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stringlengths 96
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|---|---|---|
export default function ArrowRightIcon(props) {
return (
<svg
{...props}
xmlns="http://www.w3.org/2000/svg"
width="24"
height="24"
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
strokeWidth="2"
strokeLinecap="round"
strokeLinejoin="round"
>
<path d="M5 12h14" />
<path d="m12 5 7 7-7 7" />
</svg>
)
} | transformers.js/examples/webgpu-vlm/src/components/icons/ArrowRightIcon.jsx/0 | {
"file_path": "transformers.js/examples/webgpu-vlm/src/components/icons/ArrowRightIcon.jsx",
"repo_id": "transformers.js",
"token_count": 289
} |
import json
from transformers.utils import cached_file
def generate_tokenizer_json(model_path, tokenizer):
# Marian models use two separate tokenizers for source and target languages.
# So, we merge them into a single tokenizer.
vocab_file = cached_file(model_path, 'vocab.json')
with open(vocab_file) as fp:
vocab = json.load(fp)
added_tokens = [
dict(
id=vocab.get(x),
special=True,
content=x,
single_word=False,
lstrip=False,
rstrip=False,
normalized=False
)
for x in tokenizer.all_special_tokens
]
tokenizer_json = {
'version': '1.0',
'truncation': None,
'padding': None,
'added_tokens': added_tokens,
'normalizer': {
'type': 'Precompiled',
'precompiled_charsmap': None # TODO add this
},
'pre_tokenizer': {
'type': 'Sequence',
'pretokenizers': [
{
'type': 'WhitespaceSplit'
},
{
'type': 'Metaspace',
'replacement': '\u2581',
'add_prefix_space': True
}
]
},
'post_processor': {
'type': 'TemplateProcessing', 'single': [
{'Sequence': {'id': 'A', 'type_id': 0}},
{'SpecialToken': {'id': tokenizer.eos_token, 'type_id': 0}}
],
'pair': [
{'Sequence': {'id': 'A', 'type_id': 0}},
{'SpecialToken': {'id': tokenizer.eos_token, 'type_id': 0}},
{'Sequence': {'id': 'B', 'type_id': 0}},
{'SpecialToken': {'id': tokenizer.eos_token, 'type_id': 0}}
],
'special_tokens': {
tokenizer.eos_token: {
'id': tokenizer.eos_token,
'ids': [tokenizer.eos_token_id],
'tokens': [tokenizer.eos_token]
}
}
},
'decoder': {
'type': 'Metaspace',
'replacement': '\u2581',
'add_prefix_space': True
},
'model': {
'type': 'Unigram',
'unk_id': 2,
}
}
# NOTE: Must have sentencepiece installed
spm_source = tokenizer.spm_source
spm_target = tokenizer.spm_target
src_vocab_dict = {
spm_source.IdToPiece(i): spm_source.GetScore(i)
for i in range(spm_source.GetPieceSize())
}
tgt_vocab_dict = {
spm_target.IdToPiece(i): spm_target.GetScore(i)
for i in range(spm_target.GetPieceSize())
}
tokenizer_json['model']['vocab'] = [
[
k,
0.0 if k in tokenizer.all_special_tokens else max(
src_vocab_dict.get(k, -100), tgt_vocab_dict.get(k, -100))
]
for k in vocab
]
return tokenizer_json
| transformers.js/scripts/extra/marian.py/0 | {
"file_path": "transformers.js/scripts/extra/marian.py",
"repo_id": "transformers.js",
"token_count": 1677
} |
/**
* @module generation/logits_process
*/
import { Callable } from "../utils/generic.js";
import { Tensor } from "../utils/tensor.js";
import { max, log_softmax } from "../utils/maths.js";
/**
* Abstract base class for all logit processors that can be applied during generation.
*/
export class LogitsProcessor extends Callable {
/**
* Apply the processor to the input logits.
*
* @abstract
* @param {bigint[][]} input_ids The input ids.
* @param {Tensor} logits The logits to process.
* @throws {Error} Throws an error if `_call` is not implemented in the subclass.
*/
_call(input_ids, logits) {
throw Error("`_call` should be implemented in a subclass")
}
}
/**
* Abstract base class for all logit warpers that can be applied during generation with multinomial sampling.
*/
export class LogitsWarper extends Callable {
/**
* Apply the processor to the input logits.
*
* @abstract
* @param {bigint[][]} input_ids The input ids.
* @param {Tensor} logits The logits to process.
* @throws {Error} Throws an error if `_call` is not implemented in the subclass.
*/
_call(input_ids, logits) {
throw Error("`_call` should be implemented in a subclass")
}
}
/**
* A class representing a list of logits processors. A logits processor is a function that modifies the logits
* output of a language model. This class provides methods for adding new processors and applying all processors to a
* batch of logits.
*/
export class LogitsProcessorList extends Callable {
/**
* Constructs a new instance of `LogitsProcessorList`.
*/
constructor() {
super();
this.processors = [];
}
/**
* Adds a new logits processor to the list.
*
* @param {LogitsProcessor} item The logits processor function to add.
*/
push(item) {
this.processors.push(item);
}
/**
* Adds multiple logits processors to the list.
*
* @param {LogitsProcessor[]} items The logits processor functions to add.
*/
extend(items) {
this.processors.push(...items);
}
/**
* Applies all logits processors in the list to a batch of logits, modifying them in-place.
*
* @param {bigint[][]} input_ids The input IDs for the language model.
* @param {Tensor} logits
*/
_call(input_ids, logits) {
let toReturn = logits;
// NOTE: Most processors modify logits inplace
for (const processor of this.processors) {
toReturn = processor(input_ids, toReturn);
}
return toReturn;
}
[Symbol.iterator]() {
return this.processors.values();
}
}
// DEPRECATED: https://github.com/huggingface/transformers/pull/29485
// /**
// * A logits processor that forces a specific token to be generated by the decoder.
// */
// export class ForceTokensLogitsProcessor extends LogitsProcessor {
// /**
// * Constructs a new instance of `ForceTokensLogitsProcessor`.
// *
// * @param {[number, number][]} forced_decoder_ids The ids of tokens that should be forced.
// */
// constructor(forced_decoder_ids) {
// super();
// // TODO: convert to `new Map(forced_decoder_ids)`
// this.force_token_map = Object.fromEntries(forced_decoder_ids ?? []);
// }
// /**
// * Apply the processor to the input logits.
// *
// * @param {bigint[][]} input_ids The input ids.
// * @param {Tensor} logits The logits to process.
// * @returns {Tensor} The processed logits.
// */
// _call(input_ids, logits) {
// console.log('this.force_token_map', this.force_token_map)
// console.log('call ForceTokensLogitsProcessor', input_ids, logits)
// console.log('input_ids.length', input_ids.length)
// let map = this.force_token_map[input_ids.length];
// if (map) { // There exists a mapping
// logits.data.fill(-Infinity)
// logits.data[map] = 0;
// }
// console.log('map', map)
// // throw Error("Not implemented")
// return logits;
// }
// }
/**
* A LogitsProcessor that forces a BOS token at the beginning of the generated sequence.
*/
export class ForcedBOSTokenLogitsProcessor extends LogitsProcessor {
/**
* Create a ForcedBOSTokenLogitsProcessor.
* @param {number} bos_token_id The ID of the beginning-of-sequence token to be forced.
*/
constructor(bos_token_id) {
super();
this.bos_token_id = bos_token_id;
}
/**
* Apply the BOS token forcing to the logits.
* @param {bigint[][]} input_ids The input IDs.
* @param {Tensor} logits The logits.
* @returns {Tensor} The logits with BOS token forcing.
*/
_call(input_ids, logits) {
for (let i = 0; i < input_ids.length; ++i) {
if (input_ids[i].length === 1) {
const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
batch_logits_data.fill(-Infinity);
batch_logits_data[this.bos_token_id] = 0;
}
}
return logits;
}
}
/**
* A logits processor that enforces the specified token as the last generated token when `max_length` is reached.
*/
export class ForcedEOSTokenLogitsProcessor extends LogitsProcessor {
/**
* Create a ForcedEOSTokenLogitsProcessor.
* @param {number} max_length The maximum length of the sequence to be generated.
* @param {number|number[]} eos_token_id The id(s) of the *end-of-sequence* token.
*/
constructor(max_length, eos_token_id) {
super();
this.max_length = max_length;
this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id];
}
/**
* Apply the processor to input_ids and logits.
*
* @param {bigint[][]} input_ids The input ids.
* @param {Tensor} logits The logits tensor.
*/
_call(input_ids, logits) {
for (let i = 0; i < input_ids.length; ++i) {
if (input_ids[i].length === this.max_length - 1) {
const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
batch_logits_data.fill(-Infinity);
for (const eos_token of this.eos_token_id) {
batch_logits_data[eos_token] = 0;
}
}
}
return logits;
}
}
/**
* A LogitsProcessor that suppresses a list of tokens as soon as the `generate` function starts
* generating using `begin_index` tokens. This should ensure that the tokens defined by
* `begin_suppress_tokens` at not sampled at the begining of the generation.
*/
export class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor {
/**
* Create a SuppressTokensAtBeginLogitsProcessor.
* @param {number[]} begin_suppress_tokens The IDs of the tokens to suppress.
* @param {number} begin_index The number of tokens to generate before suppressing tokens.
*/
constructor(begin_suppress_tokens, begin_index) {
super();
this.begin_suppress_tokens = begin_suppress_tokens;
this.begin_index = begin_index;
}
/**
* Apply the BOS token forcing to the logits.
* @param {bigint[][]} input_ids The input IDs.
* @param {Tensor} logits The logits.
* @returns {Tensor} The logits with BOS token forcing.
*/
_call(input_ids, logits) {
for (let i = 0; i < input_ids.length; ++i) {
if (input_ids[i].length === this.begin_index) {
const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
for (const token_id of this.begin_suppress_tokens) {
batch_logits_data[token_id] = -Infinity;
}
}
}
return logits;
}
}
/**
* A LogitsProcessor that handles adding timestamps to generated text.
*/
export class WhisperTimeStampLogitsProcessor extends LogitsProcessor {
/**
* Constructs a new WhisperTimeStampLogitsProcessor.
* @param {import('../models/whisper/generation_whisper.js').WhisperGenerationConfig} generate_config The config object passed to the `generate()` method of a transformer model.
* @param {number[]} init_tokens The initial tokens of the input sequence.
*/
constructor(generate_config, init_tokens) {
super();
this.eos_token_id =
Array.isArray(generate_config.eos_token_id)
? generate_config.eos_token_id[0]
: generate_config.eos_token_id;
this.no_timestamps_token_id = generate_config.no_timestamps_token_id;
this.timestamp_begin = this.no_timestamps_token_id + 1;
this.begin_index = init_tokens.length;
if (init_tokens.at(-1) === this.no_timestamps_token_id) {
this.begin_index -= 1;
}
this.max_initial_timestamp_index = generate_config.max_initial_timestamp_index;
}
/**
* Modify the logits to handle timestamp tokens.
* @param {bigint[][]} input_ids The input sequence of tokens.
* @param {Tensor} logits The logits output by the model.
* @returns {Tensor} The modified logits.
*/
_call(input_ids, logits) {
for (let i = 0; i < input_ids.length; ++i) {
const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
// suppress <|notimestamps|> which is handled by without_timestamps
batch_logits_data[this.no_timestamps_token_id] = -Infinity;
if (input_ids[i].length === this.begin_index - 1) {
batch_logits_data.fill(-Infinity);
batch_logits_data[this.timestamp_begin] = 0;
continue;
}
// timestamps have to appear in pairs, except directly before eos_token; mask logits accordingly
const seq = input_ids[i].slice(this.begin_index);
const last_was_timestamp = seq.length >= 1 && seq[seq.length - 1] >= this.timestamp_begin;
const penultimate_was_timestamp = seq.length < 2 || seq[seq.length - 2] >= this.timestamp_begin;
if (last_was_timestamp) {
if (penultimate_was_timestamp) { // has to be non-timestamp
batch_logits_data.subarray(this.timestamp_begin).fill(-Infinity);
} else { // cannot be normal text tokens
batch_logits_data.subarray(0, this.eos_token_id).fill(-Infinity);
}
}
// apply the `max_initial_timestamp` option
if (input_ids[i].length === this.begin_index && this.max_initial_timestamp_index !== null) {
const last_allowed = this.timestamp_begin + this.max_initial_timestamp_index;
batch_logits_data.subarray(last_allowed + 1).fill(-Infinity);
}
// if sum of probability over timestamps is above any other token, sample timestamp
const logprobs = log_softmax(batch_logits_data);
const timestamp_logprob = Math.log(logprobs.subarray(this.timestamp_begin).map(Math.exp).reduce((a, b) => a + b));
const max_text_token_logprob = max(logprobs.subarray(0, this.timestamp_begin))[0];
if (timestamp_logprob > max_text_token_logprob) {
batch_logits_data.subarray(0, this.timestamp_begin).fill(-Infinity);
}
}
return logits;
}
}
/**
* A logits processor that disallows ngrams of a certain size to be repeated.
*/
export class NoRepeatNGramLogitsProcessor extends LogitsProcessor {
/**
* Create a NoRepeatNGramLogitsProcessor.
* @param {number} no_repeat_ngram_size The no-repeat-ngram size. All ngrams of this size can only occur once.
*/
constructor(no_repeat_ngram_size) {
super();
this.no_repeat_ngram_size = no_repeat_ngram_size;
}
/**
* Generate n-grams from a sequence of token ids.
* @param {bigint[]} prevInputIds List of previous input ids
* @returns {Map<string, number[]>} Map of generated n-grams
*/
getNgrams(prevInputIds) {
const curLen = prevInputIds.length;
/**@type {number[][]} */
const ngrams = [];
for (let j = 0; j < curLen + 1 - this.no_repeat_ngram_size; ++j) {
const ngram = [];
for (let k = 0; k < this.no_repeat_ngram_size; ++k) {
ngram.push(prevInputIds[j + k]);
}
ngrams.push(ngram.map(Number));
}
/** @type {Map<string, number[]>} */
const generatedNgram = new Map();
for (const ngram of ngrams) {
const prevNgram = ngram.slice(0, ngram.length - 1);
const prevNgramKey = JSON.stringify(prevNgram);
const prevNgramValue = generatedNgram.get(prevNgramKey) ?? [];
prevNgramValue.push(ngram[ngram.length - 1]);
generatedNgram.set(prevNgramKey, prevNgramValue);
}
return generatedNgram;
}
/**
* Generate n-grams from a sequence of token ids.
* @param {Map<string, number[]>} bannedNgrams Map of banned n-grams
* @param {bigint[]} prevInputIds List of previous input ids
* @returns {number[]} Map of generated n-grams
*/
getGeneratedNgrams(bannedNgrams, prevInputIds) {
const ngramIdx = prevInputIds.slice(prevInputIds.length + 1 - this.no_repeat_ngram_size, prevInputIds.length);
const banned = bannedNgrams.get(JSON.stringify(ngramIdx.map(Number))) ?? [];
return banned;
}
/**
* Calculate banned n-gram tokens
* @param {bigint[]} prevInputIds List of previous input ids
* @returns {number[]} Map of generated n-grams
*/
calcBannedNgramTokens(prevInputIds) {
const bannedTokens = [];
if (prevInputIds.length + 1 < this.no_repeat_ngram_size) {
// return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
return bannedTokens;
} else {
const generatedNgrams = this.getNgrams(prevInputIds);
const bannedTokens = this.getGeneratedNgrams(generatedNgrams, prevInputIds);
return bannedTokens;
}
}
/**
* Apply the no-repeat-ngram processor to the logits.
* @param {bigint[][]} input_ids The input IDs.
* @param {Tensor} logits The logits.
* @returns {Tensor} The logits with no-repeat-ngram processing.
*/
_call(input_ids, logits) {
for (let i = 0; i < input_ids.length; ++i) {
const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
const bannedTokens = this.calcBannedNgramTokens(input_ids[i]);
for (const token of bannedTokens) {
batch_logits_data[token] = -Infinity;
}
}
return logits;
}
}
/**
* A logits processor that prevents the repetition of previous tokens through a penalty.
* This penalty is applied at most once per token. Note that, for decoder-only models like most LLMs,
* the considered tokens include the prompt.
*
* In the original [paper](https://arxiv.org/pdf/1909.05858.pdf), the authors suggest the use of a
* penalty of around 1.2 to achieve a good balance between truthful generation and lack of repetition.
* To penalize and reduce repetition, use `penalty` values above 1.0, where a higher value penalizes
* more strongly. To reward and encourage repetition, use `penalty` values between 0.0 and 1.0, where
* a lower value rewards more strongly.
*/
export class RepetitionPenaltyLogitsProcessor extends LogitsProcessor {
/**
* Create a RepetitionPenaltyLogitsProcessor.
* @param {number} penalty The parameter for repetition penalty.
* - 1.0 means no penalty. Above 1.0 penalizes previously generated tokens.
* - Between 0.0 and 1.0 rewards previously generated tokens.
*/
constructor(penalty) {
super();
this.penalty = penalty;
}
/**
* Apply the repetition penalty to the logits.
* @param {bigint[][]} input_ids The input IDs.
* @param {Tensor} logits The logits.
* @returns {Tensor} The logits with repetition penalty processing.
*/
_call(input_ids, logits) {
for (let i = 0; i < input_ids.length; ++i) {
const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
for (const input_id of new Set(input_ids[i])) {
const token = Number(input_id);
if (batch_logits_data[token] < 0) {
batch_logits_data[token] *= this.penalty;
} else {
batch_logits_data[token] /= this.penalty;
}
}
}
return logits
}
}
/**
* A logits processor that enforces a minimum number of tokens.
*/
export class MinLengthLogitsProcessor extends LogitsProcessor {
/**
* Create a MinLengthLogitsProcessor.
* @param {number} min_length The minimum length below which the score of `eos_token_id` is set to negative infinity.
* @param {number|number[]} eos_token_id The ID/IDs of the end-of-sequence token.
*/
constructor(min_length, eos_token_id) {
super();
this.min_length = min_length;
this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id];
}
/**
* Apply logit processor.
* @param {bigint[][]} input_ids The input IDs.
* @param {Tensor} logits The logits.
* @returns {Tensor} The processed logits.
*/
_call(input_ids, logits) {
for (let i = 0; i < input_ids.length; ++i) {
if (input_ids[i].length < this.min_length) {
const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
for (const eos_token of this.eos_token_id) {
batch_logits_data[eos_token] = -Infinity;
}
}
}
return logits
}
}
/**
* A logits processor that enforces a minimum number of new tokens.
*/
export class MinNewTokensLengthLogitsProcessor extends LogitsProcessor {
/**
* Create a MinNewTokensLengthLogitsProcessor.
* @param {number} prompt_length_to_skip The input tokens length.
* @param {number} min_new_tokens The minimum *new* tokens length below which the score of `eos_token_id` is set to negative infinity.
* @param {number|number[]} eos_token_id The ID/IDs of the end-of-sequence token.
*/
constructor(prompt_length_to_skip, min_new_tokens, eos_token_id) {
super();
this.prompt_length_to_skip = prompt_length_to_skip;
this.min_new_tokens = min_new_tokens;
this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id];
}
/**
* Apply logit processor.
* @param {bigint[][]} input_ids The input IDs.
* @param {Tensor} logits The logits.
* @returns {Tensor} The processed logits.
*/
_call(input_ids, logits) {
for (let i = 0; i < input_ids.length; ++i) {
const new_tokens_length = input_ids[i].length - this.prompt_length_to_skip;
if (new_tokens_length < this.min_new_tokens) {
const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
for (const eos_token of this.eos_token_id) {
batch_logits_data[eos_token] = -Infinity;
}
}
}
return logits
}
}
export class NoBadWordsLogitsProcessor extends LogitsProcessor {
/**
* Create a `NoBadWordsLogitsProcessor`.
* @param {number[][]} bad_words_ids List of list of token ids that are not allowed to be generated.
* @param {number|number[]} eos_token_id The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
*/
constructor(bad_words_ids, eos_token_id) {
super();
this.bad_words_ids = bad_words_ids;
this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id];
}
/**
* Apply logit processor.
* @param {bigint[][]} input_ids The input IDs.
* @param {Tensor} logits The logits.
* @returns {Tensor} The processed logits.
*/
_call(input_ids, logits) {
for (let i = 0; i < input_ids.length; ++i) {
const batch_logits_data = /** @type {Float32Array} */(logits[i].data);
const ids = input_ids[i];
for (const bad_word_ids of this.bad_words_ids) {
// Whether to modify the logits of the last token in the bad word id sequence
let mark = true;
// For each bad word in the list, if the current sequence of input ids ends with this sequence (excluding the last),
// then we set the logits of the last bad word id to -Infinity.
for (let j = 1; j <= bad_word_ids.length - 1 && bad_word_ids.length < ids.length; ++j) {
// NOTE: We use != instead of !== to compare bigint and number
// @ts-ignore
if (bad_word_ids.at(-j - 1) != ids.at(-j)) {
// We have found a mismatch
mark = false;
break;
}
}
if (mark) {
batch_logits_data[bad_word_ids.at(-1)] = -Infinity;
}
}
}
return logits
}
}
/**
* [`LogitsProcessor`] for classifier free guidance (CFG). The scores are split over the batch dimension,
* where the first half correspond to the conditional logits (predicted from the input prompt) and the second half
* correspond to the unconditional logits (predicted from an empty or 'null' prompt). The processor computes a
* weighted average across the conditional and unconditional logits, parameterised by the `guidance_scale`.
*
* See [the paper](https://arxiv.org/abs/2306.05284) for more information.
*/
export class ClassifierFreeGuidanceLogitsProcessor extends LogitsProcessor {
/**
* Create a `ClassifierFreeGuidanceLogitsProcessor`.
* @param {number} guidance_scale The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`.
* Higher guidance scale encourages the model to generate samples that are more closely linked to the input
* prompt, usually at the expense of poorer quality.
*/
constructor(guidance_scale) {
super();
if (guidance_scale <= 1) {
throw new Error(
`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${guidance_scale}.`
)
}
this.guidance_scale = guidance_scale;
}
/**
* Apply logit processor.
* @param {bigint[][]} input_ids The input IDs.
* @param {Tensor} logits The logits.
* @returns {Tensor} The processed logits.
*/
_call(input_ids, logits) {
if (logits.dims[0] !== 2 * input_ids.length) {
throw new Error(
`Logits should have twice the batch size of the input ids, the first half of batches corresponding to ` +
`the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got ` +
`batch size ${logits.dims[0]} for the logits and ${input_ids.length} for the input ids.`
)
}
const unguided_bsz = input_ids.length;
const cond_logits = logits.slice([0, unguided_bsz], null);
const uncond_logits = logits.slice([unguided_bsz, logits.dims[0]], null);
// Merge into uncond_logits (to save memory). This is equivalent to the following:
// scores = uncond_logits + (cond_logits - uncond_logits) * guidance_scale
for (let i = 0; i < uncond_logits.data.length; ++i) {
uncond_logits.data[i] += (cond_logits.data[i] - uncond_logits.data[i]) * this.guidance_scale;
}
return uncond_logits;
}
}
/**
* [`LogitsWarper`] for temperature (exponential scaling output probability distribution), which effectively means
* that it can control the randomness of the predicted tokens. Often used together with [`TopPLogitsWarper`] and [`TopKLogitsWarper`].
*/
export class TemperatureLogitsWarper extends LogitsWarper {
/**
* Create a `TemperatureLogitsWarper`.
* @param {number} temperature Strictly positive float value used to modulate the logits distribution.
* A value smaller than `1` decreases randomness (and vice versa), with `0` being equivalent to shifting
* all probability mass to the most likely token.
*/
constructor(temperature) {
super();
if (typeof temperature !== 'number' || temperature <= 0) {
let errorMessage =
`\`temperature\` (=${temperature}) must be a strictly positive float, otherwise your next token scores will be invalid.`;
if (temperature === 0) {
errorMessage += " If you're looking for greedy decoding strategies, set `do_sample=false`."
}
}
this.temperature = temperature;
}
/**
* Apply logit warper.
* @param {bigint[][]} input_ids The input IDs.
* @param {Tensor} logits The logits.
* @returns {Tensor} The processed logits.
*/
_call(input_ids, logits) {
const batch_logits_data = /** @type {Float32Array} */(logits.data);
for (let i = 0; i < batch_logits_data.length; ++i) {
batch_logits_data[i] /= this.temperature;
}
return logits;
}
}
/**
* [`LogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
* Often used together with [`TemperatureLogitsWarper`] and [`TopKLogitsWarper`].
*/
export class TopPLogitsWarper extends LogitsWarper {
/**
* Create a `TopPLogitsWarper`.
* @param {number} top_p If set to < 1, only the smallest set of most probable tokens with
* probabilities that add up to `top_p` or higher are kept for generation.
* @param {Object} options Additional options for the top-p sampling.
* @param {number} [options.filter_value=-Infinity] All filtered values will be set to this float value.
* @param {number} [options.min_tokens_to_keep=1] Minimum number of tokens that cannot be filtered.
*/
constructor(top_p, {
filter_value = -Infinity,
min_tokens_to_keep = 1,
} = {}) {
super();
if (top_p < 0 || top_p > 1.0) {
throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${top_p}`)
}
if (!Number.isInteger(min_tokens_to_keep) || min_tokens_to_keep < 1) {
throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${min_tokens_to_keep}`)
}
this.top_p = top_p
this.filter_value = filter_value
this.min_tokens_to_keep = min_tokens_to_keep
}
}
/**
* [`LogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
* Often used together with [`TemperatureLogitsWarper`] and [`TopPLogitsWarper`].
*/
export class TopKLogitsWarper extends LogitsWarper {
/**
* Create a `TopKLogitsWarper`.
* @param {number} top_k If set to > 0, only the top `top_k` tokens are kept for generation.
* @param {Object} options Additional options for the top-k sampling.
* @param {number} [options.filter_value=-Infinity] All filtered values will be set to this float value.
* @param {number} [options.min_tokens_to_keep=1] Minimum number of tokens that cannot be filtered.
*/
constructor(top_k, {
filter_value = -Infinity,
min_tokens_to_keep = 1,
} = {}) {
super();
if (!Number.isInteger(top_k) || top_k < 0) {
throw new Error(`\`top_k\` must be a positive integer, but is ${top_k}`)
}
this.top_k = Math.max(top_k, min_tokens_to_keep)
this.filter_value = filter_value
}
} | transformers.js/src/generation/logits_process.js/0 | {
"file_path": "transformers.js/src/generation/logits_process.js",
"repo_id": "transformers.js",
"token_count": 11780
} |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
export class JinaCLIPProcessor extends Processor {
static tokenizer_class = AutoTokenizer
static image_processor_class = AutoImageProcessor
async _call(text=null, images=null, kwargs = {}) {
if (!text && !images){
throw new Error('Either text or images must be provided');
}
const text_inputs = text ? this.tokenizer(text, kwargs) : {};
const image_inputs = images ? await this.image_processor(images, kwargs) : {};
return {
...text_inputs,
...image_inputs,
}
}
}
| transformers.js/src/models/jina_clip/processing_jina_clip.js/0 | {
"file_path": "transformers.js/src/models/jina_clip/processing_jina_clip.js",
"repo_id": "transformers.js",
"token_count": 291
} |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
const IMAGE_TOKEN = "<image>";
function build_string_from_input(
prompt,
bos_token,
image_seq_len,
image_token,
num_images,
) {
return `${image_token.repeat(image_seq_len * num_images)}${bos_token}${prompt}\n`
}
export class PaliGemmaProcessor extends Processor {
static tokenizer_class = AutoTokenizer
static image_processor_class = AutoImageProcessor
static uses_processor_config = false;
/**
* @typedef {import('../../utils/image.js').RawImage} RawImage
*/
// `images` is required, `text` is optional
async _call(/** @type {RawImage|RawImage[]} */ images, text = null, kwargs = {}) {
if (!text) {
console.warn(
"You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."
)
text = ""
}
if (!Array.isArray(images)) {
images = [images]
}
if (!Array.isArray(text)) {
text = [text]
}
const bos_token = this.tokenizer.bos_token;
// @ts-expect-error TS2339
const image_seq_length = this.image_processor.config.image_seq_length;
let input_strings;
if (text.some((t) => t.includes(IMAGE_TOKEN))) {
input_strings = text.map(
sample => {
const expanded_sample = sample.replaceAll(IMAGE_TOKEN, IMAGE_TOKEN.repeat(image_seq_length));
const bos_rfind_index = expanded_sample.lastIndexOf(IMAGE_TOKEN);
const bos_index = bos_rfind_index === -1 ? 0 : bos_rfind_index + IMAGE_TOKEN.length;
return expanded_sample.slice(0, bos_index) + bos_token + expanded_sample.slice(bos_index) + "\n";
}
)
} else {
console.warn(
"You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special " +
"image tokens in the text, as many tokens as there are images per each text. It is recommended to " +
"add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images " +
"each text has and add special tokens."
)
input_strings = text.map(
sample => build_string_from_input(
sample,
bos_token,
image_seq_length,
IMAGE_TOKEN,
images.length,
)
)
}
const text_inputs = this.tokenizer(input_strings, kwargs);
const image_inputs = await this.image_processor(images, kwargs);
return {
...image_inputs,
...text_inputs,
}
}
}
| transformers.js/src/models/paligemma/processing_paligemma.js/0 | {
"file_path": "transformers.js/src/models/paligemma/processing_paligemma.js",
"repo_id": "transformers.js",
"token_count": 1386
} |
/**
* @file Pipelines provide a high-level, easy to use, API for running machine learning models.
*
* **Example:** Instantiate pipeline using the `pipeline` function.
* ```javascript
* import { pipeline } from '@huggingface/transformers';
*
* const classifier = await pipeline('sentiment-analysis');
* const output = await classifier('I love transformers!');
* // [{'label': 'POSITIVE', 'score': 0.999817686}]
* ```
*
* @module pipelines
*/
import {
AutoTokenizer,
PreTrainedTokenizer,
} from './tokenizers.js';
import {
AutoModel,
AutoModelForSequenceClassification,
AutoModelForAudioClassification,
AutoModelForTokenClassification,
AutoModelForQuestionAnswering,
AutoModelForMaskedLM,
AutoModelForSeq2SeqLM,
AutoModelForSpeechSeq2Seq,
AutoModelForTextToWaveform,
AutoModelForTextToSpectrogram,
AutoModelForCTC,
AutoModelForCausalLM,
AutoModelForVision2Seq,
AutoModelForImageClassification,
AutoModelForImageSegmentation,
AutoModelForSemanticSegmentation,
AutoModelForUniversalSegmentation,
AutoModelForObjectDetection,
AutoModelForZeroShotObjectDetection,
AutoModelForDocumentQuestionAnswering,
AutoModelForImageToImage,
AutoModelForDepthEstimation,
AutoModelForImageFeatureExtraction,
PreTrainedModel,
} from './models.js';
import {
AutoProcessor,
} from './models/auto/processing_auto.js';
import {
Processor,
} from './base/processing_utils.js';
import {
Callable,
} from './utils/generic.js';
import {
dispatchCallback,
product,
} from './utils/core.js';
import {
softmax,
max,
round,
} from './utils/maths.js';
import {
read_audio,
RawAudio
} from './utils/audio.js';
import {
Tensor,
mean_pooling,
interpolate_4d,
quantize_embeddings,
topk,
} from './utils/tensor.js';
import { RawImage } from './utils/image.js';
/**
* @typedef {string | RawImage | URL} ImageInput
* @typedef {ImageInput|ImageInput[]} ImagePipelineInputs
*/
/**
* Prepare images for further tasks.
* @param {ImagePipelineInputs} images images to prepare.
* @returns {Promise<RawImage[]>} returns processed images.
* @private
*/
async function prepareImages(images) {
if (!Array.isArray(images)) {
images = [images];
}
// Possibly convert any non-images to images
return await Promise.all(images.map(x => RawImage.read(x)));
}
/**
* @typedef {string | URL | Float32Array | Float64Array} AudioInput
* @typedef {AudioInput|AudioInput[]} AudioPipelineInputs
*/
/**
* Prepare audios for further tasks.
* @param {AudioPipelineInputs} audios audios to prepare.
* @param {number} sampling_rate sampling rate of the audios.
* @returns {Promise<Float32Array[]>} The preprocessed audio data.
* @private
*/
async function prepareAudios(audios, sampling_rate) {
if (!Array.isArray(audios)) {
audios = [audios];
}
return await Promise.all(audios.map(x => {
if (typeof x === 'string' || x instanceof URL) {
return read_audio(x, sampling_rate);
} else if (x instanceof Float64Array) {
return new Float32Array(x);
}
return x;
}));
}
/**
* @typedef {Object} BoundingBox
* @property {number} xmin The minimum x coordinate of the bounding box.
* @property {number} ymin The minimum y coordinate of the bounding box.
* @property {number} xmax The maximum x coordinate of the bounding box.
* @property {number} ymax The maximum y coordinate of the bounding box.
*/
/**
* Helper function to convert list [xmin, xmax, ymin, ymax] into object { "xmin": xmin, ... }
* @param {number[]} box The bounding box as a list.
* @param {boolean} asInteger Whether to cast to integers.
* @returns {BoundingBox} The bounding box as an object.
* @private
*/
function get_bounding_box(box, asInteger) {
if (asInteger) {
box = box.map(x => x | 0);
}
const [xmin, ymin, xmax, ymax] = box;
return { xmin, ymin, xmax, ymax };
}
/**
* @callback DisposeType Disposes the item.
* @returns {Promise<void>} A promise that resolves when the item has been disposed.
*
* @typedef {Object} Disposable
* @property {DisposeType} dispose A promise that resolves when the pipeline has been disposed.
*/
/**
* The Pipeline class is the class from which all pipelines inherit.
* Refer to this class for methods shared across different pipelines.
*/
export class Pipeline extends Callable {
/**
* Create a new Pipeline.
* @param {Object} options An object containing the following properties:
* @param {string} [options.task] The task of the pipeline. Useful for specifying subtasks.
* @param {PreTrainedModel} [options.model] The model used by the pipeline.
* @param {PreTrainedTokenizer} [options.tokenizer=null] The tokenizer used by the pipeline (if any).
* @param {Processor} [options.processor=null] The processor used by the pipeline (if any).
*/
constructor({ task, model, tokenizer = null, processor = null }) {
super();
this.task = task;
this.model = model;
this.tokenizer = tokenizer;
this.processor = processor;
}
/** @type {DisposeType} */
async dispose() {
await this.model.dispose();
}
}
/**
* @typedef {Object} ModelTokenizerConstructorArgs
* @property {string} task The task of the pipeline. Useful for specifying subtasks.
* @property {PreTrainedModel} model The model used by the pipeline.
* @property {PreTrainedTokenizer} tokenizer The tokenizer used by the pipeline.
*
* @typedef {ModelTokenizerConstructorArgs} TextPipelineConstructorArgs An object used to instantiate a text-based pipeline.
*/
/**
* @typedef {Object} ModelProcessorConstructorArgs
* @property {string} task The task of the pipeline. Useful for specifying subtasks.
* @property {PreTrainedModel} model The model used by the pipeline.
* @property {Processor} processor The processor used by the pipeline.
*
* @typedef {ModelProcessorConstructorArgs} AudioPipelineConstructorArgs An object used to instantiate an audio-based pipeline.
* @typedef {ModelProcessorConstructorArgs} ImagePipelineConstructorArgs An object used to instantiate an image-based pipeline.
*/
/**
* @typedef {Object} ModelTokenizerProcessorConstructorArgs
* @property {string} task The task of the pipeline. Useful for specifying subtasks.
* @property {PreTrainedModel} model The model used by the pipeline.
* @property {PreTrainedTokenizer} tokenizer The tokenizer used by the pipeline.
* @property {Processor} processor The processor used by the pipeline.
*
* @typedef {ModelTokenizerProcessorConstructorArgs} TextAudioPipelineConstructorArgs An object used to instantiate a text- and audio-based pipeline.
* @typedef {ModelTokenizerProcessorConstructorArgs} TextImagePipelineConstructorArgs An object used to instantiate a text- and image-based pipeline.
*/
/**
* @typedef {Object} TextClassificationSingle
* @property {string} label The label predicted.
* @property {number} score The corresponding probability.
* @typedef {TextClassificationSingle[]} TextClassificationOutput
*
* @typedef {Object} TextClassificationPipelineOptions Parameters specific to text classification pipelines.
* @property {number} [top_k=1] The number of top predictions to be returned.
*
* @callback TextClassificationPipelineCallback Classify the text(s) given as inputs.
* @param {string|string[]} texts The input text(s) to be classified.
* @param {TextClassificationPipelineOptions} [options] The options to use for text classification.
* @returns {Promise<TextClassificationOutput|TextClassificationOutput[]>} An array or object containing the predicted labels and scores.
*
* @typedef {TextPipelineConstructorArgs & TextClassificationPipelineCallback & Disposable} TextClassificationPipelineType
*/
/**
* Text classification pipeline using any `ModelForSequenceClassification`.
*
* **Example:** Sentiment-analysis w/ `Xenova/distilbert-base-uncased-finetuned-sst-2-english`.
* ```javascript
* const classifier = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english');
* const output = await classifier('I love transformers!');
* // [{ label: 'POSITIVE', score: 0.999788761138916 }]
* ```
*
* **Example:** Multilingual sentiment-analysis w/ `Xenova/bert-base-multilingual-uncased-sentiment` (and return top 5 classes).
* ```javascript
* const classifier = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
* const output = await classifier('Le meilleur film de tous les temps.', { top_k: 5 });
* // [
* // { label: '5 stars', score: 0.9610759615898132 },
* // { label: '4 stars', score: 0.03323351591825485 },
* // { label: '3 stars', score: 0.0036155181005597115 },
* // { label: '1 star', score: 0.0011325967498123646 },
* // { label: '2 stars', score: 0.0009423971059732139 }
* // ]
* ```
*
* **Example:** Toxic comment classification w/ `Xenova/toxic-bert` (and return all classes).
* ```javascript
* const classifier = await pipeline('text-classification', 'Xenova/toxic-bert');
* const output = await classifier('I hate you!', { top_k: null });
* // [
* // { label: 'toxic', score: 0.9593140482902527 },
* // { label: 'insult', score: 0.16187334060668945 },
* // { label: 'obscene', score: 0.03452680632472038 },
* // { label: 'identity_hate', score: 0.0223250575363636 },
* // { label: 'threat', score: 0.019197041168808937 },
* // { label: 'severe_toxic', score: 0.005651099607348442 }
* // ]
* ```
*/
export class TextClassificationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TextClassificationPipelineType} */ (Pipeline)) {
/**
* Create a new TextClassificationPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {TextClassificationPipelineCallback} */
async _call(texts, {
top_k = 1
} = {}) {
// Run tokenization
const model_inputs = this.tokenizer(texts, {
padding: true,
truncation: true,
});
// Run model
const outputs = await this.model(model_inputs)
// TODO: Use softmax tensor function
const function_to_apply =
// @ts-expect-error TS2339
this.model.config.problem_type === 'multi_label_classification'
? batch => batch.sigmoid()
: batch => new Tensor(
'float32',
softmax(batch.data),
batch.dims,
); // single_label_classification (default)
// @ts-expect-error TS2339
const id2label = this.model.config.id2label;
const toReturn = [];
for (const batch of outputs.logits) {
const output = function_to_apply(batch);
const scores = await topk(output, top_k);
const values = scores[0].tolist();
const indices = scores[1].tolist();
const vals = indices.map((x, i) => ({
label: id2label ? id2label[x] : `LABEL_${x}`,
score: values[i],
}));
if (top_k === 1) {
toReturn.push(...vals);
} else {
toReturn.push(vals);
}
}
return Array.isArray(texts) || top_k === 1 ? /** @type {TextClassificationOutput} */ (toReturn) : /** @type {TextClassificationOutput[]} */ (toReturn)[0];
}
}
/**
* @typedef {Object} TokenClassificationSingle
* @property {string} word The token/word classified. This is obtained by decoding the selected tokens.
* @property {number} score The corresponding probability for `entity`.
* @property {string} entity The entity predicted for that token/word.
* @property {number} index The index of the corresponding token in the sentence.
* @property {number} [start] The index of the start of the corresponding entity in the sentence.
* @property {number} [end] The index of the end of the corresponding entity in the sentence.
* @typedef {TokenClassificationSingle[]} TokenClassificationOutput
*
* @typedef {Object} TokenClassificationPipelineOptions Parameters specific to token classification pipelines.
* @property {string[]} [ignore_labels] A list of labels to ignore.
*
* @callback TokenClassificationPipelineCallback Classify each token of the text(s) given as inputs.
* @param {string|string[]} texts One or several texts (or one list of texts) for token classification.
* @param {TokenClassificationPipelineOptions} [options] The options to use for token classification.
* @returns {Promise<TokenClassificationOutput|TokenClassificationOutput[]>} The result.
*
* @typedef {TextPipelineConstructorArgs & TokenClassificationPipelineCallback & Disposable} TokenClassificationPipelineType
*/
/**
* Named Entity Recognition pipeline using any `ModelForTokenClassification`.
*
* **Example:** Perform named entity recognition with `Xenova/bert-base-NER`.
* ```javascript
* const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
* const output = await classifier('My name is Sarah and I live in London');
* // [
* // { entity: 'B-PER', score: 0.9980202913284302, index: 4, word: 'Sarah' },
* // { entity: 'B-LOC', score: 0.9994474053382874, index: 9, word: 'London' }
* // ]
* ```
*
* **Example:** Perform named entity recognition with `Xenova/bert-base-NER` (and return all labels).
* ```javascript
* const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
* const output = await classifier('Sarah lives in the United States of America', { ignore_labels: [] });
* // [
* // { entity: 'B-PER', score: 0.9966587424278259, index: 1, word: 'Sarah' },
* // { entity: 'O', score: 0.9987385869026184, index: 2, word: 'lives' },
* // { entity: 'O', score: 0.9990072846412659, index: 3, word: 'in' },
* // { entity: 'O', score: 0.9988298416137695, index: 4, word: 'the' },
* // { entity: 'B-LOC', score: 0.9995510578155518, index: 5, word: 'United' },
* // { entity: 'I-LOC', score: 0.9990395307540894, index: 6, word: 'States' },
* // { entity: 'I-LOC', score: 0.9986724853515625, index: 7, word: 'of' },
* // { entity: 'I-LOC', score: 0.9975294470787048, index: 8, word: 'America' }
* // ]
* ```
*/
export class TokenClassificationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TokenClassificationPipelineType} */ (Pipeline)) {
/**
* Create a new TokenClassificationPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {TokenClassificationPipelineCallback} */
async _call(texts, {
ignore_labels = ['O'],
} = {}) {
const isBatched = Array.isArray(texts);
// Run tokenization
const model_inputs = this.tokenizer(isBatched ? texts : [texts], {
padding: true,
truncation: true,
});
// Run model
const outputs = await this.model(model_inputs)
const logits = outputs.logits;
// @ts-expect-error TS2339
const id2label = this.model.config.id2label;
const toReturn = [];
for (let i = 0; i < logits.dims[0]; ++i) {
const ids = model_inputs.input_ids[i];
const batch = logits[i];
// List of tokens that aren't ignored
const tokens = [];
for (let j = 0; j < batch.dims[0]; ++j) {
const tokenData = batch[j];
const topScoreIndex = max(tokenData.data)[1];
const entity = id2label ? id2label[topScoreIndex] : `LABEL_${topScoreIndex}`;
if (ignore_labels.includes(entity)) {
// We predicted a token that should be ignored. So, we skip it.
continue;
}
// TODO add option to keep special tokens?
const word = this.tokenizer.decode([ids[j].item()], { skip_special_tokens: true });
if (word === '') {
// Was a special token. So, we skip it.
continue;
}
const scores = softmax(tokenData.data);
tokens.push({
entity: entity,
score: scores[topScoreIndex],
index: j,
word: word,
// TODO: Add support for start and end
// start: null,
// end: null,
});
}
toReturn.push(tokens);
}
return isBatched ? toReturn : toReturn[0];
}
}
/**
* @typedef {Object} QuestionAnsweringOutput
* @property {number} score The probability associated to the answer.
* @property {number} [start] The character start index of the answer (in the tokenized version of the input).
* @property {number} [end] The character end index of the answer (in the tokenized version of the input).
* @property {string} answer The answer to the question.
*
* @typedef {Object} QuestionAnsweringPipelineOptions Parameters specific to question answering pipelines.
* @property {number} [top_k=1] The number of top answer predictions to be returned.
*
* @callback QuestionAnsweringPipelineCallback Answer the question(s) given as inputs by using the context(s).
* @param {string|string[]} question One or several question(s) (must be used in conjunction with the `context` argument).
* @param {string|string[]} context One or several context(s) associated with the question(s) (must be used in conjunction with the `question` argument).
* @param {QuestionAnsweringPipelineOptions} [options] The options to use for question answering.
* @returns {Promise<QuestionAnsweringOutput|QuestionAnsweringOutput[]>} An array or object containing the predicted answers and scores.
*
* @typedef {TextPipelineConstructorArgs & QuestionAnsweringPipelineCallback & Disposable} QuestionAnsweringPipelineType
*/
/**
* Question Answering pipeline using any `ModelForQuestionAnswering`.
*
* **Example:** Run question answering with `Xenova/distilbert-base-uncased-distilled-squad`.
* ```javascript
* const answerer = await pipeline('question-answering', 'Xenova/distilbert-base-uncased-distilled-squad');
* const question = 'Who was Jim Henson?';
* const context = 'Jim Henson was a nice puppet.';
* const output = await answerer(question, context);
* // {
* // answer: "a nice puppet",
* // score: 0.5768911502526741
* // }
* ```
*/
export class QuestionAnsweringPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => QuestionAnsweringPipelineType} */ (Pipeline)) {
/**
* Create a new QuestionAnsweringPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {QuestionAnsweringPipelineCallback} */
async _call(question, context, {
top_k = 1
} = {}) {
// Run tokenization
const inputs = this.tokenizer(question, {
text_pair: context,
padding: true,
truncation: true,
});
const { start_logits, end_logits } = await this.model(inputs);
const input_ids = inputs.input_ids.tolist();
const attention_mask = inputs.attention_mask.tolist();
// TODO: add support for `return_special_tokens_mask`
const special_tokens = this.tokenizer.all_special_ids;
/** @type {QuestionAnsweringOutput[]} */
const toReturn = [];
for (let j = 0; j < start_logits.dims[0]; ++j) {
const ids = input_ids[j];
const sepIndex = ids.findIndex(x =>
// We use == to match bigint with number
// @ts-ignore
x == this.tokenizer.sep_token_id
);
const valid_mask = attention_mask[j].map((y, ix) => (
y == 1
&& (
ix === 0 // is cls_token
|| (
ix > sepIndex
&& special_tokens.findIndex(x => x == ids[ix]) === -1 // token is not a special token (special_tokens_mask == 0)
)
)
));
const start = start_logits[j].tolist();
const end = end_logits[j].tolist();
// Now, we mask out values that can't be in the answer
// NOTE: We keep the cls_token unmasked (some models use it to indicate unanswerable questions)
for (let i = 1; i < start.length; ++i) {
if (
attention_mask[j] == 0 // is part of padding
|| i <= sepIndex // is before the sep_token
|| special_tokens.findIndex(x => x == ids[i]) !== -1 // Is a special token
) {
// Make sure non-context indexes in the tensor cannot contribute to the softmax
start[i] = -Infinity;
end[i] = -Infinity;
}
}
// Normalize logits and spans to retrieve the answer
const start_scores = softmax(start).map((x, i) => [x, i]);
const end_scores = softmax(end).map((x, i) => [x, i]);
// Mask CLS
start_scores[0][0] = 0;
end_scores[0][0] = 0;
// Generate all valid spans and select best ones
const options = product(start_scores, end_scores)
.filter(x => x[0][1] <= x[1][1])
.map(x => [x[0][1], x[1][1], x[0][0] * x[1][0]])
.sort((a, b) => b[2] - a[2]);
for (let k = 0; k < Math.min(options.length, top_k); ++k) {
const [start, end, score] = options[k];
const answer_tokens = ids.slice(start, end + 1)
const answer = this.tokenizer.decode(answer_tokens, {
skip_special_tokens: true,
});
// TODO add start and end?
// NOTE: HF returns character index
toReturn.push({
answer, score
});
}
}
// Mimic HF's return type based on top_k
return (top_k === 1) ? toReturn[0] : toReturn;
}
}
/**
* @typedef {Object} FillMaskSingle
* @property {string} sequence The corresponding input with the mask token prediction.
* @property {number} score The corresponding probability.
* @property {number} token The predicted token id (to replace the masked one).
* @property {string} token_str The predicted token (to replace the masked one).
* @typedef {FillMaskSingle[]} FillMaskOutput
*
* @typedef {Object} FillMaskPipelineOptions Parameters specific to fill mask pipelines.
* @property {number} [top_k=5] When passed, overrides the number of predictions to return.
*
* @callback FillMaskPipelineCallback Fill the masked token in the text(s) given as inputs.
* @param {string|string[]} texts One or several texts (or one list of prompts) with masked tokens.
* @param {FillMaskPipelineOptions} [options] The options to use for masked language modelling.
* @returns {Promise<FillMaskOutput|FillMaskOutput[]>} An array of objects containing the score, predicted token, predicted token string,
* and the sequence with the predicted token filled in, or an array of such arrays (one for each input text).
* If only one input text is given, the output will be an array of objects.
* @throws {Error} When the mask token is not found in the input text.
*
* @typedef {TextPipelineConstructorArgs & FillMaskPipelineCallback & Disposable} FillMaskPipelineType
*/
/**
* Masked language modeling prediction pipeline using any `ModelWithLMHead`.
*
* **Example:** Perform masked language modelling (a.k.a. "fill-mask") with `Xenova/bert-base-uncased`.
* ```javascript
* const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased');
* const output = await unmasker('The goal of life is [MASK].');
* // [
* // { token_str: 'survival', score: 0.06137419492006302, token: 8115, sequence: 'The goal of life is survival.' },
* // { token_str: 'love', score: 0.03902450203895569, token: 1567, sequence: 'The goal of life is love.' },
* // { token_str: 'happiness', score: 0.03253183513879776, token: 9266, sequence: 'The goal of life is happiness.' },
* // { token_str: 'freedom', score: 0.018736306577920914, token: 4438, sequence: 'The goal of life is freedom.' },
* // { token_str: 'life', score: 0.01859794743359089, token: 1297, sequence: 'The goal of life is life.' }
* // ]
* ```
*
* **Example:** Perform masked language modelling (a.k.a. "fill-mask") with `Xenova/bert-base-cased` (and return top result).
* ```javascript
* const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased');
* const output = await unmasker('The Milky Way is a [MASK] galaxy.', { top_k: 1 });
* // [{ token_str: 'spiral', score: 0.6299987435340881, token: 14061, sequence: 'The Milky Way is a spiral galaxy.' }]
* ```
*/
export class FillMaskPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => FillMaskPipelineType} */ (Pipeline)) {
/**
* Create a new FillMaskPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {FillMaskPipelineCallback} */
async _call(texts, {
top_k = 5
} = {}) {
// Run tokenization
const model_inputs = this.tokenizer(texts, {
padding: true,
truncation: true,
});
// Run model
const { logits } = await this.model(model_inputs)
const toReturn = [];
/** @type {bigint[][]} */
const input_ids = model_inputs.input_ids.tolist();
for (let i = 0; i < input_ids.length; ++i) {
const ids = input_ids[i];
const mask_token_index = ids.findIndex(x =>
// We use == to match bigint with number
// @ts-ignore
x == this.tokenizer.mask_token_id
);
if (mask_token_index === -1) {
throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`)
}
const itemLogits = logits[i][mask_token_index];
const scores = await topk(new Tensor(
'float32',
softmax(itemLogits.data),
itemLogits.dims,
), top_k);
const values = scores[0].tolist();
const indices = scores[1].tolist();
toReturn.push(indices.map((x, i) => {
const sequence = ids.slice();
sequence[mask_token_index] = x;
return {
score: values[i],
token: Number(x),
token_str: this.tokenizer.decode([x]),
sequence: this.tokenizer.decode(sequence, { skip_special_tokens: true }),
}
}));
}
return Array.isArray(texts) ? toReturn : toReturn[0];
}
}
/**
* @typedef {Object} Text2TextGenerationSingle
* @property {string} generated_text The generated text.
* @typedef {Text2TextGenerationSingle[]} Text2TextGenerationOutput
*
* @callback Text2TextGenerationPipelineCallback Generate the output text(s) using text(s) given as inputs.
* @param {string|string[]} texts Input text for the encoder.
* @param {Partial<import('./generation/configuration_utils.js').GenerationConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
* @returns {Promise<Text2TextGenerationOutput|Text2TextGenerationOutput[]>}
*
* @typedef {TextPipelineConstructorArgs & Text2TextGenerationPipelineCallback & Disposable} Text2TextGenerationPipelineType
*/
/**
* Text2TextGenerationPipeline class for generating text using a model that performs text-to-text generation tasks.
*
* **Example:** Text-to-text generation w/ `Xenova/LaMini-Flan-T5-783M`.
* ```javascript
* const generator = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
* const output = await generator('how can I become more healthy?', {
* max_new_tokens: 100,
* });
* // [{ generated_text: "To become more healthy, you can: 1. Eat a balanced diet with plenty of fruits, vegetables, whole grains, lean proteins, and healthy fats. 2. Stay hydrated by drinking plenty of water. 3. Get enough sleep and manage stress levels. 4. Avoid smoking and excessive alcohol consumption. 5. Regularly exercise and maintain a healthy weight. 6. Practice good hygiene and sanitation. 7. Seek medical attention if you experience any health issues." }]
* ```
*/
export class Text2TextGenerationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => Text2TextGenerationPipelineType} */ (Pipeline)) {
/** @type {'generated_text'} */
_key = 'generated_text';
/**
* Create a new Text2TextGenerationPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {Text2TextGenerationPipelineCallback} */
async _call(texts, generate_kwargs = {}) {
if (!Array.isArray(texts)) {
texts = [texts];
}
// Add global prefix, if present
// @ts-expect-error TS2339
if (this.model.config.prefix) {
// @ts-expect-error TS2339
texts = texts.map(x => this.model.config.prefix + x)
}
// Handle task specific params:
// @ts-expect-error TS2339
const task_specific_params = this.model.config.task_specific_params
if (task_specific_params && task_specific_params[this.task]) {
// Add prefixes, if present
if (task_specific_params[this.task].prefix) {
texts = texts.map(x => task_specific_params[this.task].prefix + x)
}
// TODO update generation config
}
const tokenizer = this.tokenizer;
const tokenizer_options = {
padding: true,
truncation: true,
}
let inputs;
if (this instanceof TranslationPipeline && '_build_translation_inputs' in tokenizer) {
// TODO: move to Translation pipeline?
// Currently put here to avoid code duplication
// @ts-ignore
inputs = tokenizer._build_translation_inputs(texts, tokenizer_options, generate_kwargs);
} else {
inputs = tokenizer(texts, tokenizer_options);
}
const outputTokenIds = await this.model.generate({ ...inputs, ...generate_kwargs });
return tokenizer.batch_decode(/** @type {Tensor} */(outputTokenIds), {
skip_special_tokens: true,
}).map(text => ({ [this._key]: text }));
}
}
/**
* @typedef {Object} SummarizationSingle
* @property {string} summary_text The summary text.
* @typedef {SummarizationSingle[]} SummarizationOutput
*
* @callback SummarizationPipelineCallback Summarize the text(s) given as inputs.
* @param {string|string[]} texts One or several articles (or one list of articles) to summarize.
* @param {import('./generation/configuration_utils.js').GenerationConfig} [options] Additional keyword arguments to pass along to the generate method of the model.
* @returns {Promise<SummarizationOutput|SummarizationOutput[]>}
*
* @typedef {TextPipelineConstructorArgs & SummarizationPipelineCallback & Disposable} SummarizationPipelineType
*/
/**
* A pipeline for summarization tasks, inheriting from Text2TextGenerationPipeline.
*
* **Example:** Summarization w/ `Xenova/distilbart-cnn-6-6`.
* ```javascript
* const generator = await pipeline('summarization', 'Xenova/distilbart-cnn-6-6');
* const text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, ' +
* 'and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. ' +
* 'During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest ' +
* 'man-made structure in the world, a title it held for 41 years until the Chrysler Building in New ' +
* 'York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to ' +
* 'the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the ' +
* 'Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second ' +
* 'tallest free-standing structure in France after the Millau Viaduct.';
* const output = await generator(text, {
* max_new_tokens: 100,
* });
* // [{ summary_text: ' The Eiffel Tower is about the same height as an 81-storey building and the tallest structure in Paris. It is the second tallest free-standing structure in France after the Millau Viaduct.' }]
* ```
*/
export class SummarizationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => SummarizationPipelineType} */ (/** @type {any} */ (Text2TextGenerationPipeline))) {
/** @type {'summary_text'} */
_key = 'summary_text';
/**
* Create a new SummarizationPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
}
/**
* @typedef {Object} TranslationSingle
* @property {string} translation_text The translated text.
* @typedef {TranslationSingle[]} TranslationOutput
*
* @callback TranslationPipelineCallback Translate the text(s) given as inputs.
* @param {string|string[]} texts Texts to be translated.
* @param {import('./generation/configuration_utils.js').GenerationConfig} [options] Additional keyword arguments to pass along to the generate method of the model.
* @returns {Promise<TranslationOutput|TranslationOutput[]>}
*
* @typedef {TextPipelineConstructorArgs & TranslationPipelineCallback & Disposable} TranslationPipelineType
*/
/**
* Translates text from one language to another.
*
* **Example:** Multilingual translation w/ `Xenova/nllb-200-distilled-600M`.
*
* See [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
* for the full list of languages and their corresponding codes.
*
* ```javascript
* const translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
* const output = await translator('जीवन एक चॉकलेट बॉक्स की तरह है।', {
* src_lang: 'hin_Deva', // Hindi
* tgt_lang: 'fra_Latn', // French
* });
* // [{ translation_text: 'La vie est comme une boîte à chocolat.' }]
* ```
*
* **Example:** Multilingual translation w/ `Xenova/m2m100_418M`.
*
* See [here](https://huggingface.co/facebook/m2m100_418M#languages-covered)
* for the full list of languages and their corresponding codes.
*
* ```javascript
* const translator = await pipeline('translation', 'Xenova/m2m100_418M');
* const output = await translator('生活就像一盒巧克力。', {
* src_lang: 'zh', // Chinese
* tgt_lang: 'en', // English
* });
* // [{ translation_text: 'Life is like a box of chocolate.' }]
* ```
*
* **Example:** Multilingual translation w/ `Xenova/mbart-large-50-many-to-many-mmt`.
*
* See [here](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt#languages-covered)
* for the full list of languages and their corresponding codes.
*
* ```javascript
* const translator = await pipeline('translation', 'Xenova/mbart-large-50-many-to-many-mmt');
* const output = await translator('संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है', {
* src_lang: 'hi_IN', // Hindi
* tgt_lang: 'fr_XX', // French
* });
* // [{ translation_text: 'Le chef des Nations affirme qu 'il n 'y a military solution in Syria.' }]
* ```
*/
export class TranslationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TranslationPipelineType} */ (/** @type {any} */ (Text2TextGenerationPipeline))) {
/** @type {'translation_text'} */
_key = 'translation_text';
/**
* Create a new TranslationPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
}
function isChat(x) {
return Array.isArray(x) && x.every(x => 'role' in x && 'content' in x);
}
/**
* @typedef {import('./tokenizers.js').Message[]} Chat
*
* @typedef {Object} TextGenerationSingle
* @property {string|Chat} generated_text The generated text.
* @typedef {TextGenerationSingle[]} TextGenerationOutput
*
* @typedef {Object} TextGenerationSpecificParams Parameters specific to text-generation pipelines.
* @property {boolean} [add_special_tokens] Whether or not to add special tokens when tokenizing the sequences.
* @property {boolean} [return_full_text=true] If set to `false` only added text is returned, otherwise the full text is returned.
* @typedef {import('./generation/configuration_utils.js').GenerationConfig & TextGenerationSpecificParams} TextGenerationConfig
*
* @callback TextGenerationPipelineCallback Complete the prompt(s) given as inputs.
* @param {string|string[]|Chat|Chat[]} texts One or several prompts (or one list of prompts) to complete.
* @param {Partial<TextGenerationConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
* @returns {Promise<TextGenerationOutput|TextGenerationOutput[]>} An array or object containing the generated texts.
*
* @typedef {TextPipelineConstructorArgs & TextGenerationPipelineCallback & Disposable} TextGenerationPipelineType
*/
/**
* Language generation pipeline using any `ModelWithLMHead` or `ModelForCausalLM`.
* This pipeline predicts the words that will follow a specified text prompt.
* NOTE: For the full list of generation parameters, see [`GenerationConfig`](./utils/generation#module_utils/generation.GenerationConfig).
*
* **Example:** Text generation with `Xenova/distilgpt2` (default settings).
* ```javascript
* const generator = await pipeline('text-generation', 'Xenova/distilgpt2');
* const text = 'I enjoy walking with my cute dog,';
* const output = await generator(text);
* // [{ generated_text: "I enjoy walking with my cute dog, and I love to play with the other dogs." }]
* ```
*
* **Example:** Text generation with `Xenova/distilgpt2` (custom settings).
* ```javascript
* const generator = await pipeline('text-generation', 'Xenova/distilgpt2');
* const text = 'Once upon a time, there was';
* const output = await generator(text, {
* temperature: 2,
* max_new_tokens: 10,
* repetition_penalty: 1.5,
* no_repeat_ngram_size: 2,
* num_beams: 2,
* num_return_sequences: 2,
* });
* // [{
* // "generated_text": "Once upon a time, there was an abundance of information about the history and activities that"
* // }, {
* // "generated_text": "Once upon a time, there was an abundance of information about the most important and influential"
* // }]
* ```
*
* **Example:** Run code generation with `Xenova/codegen-350M-mono`.
* ```javascript
* const generator = await pipeline('text-generation', 'Xenova/codegen-350M-mono');
* const text = 'def fib(n):';
* const output = await generator(text, {
* max_new_tokens: 44,
* });
* // [{
* // generated_text: 'def fib(n):\n' +
* // ' if n == 0:\n' +
* // ' return 0\n' +
* // ' elif n == 1:\n' +
* // ' return 1\n' +
* // ' else:\n' +
* // ' return fib(n-1) + fib(n-2)\n'
* // }]
* ```
*/
export class TextGenerationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TextGenerationPipelineType} */ (Pipeline)) {
/**
* Create a new TextGenerationPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {TextGenerationPipelineCallback} */
async _call(texts, generate_kwargs = {}) {
let isBatched = false;
let isChatInput = false;
// Normalize inputs
/** @type {string[]} */
let inputs;
if (typeof texts === 'string') {
inputs = texts = [texts];
} else if (Array.isArray(texts) && texts.every(x => typeof x === 'string')) {
isBatched = true;
inputs = /** @type {string[]} */(texts);
} else {
if (isChat(texts)) {
texts = [/** @type {Chat} */(texts)];
} else if (Array.isArray(texts) && texts.every(isChat)) {
isBatched = true;
} else {
throw new Error('Input must be a string, an array of strings, a Chat, or an array of Chats');
}
isChatInput = true;
// If the input is a chat, we need to apply the chat template
inputs = /** @type {string[]} */(/** @type {Chat[]} */ (texts).map(
x => this.tokenizer.apply_chat_template(x, {
tokenize: false,
add_generation_prompt: true,
})
));
}
// By default, do not add special tokens
const add_special_tokens = generate_kwargs.add_special_tokens ?? false;
// By default, return full text
const return_full_text = isChatInput
? false
: generate_kwargs.return_full_text ?? true;
this.tokenizer.padding_side = 'left';
const text_inputs = this.tokenizer(inputs, {
add_special_tokens,
padding: true,
truncation: true,
});
const outputTokenIds = /** @type {Tensor} */(await this.model.generate({
...text_inputs,
...generate_kwargs
}));
const decoded = this.tokenizer.batch_decode(outputTokenIds, {
skip_special_tokens: true,
});
let promptLengths;
if (!return_full_text && text_inputs.input_ids.dims.at(-1) > 0) {
promptLengths = this.tokenizer.batch_decode(text_inputs.input_ids, {
skip_special_tokens: true,
}).map(x => x.length);
}
/** @type {TextGenerationOutput[]} */
const toReturn = Array.from({ length: texts.length }, _ => []);
for (let i = 0; i < decoded.length; ++i) {
const textIndex = Math.floor(i / outputTokenIds.dims[0] * texts.length);
if (promptLengths) {
// Trim the decoded text to only include the generated part
decoded[i] = decoded[i].slice(promptLengths[textIndex]);
}
toReturn[textIndex].push({
generated_text: isChatInput
? [
...((/** @type {Chat[]} */(texts)[textIndex])),
{ role: 'assistant', content: decoded[i] },
]
: decoded[i]
});
}
return (!isBatched && toReturn.length === 1) ? toReturn[0] : toReturn;
}
}
/**
* @typedef {Object} ZeroShotClassificationOutput
* @property {string} sequence The sequence for which this is the output.
* @property {string[]} labels The labels sorted by order of likelihood.
* @property {number[]} scores The probabilities for each of the labels.
*
* @typedef {Object} ZeroShotClassificationPipelineOptions Parameters specific to zero-shot classification pipelines.
* @property {string} [hypothesis_template="This example is {}."] The template used to turn each
* candidate label into an NLI-style hypothesis. The candidate label will replace the {} placeholder.
* @property {boolean} [multi_label=false] Whether or not multiple candidate labels can be true.
* If `false`, the scores are normalized such that the sum of the label likelihoods for each sequence
* is 1. If `true`, the labels are considered independent and probabilities are normalized for each
* candidate by doing a softmax of the entailment score vs. the contradiction score.
*
* @callback ZeroShotClassificationPipelineCallback Classify the sequence(s) given as inputs.
* @param {string|string[]} texts The sequence(s) to classify, will be truncated if the model input is too large.
* @param {string|string[]} candidate_labels The set of possible class labels to classify each sequence into.
* Can be a single label, a string of comma-separated labels, or a list of labels.
* @param {ZeroShotClassificationPipelineOptions} [options] The options to use for zero-shot classification.
* @returns {Promise<ZeroShotClassificationOutput|ZeroShotClassificationOutput[]>} An array or object containing the predicted labels and scores.
*
* @typedef {TextPipelineConstructorArgs & ZeroShotClassificationPipelineCallback & Disposable} ZeroShotClassificationPipelineType
*/
/**
* NLI-based zero-shot classification pipeline using a `ModelForSequenceClassification`
* trained on NLI (natural language inference) tasks. Equivalent of `text-classification`
* pipelines, but these models don't require a hardcoded number of potential classes, they
* can be chosen at runtime. It usually means it's slower but it is **much** more flexible.
*
* **Example:** Zero shot classification with `Xenova/mobilebert-uncased-mnli`.
* ```javascript
* const classifier = await pipeline('zero-shot-classification', 'Xenova/mobilebert-uncased-mnli');
* const text = 'Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.';
* const labels = [ 'mobile', 'billing', 'website', 'account access' ];
* const output = await classifier(text, labels);
* // {
* // sequence: 'Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.',
* // labels: [ 'mobile', 'website', 'billing', 'account access' ],
* // scores: [ 0.5562091040482018, 0.1843621307860853, 0.13942646639336376, 0.12000229877234923 ]
* // }
* ```
*
* **Example:** Zero shot classification with `Xenova/nli-deberta-v3-xsmall` (multi-label).
* ```javascript
* const classifier = await pipeline('zero-shot-classification', 'Xenova/nli-deberta-v3-xsmall');
* const text = 'I have a problem with my iphone that needs to be resolved asap!';
* const labels = [ 'urgent', 'not urgent', 'phone', 'tablet', 'computer' ];
* const output = await classifier(text, labels, { multi_label: true });
* // {
* // sequence: 'I have a problem with my iphone that needs to be resolved asap!',
* // labels: [ 'urgent', 'phone', 'computer', 'tablet', 'not urgent' ],
* // scores: [ 0.9958870956360275, 0.9923963400697035, 0.002333537946160235, 0.0015134138567598765, 0.0010699384208377163 ]
* // }
* ```
*/
export class ZeroShotClassificationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => ZeroShotClassificationPipelineType} */ (Pipeline)) {
/**
* Create a new ZeroShotClassificationPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
// Use model config to get label2id mapping
this.label2id = Object.fromEntries(
Object.entries((/** @type {any} */(this).model).config.label2id).map(
([k, v]) => [k.toLowerCase(), v]
)
);
this.entailment_id = this.label2id['entailment'];
if (this.entailment_id === undefined) {
console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id.");
this.entailment_id = 2;
}
this.contradiction_id = this.label2id['contradiction'] ?? this.label2id['not_entailment'];
if (this.contradiction_id === undefined) {
console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id.");
this.contradiction_id = 0;
}
}
/** @type {ZeroShotClassificationPipelineCallback} */
async _call(texts, candidate_labels, {
hypothesis_template = "This example is {}.",
multi_label = false,
} = {}) {
const isBatched = Array.isArray(texts);
if (!isBatched) {
texts = [/** @type {string} */ (texts)];
}
if (!Array.isArray(candidate_labels)) {
candidate_labels = [candidate_labels];
}
// Insert labels into hypothesis template
const hypotheses = candidate_labels.map(
x => hypothesis_template.replace('{}', x)
);
// How to perform the softmax over the logits:
// - true: softmax over the entailment vs. contradiction dim for each label independently
// - false: softmax the "entailment" logits over all candidate labels
const softmaxEach = multi_label || candidate_labels.length === 1;
/** @type {ZeroShotClassificationOutput[]} */
const toReturn = [];
for (const premise of texts) {
const entails_logits = [];
for (const hypothesis of hypotheses) {
const inputs = this.tokenizer(premise, {
text_pair: hypothesis,
padding: true,
truncation: true,
})
const outputs = await this.model(inputs)
if (softmaxEach) {
entails_logits.push([
outputs.logits.data[this.contradiction_id],
outputs.logits.data[this.entailment_id]
])
} else {
entails_logits.push(outputs.logits.data[this.entailment_id])
}
}
/** @type {number[]} */
const scores = softmaxEach
? entails_logits.map(x => softmax(x)[1])
: softmax(entails_logits);
// Sort by scores (desc) and return scores with indices
const scores_sorted = scores
.map((x, i) => [x, i])
.sort((a, b) => (b[0] - a[0]));
toReturn.push({
sequence: premise,
labels: scores_sorted.map(x => candidate_labels[x[1]]),
scores: scores_sorted.map(x => x[0]),
});
}
return isBatched ? toReturn : toReturn[0];
}
}
/**
* @typedef {Object} FeatureExtractionPipelineOptions Parameters specific to feature extraction pipelines.
* @property {'none'|'mean'|'cls'} [pooling="none"] The pooling method to use.
* @property {boolean} [normalize=false] Whether or not to normalize the embeddings in the last dimension.
* @property {boolean} [quantize=false] Whether or not to quantize the embeddings.
* @property {'binary'|'ubinary'} [precision='binary'] The precision to use for quantization.
*
* @callback FeatureExtractionPipelineCallback Extract the features of the input(s).
* @param {string|string[]} texts One or several texts (or one list of texts) to get the features of.
* @param {FeatureExtractionPipelineOptions} [options] The options to use for feature extraction.
* @returns {Promise<Tensor>} The features computed by the model.
*
* @typedef {TextPipelineConstructorArgs & FeatureExtractionPipelineCallback & Disposable} FeatureExtractionPipelineType
*/
/**
* Feature extraction pipeline using no model head. This pipeline extracts the hidden
* states from the base transformer, which can be used as features in downstream tasks.
*
* **Example:** Run feature extraction with `bert-base-uncased` (without pooling/normalization).
* ```javascript
* const extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' });
* const output = await extractor('This is a simple test.');
* // Tensor {
* // type: 'float32',
* // data: Float32Array [0.05939924716949463, 0.021655935794115067, ...],
* // dims: [1, 8, 768]
* // }
* ```
*
* **Example:** Run feature extraction with `bert-base-uncased` (with pooling/normalization).
* ```javascript
* const extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' });
* const output = await extractor('This is a simple test.', { pooling: 'mean', normalize: true });
* // Tensor {
* // type: 'float32',
* // data: Float32Array [0.03373778983950615, -0.010106077417731285, ...],
* // dims: [1, 768]
* // }
* ```
*
* **Example:** Calculating embeddings with `sentence-transformers` models.
* ```javascript
* const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
* const output = await extractor('This is a simple test.', { pooling: 'mean', normalize: true });
* // Tensor {
* // type: 'float32',
* // data: Float32Array [0.09094982594251633, -0.014774246141314507, ...],
* // dims: [1, 384]
* // }
* ```
* **Example:** Calculating binary embeddings with `sentence-transformers` models.
* ```javascript
* const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
* const output = await extractor('This is a simple test.', { pooling: 'mean', quantize: true, precision: 'binary' });
* // Tensor {
* // type: 'int8',
* // data: Int8Array [49, 108, 24, ...],
* // dims: [1, 48]
* // }
* ```
*/
export class FeatureExtractionPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => FeatureExtractionPipelineType} */ (Pipeline)) {
/**
* Create a new FeatureExtractionPipeline.
* @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {FeatureExtractionPipelineCallback} */
async _call(texts, {
pooling = /** @type {'none'} */('none'),
normalize = false,
quantize = false,
precision = /** @type {'binary'} */('binary'),
} = {}) {
// Run tokenization
const model_inputs = this.tokenizer(texts, {
padding: true,
truncation: true,
});
// Run model
const outputs = await this.model(model_inputs)
// TODO: Provide warning to the user that they might be using model which was not exported
// specifically for feature extraction
// console.log(this.model.config)
// console.log(outputs)
/** @type {Tensor} */
let result = outputs.last_hidden_state ?? outputs.logits ?? outputs.token_embeddings;
if (pooling === 'none') {
// Skip pooling
} else if (pooling === 'mean') {
result = mean_pooling(result, model_inputs.attention_mask);
} else if (pooling === 'cls') {
result = result.slice(null, 0);
} else {
throw Error(`Pooling method '${pooling}' not supported.`);
}
if (normalize) {
result = result.normalize(2, -1);
}
if (quantize) {
result = quantize_embeddings(result, precision);
}
return result;
}
}
/**
* @typedef {Object} ImageFeatureExtractionPipelineOptions Parameters specific to image feature extraction pipelines.
* @property {boolean} [pool=null] Whether or not to return the pooled output. If set to `false`, the model will return the raw hidden states.
*
* @callback ImageFeatureExtractionPipelineCallback Extract the features of the input(s).
* @param {ImagePipelineInputs} images One or several images (or one list of images) to get the features of.
* @param {ImageFeatureExtractionPipelineOptions} [options] The options to use for image feature extraction.
* @returns {Promise<Tensor>} The image features computed by the model.
*
* @typedef {ImagePipelineConstructorArgs & ImageFeatureExtractionPipelineCallback & Disposable} ImageFeatureExtractionPipelineType
*/
/**
* Image feature extraction pipeline using no model head. This pipeline extracts the hidden
* states from the base transformer, which can be used as features in downstream tasks.
*
* **Example:** Perform image feature extraction with `Xenova/vit-base-patch16-224-in21k`.
* ```javascript
* const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/vit-base-patch16-224-in21k');
* const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png';
* const features = await image_feature_extractor(url);
* // Tensor {
* // dims: [ 1, 197, 768 ],
* // type: 'float32',
* // data: Float32Array(151296) [ ... ],
* // size: 151296
* // }
* ```
*
* **Example:** Compute image embeddings with `Xenova/clip-vit-base-patch32`.
* ```javascript
* const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/clip-vit-base-patch32');
* const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png';
* const features = await image_feature_extractor(url);
* // Tensor {
* // dims: [ 1, 512 ],
* // type: 'float32',
* // data: Float32Array(512) [ ... ],
* // size: 512
* // }
* ```
*/
export class ImageFeatureExtractionPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageFeatureExtractionPipelineType} */ (Pipeline)) {
/**
* Create a new ImageFeatureExtractionPipeline.
* @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {ImageFeatureExtractionPipelineCallback} */
async _call(images, {
pool = null,
} = {}) {
const preparedImages = await prepareImages(images);
const { pixel_values } = await this.processor(preparedImages);
const outputs = await this.model({ pixel_values });
/** @type {Tensor} */
let result;
if (pool) {
if (!('pooler_output' in outputs)) {
throw Error(`No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.`);
}
result = outputs.pooler_output;
} else {
result = outputs.last_hidden_state ?? outputs.logits ?? outputs.image_embeds;
}
return result;
}
}
// TODO
// export class SentenceSimilarityPipeline extends Pipeline {
// }
/**
* @typedef {Object} AudioClassificationSingle
* @property {string} label The label predicted.
* @property {number} score The corresponding probability.
* @typedef {AudioClassificationSingle[]} AudioClassificationOutput
*
* @typedef {Object} AudioClassificationPipelineOptions Parameters specific to audio classification pipelines.
* @property {number} [top_k=5] The number of top labels that will be returned by the pipeline.
* If the provided number is `null` or higher than the number of labels available in the model configuration,
* it will default to the number of labels.
*
* @callback AudioClassificationPipelineCallback Classify the sequence(s) given as inputs.
* @param {AudioPipelineInputs} audio The input audio file(s) to be classified. The input is either:
* - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate
* to get the waveform using the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/AudioContext) API.
* If `AudioContext` is not available, you should pass the raw waveform in as a Float32Array of shape `(n, )`.
* - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done).
* @param {AudioClassificationPipelineOptions} [options] The options to use for audio classification.
* @returns {Promise<AudioClassificationOutput|AudioClassificationOutput[]>} An array or object containing the predicted labels and scores.
*
* @typedef {AudioPipelineConstructorArgs & AudioClassificationPipelineCallback & Disposable} AudioClassificationPipelineType
*/
/**
* Audio classification pipeline using any `AutoModelForAudioClassification`.
* This pipeline predicts the class of a raw waveform or an audio file.
*
* **Example:** Perform audio classification with `Xenova/wav2vec2-large-xlsr-53-gender-recognition-librispeech`.
* ```javascript
* const classifier = await pipeline('audio-classification', 'Xenova/wav2vec2-large-xlsr-53-gender-recognition-librispeech');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
* const output = await classifier(url);
* // [
* // { label: 'male', score: 0.9981542229652405 },
* // { label: 'female', score: 0.001845747814513743 }
* // ]
* ```
*
* **Example:** Perform audio classification with `Xenova/ast-finetuned-audioset-10-10-0.4593` and return top 4 results.
* ```javascript
* const classifier = await pipeline('audio-classification', 'Xenova/ast-finetuned-audioset-10-10-0.4593');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cat_meow.wav';
* const output = await classifier(url, { top_k: 4 });
* // [
* // { label: 'Meow', score: 0.5617874264717102 },
* // { label: 'Cat', score: 0.22365376353263855 },
* // { label: 'Domestic animals, pets', score: 0.1141069084405899 },
* // { label: 'Animal', score: 0.08985692262649536 },
* // ]
* ```
*/
export class AudioClassificationPipeline extends (/** @type {new (options: AudioPipelineConstructorArgs) => AudioClassificationPipelineType} */ (Pipeline)) {
/**
* Create a new AudioClassificationPipeline.
* @param {AudioPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {AudioClassificationPipelineCallback} */
async _call(audio, {
top_k = 5
} = {}) {
const sampling_rate = this.processor.feature_extractor.config.sampling_rate;
const preparedAudios = await prepareAudios(audio, sampling_rate);
// @ts-expect-error TS2339
const id2label = this.model.config.id2label;
const toReturn = [];
for (const aud of preparedAudios) {
const inputs = await this.processor(aud);
const output = await this.model(inputs);
const logits = output.logits[0];
const scores = await topk(new Tensor(
'float32',
softmax(logits.data),
logits.dims,
), top_k);
const values = scores[0].tolist();
const indices = scores[1].tolist();
const vals = indices.map((x, i) => ({
label: /** @type {string} */ (id2label ? id2label[x] : `LABEL_${x}`),
score: /** @type {number} */ (values[i]),
}));
toReturn.push(vals);
};
return Array.isArray(audio) ? toReturn : toReturn[0];
}
}
/**
* @typedef {Object} ZeroShotAudioClassificationOutput
* @property {string} label The label identified by the model. It is one of the suggested `candidate_label`.
* @property {number} score The score attributed by the model for that label (between 0 and 1).
*
* @typedef {Object} ZeroShotAudioClassificationPipelineOptions Parameters specific to zero-shot audio classification pipelines.
* @property {string} [hypothesis_template="This is a sound of {}."] The sentence used in conjunction with `candidate_labels`
* to attempt the audio classification by replacing the placeholder with the candidate_labels.
* Then likelihood is estimated by using `logits_per_audio`.
*
* @callback ZeroShotAudioClassificationPipelineCallback Classify the sequence(s) given as inputs.
* @param {AudioPipelineInputs} audio The input audio file(s) to be classified. The input is either:
* - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate
* to get the waveform using the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/AudioContext) API.
* If `AudioContext` is not available, you should pass the raw waveform in as a Float32Array of shape `(n, )`.
* - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done).
* @param {string[]} candidate_labels The candidate labels for this audio.
* @param {ZeroShotAudioClassificationPipelineOptions} [options] The options to use for zero-shot audio classification.
* @returns {Promise<ZeroShotAudioClassificationOutput[]|ZeroShotAudioClassificationOutput[][]>} An array of objects containing the predicted labels and scores.
*
* @typedef {TextAudioPipelineConstructorArgs & ZeroShotAudioClassificationPipelineCallback & Disposable} ZeroShotAudioClassificationPipelineType
*/
/**
* Zero shot audio classification pipeline using `ClapModel`. This pipeline predicts the class of an audio when you
* provide an audio and a set of `candidate_labels`.
*
* **Example**: Perform zero-shot audio classification with `Xenova/clap-htsat-unfused`.
* ```javascript
* const classifier = await pipeline('zero-shot-audio-classification', 'Xenova/clap-htsat-unfused');
* const audio = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/dog_barking.wav';
* const candidate_labels = ['dog', 'vaccum cleaner'];
* const scores = await classifier(audio, candidate_labels);
* // [
* // { score: 0.9993992447853088, label: 'dog' },
* // { score: 0.0006007603369653225, label: 'vaccum cleaner' }
* // ]
* ```
*/
export class ZeroShotAudioClassificationPipeline extends (/** @type {new (options: TextAudioPipelineConstructorArgs) => ZeroShotAudioClassificationPipelineType} */ (Pipeline)) {
/**
* Create a new ZeroShotAudioClassificationPipeline.
* @param {TextAudioPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {ZeroShotAudioClassificationPipelineCallback} */
async _call(audio, candidate_labels, {
hypothesis_template = "This is a sound of {}."
} = {}) {
const single = !Array.isArray(audio);
if (single) {
audio = [/** @type {AudioInput} */ (audio)];
}
// Insert label into hypothesis template
const texts = candidate_labels.map(
x => hypothesis_template.replace('{}', x)
);
// Run tokenization
const text_inputs = this.tokenizer(texts, {
padding: true,
truncation: true,
});
const sampling_rate = this.processor.feature_extractor.config.sampling_rate;
const preparedAudios = await prepareAudios(audio, sampling_rate);
const toReturn = [];
for (const aud of preparedAudios) {
const audio_inputs = await this.processor(aud);
// Run model with both text and audio inputs
const output = await this.model({ ...text_inputs, ...audio_inputs });
// Compute softmax per audio
const probs = softmax(output.logits_per_audio.data);
toReturn.push([...probs].map((x, i) => ({
score: x,
label: candidate_labels[i]
})));
}
return single ? toReturn[0] : toReturn;
}
}
/**
* @typedef {Object} Chunk
* @property {[number, number]} timestamp The start and end timestamp of the chunk in seconds.
* @property {string} text The recognized text.
*/
/**
* @typedef {Object} AutomaticSpeechRecognitionOutput
* @property {string} text The recognized text.
* @property {Chunk[]} [chunks] When using `return_timestamps`, the `chunks` will become a list
* containing all the various text chunks identified by the model.
*
* @typedef {Object} AutomaticSpeechRecognitionSpecificParams Parameters specific to automatic-speech-recognition pipelines.
* @property {boolean|'word'} [return_timestamps] Whether to return timestamps or not. Default is `false`.
* @property {number} [chunk_length_s] The length of audio chunks to process in seconds. Default is 0 (no chunking).
* @property {number} [stride_length_s] The length of overlap between consecutive audio chunks in seconds. If not provided, defaults to `chunk_length_s / 6`.
* @property {boolean} [force_full_sequences] Whether to force outputting full sequences or not. Default is `false`.
* @property {string} [language] The source language. Default is `null`, meaning it should be auto-detected. Use this to potentially improve performance if the source language is known.
* @property {string} [task] The task to perform. Default is `null`, meaning it should be auto-detected.
* @property {number} [num_frames] The number of frames in the input audio.
* @typedef {import('./generation/configuration_utils.js').GenerationConfig & AutomaticSpeechRecognitionSpecificParams} AutomaticSpeechRecognitionConfig
*
* @callback AutomaticSpeechRecognitionPipelineCallback Transcribe the audio sequence(s) given as inputs to text.
* @param {AudioPipelineInputs} audio The input audio file(s) to be transcribed. The input is either:
* - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate
* to get the waveform using the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/AudioContext) API.
* If `AudioContext` is not available, you should pass the raw waveform in as a Float32Array of shape `(n, )`.
* - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done).
* @param {Partial<AutomaticSpeechRecognitionConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
* @returns {Promise<AutomaticSpeechRecognitionOutput|AutomaticSpeechRecognitionOutput[]>} An object containing the transcription text and optionally timestamps if `return_timestamps` is `true`.
*
* @typedef {TextAudioPipelineConstructorArgs & AutomaticSpeechRecognitionPipelineCallback & Disposable} AutomaticSpeechRecognitionPipelineType
*/
/**
* Pipeline that aims at extracting spoken text contained within some audio.
*
* **Example:** Transcribe English.
* ```javascript
* const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
* const output = await transcriber(url);
* // { text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country." }
* ```
*
* **Example:** Transcribe English w/ timestamps.
* ```javascript
* const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
* const output = await transcriber(url, { return_timestamps: true });
* // {
* // text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country."
* // chunks: [
* // { timestamp: [0, 8], text: " And so my fellow Americans ask not what your country can do for you" }
* // { timestamp: [8, 11], text: " ask what you can do for your country." }
* // ]
* // }
* ```
*
* **Example:** Transcribe English w/ word-level timestamps.
* ```javascript
* const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
* const output = await transcriber(url, { return_timestamps: 'word' });
* // {
* // "text": " And so my fellow Americans ask not what your country can do for you ask what you can do for your country.",
* // "chunks": [
* // { "text": " And", "timestamp": [0, 0.78] },
* // { "text": " so", "timestamp": [0.78, 1.06] },
* // { "text": " my", "timestamp": [1.06, 1.46] },
* // ...
* // { "text": " for", "timestamp": [9.72, 9.92] },
* // { "text": " your", "timestamp": [9.92, 10.22] },
* // { "text": " country.", "timestamp": [10.22, 13.5] }
* // ]
* // }
* ```
*
* **Example:** Transcribe French.
* ```javascript
* const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/french-audio.mp3';
* const output = await transcriber(url, { language: 'french', task: 'transcribe' });
* // { text: " J'adore, j'aime, je n'aime pas, je déteste." }
* ```
*
* **Example:** Translate French to English.
* ```javascript
* const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/french-audio.mp3';
* const output = await transcriber(url, { language: 'french', task: 'translate' });
* // { text: " I love, I like, I don't like, I hate." }
* ```
*
* **Example:** Transcribe/translate audio longer than 30 seconds.
* ```javascript
* const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/ted_60.wav';
* const output = await transcriber(url, { chunk_length_s: 30, stride_length_s: 5 });
* // { text: " So in college, I was a government major, which means [...] So I'd start off light and I'd bump it up" }
* ```
*/
export class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options: TextAudioPipelineConstructorArgs) => AutomaticSpeechRecognitionPipelineType} */ (Pipeline)) {
/**
* Create a new AutomaticSpeechRecognitionPipeline.
* @param {TextAudioPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {AutomaticSpeechRecognitionPipelineCallback} */
async _call(audio, kwargs = {}) {
switch (this.model.config.model_type) {
case 'whisper':
return this._call_whisper(audio, kwargs)
case 'wav2vec2':
case 'wav2vec2-bert':
case 'unispeech':
case 'unispeech-sat':
case 'hubert':
return this._call_wav2vec2(audio, kwargs)
case 'moonshine':
return this._call_moonshine(audio, kwargs)
default:
throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)
}
}
/**
* @type {AutomaticSpeechRecognitionPipelineCallback}
* @private
*/
async _call_wav2vec2(audio, kwargs) {
// TODO use kwargs
if (kwargs.language) {
console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".');
}
if (kwargs.task) {
console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');
}
const single = !Array.isArray(audio);
if (single) {
audio = [/** @type {AudioInput} */ (audio)];
}
const sampling_rate = this.processor.feature_extractor.config.sampling_rate;
const preparedAudios = await prepareAudios(audio, sampling_rate);
const toReturn = [];
for (const aud of preparedAudios) {
const inputs = await this.processor(aud);
const output = await this.model(inputs);
const logits = output.logits[0];
const predicted_ids = [];
for (const item of logits) {
predicted_ids.push(max(item.data)[1])
}
const predicted_sentences = this.tokenizer.decode(predicted_ids)
toReturn.push({ text: predicted_sentences })
}
return single ? toReturn[0] : toReturn;
}
/**
* @type {AutomaticSpeechRecognitionPipelineCallback}
* @private
*/
async _call_whisper(audio, kwargs) {
const return_timestamps = kwargs.return_timestamps ?? false;
const chunk_length_s = kwargs.chunk_length_s ?? 0;
const force_full_sequences = kwargs.force_full_sequences ?? false;
let stride_length_s = kwargs.stride_length_s ?? null;
const generation_config = { ...kwargs }
if (return_timestamps === 'word') {
generation_config['return_token_timestamps'] = true;
generation_config['return_timestamps'] = false; // Do not predict timestamp tokens
}
const single = !Array.isArray(audio);
if (single) {
audio = [/** @type {AudioInput} */ (audio)];
}
// @ts-expect-error TS2339
const time_precision = this.processor.feature_extractor.config.chunk_length / this.model.config.max_source_positions;
const hop_length = this.processor.feature_extractor.config.hop_length;
const sampling_rate = this.processor.feature_extractor.config.sampling_rate;
const preparedAudios = await prepareAudios(audio, sampling_rate);
const toReturn = [];
for (const aud of preparedAudios) {
/** @type {{stride: number[], input_features: Tensor, is_last: boolean, tokens?: bigint[], token_timestamps?: number[]}[]} */
let chunks = [];
if (chunk_length_s > 0) {
if (stride_length_s === null) {
stride_length_s = chunk_length_s / 6;
} else if (chunk_length_s <= stride_length_s) {
throw Error("`chunk_length_s` must be larger than `stride_length_s`.")
}
// TODO support different stride_length_s (for left and right)
const window = sampling_rate * chunk_length_s;
const stride = sampling_rate * stride_length_s;
const jump = window - 2 * stride;
let offset = 0;
// Create subarrays of audio with overlaps
while (true) {
const offset_end = offset + window;
const subarr = aud.subarray(offset, offset_end);
const feature = await this.processor(subarr);
const is_first = offset === 0;
const is_last = offset_end >= aud.length;
chunks.push({
stride: [
subarr.length,
is_first ? 0 : stride,
is_last ? 0 : stride
],
input_features: feature.input_features,
is_last,
})
if (is_last) break;
offset += jump;
}
} else {
chunks = [{
stride: [aud.length, 0, 0],
input_features: (await this.processor(aud)).input_features,
is_last: true
}]
}
// Generate for each set of input features
for (const chunk of chunks) {
generation_config.num_frames = Math.floor(chunk.stride[0] / hop_length);
// NOTE: doing sequentially for now
const data = await this.model.generate({
inputs: chunk.input_features,
...generation_config
});
// TODO: Right now we only get top beam
if (return_timestamps === 'word') {
// @ts-expect-error TS2339
chunk.tokens = data.sequences.tolist()[0];
// @ts-expect-error TS2339
chunk.token_timestamps = data.token_timestamps.tolist()[0].map(
(/** @type {number} */ x) => round(x, 2)
);
} else {
chunk.tokens = (/** @type {Tensor} */(data))[0].tolist();
}
// convert stride to seconds
chunk.stride = chunk.stride.map(x => x / sampling_rate);
}
// Merge text chunks
// @ts-ignore
const [full_text, optional] = this.tokenizer._decode_asr(chunks, {
time_precision, return_timestamps, force_full_sequences
});
toReturn.push({ text: full_text, ...optional })
}
return single ? toReturn[0] : toReturn;
}
/**
* @type {AutomaticSpeechRecognitionPipelineCallback}
* @private
*/
async _call_moonshine(audio, kwargs) {
const single = !Array.isArray(audio);
if (single) {
audio = [/** @type {AudioInput} */ (audio)];
}
const sampling_rate = this.processor.feature_extractor.config.sampling_rate;
const preparedAudios = await prepareAudios(audio, sampling_rate);
const toReturn = [];
for (const aud of preparedAudios) {
const inputs = await this.processor(aud);
// According to the [paper](https://arxiv.org/pdf/2410.15608):
// "We use greedy decoding, with a heuristic limit of 6 output tokens
// per second of audio to avoid repeated output sequences."
const max_new_tokens = Math.floor(aud.length / sampling_rate) * 6;
const outputs = await this.model.generate({ max_new_tokens, ...kwargs, ...inputs });
const text = this.processor.batch_decode(/** @type {Tensor} */(outputs), { skip_special_tokens: true })[0];
toReturn.push({ text });
}
return single ? toReturn[0] : toReturn;
}
}
/**
* @typedef {Object} ImageToTextSingle
* @property {string} generated_text The generated text.
* @typedef {ImageToTextSingle[]} ImageToTextOutput
*
* @callback ImageToTextPipelineCallback Assign labels to the image(s) passed as inputs.
* @param {ImagePipelineInputs} texts The images to be captioned.
* @param {Partial<import('./generation/configuration_utils.js').GenerationConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
* @returns {Promise<ImageToTextOutput|ImageToTextOutput[]>} An object (or array of objects) containing the generated text(s).
*
* @typedef {TextImagePipelineConstructorArgs & ImageToTextPipelineCallback & Disposable} ImageToTextPipelineType
*/
/**
* Image To Text pipeline using a `AutoModelForVision2Seq`. This pipeline predicts a caption for a given image.
*
* **Example:** Generate a caption for an image w/ `Xenova/vit-gpt2-image-captioning`.
* ```javascript
* const captioner = await pipeline('image-to-text', 'Xenova/vit-gpt2-image-captioning');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
* const output = await captioner(url);
* // [{ generated_text: 'a cat laying on a couch with another cat' }]
* ```
*
* **Example:** Optical Character Recognition (OCR) w/ `Xenova/trocr-small-handwritten`.
* ```javascript
* const captioner = await pipeline('image-to-text', 'Xenova/trocr-small-handwritten');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/handwriting.jpg';
* const output = await captioner(url);
* // [{ generated_text: 'Mr. Brown commented icily.' }]
* ```
*/
export class ImageToTextPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => ImageToTextPipelineType} */ (Pipeline)) {
/**
* Create a new ImageToTextPipeline.
* @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {ImageToTextPipelineCallback} */
async _call(images, generate_kwargs = {}) {
const isBatched = Array.isArray(images);
const preparedImages = await prepareImages(images);
const { pixel_values } = await this.processor(preparedImages);
const toReturn = [];
for (const batch of pixel_values) {
batch.dims = [1, ...batch.dims]
const output = await this.model.generate({ inputs: batch, ...generate_kwargs });
const decoded = this.tokenizer.batch_decode(/** @type {Tensor} */(output), {
skip_special_tokens: true,
}).map(x => ({ generated_text: x.trim() }))
toReturn.push(decoded);
}
return isBatched ? toReturn : toReturn[0];
}
}
/**
* @typedef {Object} ImageClassificationSingle
* @property {string} label The label identified by the model.
* @property {number} score The score attributed by the model for that label.
* @typedef {ImageClassificationSingle[]} ImageClassificationOutput
*
* @typedef {Object} ImageClassificationPipelineOptions Parameters specific to image classification pipelines.
* @property {number} [top_k=1] The number of top labels that will be returned by the pipeline.
*
* @callback ImageClassificationPipelineCallback Assign labels to the image(s) passed as inputs.
* @param {ImagePipelineInputs} images The input images(s) to be classified.
* @param {ImageClassificationPipelineOptions} [options] The options to use for image classification.
* @returns {Promise<ImageClassificationOutput|ImageClassificationOutput[]>} An array or object containing the predicted labels and scores.
*
* @typedef {ImagePipelineConstructorArgs & ImageClassificationPipelineCallback & Disposable} ImageClassificationPipelineType
*/
/**
* Image classification pipeline using any `AutoModelForImageClassification`.
* This pipeline predicts the class of an image.
*
* **Example:** Classify an image.
* ```javascript
* const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
* const output = await classifier(url);
* // [
* // { label: 'tiger, Panthera tigris', score: 0.632695734500885 },
* // ]
* ```
*
* **Example:** Classify an image and return top `n` classes.
* ```javascript
* const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
* const output = await classifier(url, { top_k: 3 });
* // [
* // { label: 'tiger, Panthera tigris', score: 0.632695734500885 },
* // { label: 'tiger cat', score: 0.3634825646877289 },
* // { label: 'lion, king of beasts, Panthera leo', score: 0.00045060308184474707 },
* // ]
* ```
*
* **Example:** Classify an image and return all classes.
* ```javascript
* const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
* const output = await classifier(url, { top_k: 0 });
* // [
* // { label: 'tiger, Panthera tigris', score: 0.632695734500885 },
* // { label: 'tiger cat', score: 0.3634825646877289 },
* // { label: 'lion, king of beasts, Panthera leo', score: 0.00045060308184474707 },
* // { label: 'jaguar, panther, Panthera onca, Felis onca', score: 0.00035465499968267977 },
* // ...
* // ]
* ```
*/
export class ImageClassificationPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageClassificationPipelineType} */ (Pipeline)) {
/**
* Create a new ImageClassificationPipeline.
* @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {ImageClassificationPipelineCallback} */
async _call(images, {
top_k = 5
} = {}) {
const preparedImages = await prepareImages(images);
const { pixel_values } = await this.processor(preparedImages);
const output = await this.model({ pixel_values });
// @ts-expect-error TS2339
const id2label = this.model.config.id2label;
/** @type {ImageClassificationOutput[]} */
const toReturn = [];
for (const batch of output.logits) {
const scores = await topk(new Tensor(
'float32',
softmax(batch.data),
batch.dims,
), top_k);
const values = scores[0].tolist();
const indices = scores[1].tolist();
const vals = indices.map((x, i) => ({
label: /** @type {string} */ (id2label ? id2label[x] : `LABEL_${x}`),
score: /** @type {number} */ (values[i]),
}));
toReturn.push(vals);
}
return Array.isArray(images) ? toReturn : toReturn[0];
}
}
/**
* @typedef {Object} ImageSegmentationPipelineOutput
* @property {string} label The label of the segment.
* @property {number|null} score The score of the segment.
* @property {RawImage} mask The mask of the segment.
*
* @typedef {Object} ImageSegmentationPipelineOptions Parameters specific to image segmentation pipelines.
* @property {number} [threshold=0.5] Probability threshold to filter out predicted masks.
* @property {number} [mask_threshold=0.5] Threshold to use when turning the predicted masks into binary values.
* @property {number} [overlap_mask_area_threshold=0.8] Mask overlap threshold to eliminate small, disconnected segments.
* @property {null|string} [subtask=null] Segmentation task to be performed. One of [`panoptic`, `instance`, and `semantic`],
* depending on model capabilities. If not set, the pipeline will attempt to resolve (in that order).
* @property {number[]} [label_ids_to_fuse=null] List of label ids to fuse. If not set, do not fuse any labels.
* @property {number[][]} [target_sizes=null] List of target sizes for the input images. If not set, use the original image sizes.
*
* @callback ImageSegmentationPipelineCallback Segment the input images.
* @param {ImagePipelineInputs} images The input images.
* @param {ImageSegmentationPipelineOptions} [options] The options to use for image segmentation.
* @returns {Promise<ImageSegmentationPipelineOutput[]>} The annotated segments.
*
* @typedef {ImagePipelineConstructorArgs & ImageSegmentationPipelineCallback & Disposable} ImageSegmentationPipelineType
*/
/**
* Image segmentation pipeline using any `AutoModelForXXXSegmentation`.
* This pipeline predicts masks of objects and their classes.
*
* **Example:** Perform image segmentation with `Xenova/detr-resnet-50-panoptic`.
* ```javascript
* const segmenter = await pipeline('image-segmentation', 'Xenova/detr-resnet-50-panoptic');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
* const output = await segmenter(url);
* // [
* // { label: 'remote', score: 0.9984649419784546, mask: RawImage { ... } },
* // { label: 'cat', score: 0.9994316101074219, mask: RawImage { ... } }
* // ]
* ```
*/
export class ImageSegmentationPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageSegmentationPipelineType} */ (Pipeline)) {
/**
* Create a new ImageSegmentationPipeline.
* @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
this.subtasks_mapping = {
// Mapping of subtasks to their corresponding post-processing function names.
panoptic: 'post_process_panoptic_segmentation',
instance: 'post_process_instance_segmentation',
semantic: 'post_process_semantic_segmentation'
}
}
/** @type {ImageSegmentationPipelineCallback} */
async _call(images, {
threshold = 0.5,
mask_threshold = 0.5,
overlap_mask_area_threshold = 0.8,
label_ids_to_fuse = null,
target_sizes = null,
subtask = null,
} = {}) {
const isBatched = Array.isArray(images);
if (isBatched && images.length !== 1) {
throw Error("Image segmentation pipeline currently only supports a batch size of 1.");
}
const preparedImages = await prepareImages(images);
const imageSizes = preparedImages.map(x => [x.height, x.width]);
const { pixel_values, pixel_mask } = await this.processor(preparedImages);
const output = await this.model({ pixel_values, pixel_mask });
let fn = null;
if (subtask !== null) {
fn = this.subtasks_mapping[subtask];
} else {
for (let [task, func] of Object.entries(this.subtasks_mapping)) {
if (func in this.processor.image_processor) {
fn = this.processor.image_processor[func].bind(this.processor.image_processor);
subtask = task;
break;
}
}
}
// @ts-expect-error TS2339
const id2label = this.model.config.id2label;
/** @type {ImageSegmentationPipelineOutput[]} */
const annotation = [];
if (subtask === 'panoptic' || subtask === 'instance') {
const processed = fn(
output,
threshold,
mask_threshold,
overlap_mask_area_threshold,
label_ids_to_fuse,
target_sizes ?? imageSizes, // TODO FIX?
)[0];
const segmentation = processed.segmentation;
for (const segment of processed.segments_info) {
const maskData = new Uint8ClampedArray(segmentation.data.length);
for (let i = 0; i < segmentation.data.length; ++i) {
if (segmentation.data[i] === segment.id) {
maskData[i] = 255;
}
}
const mask = new RawImage(maskData, segmentation.dims[1], segmentation.dims[0], 1)
annotation.push({
score: segment.score,
label: id2label[segment.label_id],
mask: mask
})
}
} else if (subtask === 'semantic') {
const { segmentation, labels } = fn(output, target_sizes ?? imageSizes)[0];
for (const label of labels) {
const maskData = new Uint8ClampedArray(segmentation.data.length);
for (let i = 0; i < segmentation.data.length; ++i) {
if (segmentation.data[i] === label) {
maskData[i] = 255;
}
}
const mask = new RawImage(maskData, segmentation.dims[1], segmentation.dims[0], 1);
annotation.push({
score: null,
label: id2label[label],
mask: mask
});
}
} else {
throw Error(`Subtask ${subtask} not supported.`);
}
return annotation;
}
}
/**
* @typedef {Object} ZeroShotImageClassificationOutput
* @property {string} label The label identified by the model. It is one of the suggested `candidate_label`.
* @property {number} score The score attributed by the model for that label (between 0 and 1).
*
* @typedef {Object} ZeroShotImageClassificationPipelineOptions Parameters specific to zero-shot image classification pipelines.
* @property {string} [hypothesis_template="This is a photo of {}"] The sentence used in conjunction with `candidate_labels`
* to attempt the image classification by replacing the placeholder with the candidate_labels.
* Then likelihood is estimated by using `logits_per_image`.
*
* @callback ZeroShotImageClassificationPipelineCallback Assign labels to the image(s) passed as inputs.
* @param {ImagePipelineInputs} images The input images.
* @param {string[]} candidate_labels The candidate labels for this image.
* @param {ZeroShotImageClassificationPipelineOptions} [options] The options to use for zero-shot image classification.
* @returns {Promise<ZeroShotImageClassificationOutput[]|ZeroShotImageClassificationOutput[][]>} An array of objects containing the predicted labels and scores.
*
* @typedef {TextImagePipelineConstructorArgs & ZeroShotImageClassificationPipelineCallback & Disposable} ZeroShotImageClassificationPipelineType
*/
/**
* Zero shot image classification pipeline. This pipeline predicts the class of
* an image when you provide an image and a set of `candidate_labels`.
*
* **Example:** Zero shot image classification w/ `Xenova/clip-vit-base-patch32`.
* ```javascript
* const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
* const output = await classifier(url, ['tiger', 'horse', 'dog']);
* // [
* // { score: 0.9993917942047119, label: 'tiger' },
* // { score: 0.0003519294841680676, label: 'horse' },
* // { score: 0.0002562698791734874, label: 'dog' }
* // ]
* ```
*/
export class ZeroShotImageClassificationPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => ZeroShotImageClassificationPipelineType} */ (Pipeline)) {
/**
* Create a new ZeroShotImageClassificationPipeline.
* @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {ZeroShotImageClassificationPipelineCallback} */
async _call(images, candidate_labels, {
hypothesis_template = "This is a photo of {}"
} = {}) {
const isBatched = Array.isArray(images);
const preparedImages = await prepareImages(images);
// Insert label into hypothesis template
const texts = candidate_labels.map(
x => hypothesis_template.replace('{}', x)
);
// Run tokenization
const text_inputs = this.tokenizer(texts, {
padding: this.model.config.model_type === 'siglip' ? 'max_length' : true,
truncation: true,
});
// Run processor
const { pixel_values } = await this.processor(preparedImages);
// Run model with both text and pixel inputs
const output = await this.model({ ...text_inputs, pixel_values });
const function_to_apply =
this.model.config.model_type === 'siglip'
? batch => batch.sigmoid().data
: batch => softmax(batch.data);
// Compare each image with each candidate label
const toReturn = [];
for (const batch of output.logits_per_image) {
// Compute softmax per image
const probs = function_to_apply(batch);
const result = [...probs].map((x, i) => ({
score: x,
label: candidate_labels[i]
}));
result.sort((a, b) => b.score - a.score); // sort by score in descending order
toReturn.push(result);
}
return isBatched ? toReturn : toReturn[0];
}
}
/**
* @typedef {Object} ObjectDetectionPipelineSingle
* @property {string} label The class label identified by the model.
* @property {number} score The score attributed by the model for that label.
* @property {BoundingBox} box The bounding box of detected object in image's original size, or as a percentage if `percentage` is set to true.
* @typedef {ObjectDetectionPipelineSingle[]} ObjectDetectionPipelineOutput
*
* @typedef {Object} ObjectDetectionPipelineOptions Parameters specific to object detection pipelines.
* @property {number} [threshold=0.9] The threshold used to filter boxes by score.
* @property {boolean} [percentage=false] Whether to return the boxes coordinates in percentage (true) or in pixels (false).
*
* @callback ObjectDetectionPipelineCallback Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
* @param {ImagePipelineInputs} images The input images.
* @param {ObjectDetectionPipelineOptions} [options] The options to use for object detection.
* @returns {Promise<ObjectDetectionPipelineOutput|ObjectDetectionPipelineOutput[]>} A list of objects or a list of list of objects.
*
* @typedef {ImagePipelineConstructorArgs & ObjectDetectionPipelineCallback & Disposable} ObjectDetectionPipelineType
*/
/**
* Object detection pipeline using any `AutoModelForObjectDetection`.
* This pipeline predicts bounding boxes of objects and their classes.
*
* **Example:** Run object-detection with `Xenova/detr-resnet-50`.
* ```javascript
* const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
* const img = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
* const output = await detector(img, { threshold: 0.9 });
* // [{
* // score: 0.9976370930671692,
* // label: "remote",
* // box: { xmin: 31, ymin: 68, xmax: 190, ymax: 118 }
* // },
* // ...
* // {
* // score: 0.9984092116355896,
* // label: "cat",
* // box: { xmin: 331, ymin: 19, xmax: 649, ymax: 371 }
* // }]
* ```
*/
export class ObjectDetectionPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ObjectDetectionPipelineType} */ (Pipeline)) {
/**
* Create a new ObjectDetectionPipeline.
* @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {ObjectDetectionPipelineCallback} */
async _call(images, {
threshold = 0.9,
percentage = false,
} = {}) {
const isBatched = Array.isArray(images);
if (isBatched && images.length !== 1) {
throw Error("Object detection pipeline currently only supports a batch size of 1.");
}
const preparedImages = await prepareImages(images);
const imageSizes = percentage ? null : preparedImages.map(x => [x.height, x.width]);
const { pixel_values, pixel_mask } = await this.processor(preparedImages);
const output = await this.model({ pixel_values, pixel_mask });
// @ts-ignore
const processed = this.processor.image_processor.post_process_object_detection(output, threshold, imageSizes);
// Add labels
// @ts-expect-error TS2339
const id2label = this.model.config.id2label;
// Format output
/** @type {ObjectDetectionPipelineOutput[]} */
const result = processed.map(batch => (
batch.boxes.map((box, i) => ({
score: batch.scores[i],
label: id2label[batch.classes[i]],
box: get_bounding_box(box, !percentage),
}))
))
return isBatched ? result : result[0];
}
}
/**
* @typedef {Object} ZeroShotObjectDetectionOutput
* @property {string} label Text query corresponding to the found object.
* @property {number} score Score corresponding to the object (between 0 and 1).
* @property {BoundingBox} box Bounding box of the detected object in image's original size, or as a percentage if `percentage` is set to true.
*
* @typedef {Object} ZeroShotObjectDetectionPipelineOptions Parameters specific to zero-shot object detection pipelines.
* @property {number} [threshold=0.1] The probability necessary to make a prediction.
* @property {number} [top_k=null] The number of top predictions that will be returned by the pipeline.
* If the provided number is `null` or higher than the number of predictions available, it will default
* to the number of predictions.
* @property {boolean} [percentage=false] Whether to return the boxes coordinates in percentage (true) or in pixels (false).
*
* @callback ZeroShotObjectDetectionPipelineCallback Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
* @param {ImagePipelineInputs} images The input images.
* @param {string[]} candidate_labels What the model should recognize in the image.
* @param {ZeroShotObjectDetectionPipelineOptions} [options] The options to use for zero-shot object detection.
* @returns {Promise<ZeroShotObjectDetectionOutput[]|ZeroShotObjectDetectionOutput[][]>} An array of objects containing the predicted labels, scores, and bounding boxes.
*
* @typedef {TextImagePipelineConstructorArgs & ZeroShotObjectDetectionPipelineCallback & Disposable} ZeroShotObjectDetectionPipelineType
*/
/**
* Zero-shot object detection pipeline. This pipeline predicts bounding boxes of
* objects when you provide an image and a set of `candidate_labels`.
*
* **Example:** Zero-shot object detection w/ `Xenova/owlvit-base-patch32`.
* ```javascript
* const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/astronaut.png';
* const candidate_labels = ['human face', 'rocket', 'helmet', 'american flag'];
* const output = await detector(url, candidate_labels);
* // [
* // {
* // score: 0.24392342567443848,
* // label: 'human face',
* // box: { xmin: 180, ymin: 67, xmax: 274, ymax: 175 }
* // },
* // {
* // score: 0.15129457414150238,
* // label: 'american flag',
* // box: { xmin: 0, ymin: 4, xmax: 106, ymax: 513 }
* // },
* // {
* // score: 0.13649864494800568,
* // label: 'helmet',
* // box: { xmin: 277, ymin: 337, xmax: 511, ymax: 511 }
* // },
* // {
* // score: 0.10262022167444229,
* // label: 'rocket',
* // box: { xmin: 352, ymin: -1, xmax: 463, ymax: 287 }
* // }
* // ]
* ```
*
* **Example:** Zero-shot object detection w/ `Xenova/owlvit-base-patch32` (returning top 4 matches and setting a threshold).
* ```javascript
* const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/beach.png';
* const candidate_labels = ['hat', 'book', 'sunglasses', 'camera'];
* const output = await detector(url, candidate_labels, { top_k: 4, threshold: 0.05 });
* // [
* // {
* // score: 0.1606510728597641,
* // label: 'sunglasses',
* // box: { xmin: 347, ymin: 229, xmax: 429, ymax: 264 }
* // },
* // {
* // score: 0.08935828506946564,
* // label: 'hat',
* // box: { xmin: 38, ymin: 174, xmax: 258, ymax: 364 }
* // },
* // {
* // score: 0.08530698716640472,
* // label: 'camera',
* // box: { xmin: 187, ymin: 350, xmax: 260, ymax: 411 }
* // },
* // {
* // score: 0.08349756896495819,
* // label: 'book',
* // box: { xmin: 261, ymin: 280, xmax: 494, ymax: 425 }
* // }
* // ]
* ```
*/
export class ZeroShotObjectDetectionPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => ZeroShotObjectDetectionPipelineType} */ (Pipeline)) {
/**
* Create a new ZeroShotObjectDetectionPipeline.
* @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {ZeroShotObjectDetectionPipelineCallback} */
async _call(images, candidate_labels, {
threshold = 0.1,
top_k = null,
percentage = false,
} = {}) {
const isBatched = Array.isArray(images);
const preparedImages = await prepareImages(images);
// Run tokenization
const text_inputs = this.tokenizer(candidate_labels, {
padding: true,
truncation: true,
});
// Run processor
const model_inputs = await this.processor(preparedImages);
// Since non-maximum suppression is performed for exporting, we need to
// process each image separately. For more information, see:
// https://github.com/huggingface/optimum/blob/e3b7efb1257c011db907ef40ab340e795cc5684c/optimum/exporters/onnx/model_configs.py#L1028-L1032
const toReturn = [];
for (let i = 0; i < preparedImages.length; ++i) {
const image = preparedImages[i];
const imageSize = percentage ? null : [[image.height, image.width]];
const pixel_values = model_inputs.pixel_values[i].unsqueeze_(0);
// Run model with both text and pixel inputs
const output = await this.model({ ...text_inputs, pixel_values });
let result;
if('post_process_grounded_object_detection' in this.processor) {
// @ts-ignore
const processed = this.processor.post_process_grounded_object_detection(
output,
text_inputs.input_ids,
{
// TODO: support separate threshold values
box_threshold: threshold,
text_threshold: threshold,
target_sizes: imageSize,
},
)[0];
result = processed.boxes.map((box, i) => ({
score: processed.scores[i],
label: processed.labels[i],
box: get_bounding_box(box, !percentage),
}))
} else {
// @ts-ignore
const processed = this.processor.image_processor.post_process_object_detection(output, threshold, imageSize, true)[0];
result = processed.boxes.map((box, i) => ({
score: processed.scores[i],
label: candidate_labels[processed.classes[i]],
box: get_bounding_box(box, !percentage),
}))
}
result.sort((a, b) => b.score - a.score);
if (top_k !== null) {
result = result.slice(0, top_k);
}
toReturn.push(result)
}
return isBatched ? toReturn : toReturn[0];
}
}
/**
* @typedef {Object} DocumentQuestionAnsweringSingle
* @property {string} answer The generated text.
* @typedef {DocumentQuestionAnsweringSingle[]} DocumentQuestionAnsweringOutput
*
* @callback DocumentQuestionAnsweringPipelineCallback Answer the question given as input by using the document.
* @param {ImageInput} image The image of the document to use.
* @param {string} question A question to ask of the document.
* @param {Partial<import('./generation/configuration_utils.js').GenerationConfig>} [options] Additional keyword arguments to pass along to the generate method of the model.
* @returns {Promise<DocumentQuestionAnsweringOutput|DocumentQuestionAnsweringOutput[]>} An object (or array of objects) containing the answer(s).
*
* @typedef {TextImagePipelineConstructorArgs & DocumentQuestionAnsweringPipelineCallback & Disposable} DocumentQuestionAnsweringPipelineType
*/
/**
* Document Question Answering pipeline using any `AutoModelForDocumentQuestionAnswering`.
* The inputs/outputs are similar to the (extractive) question answering pipeline; however,
* the pipeline takes an image (and optional OCR'd words/boxes) as input instead of text context.
*
* **Example:** Answer questions about a document with `Xenova/donut-base-finetuned-docvqa`.
* ```javascript
* const qa_pipeline = await pipeline('document-question-answering', 'Xenova/donut-base-finetuned-docvqa');
* const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png';
* const question = 'What is the invoice number?';
* const output = await qa_pipeline(image, question);
* // [{ answer: 'us-001' }]
* ```
*/
export class DocumentQuestionAnsweringPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => DocumentQuestionAnsweringPipelineType} */ (Pipeline)) {
/**
* Create a new DocumentQuestionAnsweringPipeline.
* @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {DocumentQuestionAnsweringPipelineCallback} */
async _call(image, question, generate_kwargs = {}) {
// NOTE: For now, we only support a batch size of 1
// Preprocess image
const preparedImage = (await prepareImages(image))[0];
const { pixel_values } = await this.processor(preparedImage);
// Run tokenization
const task_prompt = `<s_docvqa><s_question>${question}</s_question><s_answer>`;
const decoder_input_ids = this.tokenizer(task_prompt, {
add_special_tokens: false,
padding: true,
truncation: true,
}).input_ids;
// Run model
const output = await this.model.generate({
inputs: pixel_values,
// @ts-expect-error TS2339
max_length: this.model.config.decoder.max_position_embeddings,
decoder_input_ids,
...generate_kwargs,
});
// Decode output
const decoded = this.tokenizer.batch_decode(/** @type {Tensor} */(output))[0];
// Parse answer
const match = decoded.match(/<s_answer>(.*?)<\/s_answer>/);
let answer = null;
if (match && match.length >= 2) {
answer = match[1].trim();
}
return [{ answer }];
}
}
/**
* @typedef {Object} VocoderOptions
* @property {PreTrainedModel} [vocoder] The vocoder used by the pipeline (if the model uses one). If not provided, use the default HifiGan vocoder.
* @typedef {TextAudioPipelineConstructorArgs & VocoderOptions} TextToAudioPipelineConstructorArgs
*/
/**
* @typedef {Object} TextToAudioOutput
* @property {Float32Array} audio The generated audio waveform.
* @property {number} sampling_rate The sampling rate of the generated audio waveform.
*
* @typedef {Object} TextToAudioPipelineOptions Parameters specific to text-to-audio pipelines.
* @property {Tensor|Float32Array|string|URL} [speaker_embeddings=null] The speaker embeddings (if the model requires it).
*
* @callback TextToAudioPipelineCallback Generates speech/audio from the inputs.
* @param {string|string[]} texts The text(s) to generate.
* @param {TextToAudioPipelineOptions} options Parameters passed to the model generation/forward method.
* @returns {Promise<TextToAudioOutput>} An object containing the generated audio and sampling rate.
*
* @typedef {TextToAudioPipelineConstructorArgs & TextToAudioPipelineCallback & Disposable} TextToAudioPipelineType
*/
/**
* Text-to-audio generation pipeline using any `AutoModelForTextToWaveform` or `AutoModelForTextToSpectrogram`.
* This pipeline generates an audio file from an input text and optional other conditional inputs.
*
* **Example:** Generate audio from text with `Xenova/speecht5_tts`.
* ```javascript
* const synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { quantized: false });
* const speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin';
* const out = await synthesizer('Hello, my dog is cute', { speaker_embeddings });
* // RawAudio {
* // audio: Float32Array(26112) [-0.00005657337896991521, 0.00020583874720614403, ...],
* // sampling_rate: 16000
* // }
* ```
*
* You can then save the audio to a .wav file with the `wavefile` package:
* ```javascript
* import wavefile from 'wavefile';
* import fs from 'fs';
*
* const wav = new wavefile.WaveFile();
* wav.fromScratch(1, out.sampling_rate, '32f', out.audio);
* fs.writeFileSync('out.wav', wav.toBuffer());
* ```
*
* **Example:** Multilingual speech generation with `Xenova/mms-tts-fra`. See [here](https://huggingface.co/models?pipeline_tag=text-to-speech&other=vits&sort=trending) for the full list of available languages (1107).
* ```javascript
* const synthesizer = await pipeline('text-to-speech', 'Xenova/mms-tts-fra');
* const out = await synthesizer('Bonjour');
* // RawAudio {
* // audio: Float32Array(23808) [-0.00037693005288019776, 0.0003325853613205254, ...],
* // sampling_rate: 16000
* // }
* ```
*/
export class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPipelineConstructorArgs) => TextToAudioPipelineType} */ (Pipeline)) {
DEFAULT_VOCODER_ID = "Xenova/speecht5_hifigan"
/**
* Create a new TextToAudioPipeline.
* @param {TextToAudioPipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
// TODO: Find a better way for `pipeline` to set the default vocoder
this.vocoder = options.vocoder ?? null;
}
/** @type {TextToAudioPipelineCallback} */
async _call(text_inputs, {
speaker_embeddings = null,
} = {}) {
// If this.processor is not set, we are using a `AutoModelForTextToWaveform` model
if (this.processor) {
return this._call_text_to_spectrogram(text_inputs, { speaker_embeddings });
} else {
return this._call_text_to_waveform(text_inputs);
}
}
async _call_text_to_waveform(text_inputs) {
// Run tokenization
const inputs = this.tokenizer(text_inputs, {
padding: true,
truncation: true,
});
// Generate waveform
const { waveform } = await this.model(inputs);
// @ts-expect-error TS2339
const sampling_rate = this.model.config.sampling_rate;
return new RawAudio(
waveform.data,
sampling_rate,
)
}
async _call_text_to_spectrogram(text_inputs, { speaker_embeddings }) {
// Load vocoder, if not provided
if (!this.vocoder) {
console.log('No vocoder specified, using default HifiGan vocoder.');
this.vocoder = await AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID, { dtype: 'fp32' });
}
// Load speaker embeddings as Float32Array from path/URL
if (typeof speaker_embeddings === 'string' || speaker_embeddings instanceof URL) {
// Load from URL with fetch
speaker_embeddings = new Float32Array(
await (await fetch(speaker_embeddings)).arrayBuffer()
);
}
if (speaker_embeddings instanceof Float32Array) {
speaker_embeddings = new Tensor(
'float32',
speaker_embeddings,
[1, speaker_embeddings.length]
)
} else if (!(speaker_embeddings instanceof Tensor)) {
throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.")
}
// Run tokenization
const { input_ids } = this.tokenizer(text_inputs, {
padding: true,
truncation: true,
});
// NOTE: At this point, we are guaranteed that `speaker_embeddings` is a `Tensor`
// @ts-ignore
const { waveform } = await this.model.generate_speech(input_ids, speaker_embeddings, { vocoder: this.vocoder });
const sampling_rate = this.processor.feature_extractor.config.sampling_rate;
return new RawAudio(
waveform.data,
sampling_rate,
)
}
}
/**
* @callback ImageToImagePipelineCallback Transform the image(s) passed as inputs.
* @param {ImagePipelineInputs} images The images to transform.
* @returns {Promise<RawImage|RawImage[]>} The transformed image or list of images.
*
* @typedef {ImagePipelineConstructorArgs & ImageToImagePipelineCallback & Disposable} ImageToImagePipelineType
*/
/**
* Image to Image pipeline using any `AutoModelForImageToImage`. This pipeline generates an image based on a previous image input.
*
* **Example:** Super-resolution w/ `Xenova/swin2SR-classical-sr-x2-64`
* ```javascript
* const upscaler = await pipeline('image-to-image', 'Xenova/swin2SR-classical-sr-x2-64');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/butterfly.jpg';
* const output = await upscaler(url);
* // RawImage {
* // data: Uint8Array(786432) [ 41, 31, 24, 43, ... ],
* // width: 512,
* // height: 512,
* // channels: 3
* // }
* ```
*/
export class ImageToImagePipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageToImagePipelineType} */ (Pipeline)) {
/**
* Create a new ImageToImagePipeline.
* @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {ImageToImagePipelineCallback} */
async _call(images) {
const preparedImages = await prepareImages(images);
const inputs = await this.processor(preparedImages);
const outputs = await this.model(inputs);
/** @type {RawImage[]} */
const toReturn = [];
for (const batch of outputs.reconstruction) {
const output = batch.squeeze().clamp_(0, 1).mul_(255).round_().to('uint8');
toReturn.push(RawImage.fromTensor(output));
}
return toReturn.length > 1 ? toReturn : toReturn[0];
}
}
/**
* @typedef {Object} DepthEstimationPipelineOutput
* @property {Tensor} predicted_depth The raw depth map predicted by the model.
* @property {RawImage} depth The processed depth map as an image (with the same size as the input image).
*
* @callback DepthEstimationPipelineCallback Predicts the depth for the image(s) passed as inputs.
* @param {ImagePipelineInputs} images The images to compute depth for.
* @returns {Promise<DepthEstimationPipelineOutput|DepthEstimationPipelineOutput[]>} An image or a list of images containing result(s).
*
* @typedef {ImagePipelineConstructorArgs & DepthEstimationPipelineCallback & Disposable} DepthEstimationPipelineType
*/
/**
* Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image.
*
* **Example:** Depth estimation w/ `Xenova/dpt-hybrid-midas`
* ```javascript
* const depth_estimator = await pipeline('depth-estimation', 'Xenova/dpt-hybrid-midas');
* const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
* const out = await depth_estimator(url);
* // {
* // predicted_depth: Tensor {
* // dims: [ 384, 384 ],
* // type: 'float32',
* // data: Float32Array(147456) [ 542.859130859375, 545.2833862304688, 546.1649169921875, ... ],
* // size: 147456
* // },
* // depth: RawImage {
* // data: Uint8Array(307200) [ 86, 86, 86, ... ],
* // width: 640,
* // height: 480,
* // channels: 1
* // }
* // }
* ```
*/
export class DepthEstimationPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => DepthEstimationPipelineType} */ (Pipeline)) {
/**
* Create a new DepthEstimationPipeline.
* @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline.
*/
constructor(options) {
super(options);
}
/** @type {DepthEstimationPipelineCallback} */
async _call(images) {
const preparedImages = await prepareImages(images);
const inputs = await this.processor(preparedImages);
const { predicted_depth } = await this.model(inputs);
const toReturn = [];
for (let i = 0; i < preparedImages.length; ++i) {
const batch = predicted_depth[i];
const [height, width] = batch.dims.slice(-2);
const [new_width, new_height] = preparedImages[i].size;
// Interpolate to original size
const prediction = (await interpolate_4d(batch.view(1, 1, height, width), {
size: [new_height, new_width],
mode: 'bilinear',
})).view(new_height, new_width);
const minval = /** @type {number} */(prediction.min().item());
const maxval = /** @type {number} */(prediction.max().item());
const formatted = prediction.sub(minval).div_(maxval - minval).mul_(255).to('uint8').unsqueeze(0);
const depth = RawImage.fromTensor(formatted);
toReturn.push({
predicted_depth: prediction,
depth,
});
}
return toReturn.length > 1 ? toReturn : toReturn[0];
}
}
const SUPPORTED_TASKS = Object.freeze({
"text-classification": {
"tokenizer": AutoTokenizer,
"pipeline": TextClassificationPipeline,
"model": AutoModelForSequenceClassification,
"default": {
// TODO: replace with original
// "model": "distilbert-base-uncased-finetuned-sst-2-english",
"model": "Xenova/distilbert-base-uncased-finetuned-sst-2-english",
},
"type": "text",
},
"token-classification": {
"tokenizer": AutoTokenizer,
"pipeline": TokenClassificationPipeline,
"model": AutoModelForTokenClassification,
"default": {
// TODO: replace with original
// "model": "Davlan/bert-base-multilingual-cased-ner-hrl",
"model": "Xenova/bert-base-multilingual-cased-ner-hrl",
},
"type": "text",
},
"question-answering": {
"tokenizer": AutoTokenizer,
"pipeline": QuestionAnsweringPipeline,
"model": AutoModelForQuestionAnswering,
"default": {
// TODO: replace with original
// "model": "distilbert-base-cased-distilled-squad",
"model": "Xenova/distilbert-base-cased-distilled-squad",
},
"type": "text",
},
"fill-mask": {
"tokenizer": AutoTokenizer,
"pipeline": FillMaskPipeline,
"model": AutoModelForMaskedLM,
"default": {
// TODO: replace with original
// "model": "bert-base-uncased",
"model": "Xenova/bert-base-uncased",
},
"type": "text",
},
"summarization": {
"tokenizer": AutoTokenizer,
"pipeline": SummarizationPipeline,
"model": AutoModelForSeq2SeqLM,
"default": {
// TODO: replace with original
// "model": "sshleifer/distilbart-cnn-6-6",
"model": "Xenova/distilbart-cnn-6-6",
},
"type": "text",
},
"translation": {
"tokenizer": AutoTokenizer,
"pipeline": TranslationPipeline,
"model": AutoModelForSeq2SeqLM,
"default": {
// TODO: replace with original
// "model": "t5-small",
"model": "Xenova/t5-small",
},
"type": "text",
},
"text2text-generation": {
"tokenizer": AutoTokenizer,
"pipeline": Text2TextGenerationPipeline,
"model": AutoModelForSeq2SeqLM,
"default": {
// TODO: replace with original
// "model": "google/flan-t5-small",
"model": "Xenova/flan-t5-small",
},
"type": "text",
},
"text-generation": {
"tokenizer": AutoTokenizer,
"pipeline": TextGenerationPipeline,
"model": AutoModelForCausalLM,
"default": {
// TODO: replace with original
// "model": "gpt2",
"model": "Xenova/gpt2",
},
"type": "text",
},
"zero-shot-classification": {
"tokenizer": AutoTokenizer,
"pipeline": ZeroShotClassificationPipeline,
"model": AutoModelForSequenceClassification,
"default": {
// TODO: replace with original
// "model": "typeform/distilbert-base-uncased-mnli",
"model": "Xenova/distilbert-base-uncased-mnli",
},
"type": "text",
},
"audio-classification": {
"pipeline": AudioClassificationPipeline,
"model": AutoModelForAudioClassification,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "superb/wav2vec2-base-superb-ks",
"model": "Xenova/wav2vec2-base-superb-ks",
},
"type": "audio",
},
"zero-shot-audio-classification": {
"tokenizer": AutoTokenizer,
"pipeline": ZeroShotAudioClassificationPipeline,
"model": AutoModel,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "laion/clap-htsat-fused",
"model": "Xenova/clap-htsat-unfused",
},
"type": "multimodal",
},
"automatic-speech-recognition": {
"tokenizer": AutoTokenizer,
"pipeline": AutomaticSpeechRecognitionPipeline,
"model": [AutoModelForSpeechSeq2Seq, AutoModelForCTC],
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "openai/whisper-tiny.en",
"model": "Xenova/whisper-tiny.en",
},
"type": "multimodal",
},
"text-to-audio": {
"tokenizer": AutoTokenizer,
"pipeline": TextToAudioPipeline,
"model": [AutoModelForTextToWaveform, AutoModelForTextToSpectrogram],
"processor": [AutoProcessor, /* Some don't use a processor */ null],
"default": {
// TODO: replace with original
// "model": "microsoft/speecht5_tts",
"model": "Xenova/speecht5_tts",
},
"type": "text",
},
"image-to-text": {
"tokenizer": AutoTokenizer,
"pipeline": ImageToTextPipeline,
"model": AutoModelForVision2Seq,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "nlpconnect/vit-gpt2-image-captioning",
"model": "Xenova/vit-gpt2-image-captioning",
},
"type": "multimodal",
},
"image-classification": {
// no tokenizer
"pipeline": ImageClassificationPipeline,
"model": AutoModelForImageClassification,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "google/vit-base-patch16-224",
"model": "Xenova/vit-base-patch16-224",
},
"type": "multimodal",
},
"image-segmentation": {
// no tokenizer
"pipeline": ImageSegmentationPipeline,
"model": [AutoModelForImageSegmentation, AutoModelForSemanticSegmentation, AutoModelForUniversalSegmentation],
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "facebook/detr-resnet-50-panoptic",
"model": "Xenova/detr-resnet-50-panoptic",
},
"type": "multimodal",
},
"zero-shot-image-classification": {
"tokenizer": AutoTokenizer,
"pipeline": ZeroShotImageClassificationPipeline,
"model": AutoModel,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "openai/clip-vit-base-patch32",
"model": "Xenova/clip-vit-base-patch32",
},
"type": "multimodal",
},
"object-detection": {
// no tokenizer
"pipeline": ObjectDetectionPipeline,
"model": AutoModelForObjectDetection,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "facebook/detr-resnet-50",
"model": "Xenova/detr-resnet-50",
},
"type": "multimodal",
},
"zero-shot-object-detection": {
"tokenizer": AutoTokenizer,
"pipeline": ZeroShotObjectDetectionPipeline,
"model": AutoModelForZeroShotObjectDetection,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "google/owlvit-base-patch32",
"model": "Xenova/owlvit-base-patch32",
},
"type": "multimodal",
},
"document-question-answering": {
"tokenizer": AutoTokenizer,
"pipeline": DocumentQuestionAnsweringPipeline,
"model": AutoModelForDocumentQuestionAnswering,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "naver-clova-ix/donut-base-finetuned-docvqa",
"model": "Xenova/donut-base-finetuned-docvqa",
},
"type": "multimodal",
},
"image-to-image": {
// no tokenizer
"pipeline": ImageToImagePipeline,
"model": AutoModelForImageToImage,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "caidas/swin2SR-classical-sr-x2-64",
"model": "Xenova/swin2SR-classical-sr-x2-64",
},
"type": "image",
},
"depth-estimation": {
// no tokenizer
"pipeline": DepthEstimationPipeline,
"model": AutoModelForDepthEstimation,
"processor": AutoProcessor,
"default": {
// TODO: replace with original
// "model": "Intel/dpt-large",
"model": "Xenova/dpt-large",
},
"type": "image",
},
// This task serves as a useful interface for dealing with sentence-transformers (https://huggingface.co/sentence-transformers).
"feature-extraction": {
"tokenizer": AutoTokenizer,
"pipeline": FeatureExtractionPipeline,
"model": AutoModel,
"default": {
// TODO: replace with original
// "model": "sentence-transformers/all-MiniLM-L6-v2",
"model": "Xenova/all-MiniLM-L6-v2",
},
"type": "text",
},
"image-feature-extraction": {
"processor": AutoProcessor,
"pipeline": ImageFeatureExtractionPipeline,
"model": [AutoModelForImageFeatureExtraction, AutoModel],
"default": {
// TODO: replace with original
// "model": "google/vit-base-patch16-224",
"model": "Xenova/vit-base-patch16-224-in21k",
},
"type": "image",
},
})
// TODO: Add types for TASK_ALIASES
const TASK_ALIASES = Object.freeze({
"sentiment-analysis": "text-classification",
"ner": "token-classification",
// "vqa": "visual-question-answering", // TODO: Add
"asr": "automatic-speech-recognition",
"text-to-speech": "text-to-audio",
// Add for backwards compatibility
"embeddings": "feature-extraction",
});
/**
* @typedef {keyof typeof SUPPORTED_TASKS} TaskType
* @typedef {keyof typeof TASK_ALIASES} AliasType
* @typedef {TaskType | AliasType} PipelineType All possible pipeline types.
* @typedef {{[K in TaskType]: InstanceType<typeof SUPPORTED_TASKS[K]["pipeline"]>}} SupportedTasks A mapping of pipeline names to their corresponding pipeline classes.
* @typedef {{[K in AliasType]: InstanceType<typeof SUPPORTED_TASKS[TASK_ALIASES[K]]["pipeline"]>}} AliasTasks A mapping from pipeline aliases to their corresponding pipeline classes.
* @typedef {SupportedTasks & AliasTasks} AllTasks A mapping from all pipeline names and aliases to their corresponding pipeline classes.
*/
/**
* Utility factory method to build a `Pipeline` object.
*
* @template {PipelineType} T The type of pipeline to return.
* @param {T} task The task defining which pipeline will be returned. Currently accepted tasks are:
* - `"audio-classification"`: will return a `AudioClassificationPipeline`.
* - `"automatic-speech-recognition"`: will return a `AutomaticSpeechRecognitionPipeline`.
* - `"depth-estimation"`: will return a `DepthEstimationPipeline`.
* - `"document-question-answering"`: will return a `DocumentQuestionAnsweringPipeline`.
* - `"feature-extraction"`: will return a `FeatureExtractionPipeline`.
* - `"fill-mask"`: will return a `FillMaskPipeline`.
* - `"image-classification"`: will return a `ImageClassificationPipeline`.
* - `"image-segmentation"`: will return a `ImageSegmentationPipeline`.
* - `"image-to-text"`: will return a `ImageToTextPipeline`.
* - `"object-detection"`: will return a `ObjectDetectionPipeline`.
* - `"question-answering"`: will return a `QuestionAnsweringPipeline`.
* - `"summarization"`: will return a `SummarizationPipeline`.
* - `"text2text-generation"`: will return a `Text2TextGenerationPipeline`.
* - `"text-classification"` (alias "sentiment-analysis" available): will return a `TextClassificationPipeline`.
* - `"text-generation"`: will return a `TextGenerationPipeline`.
* - `"token-classification"` (alias "ner" available): will return a `TokenClassificationPipeline`.
* - `"translation"`: will return a `TranslationPipeline`.
* - `"translation_xx_to_yy"`: will return a `TranslationPipeline`.
* - `"zero-shot-classification"`: will return a `ZeroShotClassificationPipeline`.
* - `"zero-shot-audio-classification"`: will return a `ZeroShotAudioClassificationPipeline`.
* - `"zero-shot-image-classification"`: will return a `ZeroShotImageClassificationPipeline`.
* - `"zero-shot-object-detection"`: will return a `ZeroShotObjectDetectionPipeline`.
* @param {string} [model=null] The name of the pre-trained model to use. If not specified, the default model for the task will be used.
* @param {import('./utils/hub.js').PretrainedModelOptions} [options] Optional parameters for the pipeline.
* @returns {Promise<AllTasks[T]>} A Pipeline object for the specified task.
* @throws {Error} If an unsupported pipeline is requested.
*/
export async function pipeline(
task,
model = null,
{
progress_callback = null,
config = null,
cache_dir = null,
local_files_only = false,
revision = 'main',
device = null,
dtype = null,
model_file_name = null,
session_options = {},
} = {}
) {
// Helper method to construct pipeline
// Apply aliases
// @ts-ignore
task = TASK_ALIASES[task] ?? task;
// Get pipeline info
const pipelineInfo = SUPPORTED_TASKS[task.split('_', 1)[0]];
if (!pipelineInfo) {
throw Error(`Unsupported pipeline: ${task}. Must be one of [${Object.keys(SUPPORTED_TASKS)}]`)
}
// Use model if specified, otherwise, use default
if (!model) {
model = pipelineInfo.default.model
console.log(`No model specified. Using default model: "${model}".`);
}
const pretrainedOptions = {
progress_callback,
config,
cache_dir,
local_files_only,
revision,
device,
dtype,
model_file_name,
session_options,
}
const classes = new Map([
['tokenizer', pipelineInfo.tokenizer],
['model', pipelineInfo.model],
['processor', pipelineInfo.processor],
]);
// Load model, tokenizer, and processor (if they exist)
const results = await loadItems(classes, model, pretrainedOptions);
results.task = task;
dispatchCallback(progress_callback, {
'status': 'ready',
'task': task,
'model': model,
});
const pipelineClass = pipelineInfo.pipeline;
return new pipelineClass(results);
}
/**
* Helper function to get applicable model, tokenizer, or processor classes for a given model.
* @param {Map<string, any>} mapping The mapping of names to classes, arrays of classes, or null.
* @param {string} model The name of the model to load.
* @param {import('./utils/hub.js').PretrainedOptions} pretrainedOptions The options to pass to the `from_pretrained` method.
* @private
*/
async function loadItems(mapping, model, pretrainedOptions) {
const result = Object.create(null);
/**@type {Promise[]} */
const promises = [];
for (const [name, cls] of mapping.entries()) {
if (!cls) continue;
/**@type {Promise} */
let promise;
if (Array.isArray(cls)) {
promise = new Promise(async (resolve, reject) => {
let e;
for (const c of cls) {
if (c === null) {
// If null, we resolve it immediately, meaning the relevant
// class was not found, but it is optional.
resolve(null);
return;
}
try {
resolve(await c.from_pretrained(model, pretrainedOptions));
return;
} catch (err) {
if (err.message?.includes('Unsupported model type')) {
// If the error is due to an unsupported model type, we
// save the error and try the next class.
e = err;
} else if (err.message?.includes('Could not locate file')) {
e = err;
} else {
reject(err);
return;
}
}
}
reject(e);
})
} else {
promise = cls.from_pretrained(model, pretrainedOptions);
}
result[name] = promise;
promises.push(promise);
}
// Wait for all promises to resolve (in parallel)
await Promise.all(promises);
// Then assign to result
for (const [name, promise] of Object.entries(result)) {
result[name] = await promise;
}
return result;
}
| transformers.js/src/pipelines.js/0 | {
"file_path": "transformers.js/src/pipelines.js",
"repo_id": "transformers.js",
"token_count": 55335
} |
import { AutoConfig, env } from "../src/transformers.js";
import { getFile } from "../src/utils/hub.js";
// Initialise the testing environment
env.allowLocalModels = false;
env.useFSCache = false;
const TEST_DATA = {
"Xenova/bert-base-uncased": {
model_type: "bert",
},
};
describe("Configs", () => {
for (const [model_id, minimal_config] of Object.entries(TEST_DATA)) {
it(model_id, async () => {
const config = await AutoConfig.from_pretrained(model_id);
for (const [key, value] of Object.entries(minimal_config)) {
expect(config[key]).toEqual(value);
}
});
}
});
| transformers.js/tests/configs.test.js/0 | {
"file_path": "transformers.js/tests/configs.test.js",
"repo_id": "transformers.js",
"token_count": 238
} |
import { AutoTokenizer, AutoProcessor, load_image, CLIPVisionModelWithProjection, CLIPTextModelWithProjection } from "../../../src/transformers.js";
import { MAX_TEST_EXECUTION_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
const models_to_test = ["hf-internal-testing/tiny-random-CLIPModel"];
it(
`CLIPTextModelWithProjection`,
async () => {
const model_id = models_to_test[0];
// Load tokenizer and text model
const tokenizer = await AutoTokenizer.from_pretrained(model_id);
const text_model = await CLIPTextModelWithProjection.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
// Run tokenization
const texts = ["a photo of a car", "a photo of a football match"];
const text_inputs = tokenizer(texts, { padding: true, truncation: true });
// Compute embeddings
const { text_embeds } = await text_model(text_inputs);
// Ensure correct shapes
const expected_shape = [texts.length, text_model.config.projection_dim];
const actual_shape = text_embeds.dims;
expect(expected_shape).toEqual(actual_shape);
await text_model.dispose();
},
MAX_TEST_EXECUTION_TIME,
);
it(
`CLIPVisionModelWithProjection`,
async () => {
const model_id = models_to_test[0];
// Load processor and vision model
const processor = await AutoProcessor.from_pretrained(model_id);
const vision_model = await CLIPVisionModelWithProjection.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
// Read image and run processor
const image = await load_image("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg");
const image_inputs = await processor(image);
// Compute embeddings
const { image_embeds } = await vision_model(image_inputs);
// Ensure correct shapes
const expected_shape = [1, vision_model.config.projection_dim];
const actual_shape = image_embeds.dims;
expect(expected_shape).toEqual(actual_shape);
await vision_model.dispose();
},
MAX_TEST_EXECUTION_TIME,
);
};
| transformers.js/tests/models/clip/test_modeling_clip.js/0 | {
"file_path": "transformers.js/tests/models/clip/test_modeling_clip.js",
"repo_id": "transformers.js",
"token_count": 787
} |
import { GemmaTokenizer, GemmaForCausalLM } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
describe("GemmaForCausalLM", () => {
const model_id = "Xenova/tiny-random-GemmaForCausalLM";
/** @type {GemmaForCausalLM} */
let model;
/** @type {GemmaTokenizer} */
let tokenizer;
beforeAll(async () => {
model = await GemmaForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
tokenizer = await GemmaTokenizer.from_pretrained(model_id);
tokenizer.padding_side = "left";
}, MAX_MODEL_LOAD_TIME);
it(
"batch_size=1",
async () => {
const inputs = tokenizer("hello");
const outputs = await model.generate({
...inputs,
max_length: 10,
});
expect(outputs.tolist()).toEqual([[2n, 17534n, 254059n, 254059n, 254059n, 254059n, 254059n, 254059n, 254059n, 254059n]]);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"batch_size>1",
async () => {
const inputs = tokenizer(["hello", "hello world"], { padding: true });
const outputs = await model.generate({
...inputs,
max_length: 10,
});
expect(outputs.tolist()).toEqual([
[0n, 2n, 17534n, 254059n, 254059n, 254059n, 254059n, 254059n, 254059n, 254059n],
[2n, 17534n, 2134n, 71055n, 71055n, 71055n, 71055n, 71055n, 71055n, 71055n],
]);
},
MAX_TEST_EXECUTION_TIME,
);
afterAll(async () => {
await model?.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/models/gemma/test_modeling_gemma.js/0 | {
"file_path": "transformers.js/tests/models/gemma/test_modeling_gemma.js",
"repo_id": "transformers.js",
"token_count": 806
} |
import { Idefics3Processor, Idefics3ForConditionalGeneration, RawImage } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
const conversation = [
{
role: "user",
content: [{ type: "image" }, { type: "text", text: "Can you describe this image?" }],
},
];
// Empty white and black images
const white_image_dims = [224, 224, 3];
const white_image = new RawImage(new Uint8ClampedArray(white_image_dims[0] * white_image_dims[1] * white_image_dims[2]).fill(255), ...white_image_dims);
const black_image_dims = [720, 360, 3];
const black_image = new RawImage(new Uint8ClampedArray(black_image_dims[0] * black_image_dims[1] * black_image_dims[2]).fill(0), ...black_image_dims);
describe("Idefics3ForConditionalGeneration", () => {
const model_id = "hf-internal-testing/tiny-random-Idefics3ForConditionalGeneration";
/** @type {Idefics3ForConditionalGeneration} */
let model;
/** @type {Idefics3Processor} */
let processor;
/** @type {string} */
let text;
beforeAll(async () => {
model = await Idefics3ForConditionalGeneration.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
processor = await Idefics3Processor.from_pretrained(model_id);
text = processor.apply_chat_template(conversation, {
add_generation_prompt: true,
});
}, MAX_MODEL_LOAD_TIME);
it(
"forward w/ image splitting (default)",
async () => {
const inputs = await processor(text, white_image, {
do_image_splitting: true,
});
const { logits } = await model(inputs);
expect(logits.dims).toEqual([1, 3041, 128259]);
expect(logits.mean().item()).toBeCloseTo(-0.0002692154666874558, 6);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"forward w/o image splitting",
async () => {
const inputs = await processor(text, white_image, {
do_image_splitting: false,
});
const { logits } = await model(inputs);
expect(logits.dims).toEqual([1, 189, 128259]);
expect(logits.mean().item()).toBeCloseTo(-0.00019743280427064747, 6);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"batch_size=1 w/ image splitting",
async () => {
const inputs = await processor(text, white_image, {
do_image_splitting: true,
});
const generate_ids = await model.generate({
...inputs,
max_new_tokens: 10,
// To obtain unique output tokens, deterministically
repetition_penalty: 2.0,
});
expect(generate_ids.dims).toEqual([1, 3051]);
const new_tokens = generate_ids.slice(null, [inputs.input_ids.dims.at(-1), null]);
expect(new_tokens.tolist()).toEqual([[64531n, 121777n, 70370n, 105334n, 12720n, 113356n, 47739n, 59240n, 102001n, 60344n]]);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"batch_size=1 w/o image splitting",
async () => {
const inputs = await processor(text, white_image, {
do_image_splitting: false,
});
const generate_ids = await model.generate({
...inputs,
max_new_tokens: 10,
// To obtain unique output tokens, deterministically
repetition_penalty: 2.0,
});
expect(generate_ids.dims).toEqual([1, 199]);
const new_tokens = generate_ids.slice(null, [inputs.input_ids.dims.at(-1), null]);
expect(new_tokens.tolist()).toEqual([[64531n, 121777n, 70370n, 105334n, 12720n, 113356n, 47739n, 59240n, 59697n, 65246n]]);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"batch_size=1 multi-image w/o image splitting",
async () => {
const multi_image_conversation = [
{
role: "user",
content: [{ type: "image" }, { type: "image" }, { type: "text", text: "Can you describe these images?" }],
},
];
const multi_image_text = processor.apply_chat_template(multi_image_conversation, {
add_generation_prompt: true,
});
const inputs = await processor(multi_image_text, [white_image, black_image], {
do_image_splitting: false,
});
const generate_ids = await model.generate({
...inputs,
max_new_tokens: 10,
// To obtain unique output tokens, deterministically
repetition_penalty: 2.0,
});
expect(generate_ids.dims).toEqual([1, 374]);
const new_tokens = generate_ids.slice(null, [inputs.input_ids.dims.at(-1), null]);
expect(new_tokens.tolist()).toEqual([[73189n, 99346n, 113252n, 51743n, 33499n, 66430n, 78739n, 89539n, 121023n, 14474n]]);
},
MAX_TEST_EXECUTION_TIME,
);
afterAll(async () => {
await model?.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/models/idefics3/test_modeling_idefics3.js/0 | {
"file_path": "transformers.js/tests/models/idefics3/test_modeling_idefics3.js",
"repo_id": "transformers.js",
"token_count": 2233
} |
import { MPNetTokenizer } from "../../../src/tokenizers.js";
import { BASE_TEST_STRINGS } from "../test_strings.js";
export const TOKENIZER_CLASS = MPNetTokenizer;
export const TEST_CONFIG = {
"Xenova/all-mpnet-base-v2": {
SIMPLE: {
text: BASE_TEST_STRINGS.SIMPLE,
tokens: ["how", "are", "you", "doing", "?"],
ids: [0, 2133, 2028, 2021, 2729, 1033, 2],
decoded: "<s> how are you doing? </s>",
},
SIMPLE_WITH_PUNCTUATION: {
text: BASE_TEST_STRINGS.SIMPLE_WITH_PUNCTUATION,
tokens: ["you", "should", "'", "ve", "done", "this"],
ids: [0, 2021, 2327, 1009, 2314, 2593, 2027, 2],
decoded: "<s> you should've done this </s>",
},
NUMBERS: {
text: BASE_TEST_STRINGS.NUMBERS,
tokens: ["01", "##23", "##45", "##6", "##7", "##8", "##9", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "100", "1000"],
ids: [0, 5894, 21930, 19965, 2579, 2585, 2624, 2687, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 2188, 2535, 6698, 2],
decoded: "<s> 0123456789 0 1 2 3 4 5 6 7 8 9 10 100 1000 </s>",
},
TEXT_WITH_NUMBERS: {
text: BASE_TEST_STRINGS.TEXT_WITH_NUMBERS,
tokens: ["the", "company", "was", "founded", "in", "2016", "."],
ids: [0, 2000, 2198, 2005, 2635, 2003, 2359, 1016, 2],
decoded: "<s> the company was founded in 2016. </s>",
},
PUNCTUATION: {
text: BASE_TEST_STRINGS.PUNCTUATION,
tokens: ["a", "'", "ll", "!", "!", "to", "?", "'", "d", "'", "'", "d", "of", ",", "can", "'", "t", "."],
ids: [0, 1041, 1009, 2226, 1003, 1003, 2004, 1033, 1009, 1044, 1009, 1009, 1044, 2001, 1014, 2068, 1009, 1060, 1016, 2],
decoded: "<s> a'll!! to?'d'' d of, can't. </s>",
},
PYTHON_CODE: {
text: BASE_TEST_STRINGS.PYTHON_CODE,
tokens: ["def", "main", "(", ")", ":", "pass"],
ids: [0, 13370, 2368, 1010, 1011, 1028, 3417, 2],
decoded: "<s> def main ( ) : pass </s>",
},
JAVASCRIPT_CODE: {
text: BASE_TEST_STRINGS.JAVASCRIPT_CODE,
tokens: ["let", "a", "=", "ob", "##j", ".", "to", "##st", "##ring", "(", ")", ";", "to", "##st", "##ring", "(", ")", ";"],
ids: [0, 2296, 1041, 1031, 27889, 3505, 1016, 2004, 3371, 4896, 1010, 1011, 1029, 2004, 3371, 4896, 1010, 1011, 1029, 2],
decoded: "<s> let a = obj. tostring ( ) ; tostring ( ) ; </s>",
},
NEWLINES: {
text: BASE_TEST_STRINGS.NEWLINES,
tokens: ["this", "is", "a", "test", "."],
ids: [0, 2027, 2007, 1041, 3235, 1016, 2],
decoded: "<s> this is a test. </s>",
},
BASIC: {
text: BASE_TEST_STRINGS.BASIC,
tokens: ["unwanted", ",", "running"],
ids: [0, 18166, 1014, 2774, 2],
decoded: "<s> unwanted, running </s>",
},
CONTROL_TOKENS: {
text: BASE_TEST_STRINGS.CONTROL_TOKENS,
tokens: ["123"],
ids: [0, 13142, 2],
decoded: "<s> 123 </s>",
},
HELLO_WORLD_TITLECASE: {
text: BASE_TEST_STRINGS.HELLO_WORLD_TITLECASE,
tokens: ["hello", "world"],
ids: [0, 7596, 2092, 2],
decoded: "<s> hello world </s>",
},
HELLO_WORLD_LOWERCASE: {
text: BASE_TEST_STRINGS.HELLO_WORLD_LOWERCASE,
tokens: ["hello", "world"],
ids: [0, 7596, 2092, 2],
decoded: "<s> hello world </s>",
},
CHINESE_ONLY: {
text: BASE_TEST_STRINGS.CHINESE_ONLY,
tokens: ["\u751f", "[UNK]", "\u7684", "\u771f", "[UNK]", "[UNK]"],
ids: [0, 1914, 104, 1920, 1925, 104, 104, 2],
decoded: "<s> \u751f [UNK] \u7684 \u771f [UNK] [UNK] </s>",
},
LEADING_SPACE: {
text: BASE_TEST_STRINGS.LEADING_SPACE,
tokens: ["leading", "space"],
ids: [0, 2881, 2690, 2],
decoded: "<s> leading space </s>",
},
TRAILING_SPACE: {
text: BASE_TEST_STRINGS.TRAILING_SPACE,
tokens: ["trailing", "space"],
ids: [0, 12546, 2690, 2],
decoded: "<s> trailing space </s>",
},
DOUBLE_SPACE: {
text: BASE_TEST_STRINGS.DOUBLE_SPACE,
tokens: ["hi", "hello"],
ids: [0, 7636, 7596, 2],
decoded: "<s> hi hello </s>",
},
CURRENCY: {
text: BASE_TEST_STRINGS.CURRENCY,
tokens: ["test", "$", "1", "r", "##2", "#", "3", "\u20ac", "##4", "\u00a35", "\u00a5", "##6", "[UNK]", "\u20b9", "##8", "\u20b1", "##9", "test"],
ids: [0, 3235, 1006, 1019, 1058, 2479, 1005, 1021, 1578, 2553, 27817, 1075, 2579, 104, 1580, 2624, 1579, 2687, 3235, 2],
decoded: "<s> test $ 1 r2 # 3 \u20ac4 \u00a35 \u00a56 [UNK] \u20b98 \u20b19 test </s>",
},
CURRENCY_WITH_DECIMALS: {
text: BASE_TEST_STRINGS.CURRENCY_WITH_DECIMALS,
tokens: ["i", "bought", "an", "apple", "for", "$", "1", ".", "00", "at", "the", "store", "."],
ids: [0, 1049, 4153, 2023, 6211, 2009, 1006, 1019, 1016, 4006, 2016, 2000, 3577, 1016, 2],
decoded: "<s> i bought an apple for $ 1. 00 at the store. </s>",
},
ELLIPSIS: {
text: BASE_TEST_STRINGS.ELLIPSIS,
tokens: ["you", "\u2026"],
ids: [0, 2021, 1533, 2],
decoded: "<s> you \u2026 </s>",
},
TEXT_WITH_ESCAPE_CHARACTERS: {
text: BASE_TEST_STRINGS.TEXT_WITH_ESCAPE_CHARACTERS,
tokens: ["you", "\u2026"],
ids: [0, 2021, 1533, 2],
decoded: "<s> you \u2026 </s>",
},
TEXT_WITH_ESCAPE_CHARACTERS_2: {
text: BASE_TEST_STRINGS.TEXT_WITH_ESCAPE_CHARACTERS_2,
tokens: ["you", "\u2026", "you", "\u2026"],
ids: [0, 2021, 1533, 2021, 1533, 2],
decoded: "<s> you \u2026 you \u2026 </s>",
},
TILDE_NORMALIZATION: {
text: BASE_TEST_STRINGS.TILDE_NORMALIZATION,
tokens: ["weird", "\uff5e", "edge", "\uff5e", "case"],
ids: [0, 6885, 1999, 3345, 1999, 2557, 2],
decoded: "<s> weird \uff5e edge \uff5e case </s>",
},
SPIECE_UNDERSCORE: {
text: BASE_TEST_STRINGS.SPIECE_UNDERSCORE,
tokens: ["[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "."],
ids: [0, 104, 104, 104, 104, 104, 1016, 2],
decoded: "<s> [UNK] [UNK] [UNK] [UNK] [UNK]. </s>",
},
POPULAR_EMOJIS: {
text: BASE_TEST_STRINGS.POPULAR_EMOJIS,
tokens: ["[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]"],
ids: [0, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 2],
decoded: "<s> [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] </s>",
},
MULTIBYTE_EMOJIS: {
text: BASE_TEST_STRINGS.MULTIBYTE_EMOJIS,
tokens: ["[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]"],
ids: [0, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 2],
decoded: "<s> [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] </s>",
},
},
};
| transformers.js/tests/models/mpnet/test_tokenization_mpnet.js/0 | {
"file_path": "transformers.js/tests/models/mpnet/test_tokenization_mpnet.js",
"repo_id": "transformers.js",
"token_count": 3834
} |
import { VitsTokenizer } from "../../../src/tokenizers.js";
import { BASE_TEST_STRINGS, VITS_TEST_STRINGS } from "../test_strings.js";
export const TOKENIZER_CLASS = VitsTokenizer;
export const TEST_CONFIG = {
"Xenova/mms-tts-eng": {
SIMPLE: {
text: BASE_TEST_STRINGS.SIMPLE,
tokens: ["k", "h", "k", "o", "k", "w", "k", " ", "k", "a", "k", "r", "k", "e", "k", " ", "k", "y", "k", "o", "k", "u", "k", " ", "k", "d", "k", "o", "k", "i", "k", "n", "k", "g", "k"],
ids: [0, 6, 0, 22, 0, 9, 0, 19, 0, 26, 0, 25, 0, 7, 0, 19, 0, 3, 0, 22, 0, 4, 0, 19, 0, 5, 0, 22, 0, 18, 0, 29, 0, 37, 0],
decoded: "how are you doing",
},
SIMPLE_WITH_PUNCTUATION: {
text: BASE_TEST_STRINGS.SIMPLE_WITH_PUNCTUATION,
tokens: ["k", "y", "k", "o", "k", "u", "k", " ", "k", "s", "k", "h", "k", "o", "k", "u", "k", "l", "k", "d", "k", "'", "k", "v", "k", "e", "k", " ", "k", "d", "k", "o", "k", "n", "k", "e", "k", " ", "k", "t", "k", "h", "k", "i", "k", "s", "k"],
ids: [0, 3, 0, 22, 0, 4, 0, 19, 0, 8, 0, 6, 0, 22, 0, 4, 0, 21, 0, 5, 0, 1, 0, 32, 0, 7, 0, 19, 0, 5, 0, 22, 0, 29, 0, 7, 0, 19, 0, 33, 0, 6, 0, 18, 0, 8, 0],
decoded: "you should've done this",
},
NUMBERS: {
text: BASE_TEST_STRINGS.NUMBERS,
tokens: ["k", "0", "k", "1", "k", "2", "k", "3", "k", "4", "k", "5", "k", "6", "k", " ", "k", "0", "k", " ", "k", "1", "k", " ", "k", "2", "k", " ", "k", "3", "k", " ", "k", "4", "k", " ", "k", "5", "k", " ", "k", "6", "k", " ", "k", " ", "k", " ", "k", " ", "k", "1", "k", "0", "k", " ", "k", "1", "k", "0", "k", "0", "k", " ", "k", "1", "k", "0", "k", "0", "k", "0", "k"],
ids: [0, 23, 0, 15, 0, 28, 0, 11, 0, 27, 0, 35, 0, 36, 0, 19, 0, 23, 0, 19, 0, 15, 0, 19, 0, 28, 0, 19, 0, 11, 0, 19, 0, 27, 0, 19, 0, 35, 0, 19, 0, 36, 0, 19, 0, 19, 0, 19, 0, 19, 0, 15, 0, 23, 0, 19, 0, 15, 0, 23, 0, 23, 0, 19, 0, 15, 0, 23, 0, 23, 0, 23, 0],
decoded: "0123456 0 1 2 3 4 5 6 10 100 1000",
},
TEXT_WITH_NUMBERS: {
text: BASE_TEST_STRINGS.TEXT_WITH_NUMBERS,
tokens: ["k", "t", "k", "h", "k", "e", "k", " ", "k", "c", "k", "o", "k", "m", "k", "p", "k", "a", "k", "n", "k", "y", "k", " ", "k", "w", "k", "a", "k", "s", "k", " ", "k", "f", "k", "o", "k", "u", "k", "n", "k", "d", "k", "e", "k", "d", "k", " ", "k", "i", "k", "n", "k", " ", "k", "2", "k", "0", "k", "1", "k", "6", "k"],
ids: [0, 33, 0, 6, 0, 7, 0, 19, 0, 12, 0, 22, 0, 17, 0, 13, 0, 26, 0, 29, 0, 3, 0, 19, 0, 9, 0, 26, 0, 8, 0, 19, 0, 20, 0, 22, 0, 4, 0, 29, 0, 5, 0, 7, 0, 5, 0, 19, 0, 18, 0, 29, 0, 19, 0, 28, 0, 23, 0, 15, 0, 36, 0],
decoded: "the company was founded in 2016",
},
NEWLINES: {
text: BASE_TEST_STRINGS.NEWLINES,
tokens: ["k", "t", "k", "h", "k", "i", "k", "s", "k", "i", "k", "s", "k", "a", "k", "t", "k", "e", "k", "s", "k", "t", "k"],
ids: [0, 33, 0, 6, 0, 18, 0, 8, 0, 18, 0, 8, 0, 26, 0, 33, 0, 7, 0, 8, 0, 33, 0],
decoded: "thisisatest",
},
CHINESE_ONLY: {
text: BASE_TEST_STRINGS.CHINESE_ONLY,
tokens: [],
ids: [],
decoded: "",
},
LEADING_SPACE: {
text: BASE_TEST_STRINGS.LEADING_SPACE,
tokens: ["k", "l", "k", "e", "k", "a", "k", "d", "k", "i", "k", "n", "k", "g", "k", " ", "k", "s", "k", "p", "k", "a", "k", "c", "k", "e", "k"],
ids: [0, 21, 0, 7, 0, 26, 0, 5, 0, 18, 0, 29, 0, 37, 0, 19, 0, 8, 0, 13, 0, 26, 0, 12, 0, 7, 0],
decoded: "leading space",
},
TRAILING_SPACE: {
text: BASE_TEST_STRINGS.TRAILING_SPACE,
tokens: ["k", "t", "k", "r", "k", "a", "k", "i", "k", "l", "k", "i", "k", "n", "k", "g", "k", " ", "k", "s", "k", "p", "k", "a", "k", "c", "k", "e", "k"],
ids: [0, 33, 0, 25, 0, 26, 0, 18, 0, 21, 0, 18, 0, 29, 0, 37, 0, 19, 0, 8, 0, 13, 0, 26, 0, 12, 0, 7, 0],
decoded: "trailing space",
},
SURROUNDING_SPACE: {
text: BASE_TEST_STRINGS.SURROUNDING_SPACE,
tokens: ["k", "s", "k", "u", "k", "r", "k", "r", "k", "o", "k", "u", "k", "n", "k", "d", "k", "i", "k", "n", "k", "g", "k", " ", "k", "s", "k", "p", "k", "a", "k", "c", "k", "e", "k"],
ids: [0, 8, 0, 4, 0, 25, 0, 25, 0, 22, 0, 4, 0, 29, 0, 5, 0, 18, 0, 29, 0, 37, 0, 19, 0, 8, 0, 13, 0, 26, 0, 12, 0, 7, 0],
decoded: "surrounding space",
},
SPECIAL_CHARACTERS: {
text: VITS_TEST_STRINGS.SPECIAL_CHARACTERS,
tokens: [],
ids: [],
decoded: "",
},
},
"Xenova/mms-tts-ron": {
SPECIAL_CHARACTERS: {
text: VITS_TEST_STRINGS.SPECIAL_CHARACTERS,
tokens: ["c", "\u0163", "c", " ", "c", "\u0163", "c"],
ids: [0, 32, 0, 28, 0, 32, 0],
decoded: "\u0163 \u0163",
},
},
};
| transformers.js/tests/models/vits/test_tokenization_vits.js/0 | {
"file_path": "transformers.js/tests/models/vits/test_tokenization_vits.js",
"repo_id": "transformers.js",
"token_count": 2597
} |
import { pipeline, ImageFeatureExtractionPipeline } from "../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
import { load_cached_image } from "../asset_cache.js";
const PIPELINE_ID = "image-feature-extraction";
export default () => {
describe("Image Feature Extraction", () => {
describe("Default", () => {
const model_id = "hf-internal-testing/tiny-random-ViTMAEModel";
/** @type {ImageFeatureExtractionPipeline} */
let pipe;
let images;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
images = await Promise.all([load_cached_image("white_image"), load_cached_image("blue_image")]);
}, MAX_MODEL_LOAD_TIME);
it("should be an instance of ImageFeatureExtractionPipeline", () => {
expect(pipe).toBeInstanceOf(ImageFeatureExtractionPipeline);
});
describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe(images[0]);
expect(output.dims).toEqual([1, 91, 32]);
expect(output.mean().item()).toBeCloseTo(-8.507473614471905e-10, 6);
},
MAX_TEST_EXECUTION_TIME,
);
});
describe("batch_size>1", () => {
it(
"default",
async () => {
const output = await pipe(images);
expect(output.dims).toEqual([images.length, 91, 32]);
expect(output.mean().item()).toBeCloseTo(-5.997602414709036e-10, 6);
},
MAX_TEST_EXECUTION_TIME,
);
});
afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
describe("CLIP-like", () => {
const model_id = "hf-internal-testing/tiny-random-CLIPModel";
/** @type {ImageFeatureExtractionPipeline} */
let pipe;
let images;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
images = await Promise.all([load_cached_image("white_image"), load_cached_image("blue_image")]);
}, MAX_MODEL_LOAD_TIME);
it("should be an instance of ImageFeatureExtractionPipeline", () => {
expect(pipe).toBeInstanceOf(ImageFeatureExtractionPipeline);
});
describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe(images[0]);
expect(output.dims).toEqual([1, 64]);
expect(output.mean().item()).toBeCloseTo(-0.11340035498142242, 6);
},
MAX_TEST_EXECUTION_TIME,
);
});
describe("batch_size>1", () => {
it(
"default",
async () => {
const output = await pipe(images);
expect(output.dims).toEqual([images.length, 64]);
expect(output.mean().item()).toBeCloseTo(-0.11006651818752289, 6);
},
MAX_TEST_EXECUTION_TIME,
);
});
afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
});
};
| transformers.js/tests/pipelines/test_pipelines_image_feature_extraction.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_image_feature_extraction.js",
"repo_id": "transformers.js",
"token_count": 1475
} |
import { pipeline, ZeroShotObjectDetectionPipeline } from "../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
import { load_cached_image } from "../asset_cache.js";
const PIPELINE_ID = "zero-shot-object-detection";
export default () => {
describe("Zero-shot Object Detection", () => {
describe("w/ post_process_object_detection", () => {
const model_id = "hf-internal-testing/tiny-random-OwlViTForObjectDetection";
const candidate_labels = ["hello", "hello world"];
/** @type {ZeroShotObjectDetectionPipeline} */
let pipe;
let images;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
images = await Promise.all([load_cached_image("white_image"), load_cached_image("blue_image")]);
}, MAX_MODEL_LOAD_TIME);
const targets = {
white_image: [
{
score: 0.6028420329093933,
label: "hello",
box: { xmin: 47, ymin: 117, xmax: 62, ymax: 134 },
},
{
score: 0.6026064157485962,
label: "hello world",
box: { xmin: 47, ymin: 117, xmax: 62, ymax: 134 },
},
{
score: 0.5987668037414551,
label: "hello world",
box: { xmin: 145, ymin: 47, xmax: 160, ymax: 63 },
},
{
score: 0.5986272692680359,
label: "hello",
box: { xmin: 89, ymin: 131, xmax: 104, ymax: 148 },
},
{
score: 0.5985949039459229,
label: "hello world",
box: { xmin: 89, ymin: 131, xmax: 104, ymax: 148 },
},
// ... many more
],
blue_image: [
{
score: 0.6622366309165955,
label: "hello",
box: { xmin: 48, ymin: 45, xmax: 62, ymax: 61 },
},
{
score: 0.6562080383300781,
label: "hello world",
box: { xmin: 48, ymin: 45, xmax: 62, ymax: 61 },
},
{
score: 0.6493991613388062,
label: "hello world",
box: { xmin: 34, ymin: 58, xmax: 48, ymax: 74 },
},
{
score: 0.6476974487304688,
label: "hello",
box: { xmin: 34, ymin: 58, xmax: 48, ymax: 74 },
},
{
score: 0.6391685009002686,
label: "hello",
box: { xmin: 103, ymin: 59, xmax: 117, ymax: 75 },
},
// ... many more
],
};
it("should be an instance of ZeroShotObjectDetectionPipeline", () => {
expect(pipe).toBeInstanceOf(ZeroShotObjectDetectionPipeline);
});
describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe(images[0], candidate_labels);
expect(output).toHaveLength(512);
expect(output.slice(0, targets.white_image.length)).toBeCloseToNested(targets.white_image, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ top_k & threshold)",
async () => {
const top_k = 3;
const output = await pipe(images[0], candidate_labels, { top_k, threshold: 0.05 });
expect(output).toBeCloseToNested(targets.white_image.slice(0, top_k), 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
describe("batch_size>1", () => {
it(
"default",
async () => {
const output = await pipe(images, candidate_labels);
const target = Object.values(targets);
expect(output.map((x, i) => x.slice(0, target[i].length))).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ top_k & threshold)",
async () => {
const top_k = 3;
const output = await pipe(images, candidate_labels, { top_k, threshold: 0.05 });
const target = Object.values(targets).map((x) => x.slice(0, top_k));
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
describe("w/ post_process_grounded_object_detection", () => {
const model_id = "hf-internal-testing/tiny-random-GroundingDinoForObjectDetection";
const candidate_labels = ["a cat."];
/** @type {ZeroShotObjectDetectionPipeline} */
let pipe;
let image;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
image = await load_cached_image("white_image");
}, MAX_MODEL_LOAD_TIME);
const target = [
{ box: { xmax: 112, xmin: -111, ymax: 0, ymin: 0 }, label: "a cat. [SEP]", score: 1 },
{ box: { xmax: 112, xmin: -111, ymax: 0, ymin: 0 }, label: "a cat. [SEP]", score: 1 },
{ box: { xmax: 112, xmin: -111, ymax: 0, ymin: 0 }, label: "a cat. [SEP]", score: 1 },
// ... many more
];
it("should be an instance of ZeroShotObjectDetectionPipeline", () => {
expect(pipe).toBeInstanceOf(ZeroShotObjectDetectionPipeline);
});
describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe(image, candidate_labels);
expect(output).toHaveLength(900);
expect(output.slice(0, target.length)).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ top_k & threshold)",
async () => {
const top_k = 3;
const output = await pipe(image, candidate_labels, { top_k, threshold: 0.05 });
expect(output).toBeCloseToNested(target.slice(0, top_k), 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
});
};
| transformers.js/tests/pipelines/test_pipelines_zero_shot_object_detection.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_zero_shot_object_detection.js",
"repo_id": "transformers.js",
"token_count": 3154
} |
version: 2.1
setup: true
orbs:
continuation: circleci/[email protected]
parameters:
nightly:
type: boolean
default: false
jobs:
# Ensure running with CircleCI/huggingface
check_circleci_user:
docker:
- image: python:3.10-slim
resource_class: small
parallelism: 1
steps:
- run: echo $CIRCLE_PROJECT_USERNAME
- run: |
if [ "$CIRCLE_PROJECT_USERNAME" = "huggingface" ]; then
exit 0
else
echo "The CI is running under $CIRCLE_PROJECT_USERNAME personal account. Please follow https://support.circleci.com/hc/en-us/articles/360008097173-Troubleshooting-why-pull-requests-are-not-triggering-jobs-on-my-organization- to fix it."; exit -1
fi
# Fetch the tests to run
fetch_tests:
working_directory: ~/transformers
docker:
- image: huggingface/transformers-quality
parallelism: 1
steps:
- checkout
- run: uv pip install -U -e .
- run: echo 'export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)"' >> "$BASH_ENV" && source "$BASH_ENV"
- run: mkdir -p test_preparation
- run: python utils/tests_fetcher.py | tee tests_fetched_summary.txt
- run: python utils/tests_fetcher.py --filter_tests
- run: export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)" && echo $GIT_COMMIT_MESSAGE && python .circleci/create_circleci_config.py --fetcher_folder test_preparation
- run: |
if [ ! -s test_preparation/generated_config.yml ]; then
echo "No tests to run, exiting early!"
circleci-agent step halt
fi
- store_artifacts:
path: test_preparation
- run:
name: "Retrieve Artifact Paths"
# [reference] https://circleci.com/docs/api/v2/index.html#operation/getJobArtifacts
# `CIRCLE_TOKEN` is defined as an environment variables set within a context, see `https://circleci.com/docs/contexts/`
command: |
project_slug="gh/${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}"
job_number=${CIRCLE_BUILD_NUM}
url="https://circleci.com/api/v2/project/${project_slug}/${job_number}/artifacts"
curl -o test_preparation/artifacts.json ${url} --header "Circle-Token: $CIRCLE_TOKEN"
- run:
name: "Prepare pipeline parameters"
command: |
python utils/process_test_artifacts.py
# To avoid too long generated_config.yaml on the continuation orb, we pass the links to the artifacts as parameters.
# Otherwise the list of tests was just too big. Explicit is good but for that it was a limitation.
# We used:
# https://circleci.com/docs/api/v2/index.html#operation/getJobArtifacts : to get the job artifacts
# We could not pass a nested dict, which is why we create the test_file_... parameters for every single job
- store_artifacts:
path: test_preparation/transformed_artifacts.json
- store_artifacts:
path: test_preparation/artifacts.json
- continuation/continue:
parameters: test_preparation/transformed_artifacts.json
configuration_path: test_preparation/generated_config.yml
# To run all tests for the nightly build
fetch_all_tests:
working_directory: ~/transformers
docker:
- image: huggingface/transformers-quality
parallelism: 1
steps:
- checkout
- run: uv pip install -U -e .
- run: echo 'export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)"' >> "$BASH_ENV" && source "$BASH_ENV"
- run: mkdir -p test_preparation
- run: python utils/tests_fetcher.py --fetch_all | tee tests_fetched_summary.txt
- run: python utils/tests_fetcher.py --filter_tests
- run: export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)" && echo $GIT_COMMIT_MESSAGE && python .circleci/create_circleci_config.py --fetcher_folder test_preparation
- run: |
if [ ! -s test_preparation/generated_config.yml ]; then
echo "No tests to run, exiting early!"
circleci-agent step halt
fi
- store_artifacts:
path: test_preparation
- run:
name: "Retrieve Artifact Paths"
env:
CIRCLE_TOKEN: ${{ secrets.CI_ARTIFACT_TOKEN }}
command: |
project_slug="gh/${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}"
job_number=${CIRCLE_BUILD_NUM}
url="https://circleci.com/api/v2/project/${project_slug}/${job_number}/artifacts"
curl -o test_preparation/artifacts.json ${url}
- run:
name: "Prepare pipeline parameters"
command: |
python utils/process_test_artifacts.py
# To avoid too long generated_config.yaml on the continuation orb, we pass the links to the artifacts as parameters.
# Otherwise the list of tests was just too big. Explicit is good but for that it was a limitation.
# We used:
# https://circleci.com/docs/api/v2/index.html#operation/getJobArtifacts : to get the job artifacts
# We could not pass a nested dict, which is why we create the test_file_... parameters for every single job
- store_artifacts:
path: test_preparation/transformed_artifacts.json
- store_artifacts:
path: test_preparation/artifacts.json
- continuation/continue:
parameters: test_preparation/transformed_artifacts.json
configuration_path: test_preparation/generated_config.yml
check_code_quality:
working_directory: ~/transformers
docker:
- image: huggingface/transformers-quality
resource_class: large
environment:
TRANSFORMERS_IS_CI: yes
PYTEST_TIMEOUT: 120
parallelism: 1
steps:
- checkout
- run: uv pip install -e ".[quality]"
- run:
name: Show installed libraries and their versions
command: pip freeze | tee installed.txt
- store_artifacts:
path: ~/transformers/installed.txt
- run: python -c "from transformers import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
- run: ruff check examples tests src utils
- run: ruff format tests src utils --check
- run: python utils/custom_init_isort.py --check_only
- run: python utils/sort_auto_mappings.py --check_only
- run: python utils/check_doc_toc.py
- run: python utils/check_docstrings.py --check_all
check_repository_consistency:
working_directory: ~/transformers
docker:
- image: huggingface/transformers-consistency
resource_class: large
environment:
TRANSFORMERS_IS_CI: yes
PYTEST_TIMEOUT: 120
parallelism: 1
steps:
- checkout
- run: uv pip install -e ".[quality]"
- run:
name: Show installed libraries and their versions
command: pip freeze | tee installed.txt
- store_artifacts:
path: ~/transformers/installed.txt
- run: python utils/check_copies.py
- run: python utils/check_modular_conversion.py
- run: python utils/check_table.py
- run: python utils/check_dummies.py
- run: python utils/check_repo.py
- run: python utils/check_inits.py
- run: python utils/check_config_docstrings.py
- run: python utils/check_config_attributes.py
- run: python utils/check_doctest_list.py
- run: make deps_table_check_updated
- run: python utils/update_metadata.py --check-only
- run: python utils/check_docstrings.py
- run: python utils/check_support_list.py
workflows:
version: 2
setup_and_quality:
when:
and:
- equal: [<<pipeline.project.git_url>>, https://github.com/huggingface/transformers]
- not: <<pipeline.parameters.nightly>>
jobs:
- check_circleci_user
- check_code_quality
- check_repository_consistency
- fetch_tests
setup_and_quality_2:
when:
not:
equal: [<<pipeline.project.git_url>>, https://github.com/huggingface/transformers]
jobs:
- check_circleci_user
- check_code_quality
- check_repository_consistency
- fetch_tests:
# [reference] https://circleci.com/docs/contexts/
context:
- TRANSFORMERS_CONTEXT
nightly:
when: <<pipeline.parameters.nightly>>
jobs:
- check_circleci_user
- check_code_quality
- check_repository_consistency
- fetch_all_tests
| transformers/.circleci/config.yml/0 | {
"file_path": "transformers/.circleci/config.yml",
"repo_id": "transformers",
"token_count": 4625
} |
CREATE TABLE IF NOT EXISTS benchmarks (
benchmark_id SERIAL PRIMARY KEY,
branch VARCHAR(255),
commit_id VARCHAR(72),
commit_message VARCHAR(70),
metadata jsonb,
created_at timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC')
);
CREATE INDEX IF NOT EXISTS benchmarks_benchmark_id_idx ON benchmarks (benchmark_id);
CREATE INDEX IF NOT EXISTS benchmarks_branch_idx ON benchmarks (branch);
CREATE TABLE IF NOT EXISTS device_measurements (
measurement_id SERIAL PRIMARY KEY,
benchmark_id int REFERENCES benchmarks (benchmark_id),
cpu_util double precision,
mem_megabytes double precision,
gpu_util double precision,
gpu_mem_megabytes double precision,
time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC')
);
CREATE INDEX IF NOT EXISTS device_measurements_branch_idx ON device_measurements (benchmark_id);
CREATE TABLE IF NOT EXISTS model_measurements (
measurement_id SERIAL PRIMARY KEY,
benchmark_id int REFERENCES benchmarks (benchmark_id),
measurements jsonb,
time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC')
);
CREATE INDEX IF NOT EXISTS model_measurements_branch_idx ON model_measurements (benchmark_id);
| transformers/benchmark/init_db.sql/0 | {
"file_path": "transformers/benchmark/init_db.sql",
"repo_id": "transformers",
"token_count": 391
} |
FROM python:3.9-slim
ENV PYTHONDONTWRITEBYTECODE=1
ARG REF=main
USER root
RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git
ENV UV_PYTHON=/usr/local/bin/python
RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
RUN uv pip install --no-deps accelerate
RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
RUN pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,audio,sklearn,sentencepiece,vision,testing]"
# RUN pip install --no-cache-dir "scipy<1.13" "transformers[flax,testing,sentencepiece,flax-speech,vision]"
RUN pip uninstall -y transformers
RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
| transformers/docker/torch-jax-light.dockerfile/0 | {
"file_path": "transformers/docker/torch-jax-light.dockerfile",
"repo_id": "transformers",
"token_count": 335
} |
FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
LABEL maintainer="Hugging Face"
ARG DEBIAN_FRONTEND=noninteractive
# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands)
SHELL ["sh", "-lc"]
# The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant
# to be used as arguments for docker build (so far).
ARG PYTORCH='2.5.1'
# Example: `cu102`, `cu113`, etc.
ARG CUDA='cu118'
RUN apt update
RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
RUN python3 -m pip install --no-cache-dir --upgrade pip
ARG REF=main
RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile
RUN echo torch=$VERSION
# `torchvision` and `torchaudio` should be installed along with `torch`, especially for nightly build.
# Currently, let's just use their latest releases (when `torch` is installed with a release version)
RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA
RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch]
RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
# needed in bnb and awq
RUN python3 -m pip install --no-cache-dir einops
# Add bitsandbytes for mixed int8 testing
RUN python3 -m pip install --no-cache-dir bitsandbytes
# Add auto-gptq for gtpq quantization testing, installed from source for pytorch==2.5.1 compatibility
# TORCH_CUDA_ARCH_LIST="7.5+PTX" is added to make the package compile for Tesla T4 gpus available for the CI.
RUN pip install gekko
RUN git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ && TORCH_CUDA_ARCH_LIST="7.5+PTX" python3 setup.py install
# Add optimum for gptq quantization testing
RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum
# Add PEFT
RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft@main#egg=peft
# Add aqlm for quantization testing
RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2
# Add vptq for quantization testing
RUN python3 -m pip install --no-cache-dir vptq
# Add hqq for quantization testing
RUN python3 -m pip install --no-cache-dir hqq
# For GGUF tests
RUN python3 -m pip install --no-cache-dir gguf
# Add autoawq for quantization testing
# >=v0.2.7 needed for compatibility with transformers > 4.46
RUN python3 -m pip install --no-cache-dir https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.7.post2/autoawq-0.2.7.post2-py3-none-any.whl
# Add quanto for quantization testing
RUN python3 -m pip install --no-cache-dir optimum-quanto
# Add eetq for quantization testing
RUN python3 -m pip install git+https://github.com/NetEase-FuXi/EETQ.git
# Add flute-kernel and fast_hadamard_transform for quantization testing
RUN python3 -m pip install --no-cache-dir flute-kernel==0.3.0 -i https://flute-ai.github.io/whl/cu118
RUN python3 -m pip install --no-cache-dir fast_hadamard_transform==1.0.4.post1
# When installing in editable mode, `transformers` is not recognized as a package.
# this line must be added in order for python to be aware of transformers.
RUN cd transformers && python3 setup.py develop
| transformers/docker/transformers-quantization-latest-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-quantization-latest-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 1191
} |
# بناء نماذج مخصصة
تم تصميم مكتبة 🤗 Transformers لتكون قابلة للتوسيع بسهولة. كل نموذج مُشفّر بالكامل في مجلد فرعي معين بالمستودع، دون أي تجريد، لذلك يمكنك بسهولة نسخ ملف النمذجة وتعديله وفقًا لاحتياجاتك.
إذا كنت تُنشئ نموذجًا جديدًا تمامًا، فقد يكون من الأسهل البدء من الصفر. في هذا البرنامج التعليمي، سنُرِيك كيفية كتابة نموذج مخصص وتكوينه ليُستخدم داخل Transformers، وكيفية مشاركته مع المجتمع (مع الكود الذي يعتمد عليه) بحيث يمكن لأي شخص استخدامه، حتى إذا لم يكن موجودًا في مكتبة 🤗 Transformers. سنرى كيفية البناء على المحولات ونوسّع الإطار باستخدام الأدوات التي يمكن استخدامها لتعديل سلوك الإطار (hooks) والتعليمات البرمجية المخصصة.
سنوضح كل هذا من خلال نموذج ResNet، بتغليف فئة ResNet من
[مكتبة timm](https://github.com/rwightman/pytorch-image-models) داخل [`PreTrainedModel`].
## كتابة إعدادات مخصصة
لنبدأ بكتابة إعدادات النموذج. إعدادات النموذج هو كائنٌ يحتوي على جميع المعلومات اللازمة لبنائه. كما سنرى لاحقًا، يتطلب النموذج كائن `config` لتهيئته، لذا يجب أن يكون هذا الكائن كاملاً.
<Tip>
تتبع النماذج في مكتبة `transformers` اتفاقية قبول كائن `config` في دالة `__init__` الخاصة بها، ثم تمرر كائن `config` بالكامل إلى الطبقات الفرعية في النموذج، بدلاً من تقسيمه إلى معامﻻت متعددة. يؤدي كتابة نموذجك بهذا الأسلوب إلى كود أبسط مع "مصدر حقيقة" واضح لأي فرط معلمات، كما يسهل إعادة استخدام الكود من نماذج أخرى في `transformers`.
</Tip>
في مثالنا، سنعدّل بعض الوسائط في فئة ResNet التي قد نرغب في ضبطها. ستعطينا التكوينات المختلفة أنواع ResNets المختلفة الممكنة. سنقوم بتخزين هذه الوسائط بعد التحقق من صحته.
```python
from transformers import PretrainedConfig
from typing import List
class ResnetConfig(PretrainedConfig):
model_type = "resnet"
def __init__(
self,
block_type="bottleneck",
layers: List[int] = [3, 4, 6, 3],
num_classes: int = 1000,
input_channels: int = 3,
cardinality: int = 1,
base_width: int = 64,
stem_width: int = 64,
stem_type: str = "",
avg_down: bool = False,
**kwargs,
):
if block_type not in ["basic", "bottleneck"]:
raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
if stem_type not in ["", "deep", "deep-tiered"]:
raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
self.block_type = block_type
self.layers = layers
self.num_classes = num_classes
self.input_channels = input_channels
self.cardinality = cardinality
self.base_width = base_width
self.stem_width = stem_width
self.stem_type = stem_type
self.avg_down = avg_down
super().__init__(**kwargs)
```
الأشياء الثلاثة المهمة التي يجب تذكرها عند كتابة تكوينك الخاص هي:
- يجب أن ترث من `PretrainedConfig`،
- يجب أن تقبل دالة `__init__` الخاصة بـ `PretrainedConfig` أي معامﻻت إضافية kwargs،
- يجب تمرير هذه المعامﻻت الإضافية إلى دالة `__init__` فى الفئة الأساسية الاعلى.
يضمن الإرث حصولك على جميع الوظائف من مكتبة 🤗 Transformers، في حين أن القيدين التانى والثالث يأتيان من حقيقة أن `PretrainedConfig` لديه المزيد من الحقول أكثر من تلك التي تقوم بتعيينها. عند إعادة تحميل تكوين باستخدام طريقة `from_pretrained`، يجب أن يقبل تكوينك هذه الحقول ثم إرسالها إلى الفئة الأساسية الأعلى.
تحديد `model_type` لتكوينك (هنا `model_type="resnet"`) ليس إلزاميًا، ما لم ترغب في
تسجيل نموذجك باستخدام الفئات التلقائية (راجع القسم الأخير).
مع القيام بذلك، يمكنك بسهولة إنشاء تكوينك وحفظه مثلما تفعل مع أي تكوين نموذج آخر في
المكتبة. إليك كيفية إنشاء تكوين resnet50d وحفظه:
```py
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d_config.save_pretrained("custom-resnet")
```
سيؤدي هذا إلى حفظ ملف باسم `config.json` داخل مجلد `custom-resnet`. يمكنك بعد ذلك إعادة تحميل تكوينك باستخدام
طريقة `from_pretrained`:
```py
resnet50d_config = ResnetConfig.from_pretrained("custom-resnet")
```
يمكنك أيضًا استخدام أي طريقة أخرى من فئة [`PretrainedConfig`]، مثل [`~PretrainedConfig.push_to_hub`] لتحميل تكوينك مباشرة إلى Hub.
## كتابة نموذج مخصص
الآن بعد أن أصبح لدينا تكوين ResNet، يمكننا المتابعة لإنشاء نموذجين: الأول يستخرج الميزات المخفية من دفعة من الصور (مثل [`BertModel`]) والآخر مناسب لتصنيف الصور (مثل [`BertForSequenceClassification`]).
كما ذكرنا سابقًا، سنقوم ببناء نموذج مبسط لتسهيل الفهم في هذا المثال. الخطوة الوحيدة المطلوبة قبل كتابة هذه الفئة هي لربط أنواع وحدات البناء بفئات ذات وحدات بناء فعلية. بعد ذلك، يُعرّف النموذج من خلال التكوين عبر تمرير كل شيء إلى فئة `ResNet`:
```py
from transformers import PreTrainedModel
from timm.models.resnet import BasicBlock, Bottleneck, ResNet
from .configuration_resnet import ResnetConfig
BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}
class ResnetModel(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
)
def forward(self, tensor):
return self.model.forward_features(tensor)
```
بالنسبة للنموذج الذي سيصنف الصور، فإننا نغير فقط طريقة التقديم:
```py
import torch
class ResnetModelForImageClassification(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
)
def forward(self, tensor, labels=None):
logits = self.model(tensor)
if labels is not None:
loss = torch.nn.cross_entropy(logits, labels)
return {"loss": loss, "logits": logits}
return {"logits": logits}
```
في كلتا الحالتين، لاحظ كيف نرث من `PreTrainedModel` ونستدعي مُهيئ الفئة الرئيسية باستخدام `config` (كما تفعل عند إنشاء وحدة `torch.nn.Module` عادية). ليس من الضروري تعريف `config_class` إلا إذا كنت ترغب في تسجيل نموذجك مع الفئات التلقائية (راجع القسم الأخير).
<Tip>
إذا كان نموذجك مشابهًا جدًا لنموذج داخل المكتبة، فيمكنك إعادة استخدام نفس التكوين مثل هذا النموذج.
</Tip>
يمكن لنموذجك أن يعيد أي شيء تريده، ولكن إعادة قاموس مثلما فعلنا لـ
`ResnetModelForImageClassification`، مع تضمين الخسارة عند تمرير العلامات، سيجعل نموذجك قابلًا للاستخدام مباشرة داخل فئة [`Trainer`]. يعد استخدام تنسيق إخراج آخر أمرًا جيدًا طالما أنك تخطط لاستخدام حلقة تدريب خاصة بك أو مكتبة أخرى للتدريب.
الآن بعد أن أصبح لدينا فئة النموذج، دعنا ننشئ واحدة:
```py
resnet50d = ResnetModelForImageClassification(resnet50d_config)
```
يمكنك استخدام أي من طرق فئة [`PreTrainedModel`]، مثل [`~PreTrainedModel.save_pretrained`] أو
[`~PreTrainedModel.push_to_hub`]. سنستخدم الثاني في القسم التالي، وسنرى كيفية دفع أوزان النموذج مع كود نموذجنا. ولكن أولاً، دعنا نحمل بعض الأوزان المُعلمة مسبقًا داخل نموذجنا.
في حالة الاستخدام الخاصة بك، فمن المحتمل أن تقوم بتدريب نموذجك المخصص على بياناتك الخاصة. للانتقال بسرعة خلال هذا البرنامج التعليمي،
سنستخدم الإصدار المُعلم مسبقًا من resnet50d. نظرًا لأن نموذجنا هو مجرد غلاف حوله، فمن السهل نقل هذه الأوزان:
```py
import timm
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())
```
الآن دعونا نرى كيفية التأكد من أنه عند قيامنا بـ [`~PreTrainedModel.save_pretrained`] أو [`~PreTrainedModel.push_to_hub`]، يتم حفظ كود النموذج.
## تسجيل نموذج مع كود مخصص للفئات التلقائية
إذا كنت تكتب مكتبة توسع 🤗 Transformers، فقد ترغب في توسيع الفئات التلقائية لتشمل نموذجك الخاص. يختلف هذا عن نشر الكود إلى Hub بمعنى أن المستخدمين سيحتاجون إلى استيراد مكتبتك للحصول على النماذج المخصصة (على عكس تنزيل كود النموذج تلقائيًا من Hub).
ما دام تكوينك يحتوي على معامل `model_type` مختلفة عن أنواع النماذج الحالية، وأن فئات نماذجك لديك لديها الخصائص الصحيحة `config_class`، فيمكنك ببساطة إضافتها إلى الفئات التلقائية مثل هذا:
```py
from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
AutoConfig.register("resnet", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification)
```
لاحظ أن الحجة الأولى المستخدمة عند تسجيل تكوينك المخصص لـ [`AutoConfig`] يجب أن تتطابق مع `model_type`
من تكوينك المخصص، والحجة الأولى المستخدمة عند تسجيل نماذجك المخصصة لأي فئة نموذج تلقائي يجب
أن تتطابق مع `config_class` من تلك النماذج.
## إرسال الكود إلى Hub
<Tip warning={true}>
هذا API تجريبي وقد يكون له بعض التغييرات الطفيفة في الإصدارات القادمة.
</Tip>
أولاً، تأكد من تعريف نموذجك بالكامل في ملف `.py`. يمكن أن يعتمد على الاستيراد النسبي لملفات أخرى طالما أن جميع الملفات موجودة في نفس الدليل (لا ندعم الوحدات الفرعية لهذه الميزة حتى الآن). في مثالنا، سنحدد ملف `modeling_resnet.py` وملف `configuration_resnet.py` في مجلد باسم "resnet_model" في دليل العمل الحالي. يحتوي ملف التكوين على كود لـ `ResnetConfig` ويحتوي ملف النمذجة على كود لـ `ResnetModel` و`ResnetModelForImageClassification`.
```
.
└── resnet_model
├── __init__.py
├── configuration_resnet.py
└── modeling_resnet.py
```
يمكن أن يكون ملف `__init__.py` فارغًا، فهو موجود فقط حتى يتمكن Python من اكتشاف أن `resnet_model` يمكن استخدامه كموديل.
<Tip warning={true}>
إذا كنت تقوم بنسخ ملفات النمذجة من المكتبة، فسوف تحتاج إلى استبدال جميع الواردات النسبية في أعلى الملف
لاستيرادها من حزمة `transformers`.
</Tip>
لاحظ أنه يمكنك إعادة استخدام (أو توسيع) تكوين/نموذج موجود.
لمشاركة نموذجك مع المجتمع، اتبع الخطوات التالية: أولاً، قم باستيراد نموذج ResNet والتكوين من الملفات التي تم إنشاؤها حديثًا:
```py
from resnet_model.configuration_resnet import ResnetConfig
from resnet_model.modeling_resnet import ResnetModel, ResnetModelForImageClassification
```
بعد ذلك، يجب عليك إخبار المكتبة بأنك تريد نسخ ملفات الكود الخاصة بهذه الكائنات عند استخدام طريقة `save_pretrained`
وتسجيلها بشكل صحيح باستخدام فئة تلقائية (خاصة للنماذج)، ما عليك سوى تشغيل:
```py
ResnetConfig.register_for_auto_class()
ResnetModel.register_for_auto_class("AutoModel")
ResnetModelForImageClassification.register_for_auto_class("AutoModelForImageClassification")
```
لاحظ أنه لا توجد حاجة لتحديد فئة تلقائية للتكوين (هناك فئة تلقائية واحدة فقط لها،
[`AutoConfig`]) ولكن الأمر يختلف بالنسبة للنماذج. قد يكون نموذجك المخصص مناسبًا للعديد من المهام المختلفة، لذلك يجب
تحديد أي من الفئات التلقائية هو الصحيح لنموذجك.
<Tip>
استخدم `register_for_auto_class()` إذا كنت تريد نسخ ملفات الكود. إذا كنت تفضل استخدام الكود على Hub من مستودع آخر،
فلا تحتاج إلى استدعائه. في الحالات التي يوجد فيها أكثر من فئة تلقائية واحدة، يمكنك تعديل ملف `config.json` مباشرة باستخدام
الهيكل التالي:
```json
"auto_map": {
"AutoConfig": "<your-repo-name>--<config-name>",
"AutoModel": "<your-repo-name>--<config-name>",
"AutoModelFor<Task>": "<your-repo-name>--<config-name>",
},
```
</Tip>
بعد ذلك، دعنا نقوم بإنشاء التكوين والنماذج كما فعلنا من قبل:
```py
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d = ResnetModelForImageClassification(resnet50d_config)
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())
```
الآن لإرسال النموذج إلى Hub، تأكد من تسجيل الدخول. إما تشغيل في المحطة الأوامر الطرفية الخاصة بك:
```bash
huggingface-cli login
```
أو من دفتر ملاحظات:
```py
from huggingface_hub import notebook_login
notebook_login()
```
يمكنك بعد ذلك الضغط على مساحة الاسم الخاصة بك (أو منظمة أنت عضو فيها) مثل هذا:
```py
resnet50d.push_to_hub("custom-resnet50d")
```
بالإضافة إلى أوزان النمذجة والتكوين بتنسيق json، فقد قام هذا أيضًا بنسخ ملفات النمذجة والتكوين `.py` في مجلد `custom-resnet50d` وتحميل النتيجة إلى Hub. يمكنك التحقق من النتيجة في هذا [مستودع النموذج](https://huggingface.co/sgugger/custom-resnet50d).
راجع [البرنامج التعليمي للمشاركة](model_sharing) لمزيد من المعلومات حول طريقة الدفع إلى المحور.
### استخدام نموذج مع كود مخصص
يمكنك استخدام أي تكوين أو نموذج أو مقسم لغوي مع ملفات برمجة مخصصة في مستودعه باستخدام الفئات التلقائية و دالة `from_pretrained`.تُفحص جميع الملفات والرموز المرفوع إلى Hub بحثًا عن البرامج الضارة (راجع وثائق [أمان Hub](https://huggingface.co/docs/hub/security#malware-scanning) لمزيد من المعلومات)، ولكن يجب عليك مراجعة كود النموذج والمؤلف لتجنب تنفيذ التعليمات البرمجية الضارة على جهازك. لتفعيل نموذج يحتوي على شفرة برمجية مخصصة، عيّن `trust_remote_code=True`:
```py
from transformers import AutoModelForImageClassification
model = AutoModelForImageClassification.from_pretrained("sgugger/custom-resnet50d", trust_remote_code=True)
```
يُنصح بشدة بتحديد رقم إصدار (commit hash) كـ `revision` للتأكد من عدم تعديل مؤلف النموذج للشفرة لاحقًابإضافة أسطر ضارة (إلا إذا كنت تثق تمامًا بمؤلفي النموذج):
```py
commit_hash = "ed94a7c6247d8aedce4647f00f20de6875b5b292"
model = AutoModelForImageClassification.from_pretrained(
"sgugger/custom-resnet50d"، trust_remote_code=True، revision=commit_hash
)
```
لاحظ وجود زرّ لنسخ رقم إصدار بسهولة عند تصفح سجل التزامات مستودع النموذج على منصة Hugging Face.
| transformers/docs/source/ar/custom_models.md/0 | {
"file_path": "transformers/docs/source/ar/custom_models.md",
"repo_id": "transformers",
"token_count": 10048
} |
# تحميل المحوّلات باستخدام 🤗 PEFT
[[open-in-colab]]
تقنية "التدريب الدقيق ذو الكفاءة البارامتيرية" (PEFT)](https://huggingface.co/blog/peft) تقوم بتجميد معلمات النموذج المُدرب مسبقًا أثناء الضبط الدقيق وتضيف عدد صغير من المعلمات القابلة للتدريب (المحولات) فوقه. يتم تدريب المحوّلات لتعلم معلومات خاصة بالمهام. وقد ثبت أن هذا النهج فعال للغاية من حيث استخدام الذاكرة مع انخفاض استخدام الكمبيوتر أثناء إنتاج نتائج قمماثلة للنموذج مضبوط دقيقًا بالكامل.
عادة ما تكون المحولات المدربة باستخدام PEFT أصغر بمقدار كبير من حيث الحجم من النموذج الكامل، مما يجعل من السهل مشاركتها وتخزينها وتحميلها.
<div class="flex flex-col justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/PEFT-hub-screenshot.png"/>
<figcaption class="text-center">تبلغ أوزان المحول لطراز OPTForCausalLM المخزن على Hub حوالي 6 ميجابايت مقارنة بالحجم الكامل لأوزان النموذج، والتي يمكن أن تكون حوالي 700 ميجابايت.</figcaption>
</div>
إذا كنت مهتمًا بمعرفة المزيد عن مكتبة 🤗 PEFT، فراجع [الوثائق](https://huggingface.co/docs/peft/index).
## الإعداد
ابدأ بتثبيت 🤗 PEFT:
```bash
pip install peft
```
إذا كنت تريد تجربة الميزات الجديدة تمامًا، فقد تكون مهتمًا بتثبيت المكتبة من المصدر:
```bash
pip install git+https://github.com/huggingface/peft.git
```
## نماذج PEFT المدعومة
يدعم 🤗 Transformers بشكلٍ أصلي بعض طرق PEFT، مما يعني أنه يمكنك تحميل أوزان المحول المخزنة محليًا أو على Hub وتشغيلها أو تدريبها ببضع سطور من التعليمات البرمجية. الطرق المدعومة هي:
- [محولات الرتبة المنخفضة](https://huggingface.co/docs/peft/conceptual_guides/lora)
- [IA3](https://huggingface.co/docs/peft/conceptual_guides/ia3)
- [AdaLoRA](https://arxiv.org/abs/2303.10512)
إذا كنت تريد استخدام طرق PEFT الأخرى، مثل تعلم المحث أو ضبط المحث، أو حول مكتبة 🤗 PEFT بشكل عام، يرجى الرجوع إلى [الوثائق](https://huggingface.co/docs/peft/index).
## تحميل محول PEFT
لتحميل نموذج محول PEFT واستخدامه من 🤗 Transformers، تأكد من أن مستودع Hub أو الدليل المحلي يحتوي على ملف `adapter_config.json` وأوزان المحوّل، كما هو موضح في صورة المثال أعلاه. بعد ذلك، يمكنك تحميل نموذج محوّل PEFT باستخدام فئة `AutoModelFor`. على سبيل المثال، لتحميل نموذج محول PEFT للنمذجة اللغوية السببية:
1. حدد معرف النموذج لPEFT
2. مرره إلى فئة [`AutoModelForCausalLM`]
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "ybelkada/opt-350m-lora"
model = AutoModelForCausalLM.from_pretrained(peft_model_id)
```
<Tip>
يمكنك تحميل محول PEFT باستخدام فئة `AutoModelFor` أو فئة النموذج الأساسي مثل `OPTForCausalLM` أو `LlamaForCausalLM`.
</Tip>
يمكنك أيضًا تحميل محول PEFT عن طريق استدعاء طريقة `load_adapter`:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "facebook/opt-350m"
peft_model_id = "ybelkada/opt-350m-lora"
model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)
```
راجع قسم [وثائق API](#transformers.integrations.PeftAdapterMixin) أدناه لمزيد من التفاصيل.
## التحميل في 8 بت أو 4 بت
راجع قسم [وثائق API](#transformers.integrations.PeftAdapterMixin) أدناه لمزيد من التفاصيل.
## التحميل في 8 بت أو 4 بت
يدعم تكامل `bitsandbytes` أنواع بيانات الدقة 8 بت و4 بت، والتي تكون مفيدة لتحميل النماذج الكبيرة لأنها توفر مساحة في الذاكرة (راجع دليل تكامل `bitsandbytes` [guide](./quantization#bitsandbytes-integration) لمعرفة المزيد). أضف المعلمات`load_in_8bit` أو `load_in_4bit` إلى [`~PreTrainedModel.from_pretrained`] وقم بتعيين `device_map="auto"` لتوزيع النموذج بشكل فعال على الأجهزة لديك:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
peft_model_id = "ybelkada/opt-350m-lora"
model = AutoModelForCausalLM.from_pretrained(peft_model_id, quantization_config=BitsAndBytesConfig(load_in_8bit=True))
```
## إضافة محول جديد
يمكنك استخدام الدالة [`~peft.PeftModel.add_adapter`] لإضافة محوّل جديد إلى نموذج يحتوي بالفعل على محوّل آخر طالما أن المحول الجديد مطابقًا للنوع الحالي. على سبيل المثال، إذا كان لديك محول LoRA موجود مرتبط بنموذج:
```py
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import LoraConfig
model_id = "facebook/opt-350m"
model = AutoModelForCausalLM.from_pretrained(model_id)
lora_config = LoraConfig(
target_modules=["q_proj", "k_proj"],
init_lora_weights=False
)
model.add_adapter(lora_config, adapter_name="adapter_1")
```
لإضافة محول جديد:
```py
# قم بتعليق محول جديد بنفس التكوين
model.add_adapter(lora_config, adapter_name="adapter_2")
```
الآن يمكنك استخدام [`~peft.PeftModel.set_adapter`] لتعيين المحول الذي سيتم استخدامه:
```py
# استخدم adapter_1
model.set_adapter("adapter_1")
output = model.generate(**inputs)
print(tokenizer.decode(output_disabled[0], skip_special_tokens=True))
# استخدم adapter_2
model.set_adapter("adapter_2")
output_enabled = model.generate(**inputs)
print(tokenizer.decode(output_enabled[0], skip_special_tokens=True))
```
## تمكين وتعطيل المحولات
بمجرد إضافة محول إلى نموذج، يمكنك تمكين أو تعطيل وحدة المحول. لتمكين وحدة المحول:
```py
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import PeftConfig
model_id = "facebook/opt-350m"
adapter_model_id = "ybelkada/opt-350m-lora"
tokenizer = AutoTokenizer.from_pretrained(model_id)
text = "Hello"
inputs = tokenizer(text, return_tensors="pt")
model = AutoModelForCausalLM.from_pretrained(model_id)
peft_config = PeftConfig.from_pretrained(adapter_model_id)
# لبدء تشغيله بأوزان عشوائية
peft_config.init_lora_weights = False
model.add_adapter(peft_config)
model.enable_adapters()
output = model.generate(**inputs)
```
لإيقاف تشغيل وحدة المحول:
```py
model.disable_adapters()
output = model.generate(**inputs)
```
## تدريب محول PEFT
يدعم محول PEFT فئة [`Trainer`] بحيث يمكنك تدريب محول لحالتك الاستخدام المحددة. فهو يتطلب فقط إضافة بضع سطور أخرى من التعليمات البرمجية. على سبيل المثال، لتدريب محول LoRA:
<Tip>
إذا لم تكن معتادًا على ضبط نموذج دقيق باستخدام [`Trainer`، فراجع البرنامج التعليمي](training) لضبط نموذج مُدرب مسبقًا.
</Tip>
1. حدد تكوين المحول باستخدام نوع المهمة والمعاملات الزائدة (راجع [`~peft.LoraConfig`] لمزيد من التفاصيل حول وظيفة هذه المعلمات).
```py
from peft import LoraConfig
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=64,
bias="none",
task_type="CAUSAL_LM"،
)
```
2. أضف المحول إلى النموذج.
```py
model.add_adapter(peft_config)
```
3. الآن يمكنك تمرير النموذج إلى [`Trainer`]!
```py
trainer = Trainer(model=model, ...)
trainer.train()
```
لحفظ محول المدرب وتحميله مرة أخرى:
```py
model.save_pretrained(save_dir)
model = AutoModelForCausalLM.from_pretrained(save_dir)
```
## إضافة طبقات قابلة للتدريب إضافية إلى محول PEFT
```py
model.save_pretrained(save_dir)
model = AutoModelForCausalLM.from_pretrained(save_dir)
```
## إضافة طبقات قابلة للتدريب إضافية إلى محول PEFT
يمكنك أيضًا إجراء تدريب دقيق لمحوّلات قابلة للتدريب إضافية فوق نموذج يحتوي بالفعل على محوّلات عن طريق تمرير معلم `modules_to_save` في تكوين PEFT الخاص بك. على سبيل المثال، إذا كنت تريد أيضًا ضبط دقيق لرأس النموذج اللغوي`lm_head` فوق نموذج بمحوّل LoRA:
```py
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import LoraConfig
model_id = "facebook/opt-350m"
model = AutoModelForCausalLM.from_pretrained(model_id)
lora_config = LoraConfig(
target_modules=["q_proj", "k_proj"],
modules_to_save=["lm_head"]،
)
model.add_adapter(lora_config)
```
## وثائق API
[[autodoc]] integrations.PeftAdapterMixin
- load_adapter
- add_adapter
- set_adapter
- disable_adapters
- enable_adapters
- active_adapters
- get_adapter_state_dict
<!--
TODO: (@younesbelkada @stevhliu)
- Link to PEFT docs for further details
- Trainer
- 8-bit / 4-bit examples ?
--> | transformers/docs/source/ar/peft.md/0 | {
"file_path": "transformers/docs/source/ar/peft.md",
"repo_id": "transformers",
"token_count": 5151
} |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
# Vortrainierte Instanzen mit einer AutoClass laden
Bei so vielen verschiedenen Transformator-Architekturen kann es eine Herausforderung sein, eine für Ihren Checkpoint zu erstellen. Als Teil der 🤗 Transformers Kernphilosophie, die Bibliothek leicht, einfach und flexibel nutzbar zu machen, leitet eine `AutoClass` automatisch die richtige Architektur aus einem gegebenen Checkpoint ab und lädt sie. Mit der Methode `from_pretrained()` kann man schnell ein vortrainiertes Modell für eine beliebige Architektur laden, so dass man keine Zeit und Ressourcen aufwenden muss, um ein Modell von Grund auf zu trainieren. Die Erstellung dieser Art von Checkpoint-agnostischem Code bedeutet, dass Ihr Code, wenn er für einen Checkpoint funktioniert, auch mit einem anderen Checkpoint funktionieren wird - solange er für eine ähnliche Aufgabe trainiert wurde - selbst wenn die Architektur unterschiedlich ist.
<Tip>
Denken Sie daran, dass sich die Architektur auf das Skelett des Modells bezieht und die Checkpoints die Gewichte für eine bestimmte Architektur sind. Zum Beispiel ist [BERT](https://huggingface.co/google-bert/bert-base-uncased) eine Architektur, während `google-bert/bert-base-uncased` ein Checkpoint ist. Modell ist ein allgemeiner Begriff, der entweder Architektur oder Prüfpunkt bedeuten kann.
</Tip>
In dieser Anleitung lernen Sie, wie man:
* Einen vortrainierten Tokenizer lädt.
* Einen vortrainierten Merkmalsextraktor lädt.
* Einen vortrainierten Prozessor lädt.
* Ein vortrainiertes Modell lädt.
## AutoTokenizer
Nahezu jede NLP-Aufgabe beginnt mit einem Tokenizer. Ein Tokenizer wandelt Ihre Eingabe in ein Format um, das vom Modell verarbeitet werden kann.
Laden Sie einen Tokenizer mit [`AutoTokenizer.from_pretrained`]:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
```
Dann tokenisieren Sie Ihre Eingabe wie unten gezeigt:
```py
>>> sequence = "In a hole in the ground there lived a hobbit."
>>> print(tokenizer(sequence))
{'input_ids': [101, 1999, 1037, 4920, 1999, 1996, 2598, 2045, 2973, 1037, 7570, 10322, 4183, 1012, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
## AutoFeatureExtractor
Für Audio- und Bildverarbeitungsaufgaben verarbeitet ein Merkmalsextraktor das Audiosignal oder Bild in das richtige Eingabeformat.
Laden Sie einen Merkmalsextraktor mit [`AutoFeatureExtractor.from_pretrained`]:
```py
>>> from transformers import AutoFeatureExtractor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained(
... "ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
... )
```
## AutoProcessor
Multimodale Aufgaben erfordern einen Prozessor, der zwei Arten von Vorverarbeitungswerkzeugen kombiniert. Das Modell [LayoutLMV2](model_doc/layoutlmv2) beispielsweise benötigt einen Feature-Extraktor für Bilder und einen Tokenizer für Text; ein Prozessor kombiniert beide.
Laden Sie einen Prozessor mit [`AutoProcessor.from_pretrained`]:
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
```
## AutoModel
<frameworkcontent>
<pt>
Mit den `AutoModelFor`-Klassen können Sie schließlich ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe [hier](model_doc/auto) für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit [`AutoModelForSequenceClassification.from_pretrained`]:
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
```py
>>> from transformers import AutoModelForTokenClassification
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
<Tip warning={true}>
Für PyTorch-Modelle verwendet die Methode `from_pretrained()` `torch.load()`, die intern `pickle` verwendet und als unsicher bekannt ist. Generell sollte man niemals ein Modell laden, das aus einer nicht vertrauenswürdigen Quelle stammen könnte, oder das manipuliert worden sein könnte. Dieses Sicherheitsrisiko wird für öffentliche Modelle, die auf dem Hugging Face Hub gehostet werden, teilweise gemildert, da diese bei jeder Übertragung [auf Malware](https://huggingface.co/docs/hub/security-malware) gescannt werden. Siehe die [Hub-Dokumentation](https://huggingface.co/docs/hub/security) für Best Practices wie [signierte Commit-Verifizierung](https://huggingface.co/docs/hub/security-gpg#signing-commits-with-gpg) mit GPG.
TensorFlow- und Flax-Checkpoints sind nicht betroffen und können in PyTorch-Architekturen mit den Kwargs `from_tf` und `from_flax` für die Methode `from_pretrained` geladen werden, um dieses Problem zu umgehen.
</Tip>
Im Allgemeinen empfehlen wir die Verwendung der Klasse "AutoTokenizer" und der Klasse "AutoModelFor", um trainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.
</pt>
<tf>
Mit den Klassen `TFAutoModelFor` schließlich können Sie ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe [hier](model_doc/auto) für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit [`TFAutoModelForSequenceClassification.from_pretrained`]:
```py
>>> from transformers import TFAutoModelForSequenceClassification
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
```py
>>> from transformers import TFAutoModelForTokenClassification
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Im Allgemeinen empfehlen wir, die Klasse "AutoTokenizer" und die Klasse "TFAutoModelFor" zu verwenden, um vortrainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.
</tf>
</frameworkcontent>
| transformers/docs/source/de/autoclass_tutorial.md/0 | {
"file_path": "transformers/docs/source/de/autoclass_tutorial.md",
"repo_id": "transformers",
"token_count": 2644
} |
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# Building custom models
The 🤗 Transformers library is designed to be easily extensible. Every model is fully coded in a given subfolder
of the repository with no abstraction, so you can easily copy a modeling file and tweak it to your needs.
If you are writing a brand new model, it might be easier to start from scratch. In this tutorial, we will show you
how to write a custom model and its configuration so it can be used inside Transformers, and how you can share it
with the community (with the code it relies on) so that anyone can use it, even if it's not present in the 🤗
Transformers library. We'll see how to build upon transformers and extend the framework with your hooks and
custom code.
We will illustrate all of this on a ResNet model, by wrapping the ResNet class of the
[timm library](https://github.com/rwightman/pytorch-image-models) into a [`PreTrainedModel`].
## Writing a custom configuration
Before we dive into the model, let's first write its configuration. The configuration of a model is an object that
will contain all the necessary information to build the model. As we will see in the next section, the model can only
take a `config` to be initialized, so we really need that object to be as complete as possible.
<Tip>
Models in the `transformers` library itself generally follow the convention that they accept a `config` object
in their `__init__` method, and then pass the whole `config` to sub-layers in the model, rather than breaking the
config object into multiple arguments that are all passed individually to sub-layers. Writing your model in this
style results in simpler code with a clear "source of truth" for any hyperparameters, and also makes it easier
to reuse code from other models in `transformers`.
</Tip>
In our example, we will take a couple of arguments of the ResNet class that we might want to tweak. Different
configurations will then give us the different types of ResNets that are possible. We then just store those arguments,
after checking the validity of a few of them.
```python
from transformers import PretrainedConfig
from typing import List
class ResnetConfig(PretrainedConfig):
model_type = "resnet"
def __init__(
self,
block_type="bottleneck",
layers: List[int] = [3, 4, 6, 3],
num_classes: int = 1000,
input_channels: int = 3,
cardinality: int = 1,
base_width: int = 64,
stem_width: int = 64,
stem_type: str = "",
avg_down: bool = False,
**kwargs,
):
if block_type not in ["basic", "bottleneck"]:
raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
if stem_type not in ["", "deep", "deep-tiered"]:
raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
self.block_type = block_type
self.layers = layers
self.num_classes = num_classes
self.input_channels = input_channels
self.cardinality = cardinality
self.base_width = base_width
self.stem_width = stem_width
self.stem_type = stem_type
self.avg_down = avg_down
super().__init__(**kwargs)
```
The three important things to remember when writing you own configuration are the following:
- you have to inherit from `PretrainedConfig`,
- the `__init__` of your `PretrainedConfig` must accept any kwargs,
- those `kwargs` need to be passed to the superclass `__init__`.
The inheritance is to make sure you get all the functionality from the 🤗 Transformers library, while the two other
constraints come from the fact a `PretrainedConfig` has more fields than the ones you are setting. When reloading a
config with the `from_pretrained` method, those fields need to be accepted by your config and then sent to the
superclass.
Defining a `model_type` for your configuration (here `model_type="resnet"`) is not mandatory, unless you want to
register your model with the auto classes (see last section).
With this done, you can easily create and save your configuration like you would do with any other model config of the
library. Here is how we can create a resnet50d config and save it:
```py
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d_config.save_pretrained("custom-resnet")
```
This will save a file named `config.json` inside the folder `custom-resnet`. You can then reload your config with the
`from_pretrained` method:
```py
resnet50d_config = ResnetConfig.from_pretrained("custom-resnet")
```
You can also use any other method of the [`PretrainedConfig`] class, like [`~PretrainedConfig.push_to_hub`] to
directly upload your config to the Hub.
## Writing a custom model
Now that we have our ResNet configuration, we can go on writing the model. We will actually write two: one that
extracts the hidden features from a batch of images (like [`BertModel`]) and one that is suitable for image
classification (like [`BertForSequenceClassification`]).
As we mentioned before, we'll only write a loose wrapper of the model to keep it simple for this example. The only
thing we need to do before writing this class is a map between the block types and actual block classes. Then the
model is defined from the configuration by passing everything to the `ResNet` class:
```py
from transformers import PreTrainedModel
from timm.models.resnet import BasicBlock, Bottleneck, ResNet
from .configuration_resnet import ResnetConfig
BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}
class ResnetModel(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
)
def forward(self, tensor):
return self.model.forward_features(tensor)
```
For the model that will classify images, we just change the forward method:
```py
import torch
class ResnetModelForImageClassification(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
)
def forward(self, tensor, labels=None):
logits = self.model(tensor)
if labels is not None:
loss = torch.nn.functional.cross_entropy(logits, labels)
return {"loss": loss, "logits": logits}
return {"logits": logits}
```
In both cases, notice how we inherit from `PreTrainedModel` and call the superclass initialization with the `config`
(a bit like when you write a regular `torch.nn.Module`). The line that sets the `config_class` is not mandatory, unless
you want to register your model with the auto classes (see last section).
<Tip>
If your model is very similar to a model inside the library, you can re-use the same configuration as this model.
</Tip>
You can have your model return anything you want, but returning a dictionary like we did for
`ResnetModelForImageClassification`, with the loss included when labels are passed, will make your model directly
usable inside the [`Trainer`] class. Using another output format is fine as long as you are planning on using your own
training loop or another library for training.
Now that we have our model class, let's create one:
```py
resnet50d = ResnetModelForImageClassification(resnet50d_config)
```
Again, you can use any of the methods of [`PreTrainedModel`], like [`~PreTrainedModel.save_pretrained`] or
[`~PreTrainedModel.push_to_hub`]. We will use the second in the next section, and see how to push the model weights
with the code of our model. But first, let's load some pretrained weights inside our model.
In your own use case, you will probably be training your custom model on your own data. To go fast for this tutorial,
we will use the pretrained version of the resnet50d. Since our model is just a wrapper around it, it's going to be
easy to transfer those weights:
```py
import timm
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())
```
Now let's see how to make sure that when we do [`~PreTrainedModel.save_pretrained`] or [`~PreTrainedModel.push_to_hub`], the
code of the model is saved.
## Registering a model with custom code to the auto classes
If you are writing a library that extends 🤗 Transformers, you may want to extend the auto classes to include your own
model. This is different from pushing the code to the Hub in the sense that users will need to import your library to
get the custom models (contrarily to automatically downloading the model code from the Hub).
As long as your config has a `model_type` attribute that is different from existing model types, and that your model
classes have the right `config_class` attributes, you can just add them to the auto classes like this:
```py
from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
AutoConfig.register("resnet", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification)
```
Note that the first argument used when registering your custom config to [`AutoConfig`] needs to match the `model_type`
of your custom config, and the first argument used when registering your custom models to any auto model class needs
to match the `config_class` of those models.
## Sending the code to the Hub
<Tip warning={true}>
This API is experimental and may have some slight breaking changes in the next releases.
</Tip>
First, make sure your model is fully defined in a `.py` file. It can rely on relative imports to some other files as
long as all the files are in the same directory (we don't support submodules for this feature yet). For our example,
we'll define a `modeling_resnet.py` file and a `configuration_resnet.py` file in a folder of the current working
directory named `resnet_model`. The configuration file contains the code for `ResnetConfig` and the modeling file
contains the code of `ResnetModel` and `ResnetModelForImageClassification`.
```
.
└── resnet_model
├── __init__.py
├── configuration_resnet.py
└── modeling_resnet.py
```
The `__init__.py` can be empty, it's just there so that Python detects `resnet_model` can be use as a module.
<Tip warning={true}>
If copying a modeling files from the library, you will need to replace all the relative imports at the top of the file
to import from the `transformers` package.
</Tip>
Note that you can re-use (or subclass) an existing configuration/model.
To share your model with the community, follow those steps: first import the ResNet model and config from the newly
created files:
```py
from resnet_model.configuration_resnet import ResnetConfig
from resnet_model.modeling_resnet import ResnetModel, ResnetModelForImageClassification
```
Then you have to tell the library you want to copy the code files of those objects when using the `save_pretrained`
method and properly register them with a given Auto class (especially for models), just run:
```py
ResnetConfig.register_for_auto_class()
ResnetModel.register_for_auto_class("AutoModel")
ResnetModelForImageClassification.register_for_auto_class("AutoModelForImageClassification")
```
Note that there is no need to specify an auto class for the configuration (there is only one auto class for them,
[`AutoConfig`]) but it's different for models. Your custom model could be suitable for many different tasks, so you
have to specify which one of the auto classes is the correct one for your model.
<Tip>
Use `register_for_auto_class()` if you want the code files to be copied. If you instead prefer to use code on the Hub from another repo,
you don't need to call it. In cases where there's more than one auto class, you can modify the `config.json` directly using the
following structure:
```json
"auto_map": {
"AutoConfig": "<your-repo-name>--<config-name>",
"AutoModel": "<your-repo-name>--<config-name>",
"AutoModelFor<Task>": "<your-repo-name>--<config-name>",
},
```
</Tip>
Next, let's create the config and models as we did before:
```py
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d = ResnetModelForImageClassification(resnet50d_config)
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())
```
Now to send the model to the Hub, make sure you are logged in. Either run in your terminal:
```bash
huggingface-cli login
```
or from a notebook:
```py
from huggingface_hub import notebook_login
notebook_login()
```
You can then push to your own namespace (or an organization you are a member of) like this:
```py
resnet50d.push_to_hub("custom-resnet50d")
```
On top of the modeling weights and the configuration in json format, this also copied the modeling and
configuration `.py` files in the folder `custom-resnet50d` and uploaded the result to the Hub. You can check the result
in this [model repo](https://huggingface.co/sgugger/custom-resnet50d).
See the [sharing tutorial](model_sharing) for more information on the push to Hub method.
## Using a model with custom code
You can use any configuration, model or tokenizer with custom code files in its repository with the auto-classes and
the `from_pretrained` method. All files and code uploaded to the Hub are scanned for malware (refer to the [Hub security](https://huggingface.co/docs/hub/security#malware-scanning) documentation for more information), but you should still
review the model code and author to avoid executing malicious code on your machine. Set `trust_remote_code=True` to use
a model with custom code:
```py
from transformers import AutoModelForImageClassification
model = AutoModelForImageClassification.from_pretrained("sgugger/custom-resnet50d", trust_remote_code=True)
```
It is also strongly encouraged to pass a commit hash as a `revision` to make sure the author of the models did not
update the code with some malicious new lines (unless you fully trust the authors of the models).
```py
commit_hash = "ed94a7c6247d8aedce4647f00f20de6875b5b292"
model = AutoModelForImageClassification.from_pretrained(
"sgugger/custom-resnet50d", trust_remote_code=True, revision=commit_hash
)
```
Note that when browsing the commit history of the model repo on the Hub, there is a button to easily copy the commit
hash of any commit.
| transformers/docs/source/en/custom_models.md/0 | {
"file_path": "transformers/docs/source/en/custom_models.md",
"repo_id": "transformers",
"token_count": 4869
} |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Custom Layers and Utilities
This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.
Most of those are only useful if you are studying the code of the models in the library.
## Pytorch custom modules
[[autodoc]] pytorch_utils.Conv1D
[[autodoc]] modeling_utils.PoolerStartLogits
- forward
[[autodoc]] modeling_utils.PoolerEndLogits
- forward
[[autodoc]] modeling_utils.PoolerAnswerClass
- forward
[[autodoc]] modeling_utils.SquadHeadOutput
[[autodoc]] modeling_utils.SQuADHead
- forward
[[autodoc]] modeling_utils.SequenceSummary
- forward
## PyTorch Helper Functions
[[autodoc]] pytorch_utils.apply_chunking_to_forward
[[autodoc]] pytorch_utils.find_pruneable_heads_and_indices
[[autodoc]] pytorch_utils.prune_layer
[[autodoc]] pytorch_utils.prune_conv1d_layer
[[autodoc]] pytorch_utils.prune_linear_layer
## TensorFlow custom layers
[[autodoc]] modeling_tf_utils.TFConv1D
[[autodoc]] modeling_tf_utils.TFSequenceSummary
## TensorFlow loss functions
[[autodoc]] modeling_tf_utils.TFCausalLanguageModelingLoss
[[autodoc]] modeling_tf_utils.TFMaskedLanguageModelingLoss
[[autodoc]] modeling_tf_utils.TFMultipleChoiceLoss
[[autodoc]] modeling_tf_utils.TFQuestionAnsweringLoss
[[autodoc]] modeling_tf_utils.TFSequenceClassificationLoss
[[autodoc]] modeling_tf_utils.TFTokenClassificationLoss
## TensorFlow Helper Functions
[[autodoc]] modeling_tf_utils.get_initializer
[[autodoc]] modeling_tf_utils.keras_serializable
[[autodoc]] modeling_tf_utils.shape_list
| transformers/docs/source/en/internal/modeling_utils.md/0 | {
"file_path": "transformers/docs/source/en/internal/modeling_utils.md",
"repo_id": "transformers",
"token_count": 729
} |
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# Feature Extractor
A feature extractor is in charge of preparing input features for audio or vision models. This includes feature extraction from sequences, e.g., pre-processing audio files to generate Log-Mel Spectrogram features, feature extraction from images, e.g., cropping image files, but also padding, normalization, and conversion to NumPy, PyTorch, and TensorFlow tensors.
## FeatureExtractionMixin
[[autodoc]] feature_extraction_utils.FeatureExtractionMixin
- from_pretrained
- save_pretrained
## SequenceFeatureExtractor
[[autodoc]] SequenceFeatureExtractor
- pad
## BatchFeature
[[autodoc]] BatchFeature
## ImageFeatureExtractionMixin
[[autodoc]] image_utils.ImageFeatureExtractionMixin
| transformers/docs/source/en/main_classes/feature_extractor.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/feature_extractor.md",
"repo_id": "transformers",
"token_count": 383
} |
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# AltCLIP
## Overview
The AltCLIP model was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679v2) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu. AltCLIP
(Altering the Language Encoder in CLIP) is a neural network trained on a variety of image-text and text-text pairs. By switching CLIP's
text encoder with a pretrained multilingual text encoder XLM-R, we could obtain very close performances with CLIP on almost all tasks, and extended original CLIP's capabilities such as multilingual understanding.
The abstract from the paper is the following:
*In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model.
Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained
multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of
teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art
performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with
CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.*
This model was contributed by [jongjyh](https://huggingface.co/jongjyh).
## Usage tips and example
The usage of AltCLIP is very similar to the CLIP. the difference between CLIP is the text encoder. Note that we use bidirectional attention instead of casual attention
and we take the [CLS] token in XLM-R to represent text embedding.
AltCLIP is a multi-modal vision and language model. It can be used for image-text similarity and for zero-shot image
classification. AltCLIP uses a ViT like transformer to get visual features and a bidirectional language model to get the text
features. Both the text and visual features are then projected to a latent space with identical dimension. The dot
product between the projected image and text features is then used as a similar score.
To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image. The authors
also add absolute position embeddings, and feed the resulting sequence of vectors to a standard Transformer encoder.
The [`CLIPImageProcessor`] can be used to resize (or rescale) and normalize images for the model.
The [`AltCLIPProcessor`] wraps a [`CLIPImageProcessor`] and a [`XLMRobertaTokenizer`] into a single instance to both
encode the text and prepare the images. The following example shows how to get the image-text similarity scores using
[`AltCLIPProcessor`] and [`AltCLIPModel`].
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AltCLIPModel, AltCLIPProcessor
>>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
>>> processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```
<Tip>
This model is based on `CLIPModel`, use it like you would use the original [CLIP](clip).
</Tip>
## AltCLIPConfig
[[autodoc]] AltCLIPConfig
- from_text_vision_configs
## AltCLIPTextConfig
[[autodoc]] AltCLIPTextConfig
## AltCLIPVisionConfig
[[autodoc]] AltCLIPVisionConfig
## AltCLIPProcessor
[[autodoc]] AltCLIPProcessor
## AltCLIPModel
[[autodoc]] AltCLIPModel
- forward
- get_text_features
- get_image_features
## AltCLIPTextModel
[[autodoc]] AltCLIPTextModel
- forward
## AltCLIPVisionModel
[[autodoc]] AltCLIPVisionModel
- forward | transformers/docs/source/en/model_doc/altclip.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/altclip.md",
"repo_id": "transformers",
"token_count": 1400
} |
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# Convolutional Vision Transformer (CvT)
## Overview
The CvT model was proposed in [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang. The Convolutional vision Transformer (CvT) improves the [Vision Transformer (ViT)](vit) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs.
The abstract from the paper is the following:
*We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT)
in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. This is accomplished through
two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer
block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs)
to the ViT architecture (\ie shift, scale, and distortion invariance) while maintaining the merits of Transformers (\ie dynamic attention,
global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves
state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. In addition,
performance gains are maintained when pretrained on larger datasets (\eg ImageNet-22k) and fine-tuned to downstream tasks. Pre-trained on
ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7\% on the ImageNet-1k val set. Finally, our results show that the positional encoding,
a crucial component in existing Vision Transformers, can be safely removed in our model, simplifying the design for higher resolution vision tasks.*
This model was contributed by [anugunj](https://huggingface.co/anugunj). The original code can be found [here](https://github.com/microsoft/CvT).
## Usage tips
- CvT models are regular Vision Transformers, but trained with convolutions. They outperform the [original model (ViT)](vit) when fine-tuned on ImageNet-1K and CIFAR-100.
- You can check out demo notebooks regarding inference as well as fine-tuning on custom data [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer) (you can just replace [`ViTFeatureExtractor`] by [`AutoImageProcessor`] and [`ViTForImageClassification`] by [`CvtForImageClassification`]).
- The available checkpoints are either (1) pre-trained on [ImageNet-22k](http://www.image-net.org/) (a collection of 14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/) (also referred to as ILSVRC 2012, a collection of 1.3 million
images and 1,000 classes).
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CvT.
<PipelineTag pipeline="image-classification"/>
- [`CvtForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
## CvtConfig
[[autodoc]] CvtConfig
<frameworkcontent>
<pt>
## CvtModel
[[autodoc]] CvtModel
- forward
## CvtForImageClassification
[[autodoc]] CvtForImageClassification
- forward
</pt>
<tf>
## TFCvtModel
[[autodoc]] TFCvtModel
- call
## TFCvtForImageClassification
[[autodoc]] TFCvtForImageClassification
- call
</tf>
</frameworkcontent>
| transformers/docs/source/en/model_doc/cvt.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/cvt.md",
"repo_id": "transformers",
"token_count": 1314
} |
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the License. You may obtain a copy of the License at
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# Gemma2
## Overview
The Gemma2 model was proposed in [Gemma2: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/google-gemma-2/) by Gemma2 Team, Google.
Two Gemma2 models are released, with parameters sizes of 9 billion (9B) and 27 billion (27B).
The abstract from the blog post is the following:
*Now we’re officially releasing Gemma 2 to researchers and developers globally. Available in both 9 billion (9B) and 27 billion (27B) parameter sizes, Gemma 2 is higher-performing and more efficient at inference than the first generation, with significant safety advancements built in. In fact, at 27B, it offers competitive alternatives to models more than twice its size, delivering the kind of performance that was only possible with proprietary models as recently as December.*
Tips:
- The original checkpoints can be converted using the conversion script `src/transformers/models/Gemma2/convert_Gemma2_weights_to_hf.py`
<Tip warning={true}>
- Gemma2 uses sliding window attention every second layer, which makes it unsuitable for typical kv caching with [`~DynamicCache`] or tuples of tensors. To enable caching in Gemma2 forward call, you must initialize a [`~HybridCache`] instance and pass it as `past_key_values` to the forward call. Note, that you also have to prepare `cache_position` if the `past_key_values` already contains previous keys and values.
</Tip>
This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ), [Pedro Cuenca](https://huggingface.co/pcuenq) and [Tom Arsen]().
## Gemma2Config
[[autodoc]] Gemma2Config
## Gemma2Model
[[autodoc]] Gemma2Model
- forward
## Gemma2ForCausalLM
[[autodoc]] Gemma2ForCausalLM
- forward
## Gemma2ForSequenceClassification
[[autodoc]] Gemma2ForSequenceClassification
- forward
## Gemma2ForTokenClassification
[[autodoc]] Gemma2ForTokenClassification
- forward
| transformers/docs/source/en/model_doc/gemma2.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/gemma2.md",
"repo_id": "transformers",
"token_count": 736
} |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# LLaVa-NeXT-Video
## Overview
The LLaVa-NeXT-Video model was proposed in [LLaVA-NeXT: A Strong Zero-shot Video Understanding Model
](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/) by Yuanhan Zhang, Bo Li, Haotian Liu, Yong Jae Lee, Liangke Gui, Di Fu, Jiashi Feng, Ziwei Liu, Chunyuan Li. LLaVa-NeXT-Video improves upon [LLaVa-NeXT](llava_next) by fine-tuning on a mix if video and image dataset thus increasing the model's performance on videos.
[LLaVA-NeXT](llava_next) surprisingly has strong performance in understanding video content in zero-shot fashion with the AnyRes technique that it uses. The AnyRes technique naturally represents a high-resolution image into multiple images. This technique is naturally generalizable to represent videos because videos can be considered as a set of frames (similar to a set of images in LLaVa-NeXT). The current version of LLaVA-NeXT makes use of AnyRes and trains with supervised fine-tuning (SFT) on top of LLaVA-Next on video data to achieves better video understanding capabilities.The model is a current SOTA among open-source models on [VideoMME bench](https://arxiv.org/abs/2405.21075).
The introduction from the blog is the following:
On January 30, 2024, we released LLaVA-NeXT, an open-source Large Multimodal Model (LMM) that has been trained exclusively on text-image data. With the proposed AnyRes technique, it boosts capabilities in reasoning, OCR, and world knowledge, demonstrating remarkable performance across a spectrum of image-based multimodal understanding tasks, and even exceeding Gemini-Pro on several image benchmarks, e.g. MMMU and MathVista.
**In today’s exploration, we delve into the performance of LLaVA-NeXT within the realm of video understanding tasks. We reveal that LLaVA-NeXT surprisingly has strong performance in understanding video content. The current version of LLaVA-NeXT for videos has several improvements:
- Zero-shot video representation capabilities with AnyRes: The AnyRes technique naturally represents a high-resolution image into multiple images that a pre-trained VIT is able to digest, and forms them into a concantenated sequence. This technique is naturally generalizable to represent videos (consisting of multiple frames), allowing the image-only-trained LLaVA-Next model to perform surprisingly well on video tasks. Notably, this is the first time that LMMs show strong zero-shot modality transfer ability.
- Inference with length generalization improves on longer videos. The linear scaling technique enables length generalization, allowing LLaVA-NeXT to effectively handle long-video beyond the limitation of the "max_token_length" of the LLM.
- Strong video understanding ability. (1) LLaVA-Next-Image, which combines the above two techniques, yields superior zero-shot performance than open-source LMMs tuned on videos. (2) LLaVA-Next-Video, further supervised fine-tuning (SFT) LLaVA-Next-Image on video data, achieves better video understanding capabilities compared to LLaVA-Next-Image. (3) LLaVA-Next-Video-DPO, which aligns the model response with AI feedback using direct preference optimization (DPO), showing significant performance boost.
- Efficient deployment and inference with SGLang. It allows 5x faster inference on video tasks, allowing more scalable serving such as million-level video re-captioning. See instructions in our repo.**
This model was contributed by [RaushanTurganbay](https://huggingface.co/RaushanTurganbay).
The original code can be found [here](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/inference).
## Usage tips
- We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simply make sure to call `processor.tokenizer.padding_side = "left"` before generating.
<Tip warning={true}>
- Llava-Next uses different number of patches for images and thus has to pad the inputs inside modeling code, aside from the padding done when processing the inputs. The default setting is "left-padding" if model is in `eval()` mode, otherwise "right-padding".
</Tip>
> [!NOTE]
> LLaVA models after release v4.46 will raise warnings about adding `processor.patch_size = {{patch_size}}`, `processor.num_additional_image_tokens = {{num_additional_image_tokens}}` and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}`. It is strongly recommended to add the attributes to the processor if you own the model checkpoint, or open a PR if it is not owned by you.
Adding these attributes means that LLaVA will try to infer the number of image tokens required per image and expand the text with as many `<image>` placeholders as there will be tokens. Usually it is around 500 tokens per image, so make sure that the text is not truncated as otherwise there will be failure when merging the embeddings.
The attributes can be obtained from model config, as `model.config.vision_config.patch_size` or `model.config.vision_feature_select_strategy`. The `num_additional_image_tokens` should be `1` if the vision backbone adds a CLS token or `0` if nothing extra is added to the vision patches.
- Note that each checkpoint has been trained with a specific prompt format, depending on which large language model (LLM) was used. You can use tokenizer's `apply_chat_template` to format your prompts correctly. Below is an example of how to do that.
We will use [LLaVA-NeXT-Video-7B-hf](https://huggingface.co/llava-hf/LLaVA-NeXT-Video-7B-hf) and a conversation history of videos and images. Each content field has to be a list of dicts, as follows:
```python
from transformers import LlavaNextVideoProcessor
processor = LlavaNextVideoProcessor.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf")
conversation = [
{
"role": "system",
"content": [
{"type": "text", "text": "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."},
],
},
{
"role": "user",
"content": [
{"type": "text", "text": "What’s shown in this image?"},
{"type": "image"},
],
},
{
"role": "assistant",
"content": [{"type": "text", "text": "This image shows a red stop sign."},]
},
{
"role": "user",
"content": [
{"type": "text", "text": "Why is this video funny?"},
{"type": "video"},
],
},
]
text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
# Note that the template simply formats your prompt, you still have to tokenize it and obtain pixel values for your visuals
print(text_prompt)
```
## Usage example
### Single Media Mode
The model can accept both images and videos as input. Here's an example code for inference in half-precision (`torch.float16`):
```python
import av
import torch
import numpy as np
from transformers import LlavaNextVideoForConditionalGeneration, LlavaNextVideoProcessor
def read_video_pyav(container, indices):
'''
Decode the video with PyAV decoder.
Args:
container (`av.container.input.InputContainer`): PyAV container.
indices (`List[int]`): List of frame indices to decode.
Returns:
result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
'''
frames = []
container.seek(0)
start_index = indices[0]
end_index = indices[-1]
for i, frame in enumerate(container.decode(video=0)):
if i > end_index:
break
if i >= start_index and i in indices:
frames.append(frame)
return np.stack([x.to_ndarray(format="rgb24") for x in frames])
# Load the model in half-precision
model = LlavaNextVideoForConditionalGeneration.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf", torch_dtype=torch.float16, device_map="auto")
processor = LlavaNextVideoProcessor.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf")
# Load the video as an np.array, sampling uniformly 8 frames (can sample more for longer videos)
video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
container = av.open(video_path)
total_frames = container.streams.video[0].frames
indices = np.arange(0, total_frames, total_frames / 8).astype(int)
video = read_video_pyav(container, indices)
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "Why is this video funny?"},
{"type": "video"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
inputs = processor(text=prompt, videos=video, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=60)
processor.batch_decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True)
```
### Mixed Media Mode
The model can also generate from an interleaved image-video inputs. However note, that it was not trained in interleaved image-video setting which might affect the performance. Below is an example usage for mixed media input, add the following lines to the above code snippet:
```python
from PIL import Image
import requests
# Generate from image and video mixed inputs
# Load and image and write a new prompt
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "How many cats are there in the image?"},
{"type": "image"},
],
},
{
"role": "assistant",
"content": [{"type": "text", "text": "There are two cats"}],
},
{
"role": "user",
"content": [
{"type": "text", "text": "Why is this video funny?"},
{"type": "video"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
inputs = processor(text=prompt, images=image, videos=clip, padding=True, return_tensors="pt")
# Generate
generate_ids = model.generate(**inputs, max_length=50)
processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
```
## Model optimization
### Quantization using Bitsandbytes for memory efficiency
The model can be loaded in lower bits, significantly reducing memory burden while maintaining the performance of the original model. This allows for efficient deployment on resource-constrained cases.
First, make sure to install bitsandbytes by running `pip install bitsandbytes` and to have access to a GPU/accelerator that is supported by the library.
<Tip>
bitsandbytes is being refactored to support multiple backends beyond CUDA. Currently, ROCm (AMD GPU) and Intel CPU implementations are mature, with Intel XPU in progress and Apple Silicon support expected by Q4/Q1. For installation instructions and the latest backend updates, visit [this link](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend).
We value your feedback to help identify bugs before the full release! Check out [these docs](https://huggingface.co/docs/bitsandbytes/main/en/non_cuda_backends) for more details and feedback links.
</Tip>
Then simply load the quantized model by adding [`BitsAndBytesConfig`](../main_classes/quantization#transformers.BitsAndBytesConfig) as shown below:
```python
from transformers import LlavaNextVideoForConditionalGeneration, LlavaNextVideoProcessor
# specify how to quantize the model
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
model = LlavaNextVideoForConditionalGeneration.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf", quantization_config=quantization_config, device_map="auto")
```
### Flash-Attention 2 to speed-up generation
Additionally, we can greatly speed-up model inference by using [Flash Attention](../perf_train_gpu_one#flash-attention-2), which is a faster implementation of the attention mechanism used inside the model.
First, make sure to install the latest version of Flash Attention 2:
```bash
pip install -U flash-attn --no-build-isolation
```
Also, you should have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention-2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.
To load and run a model using Flash Attention-2, simply add `attn_implementation="flash_attention_2"` when loading the model as follows:
```python
from transformers import LlavaNextVideoForConditionalGeneration
model = LlavaNextVideoForConditionalGeneration.from_pretrained(
"llava-hf/LLaVA-NeXT-Video-7B-hf",
torch_dtype=torch.float16,
attn_implementation="flash_attention_2",
).to(0)
```
## LlavaNextVideoConfig
[[autodoc]] LlavaNextVideoConfig
## LlavaNextVideoProcessor
[[autodoc]] LlavaNextVideoProcessor
## LlavaNextVideoImageProcessor
[[autodoc]] LlavaNextVideoImageProcessor
## LlavaNextVideoForConditionalGeneration
[[autodoc]] LlavaNextVideoForConditionalGeneration
- forward
| transformers/docs/source/en/model_doc/llava_next_video.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/llava_next_video.md",
"repo_id": "transformers",
"token_count": 4329
} |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# M-CTC-T
<Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.
</Tip>
## Overview
The M-CTC-T model was proposed in [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal.
The abstract from the paper is the following:
*Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual
speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech
recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even
with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised
learning on a target language, generate pseudo-labels for that language, and train a final model using
pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled
Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better
performance for many languages that also transfers well to LibriSpeech.*
This model was contributed by [cwkeam](https://huggingface.co/cwkeam). The original code can be found [here](https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl).
## Usage tips
The PyTorch version of this model is only available in torch 1.9 and higher.
## Resources
- [Automatic speech recognition task guide](../tasks/asr)
## MCTCTConfig
[[autodoc]] MCTCTConfig
## MCTCTFeatureExtractor
[[autodoc]] MCTCTFeatureExtractor
- __call__
## MCTCTProcessor
[[autodoc]] MCTCTProcessor
- __call__
- from_pretrained
- save_pretrained
- batch_decode
- decode
## MCTCTModel
[[autodoc]] MCTCTModel
- forward
## MCTCTForCTC
[[autodoc]] MCTCTForCTC
- forward
| transformers/docs/source/en/model_doc/mctct.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mctct.md",
"repo_id": "transformers",
"token_count": 928
} |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# ModernBERT
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=modernbert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-modernbert-blueviolet">
</a>
<a href="https://arxiv.org/abs/2412.13663">
<img alt="Paper page" src="https://img.shields.io/badge/Paper%20page-2412.13663-green">
</a>
</div>
## Overview
The ModernBERT model was proposed in [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://arxiv.org/abs/2412.13663) by Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Galalgher, Raja Bisas, Faisal Ladhak, Tom Aarsen, Nathan Cooper, Grifin Adams, Jeremy Howard and Iacopo Poli.
It is a refresh of the traditional encoder architecture, as used in previous models such as [BERT](https://huggingface.co/docs/transformers/en/model_doc/bert) and [RoBERTa](https://huggingface.co/docs/transformers/en/model_doc/roberta).
It builds on BERT and implements many modern architectural improvements which have been developed since its original release, such as:
- [Rotary Positional Embeddings](https://huggingface.co/blog/designing-positional-encoding) to support sequences of up to 8192 tokens.
- [Unpadding](https://arxiv.org/abs/2208.08124) to ensure no compute is wasted on padding tokens, speeding up processing time for batches with mixed-length sequences.
- [GeGLU](https://arxiv.org/abs/2002.05202) Replacing the original MLP layers with GeGLU layers, shown to improve performance.
- [Alternating Attention](https://arxiv.org/abs/2004.05150v2) where most attention layers employ a sliding window of 128 tokens, with Global Attention only used every 3 layers.
- [Flash Attention](https://github.com/Dao-AILab/flash-attention) to speed up processing.
- A model designed following recent [The Case for Co-Designing Model Architectures with Hardware](https://arxiv.org/abs/2401.14489), ensuring maximum efficiency across inference GPUs.
- Modern training data scales (2 trillion tokens) and mixtures (including code ande math data)
The abstract from the paper is the following:
*Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.*
The original code can be found [here](https://github.com/answerdotai/modernbert).
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ModernBert.
<PipelineTag pipeline="text-classification"/>
- A notebook on how to [finetune for General Language Understanding Evaluation (GLUE) with Transformers](https://github.com/AnswerDotAI/ModernBERT/blob/main/examples/finetune_modernbert_on_glue.ipynb), also available as a Google Colab [notebook](https://colab.research.google.com/github/AnswerDotAI/ModernBERT/blob/main/examples/finetune_modernbert_on_glue.ipynb). 🌎
<PipelineTag pipeline="sentence-similarity"/>
- A script on how to [finetune for text similarity or information retrieval with Sentence Transformers](https://github.com/AnswerDotAI/ModernBERT/blob/main/examples/train_st.py). 🌎
- A script on how to [finetune for information retrieval with PyLate](https://github.com/AnswerDotAI/ModernBERT/blob/main/examples/train_pylate.py). 🌎
<PipelineTag pipeline="fill-mask"/>
- [Masked language modeling task guide](../tasks/masked_language_modeling)
## ModernBertConfig
[[autodoc]] ModernBertConfig
<frameworkcontent>
<pt>
## ModernBertModel
[[autodoc]] ModernBertModel
- forward
## ModernBertForMaskedLM
[[autodoc]] ModernBertForMaskedLM
- forward
## ModernBertForSequenceClassification
[[autodoc]] ModernBertForSequenceClassification
- forward
## ModernBertForTokenClassification
[[autodoc]] ModernBertForTokenClassification
- forward
</pt>
</frameworkcontent>
| transformers/docs/source/en/model_doc/modernbert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/modernbert.md",
"repo_id": "transformers",
"token_count": 1529
} |
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# Nougat
## Overview
The Nougat model was proposed in [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by
Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic. Nougat uses the same architecture as [Donut](donut), meaning an image Transformer
encoder and an autoregressive text Transformer decoder to translate scientific PDFs to markdown, enabling easier access to them.
The abstract from the paper is the following:
*Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human-readable documents and machine-readable text. We release the models and code to accelerate future work on scientific text recognition.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/nougat_architecture.jpg"
alt="drawing" width="600"/>
<small> Nougat high-level overview. Taken from the <a href="https://arxiv.org/abs/2308.13418">original paper</a>. </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found
[here](https://github.com/facebookresearch/nougat).
## Usage tips
- The quickest way to get started with Nougat is by checking the [tutorial
notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Nougat), which show how to use the model
at inference time as well as fine-tuning on custom data.
- Nougat is always used within the [VisionEncoderDecoder](vision-encoder-decoder) framework. The model is identical to [Donut](donut) in terms of architecture.
## Inference
Nougat's [`VisionEncoderDecoder`] model accepts images as input and makes use of
[`~generation.GenerationMixin.generate`] to autoregressively generate text given the input image.
The [`NougatImageProcessor`] class is responsible for preprocessing the input image and
[`NougatTokenizerFast`] decodes the generated target tokens to the target string. The
[`NougatProcessor`] wraps [`NougatImageProcessor`] and [`NougatTokenizerFast`] classes
into a single instance to both extract the input features and decode the predicted token ids.
- Step-by-step PDF transcription
```py
>>> from huggingface_hub import hf_hub_download
>>> import re
>>> from PIL import Image
>>> from transformers import NougatProcessor, VisionEncoderDecoderModel
>>> from datasets import load_dataset
>>> import torch
>>> processor = NougatProcessor.from_pretrained("facebook/nougat-base")
>>> model = VisionEncoderDecoderModel.from_pretrained("facebook/nougat-base")
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> # prepare PDF image for the model
>>> filepath = hf_hub_download(repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_paper.png", repo_type="dataset")
>>> image = Image.open(filepath)
>>> pixel_values = processor(image, return_tensors="pt").pixel_values
>>> # generate transcription (here we only generate 30 tokens)
>>> outputs = model.generate(
... pixel_values.to(device),
... min_length=1,
... max_new_tokens=30,
... bad_words_ids=[[processor.tokenizer.unk_token_id]],
... )
>>> sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
>>> sequence = processor.post_process_generation(sequence, fix_markdown=False)
>>> # note: we're using repr here such for the sake of printing the \n characters, feel free to just print the sequence
>>> print(repr(sequence))
'\n\n# Nougat: Neural Optical Understanding for Academic Documents\n\n Lukas Blecher\n\nCorrespondence to: lblecher@'
```
See the [model hub](https://huggingface.co/models?filter=nougat) to look for Nougat checkpoints.
<Tip>
The model is identical to [Donut](donut) in terms of architecture.
</Tip>
## NougatImageProcessor
[[autodoc]] NougatImageProcessor
- preprocess
## NougatTokenizerFast
[[autodoc]] NougatTokenizerFast
## NougatProcessor
[[autodoc]] NougatProcessor
- __call__
- from_pretrained
- save_pretrained
- batch_decode
- decode
- post_process_generation | transformers/docs/source/en/model_doc/nougat.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/nougat.md",
"repo_id": "transformers",
"token_count": 1549
} |
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# Qwen2
## Overview
Qwen2 is the new model series of large language models from the Qwen team. Previously, we released the Qwen series, including Qwen2-0.5B, Qwen2-1.5B, Qwen2-7B, Qwen2-57B-A14B, Qwen2-72B, Qwen2-Audio, etc.
### Model Details
Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.
## Usage tips
`Qwen2-7B` and `Qwen2-7B-Instruct` can be found on the [Huggingface Hub](https://huggingface.co/Qwen)
In the following, we demonstrate how to use `Qwen2-7B-Instruct` for the inference. Note that we have used the ChatML format for dialog, in this demo we show how to leverage `apply_chat_template` for this purpose.
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto
>>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-7B-Instruct", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
>>> prompt = "Give me a short introduction to large language model."
>>> messages = [{"role": "user", "content": prompt}]
>>> text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
>>> model_inputs = tokenizer([text], return_tensors="pt").to(device)
>>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)
>>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
>>> response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Qwen2Config
[[autodoc]] Qwen2Config
## Qwen2Tokenizer
[[autodoc]] Qwen2Tokenizer
- save_vocabulary
## Qwen2TokenizerFast
[[autodoc]] Qwen2TokenizerFast
## Qwen2Model
[[autodoc]] Qwen2Model
- forward
## Qwen2ForCausalLM
[[autodoc]] Qwen2ForCausalLM
- forward
## Qwen2ForSequenceClassification
[[autodoc]] Qwen2ForSequenceClassification
- forward
## Qwen2ForTokenClassification
[[autodoc]] Qwen2ForTokenClassification
- forward
## Qwen2ForQuestionAnswering
[[autodoc]] Qwen2ForQuestionAnswering
- forward
| transformers/docs/source/en/model_doc/qwen2.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/qwen2.md",
"repo_id": "transformers",
"token_count": 995
} |
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# RoFormer
## Overview
The RoFormer model was proposed in [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
The abstract from the paper is the following:
*Position encoding in transformer architecture provides supervision for dependency modeling between elements at
different positions in the sequence. We investigate various methods to encode positional information in
transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The
proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative
position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of
being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and
capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced
transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We
release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing
experiment for English benchmark will soon be updated.*
This model was contributed by [junnyu](https://huggingface.co/junnyu). The original code can be found [here](https://github.com/ZhuiyiTechnology/roformer).
## Usage tips
RoFormer is a BERT-like autoencoding model with rotary position embeddings. Rotary position embeddings have shown
improved performance on classification tasks with long texts.
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## RoFormerConfig
[[autodoc]] RoFormerConfig
## RoFormerTokenizer
[[autodoc]] RoFormerTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## RoFormerTokenizerFast
[[autodoc]] RoFormerTokenizerFast
- build_inputs_with_special_tokens
<frameworkcontent>
<pt>
## RoFormerModel
[[autodoc]] RoFormerModel
- forward
## RoFormerForCausalLM
[[autodoc]] RoFormerForCausalLM
- forward
## RoFormerForMaskedLM
[[autodoc]] RoFormerForMaskedLM
- forward
## RoFormerForSequenceClassification
[[autodoc]] RoFormerForSequenceClassification
- forward
## RoFormerForMultipleChoice
[[autodoc]] RoFormerForMultipleChoice
- forward
## RoFormerForTokenClassification
[[autodoc]] RoFormerForTokenClassification
- forward
## RoFormerForQuestionAnswering
[[autodoc]] RoFormerForQuestionAnswering
- forward
</pt>
<tf>
## TFRoFormerModel
[[autodoc]] TFRoFormerModel
- call
## TFRoFormerForMaskedLM
[[autodoc]] TFRoFormerForMaskedLM
- call
## TFRoFormerForCausalLM
[[autodoc]] TFRoFormerForCausalLM
- call
## TFRoFormerForSequenceClassification
[[autodoc]] TFRoFormerForSequenceClassification
- call
## TFRoFormerForMultipleChoice
[[autodoc]] TFRoFormerForMultipleChoice
- call
## TFRoFormerForTokenClassification
[[autodoc]] TFRoFormerForTokenClassification
- call
## TFRoFormerForQuestionAnswering
[[autodoc]] TFRoFormerForQuestionAnswering
- call
</tf>
<jax>
## FlaxRoFormerModel
[[autodoc]] FlaxRoFormerModel
- __call__
## FlaxRoFormerForMaskedLM
[[autodoc]] FlaxRoFormerForMaskedLM
- __call__
## FlaxRoFormerForSequenceClassification
[[autodoc]] FlaxRoFormerForSequenceClassification
- __call__
## FlaxRoFormerForMultipleChoice
[[autodoc]] FlaxRoFormerForMultipleChoice
- __call__
## FlaxRoFormerForTokenClassification
[[autodoc]] FlaxRoFormerForTokenClassification
- __call__
## FlaxRoFormerForQuestionAnswering
[[autodoc]] FlaxRoFormerForQuestionAnswering
- __call__
</jax>
</frameworkcontent>
| transformers/docs/source/en/model_doc/roformer.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/roformer.md",
"repo_id": "transformers",
"token_count": 1450
} |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# Splinter
## Overview
The Splinter model was proposed in [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy. Splinter
is an encoder-only transformer (similar to BERT) pretrained using the recurring span selection task on a large corpus
comprising Wikipedia and the Toronto Book Corpus.
The abstract from the paper is the following:
In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order
of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where only a few hundred
training examples are available, and observe that standard models perform poorly, highlighting the discrepancy between
current pretraining objectives and question answering. We propose a new pretraining scheme tailored for question
answering: recurring span selection. Given a passage with multiple sets of recurring spans, we mask in each set all
recurring spans but one, and ask the model to select the correct span in the passage for each masked span. Masked spans
are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select
the answer span. The resulting model obtains surprisingly good results on multiple benchmarks (e.g., 72.7 F1 on SQuAD
with only 128 training examples), while maintaining competitive performance in the high-resource setting.
This model was contributed by [yuvalkirstain](https://huggingface.co/yuvalkirstain) and [oriram](https://huggingface.co/oriram). The original code can be found [here](https://github.com/oriram/splinter).
## Usage tips
- Splinter was trained to predict answers spans conditioned on a special [QUESTION] token. These tokens contextualize
to question representations which are used to predict the answers. This layer is called QASS, and is the default
behaviour in the [`SplinterForQuestionAnswering`] class. Therefore:
- Use [`SplinterTokenizer`] (rather than [`BertTokenizer`]), as it already
contains this special token. Also, its default behavior is to use this token when two sequences are given (for
example, in the *run_qa.py* script).
- If you plan on using Splinter outside *run_qa.py*, please keep in mind the question token - it might be important for
the success of your model, especially in a few-shot setting.
- Please note there are two different checkpoints for each size of Splinter. Both are basically the same, except that
one also has the pretrained weights of the QASS layer (*tau/splinter-base-qass* and *tau/splinter-large-qass*) and one
doesn't (*tau/splinter-base* and *tau/splinter-large*). This is done to support randomly initializing this layer at
fine-tuning, as it is shown to yield better results for some cases in the paper.
## Resources
- [Question answering task guide](../tasks/question-answering)
## SplinterConfig
[[autodoc]] SplinterConfig
## SplinterTokenizer
[[autodoc]] SplinterTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## SplinterTokenizerFast
[[autodoc]] SplinterTokenizerFast
## SplinterModel
[[autodoc]] SplinterModel
- forward
## SplinterForQuestionAnswering
[[autodoc]] SplinterForQuestionAnswering
- forward
## SplinterForPreTraining
[[autodoc]] SplinterForPreTraining
- forward
| transformers/docs/source/en/model_doc/splinter.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/splinter.md",
"repo_id": "transformers",
"token_count": 1101
} |
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# TextNet
## Overview
The TextNet model was proposed in [FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation](https://arxiv.org/abs/2111.02394) by Zhe Chen, Jiahao Wang, Wenhai Wang, Guo Chen, Enze Xie, Ping Luo, Tong Lu. TextNet is a vision backbone useful for text detection tasks. It is the result of neural architecture search (NAS) on backbones with reward function as text detection task (to provide powerful features for text detection).
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/fast_architecture.png"
alt="drawing" width="600"/>
<small> TextNet backbone as part of FAST. Taken from the <a href="https://arxiv.org/abs/2111.02394">original paper.</a> </small>
This model was contributed by [Raghavan](https://huggingface.co/Raghavan), [jadechoghari](https://huggingface.co/jadechoghari) and [nielsr](https://huggingface.co/nielsr).
## Usage tips
TextNet is mainly used as a backbone network for the architecture search of text detection. Each stage of the backbone network is comprised of a stride-2 convolution and searchable blocks.
Specifically, we present a layer-level candidate set, defined as {conv3×3, conv1×3, conv3×1, identity}. As the 1×3 and 3×1 convolutions have asymmetric kernels and oriented structure priors, they may help to capture the features of extreme aspect-ratio and rotated text lines.
TextNet is the backbone for Fast, but can also be used as an efficient text/image classification, we add a `TextNetForImageClassification` as is it would allow people to train an image classifier on top of the pre-trained textnet weights
## TextNetConfig
[[autodoc]] TextNetConfig
## TextNetImageProcessor
[[autodoc]] TextNetImageProcessor
- preprocess
## TextNetModel
[[autodoc]] TextNetModel
- forward
## TextNetForImageClassification
[[autodoc]] TextNetForImageClassification
- forward
| transformers/docs/source/en/model_doc/textnet.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/textnet.md",
"repo_id": "transformers",
"token_count": 745
} |
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# Video Vision Transformer (ViViT)
## Overview
The Vivit model was proposed in [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691) by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid.
The paper proposes one of the first successful pure-transformer based set of models for video understanding.
The abstract from the paper is the following:
*We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification. Our model extracts spatio-temporal tokens from the input video, which are then encoded by a series of transformer layers. In order to handle the long sequences of tokens encountered in video, we propose several, efficient variants of our model which factorise the spatial- and temporal-dimensions of the input. Although transformer-based models are known to only be effective when large training datasets are available, we show how we can effectively regularise the model during training and leverage pretrained image models to be able to train on comparatively small datasets. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple video classification benchmarks including Kinetics 400 and 600, Epic Kitchens, Something-Something v2 and Moments in Time, outperforming prior methods based on deep 3D convolutional networks.*
This model was contributed by [jegormeister](https://huggingface.co/jegormeister). The original code (written in JAX) can be found [here](https://github.com/google-research/scenic/tree/main/scenic/projects/vivit).
### Using Scaled Dot Product Attention (SDPA)
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
or the [GPU Inference](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention)
page for more information.
SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set
`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
```
from transformers import VivitModel
model = VivitModel.from_pretrained("google/vivit-b-16x2-kinetics400", attn_implementation="sdpa", torch_dtype=torch.float16)
...
```
For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
On a local benchmark (A100-40GB, PyTorch 2.3.0, OS Ubuntu 22.04) with `float32` and `google/vivit-b-16x2-kinetics400` model, we saw the following speedups during inference.
### Training
| num_training_steps | batch_size | is cuda | Speedup (%) | Eager peak mem (MB) | sdpa peak mem (MB) | Mem saving (%) |
|---------------------:|-------------:|----------:|--------------:|----------------------:|---------------------:|-----------------:|
| 100 | 1 | True | 7.122 | 2575.28 | 5932.54 | 130.364 |
### Inference
| num_batches | batch_size | is cuda | is half | Speedup (%) | Mem eager (MB) | Mem BT (MB) | Mem saved (%) |
|---------------|--------------|-----------|-----------|---------------|------------------|---------------|-----------------|
| 20 | 1 | True | False | 15.422 | 715.807 | 317.079 | 125.75 |
| 20 | 2 | True | False | 17.146 | 1234.75 | 447.175 | 176.122 |
| 20 | 4 | True | False | 18.093 | 2275.82 | 709.864 | 220.6 |
| 20 | 8 | True | False | 19.284 | 4358.19 | 1233.24 | 253.393 |
## VivitConfig
[[autodoc]] VivitConfig
## VivitImageProcessor
[[autodoc]] VivitImageProcessor
- preprocess
## VivitModel
[[autodoc]] VivitModel
- forward
## VivitForVideoClassification
[[autodoc]] transformers.VivitForVideoClassification
- forward
| transformers/docs/source/en/model_doc/vivit.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/vivit.md",
"repo_id": "transformers",
"token_count": 1653
} |
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# XLSR-Wav2Vec2
## Overview
The XLSR-Wav2Vec2 model was proposed in [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael
Auli.
The abstract from the paper is the following:
*This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw
waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over
masked latent speech representations and jointly learns a quantization of the latents shared across languages. The
resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly
outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction
of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to
a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong
individual models. Analysis shows that the latent discrete speech representations are shared across languages with
increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing
XLSR-53, a large model pretrained in 53 languages.*
The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).
Note: Meta (FAIR) released a new version of [Wav2Vec2-BERT 2.0](https://huggingface.co/docs/transformers/en/model_doc/wav2vec2-bert) - it's pretrained on 4.5M hours of audio. We especially recommend using it for fine-tuning tasks, e.g. as per [this guide](https://huggingface.co/blog/fine-tune-w2v2-bert).
## Usage tips
- XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be
decoded using [`Wav2Vec2CTCTokenizer`].
<Tip>
XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2).
</Tip>
| transformers/docs/source/en/model_doc/xlsr_wav2vec2.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/xlsr_wav2vec2.md",
"repo_id": "transformers",
"token_count": 813
} |
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# CPU inference
With some optimizations, it is possible to efficiently run large model inference on a CPU. One of these optimization techniques involves compiling the PyTorch code into an intermediate format for high-performance environments like C++. The other technique fuses multiple operations into one kernel to reduce the overhead of running each operation separately.
You'll learn how to use [BetterTransformer](https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/) for faster inference, and how to convert your PyTorch code to [TorchScript](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html). If you're using an Intel CPU, you can also use [graph optimizations](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features.html#graph-optimization) from [Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/index.html) to boost inference speed even more. Finally, learn how to use 🤗 Optimum to accelerate inference with ONNX Runtime or OpenVINO (if you're using an Intel CPU).
## BetterTransformer
BetterTransformer accelerates inference with its fastpath (native PyTorch specialized implementation of Transformer functions) execution. The two optimizations in the fastpath execution are:
1. fusion, which combines multiple sequential operations into a single "kernel" to reduce the number of computation steps
2. skipping the inherent sparsity of padding tokens to avoid unnecessary computation with nested tensors
BetterTransformer also converts all attention operations to use the more memory-efficient [scaled dot product attention](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention).
<Tip>
BetterTransformer is not supported for all models. Check this [list](https://huggingface.co/docs/optimum/bettertransformer/overview#supported-models) to see if a model supports BetterTransformer.
</Tip>
Before you start, make sure you have 🤗 Optimum [installed](https://huggingface.co/docs/optimum/installation).
Enable BetterTransformer with the [`PreTrainedModel.to_bettertransformer`] method:
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder", torch_dtype="auto")
```
## TorchScript
TorchScript is an intermediate PyTorch model representation that can be run in production environments where performance is important. You can train a model in PyTorch and then export it to TorchScript to free the model from Python performance constraints. PyTorch [traces](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) a model to return a [`ScriptFunction`] that is optimized with just-in-time compilation (JIT). Compared to the default eager mode, JIT mode in PyTorch typically yields better performance for inference using optimization techniques like operator fusion.
For a gentle introduction to TorchScript, see the [Introduction to PyTorch TorchScript](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html) tutorial.
With the [`Trainer`] class, you can enable JIT mode for CPU inference by setting the `--jit_mode_eval` flag:
```bash
python examples/pytorch/question-answering/run_qa.py \
--model_name_or_path csarron/bert-base-uncased-squad-v1 \
--dataset_name squad \
--do_eval \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/ \
--no_cuda \
--jit_mode_eval
```
<Tip warning={true}>
For PyTorch >= 1.14.0, JIT-mode could benefit any model for prediction and evaluation since the dict input is supported in `jit.trace`.
For PyTorch < 1.14.0, JIT-mode could benefit a model if its forward parameter order matches the tuple input order in `jit.trace`, such as a question-answering model. If the forward parameter order does not match the tuple input order in `jit.trace`, like a text classification model, `jit.trace` will fail and we are capturing this with the exception here to make it fallback. Logging is used to notify users.
</Tip>
## IPEX graph optimization
Intel® Extension for PyTorch (IPEX) provides further optimizations in JIT mode for Intel CPUs, and we recommend combining it with TorchScript for even faster performance. The IPEX [graph optimization](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/graph_optimization.html) fuses operations like Multi-head attention, Concat Linear, Linear + Add, Linear + Gelu, Add + LayerNorm, and more.
To take advantage of these graph optimizations, make sure you have IPEX [installed](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/installation.html):
```bash
pip install intel_extension_for_pytorch
```
Set the `--use_ipex` and `--jit_mode_eval` flags in the [`Trainer`] class to enable JIT mode with the graph optimizations:
```bash
python examples/pytorch/question-answering/run_qa.py \
--model_name_or_path csarron/bert-base-uncased-squad-v1 \
--dataset_name squad \
--do_eval \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/ \
--no_cuda \
--use_ipex \
--jit_mode_eval
```
## 🤗 Optimum
<Tip>
Learn more details about using ORT with 🤗 Optimum in the [Optimum Inference with ONNX Runtime](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/models) guide. This section only provides a brief and simple example.
</Tip>
ONNX Runtime (ORT) is a model accelerator that runs inference on CPUs by default. ORT is supported by 🤗 Optimum which can be used in 🤗 Transformers, without making too many changes to your code. You only need to replace the 🤗 Transformers `AutoClass` with its equivalent [`~optimum.onnxruntime.ORTModel`] for the task you're solving, and load a checkpoint in the ONNX format.
For example, if you're running inference on a question answering task, load the [optimum/roberta-base-squad2](https://huggingface.co/optimum/roberta-base-squad2) checkpoint which contains a `model.onnx` file:
```py
from transformers import AutoTokenizer, pipeline
from optimum.onnxruntime import ORTModelForQuestionAnswering
model = ORTModelForQuestionAnswering.from_pretrained("optimum/roberta-base-squad2")
tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2")
onnx_qa = pipeline("question-answering", model=model, tokenizer=tokenizer)
question = "What's my name?"
context = "My name is Philipp and I live in Nuremberg."
pred = onnx_qa(question, context)
```
If you have an Intel CPU, take a look at 🤗 [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) which supports a variety of compression techniques (quantization, pruning, knowledge distillation) and tools for converting models to the [OpenVINO](https://huggingface.co/docs/optimum/intel/inference) format for higher performance inference.
| transformers/docs/source/en/perf_infer_cpu.md/0 | {
"file_path": "transformers/docs/source/en/perf_infer_cpu.md",
"repo_id": "transformers",
"token_count": 2102
} |
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# Preprocess
[[open-in-colab]]
Before you can train a model on a dataset, it needs to be preprocessed into the expected model input format. Whether your data is text, images, or audio, it needs to be converted and assembled into batches of tensors. 🤗 Transformers provides a set of preprocessing classes to help prepare your data for the model. In this tutorial, you'll learn that for:
* Text, use a [Tokenizer](./main_classes/tokenizer) to convert text into a sequence of tokens, create a numerical representation of the tokens, and assemble them into tensors.
* Speech and audio, use a [Feature extractor](./main_classes/feature_extractor) to extract sequential features from audio waveforms and convert them into tensors.
* Image inputs use a [ImageProcessor](./main_classes/image_processor) to convert images into tensors.
* Multimodal inputs, use a [Processor](./main_classes/processors) to combine a tokenizer and a feature extractor or image processor.
<Tip>
`AutoProcessor` **always** works and automatically chooses the correct class for the model you're using, whether you're using a tokenizer, image processor, feature extractor or processor.
</Tip>
Before you begin, install 🤗 Datasets so you can load some datasets to experiment with:
```bash
pip install datasets
```
## Natural Language Processing
<Youtube id="Yffk5aydLzg"/>
The main tool for preprocessing textual data is a [tokenizer](main_classes/tokenizer). A tokenizer splits text into *tokens* according to a set of rules. The tokens are converted into numbers and then tensors, which become the model inputs. Any additional inputs required by the model are added by the tokenizer.
<Tip>
If you plan on using a pretrained model, it's important to use the associated pretrained tokenizer. This ensures the text is split the same way as the pretraining corpus, and uses the same corresponding tokens-to-index (usually referred to as the *vocab*) during pretraining.
</Tip>
Get started by loading a pretrained tokenizer with the [`AutoTokenizer.from_pretrained`] method. This downloads the *vocab* a model was pretrained with:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
```
Then pass your text to the tokenizer:
```py
>>> encoded_input = tokenizer("Do not meddle in the affairs of wizards, for they are subtle and quick to anger.")
>>> print(encoded_input)
{'input_ids': [101, 2079, 2025, 19960, 10362, 1999, 1996, 3821, 1997, 16657, 1010, 2005, 2027, 2024, 11259, 1998, 4248, 2000, 4963, 1012, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
The tokenizer returns a dictionary with three important items:
* [input_ids](glossary#input-ids) are the indices corresponding to each token in the sentence.
* [attention_mask](glossary#attention-mask) indicates whether a token should be attended to or not.
* [token_type_ids](glossary#token-type-ids) identifies which sequence a token belongs to when there is more than one sequence.
Return your input by decoding the `input_ids`:
```py
>>> tokenizer.decode(encoded_input["input_ids"])
'[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]'
```
As you can see, the tokenizer added two special tokens - `CLS` and `SEP` (classifier and separator) - to the sentence. Not all models need
special tokens, but if they do, the tokenizer automatically adds them for you.
If there are several sentences you want to preprocess, pass them as a list to the tokenizer:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_inputs = tokenizer(batch_sentences)
>>> print(encoded_inputs)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1]]}
```
### Pad
Sentences aren't always the same length which can be an issue because tensors, the model inputs, need to have a uniform shape. Padding is a strategy for ensuring tensors are rectangular by adding a special *padding token* to shorter sentences.
Set the `padding` parameter to `True` to pad the shorter sequences in the batch to match the longest sequence:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
```
The first and third sentences are now padded with `0`'s because they are shorter.
### Truncation
On the other end of the spectrum, sometimes a sequence may be too long for a model to handle. In this case, you'll need to truncate the sequence to a shorter length.
Set the `truncation` parameter to `True` to truncate a sequence to the maximum length accepted by the model:
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True)
>>> print(encoded_input)
{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
```
<Tip>
Check out the [Padding and truncation](./pad_truncation) concept guide to learn more different padding and truncation arguments.
</Tip>
### Build tensors
Finally, you want the tokenizer to return the actual tensors that get fed to the model.
Set the `return_tensors` parameter to either `pt` for PyTorch, or `tf` for TensorFlow:
<frameworkcontent>
<pt>
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
>>> print(encoded_input)
{'input_ids': tensor([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]]),
'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),
'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}
```
</pt>
<tf>
```py
>>> batch_sentences = [
... "But what about second breakfast?",
... "Don't think he knows about second breakfast, Pip.",
... "What about elevensies?",
... ]
>>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
>>> print(encoded_input)
{'input_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
[101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
[101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=int32)>,
'token_type_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>,
'attention_mask': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>}
```
</tf>
</frameworkcontent>
<Tip>
Different pipelines support tokenizer arguments in their `__call__()` differently. `text-2-text-generation` pipelines support (i.e. pass on)
only `truncation`. `text-generation` pipelines support `max_length`, `truncation`, `padding` and `add_special_tokens`.
In `fill-mask` pipelines, tokenizer arguments can be passed in the `tokenizer_kwargs` argument (dictionary).
</Tip>
## Audio
For audio tasks, you'll need a [feature extractor](main_classes/feature_extractor) to prepare your dataset for the model. The feature extractor is designed to extract features from raw audio data, and convert them into tensors.
Load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub) for more details on how to load a dataset) to see how you can use a feature extractor with audio datasets:
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
```
Access the first element of the `audio` column to take a look at the input. Calling the `audio` column automatically loads and resamples the audio file:
```py
>>> dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 8000}
```
This returns three items:
* `array` is the speech signal loaded - and potentially resampled - as a 1D array.
* `path` points to the location of the audio file.
* `sampling_rate` refers to how many data points in the speech signal are measured per second.
For this tutorial, you'll use the [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) model. Take a look at the model card, and you'll learn Wav2Vec2 is pretrained on 16kHz sampled speech audio. It is important your audio data's sampling rate matches the sampling rate of the dataset used to pretrain the model. If your data's sampling rate isn't the same, then you need to resample your data.
1. Use 🤗 Datasets' [`~datasets.Dataset.cast_column`] method to upsample the sampling rate to 16kHz:
```py
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
```
2. Call the `audio` column again to resample the audio file:
```py
>>> dataset[0]["audio"]
{'array': array([ 2.3443763e-05, 2.1729663e-04, 2.2145823e-04, ...,
3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'sampling_rate': 16000}
```
Next, load a feature extractor to normalize and pad the input. When padding textual data, a `0` is added for shorter sequences. The same idea applies to audio data. The feature extractor adds a `0` - interpreted as silence - to `array`.
Load the feature extractor with [`AutoFeatureExtractor.from_pretrained`]:
```py
>>> from transformers import AutoFeatureExtractor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
```
Pass the audio `array` to the feature extractor. We also recommend adding the `sampling_rate` argument in the feature extractor in order to better debug any silent errors that may occur.
```py
>>> audio_input = [dataset[0]["audio"]["array"]]
>>> feature_extractor(audio_input, sampling_rate=16000)
{'input_values': [array([ 3.8106556e-04, 2.7506407e-03, 2.8015103e-03, ...,
5.6335266e-04, 4.6588284e-06, -1.7142107e-04], dtype=float32)]}
```
Just like the tokenizer, you can apply padding or truncation to handle variable sequences in a batch. Take a look at the sequence length of these two audio samples:
```py
>>> dataset[0]["audio"]["array"].shape
(173398,)
>>> dataset[1]["audio"]["array"].shape
(106496,)
```
Create a function to preprocess the dataset so the audio samples are the same lengths. Specify a maximum sample length, and the feature extractor will either pad or truncate the sequences to match it:
```py
>>> def preprocess_function(examples):
... audio_arrays = [x["array"] for x in examples["audio"]]
... inputs = feature_extractor(
... audio_arrays,
... sampling_rate=16000,
... padding=True,
... max_length=100000,
... truncation=True,
... )
... return inputs
```
Apply the `preprocess_function` to the first few examples in the dataset:
```py
>>> processed_dataset = preprocess_function(dataset[:5])
```
The sample lengths are now the same and match the specified maximum length. You can pass your processed dataset to the model now!
```py
>>> processed_dataset["input_values"][0].shape
(100000,)
>>> processed_dataset["input_values"][1].shape
(100000,)
```
## Computer vision
For computer vision tasks, you'll need an [image processor](main_classes/image_processor) to prepare your dataset for the model.
Image preprocessing consists of several steps that convert images into the input expected by the model. These steps
include but are not limited to resizing, normalizing, color channel correction, and converting images to tensors.
<Tip>
Image preprocessing often follows some form of image augmentation. Both image preprocessing and image augmentation
transform image data, but they serve different purposes:
* Image augmentation alters images in a way that can help prevent overfitting and increase the robustness of the model. You can get creative in how you augment your data - adjust brightness and colors, crop, rotate, resize, zoom, etc. However, be mindful not to change the meaning of the images with your augmentations.
* Image preprocessing guarantees that the images match the model’s expected input format. When fine-tuning a computer vision model, images must be preprocessed exactly as when the model was initially trained.
You can use any library you like for image augmentation. For image preprocessing, use the `ImageProcessor` associated with the model.
</Tip>
Load the [food101](https://huggingface.co/datasets/food101) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub) for more details on how to load a dataset) to see how you can use an image processor with computer vision datasets:
<Tip>
Use 🤗 Datasets `split` parameter to only load a small sample from the training split since the dataset is quite large!
</Tip>
```py
>>> from datasets import load_dataset
>>> dataset = load_dataset("food101", split="train[:100]")
```
Next, take a look at the image with 🤗 Datasets [`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=image#datasets.Image) feature:
```py
>>> dataset[0]["image"]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vision-preprocess-tutorial.png"/>
</div>
Load the image processor with [`AutoImageProcessor.from_pretrained`]:
```py
>>> from transformers import AutoImageProcessor
>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
```
First, let's add some image augmentation. You can use any library you prefer, but in this tutorial, we'll use torchvision's [`transforms`](https://pytorch.org/vision/stable/transforms.html) module. If you're interested in using another data augmentation library, learn how in the [Albumentations](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb) or [Kornia notebooks](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb).
1. Here we use [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) to chain together a couple of
transforms - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) and [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html).
Note that for resizing, we can get the image size requirements from the `image_processor`. For some models, an exact height and
width are expected, for others only the `shortest_edge` is defined.
```py
>>> from torchvision.transforms import RandomResizedCrop, ColorJitter, Compose
>>> size = (
... image_processor.size["shortest_edge"]
... if "shortest_edge" in image_processor.size
... else (image_processor.size["height"], image_processor.size["width"])
... )
>>> _transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=0.5, hue=0.5)])
```
2. The model accepts [`pixel_values`](model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel.forward.pixel_values)
as its input. `ImageProcessor` can take care of normalizing the images, and generating appropriate tensors.
Create a function that combines image augmentation and image preprocessing for a batch of images and generates `pixel_values`:
```py
>>> def transforms(examples):
... images = [_transforms(img.convert("RGB")) for img in examples["image"]]
... examples["pixel_values"] = image_processor(images, do_resize=False, return_tensors="pt")["pixel_values"]
... return examples
```
<Tip>
In the example above we set `do_resize=False` because we have already resized the images in the image augmentation transformation,
and leveraged the `size` attribute from the appropriate `image_processor`. If you do not resize images during image augmentation,
leave this parameter out. By default, `ImageProcessor` will handle the resizing.
If you wish to normalize images as a part of the augmentation transformation, use the `image_processor.image_mean`,
and `image_processor.image_std` values.
</Tip>
3. Then use 🤗 Datasets[`~datasets.Dataset.set_transform`] to apply the transforms on the fly:
```py
>>> dataset.set_transform(transforms)
```
4. Now when you access the image, you'll notice the image processor has added `pixel_values`. You can pass your processed dataset to the model now!
```py
>>> dataset[0].keys()
```
Here is what the image looks like after the transforms are applied. The image has been randomly cropped and it's color properties are different.
```py
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> img = dataset[0]["pixel_values"]
>>> plt.imshow(img.permute(1, 2, 0))
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/preprocessed_image.png"/>
</div>
<Tip>
For tasks like object detection, semantic segmentation, instance segmentation, and panoptic segmentation, `ImageProcessor`
offers post processing methods. These methods convert model's raw outputs into meaningful predictions such as bounding boxes,
or segmentation maps.
</Tip>
### Pad
In some cases, for instance, when fine-tuning [DETR](./model_doc/detr), the model applies scale augmentation at training
time. This may cause images to be different sizes in a batch. You can use [`DetrImageProcessor.pad`]
from [`DetrImageProcessor`] and define a custom `collate_fn` to batch images together.
```py
>>> def collate_fn(batch):
... pixel_values = [item["pixel_values"] for item in batch]
... encoding = image_processor.pad(pixel_values, return_tensors="pt")
... labels = [item["labels"] for item in batch]
... batch = {}
... batch["pixel_values"] = encoding["pixel_values"]
... batch["pixel_mask"] = encoding["pixel_mask"]
... batch["labels"] = labels
... return batch
```
## Multimodal
For tasks involving multimodal inputs, you'll need a [processor](main_classes/processors) to prepare your dataset for the model. A processor couples together two processing objects such as tokenizer and feature extractor.
Load the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub) for more details on how to load a dataset) to see how you can use a processor for automatic speech recognition (ASR):
```py
>>> from datasets import load_dataset
>>> lj_speech = load_dataset("lj_speech", split="train")
```
For ASR, you're mainly focused on `audio` and `text` so you can remove the other columns:
```py
>>> lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"])
```
Now take a look at the `audio` and `text` columns:
```py
>>> lj_speech[0]["audio"]
{'array': array([-7.3242188e-04, -7.6293945e-04, -6.4086914e-04, ...,
7.3242188e-04, 2.1362305e-04, 6.1035156e-05], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav',
'sampling_rate': 22050}
>>> lj_speech[0]["text"]
'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'
```
Remember you should always [resample](preprocessing#audio) your audio dataset's sampling rate to match the sampling rate of the dataset used to pretrain a model!
```py
>>> lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000))
```
Load a processor with [`AutoProcessor.from_pretrained`]:
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
```
1. Create a function to process the audio data contained in `array` to `input_values`, and tokenize `text` to `labels`. These are the inputs to the model:
```py
>>> def prepare_dataset(example):
... audio = example["audio"]
... example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
... return example
```
2. Apply the `prepare_dataset` function to a sample:
```py
>>> prepare_dataset(lj_speech[0])
```
The processor has now added `input_values` and `labels`, and the sampling rate has also been correctly downsampled to 16kHz. You can pass your processed dataset to the model now!
| transformers/docs/source/en/preprocessing.md/0 | {
"file_path": "transformers/docs/source/en/preprocessing.md",
"repo_id": "transformers",
"token_count": 8688
} |
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# Knowledge Distillation for Computer Vision
[[open-in-colab]]
Knowledge distillation is a technique used to transfer knowledge from a larger, more complex model (teacher) to a smaller, simpler model (student). To distill knowledge from one model to another, we take a pre-trained teacher model trained on a certain task (image classification for this case) and randomly initialize a student model to be trained on image classification. Next, we train the student model to minimize the difference between its outputs and the teacher's outputs, thus making it mimic the behavior. It was first introduced in [Distilling the Knowledge in a Neural Network by Hinton et al](https://arxiv.org/abs/1503.02531). In this guide, we will do task-specific knowledge distillation. We will use the [beans dataset](https://huggingface.co/datasets/beans) for this.
This guide demonstrates how you can distill a [fine-tuned ViT model](https://huggingface.co/merve/vit-mobilenet-beans-224) (teacher model) to a [MobileNet](https://huggingface.co/google/mobilenet_v2_1.4_224) (student model) using the [Trainer API](https://huggingface.co/docs/transformers/en/main_classes/trainer#trainer) of 🤗 Transformers.
Let's install the libraries needed for distillation and evaluating the process.
```bash
pip install transformers datasets accelerate tensorboard evaluate --upgrade
```
In this example, we are using the `merve/beans-vit-224` model as teacher model. It's an image classification model, based on `google/vit-base-patch16-224-in21k` fine-tuned on beans dataset. We will distill this model to a randomly initialized MobileNetV2.
We will now load the dataset.
```python
from datasets import load_dataset
dataset = load_dataset("beans")
```
We can use an image processor from either of the models, as in this case they return the same output with same resolution. We will use the `map()` method of `dataset` to apply the preprocessing to every split of the dataset.
```python
from transformers import AutoImageProcessor
teacher_processor = AutoImageProcessor.from_pretrained("merve/beans-vit-224")
def process(examples):
processed_inputs = teacher_processor(examples["image"])
return processed_inputs
processed_datasets = dataset.map(process, batched=True)
```
Essentially, we want the student model (a randomly initialized MobileNet) to mimic the teacher model (fine-tuned vision transformer). To achieve this, we first get the logits output from the teacher and the student. Then, we divide each of them by the parameter `temperature` which controls the importance of each soft target. A parameter called `lambda` weighs the importance of the distillation loss. In this example, we will use `temperature=5` and `lambda=0.5`. We will use the Kullback-Leibler Divergence loss to compute the divergence between the student and teacher. Given two data P and Q, KL Divergence explains how much extra information we need to represent P using Q. If two are identical, their KL divergence is zero, as there's no other information needed to explain P from Q. Thus, in the context of knowledge distillation, KL divergence is useful.
```python
from transformers import TrainingArguments, Trainer
import torch
import torch.nn as nn
import torch.nn.functional as F
from accelerate.test_utils.testing import get_backend
class ImageDistilTrainer(Trainer):
def __init__(self, teacher_model=None, student_model=None, temperature=None, lambda_param=None, *args, **kwargs):
super().__init__(model=student_model, *args, **kwargs)
self.teacher = teacher_model
self.student = student_model
self.loss_function = nn.KLDivLoss(reduction="batchmean")
device, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
self.teacher.to(device)
self.teacher.eval()
self.temperature = temperature
self.lambda_param = lambda_param
def compute_loss(self, student, inputs, return_outputs=False):
student_output = self.student(**inputs)
with torch.no_grad():
teacher_output = self.teacher(**inputs)
# Compute soft targets for teacher and student
soft_teacher = F.softmax(teacher_output.logits / self.temperature, dim=-1)
soft_student = F.log_softmax(student_output.logits / self.temperature, dim=-1)
# Compute the loss
distillation_loss = self.loss_function(soft_student, soft_teacher) * (self.temperature ** 2)
# Compute the true label loss
student_target_loss = student_output.loss
# Calculate final loss
loss = (1. - self.lambda_param) * student_target_loss + self.lambda_param * distillation_loss
return (loss, student_output) if return_outputs else loss
```
We will now login to Hugging Face Hub so we can push our model to the Hugging Face Hub through the `Trainer`.
```python
from huggingface_hub import notebook_login
notebook_login()
```
Let's set the `TrainingArguments`, the teacher model and the student model.
```python
from transformers import AutoModelForImageClassification, MobileNetV2Config, MobileNetV2ForImageClassification
training_args = TrainingArguments(
output_dir="my-awesome-model",
num_train_epochs=30,
fp16=True,
logging_dir=f"{repo_name}/logs",
logging_strategy="epoch",
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="accuracy",
report_to="tensorboard",
push_to_hub=True,
hub_strategy="every_save",
hub_model_id=repo_name,
)
num_labels = len(processed_datasets["train"].features["labels"].names)
# initialize models
teacher_model = AutoModelForImageClassification.from_pretrained(
"merve/beans-vit-224",
num_labels=num_labels,
ignore_mismatched_sizes=True
)
# training MobileNetV2 from scratch
student_config = MobileNetV2Config()
student_config.num_labels = num_labels
student_model = MobileNetV2ForImageClassification(student_config)
```
We can use `compute_metrics` function to evaluate our model on the test set. This function will be used during the training process to compute the `accuracy` & `f1` of our model.
```python
import evaluate
import numpy as np
accuracy = evaluate.load("accuracy")
def compute_metrics(eval_pred):
predictions, labels = eval_pred
acc = accuracy.compute(references=labels, predictions=np.argmax(predictions, axis=1))
return {"accuracy": acc["accuracy"]}
```
Let's initialize the `Trainer` with the training arguments we defined. We will also initialize our data collator.
```python
from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()
trainer = ImageDistilTrainer(
student_model=student_model,
teacher_model=teacher_model,
training_args=training_args,
train_dataset=processed_datasets["train"],
eval_dataset=processed_datasets["validation"],
data_collator=data_collator,
processing_class=teacher_processor,
compute_metrics=compute_metrics,
temperature=5,
lambda_param=0.5
)
```
We can now train our model.
```python
trainer.train()
```
We can evaluate the model on the test set.
```python
trainer.evaluate(processed_datasets["test"])
```
On test set, our model reaches 72 percent accuracy. To have a sanity check over efficiency of distillation, we also trained MobileNet on the beans dataset from scratch with the same hyperparameters and observed 63 percent accuracy on the test set. We invite the readers to try different pre-trained teacher models, student architectures, distillation parameters and report their findings. The training logs and checkpoints for distilled model can be found in [this repository](https://huggingface.co/merve/vit-mobilenet-beans-224), and MobileNetV2 trained from scratch can be found in this [repository](https://huggingface.co/merve/resnet-mobilenet-beans-5).
| transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md/0 | {
"file_path": "transformers/docs/source/en/tasks/knowledge_distillation_for_image_classification.md",
"repo_id": "transformers",
"token_count": 2638
} |
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# Video-text-to-text
[[open-in-colab]]
Video-text-to-text models, also known as video language models or vision language models with video input, are language models that take a video input. These models can tackle various tasks, from video question answering to video captioning.
These models have nearly the same architecture as [image-text-to-text](../image_text_to_text.md) models except for some changes to accept video data, since video data is essentially image frames with temporal dependencies. Some image-text-to-text models take in multiple images, but this alone is inadequate for a model to accept videos. Moreover, video-text-to-text models are often trained with all vision modalities. Each example might have videos, multiple videos, images and multiple images. Some of these models can also take interleaved inputs. For example, you can refer to a specific video inside a string of text by adding a video token in text like "What is happening in this video? `<video>`".
In this guide, we provide a brief overview of video LMs and show how to use them with Transformers for inference.
To begin with, there are multiple types of video LMs:
- base models used for fine-tuning
- chat fine-tuned models for conversation
- instruction fine-tuned models
This guide focuses on inference with an instruction-tuned model, [llava-hf/llava-interleave-qwen-7b-hf](https://huggingface.co/llava-hf/llava-interleave-qwen-7b-hf) which can take in interleaved data. Alternatively, you can try [llava-interleave-qwen-0.5b-hf](https://huggingface.co/llava-hf/llava-interleave-qwen-0.5b-hf) if your hardware doesn't allow running a 7B model.
Let's begin installing the dependencies.
```bash
pip install -q transformers accelerate flash_attn
```
Let's initialize the model and the processor.
```python
from transformers import LlavaProcessor, LlavaForConditionalGeneration
import torch
model_id = "llava-hf/llava-interleave-qwen-0.5b-hf"
processor = LlavaProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16)
model.to("cuda") # can also be xpu, mps, npu etc. depending on your hardware accelerator
```
Some models directly consume the `<video>` token, and others accept `<image>` tokens equal to the number of sampled frames. This model handles videos in the latter fashion. We will write a simple utility to handle image tokens, and another utility to get a video from a url and sample frames from it.
```python
import uuid
import requests
import cv2
from PIL import Image
def replace_video_with_images(text, frames):
return text.replace("<video>", "<image>" * frames)
def sample_frames(url, num_frames):
response = requests.get(url)
path_id = str(uuid.uuid4())
path = f"./{path_id}.mp4"
with open(path, "wb") as f:
f.write(response.content)
video = cv2.VideoCapture(path)
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
interval = total_frames // num_frames
frames = []
for i in range(total_frames):
ret, frame = video.read()
pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
if not ret:
continue
if i % interval == 0:
frames.append(pil_img)
video.release()
return frames[:num_frames]
```
Let's get our inputs. We will sample frames and concatenate them.
```python
video_1 = "https://huggingface.co/spaces/merve/llava-interleave/resolve/main/cats_1.mp4"
video_2 = "https://huggingface.co/spaces/merve/llava-interleave/resolve/main/cats_2.mp4"
video_1 = sample_frames(video_1, 6)
video_2 = sample_frames(video_2, 6)
videos = video_1 + video_2
videos
# [<PIL.Image.Image image mode=RGB size=1920x1080>,
# <PIL.Image.Image image mode=RGB size=1920x1080>,
# <PIL.Image.Image image mode=RGB size=1920x1080>, ...]
```
Both videos have cats.
<div class="container">
<div class="video-container">
<video width="400" controls>
<source src="https://huggingface.co/spaces/merve/llava-interleave/resolve/main/cats_1.mp4" type="video/mp4">
</video>
</div>
<div class="video-container">
<video width="400" controls>
<source src="https://huggingface.co/spaces/merve/llava-interleave/resolve/main/cats_2.mp4" type="video/mp4">
</video>
</div>
</div>
Now we can preprocess the inputs.
This model has a prompt template that looks like following. First, we'll put all the sampled frames into one list. Since we have eight frames in each video, we will insert 12 `<image>` tokens to our prompt. Add `assistant` at the end of the prompt to trigger the model to give answers. Then we can preprocess.
```python
user_prompt = "Are these two cats in these two videos doing the same thing?"
toks = "<image>" * 12
prompt = "<|im_start|>user"+ toks + f"\n{user_prompt}<|im_end|><|im_start|>assistant"
inputs = processor(text=prompt, images=videos, return_tensors="pt").to(model.device, model.dtype)
```
We can now call [`~GenerationMixin.generate`] for inference. The model outputs the question in our input and answer, so we only take the text after the prompt and `assistant` part from the model output.
```python
output = model.generate(**inputs, max_new_tokens=100, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True)[len(user_prompt)+10:])
# The first cat is shown in a relaxed state, with its eyes closed and a content expression, while the second cat is shown in a more active state, with its mouth open wide, possibly in a yawn or a vocalization.
```
And voila!
To learn more about chat templates and token streaming for video-text-to-text models, refer to the [image-text-to-text](../tasks/image_text_to_text) task guide because these models work similarly. | transformers/docs/source/en/tasks/video_text_to_text.md/0 | {
"file_path": "transformers/docs/source/en/tasks/video_text_to_text.md",
"repo_id": "transformers",
"token_count": 2060
} |
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# Entrenamiento distribuido con 🤗 Accelerate
El paralelismo ha emergido como una estrategia para entrenar modelos grandes en hardware limitado e incrementar la velocidad de entrenamiento en varios órdenes de magnitud. En Hugging Face creamos la biblioteca [🤗 Accelerate](https://huggingface.co/docs/accelerate) para ayudar a los usuarios a entrenar modelos 🤗 Transformers en cualquier tipo de configuración distribuida, ya sea en una máquina con múltiples GPUs o en múltiples GPUs distribuidas entre muchas máquinas. En este tutorial aprenderás cómo personalizar tu bucle de entrenamiento de PyTorch nativo para poder entrenar en entornos distribuidos.
## Configuración
Empecemos por instalar 🤗 Accelerate:
```bash
pip install accelerate
```
Luego, importamos y creamos un objeto [`Accelerator`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator). `Accelerator` detectará automáticamente el tipo de configuración distribuida que tengas disponible e inicializará todos los componentes necesarios para el entrenamiento. No necesitas especificar el dispositivo en donde se debe colocar tu modelo.
```py
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
```
## Prepárate para acelerar
Pasa todos los objetos relevantes para el entrenamiento al método [`prepare`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.prepare). Esto incluye los DataLoaders de entrenamiento y evaluación, un modelo y un optimizador:
```py
>>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
... train_dataloader, eval_dataloader, model, optimizer
... )
```
## Backward
Por último, reemplaza el típico `loss.backward()` en tu bucle de entrenamiento con el método [`backward`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.backward) de 🤗 Accelerate:
```py
>>> for epoch in range(num_epochs):
... for batch in train_dataloader:
... outputs = model(**batch)
... loss = outputs.loss
... accelerator.backward(loss)
... optimizer.step()
... lr_scheduler.step()
... optimizer.zero_grad()
... progress_bar.update(1)
```
Como se puede ver en el siguiente código, ¡solo necesitas adicionar cuatro líneas de código a tu bucle de entrenamiento para habilitar el entrenamiento distribuido!
```diff
+ from accelerate import Accelerator
from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
+ accelerator = Accelerator()
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
optimizer = AdamW(model.parameters(), lr=3e-5)
- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
- model.to(device)
+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
+ train_dataloader, eval_dataloader, model, optimizer
+ )
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
- batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
- loss.backward()
+ accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
```
## Entrenamiento
Una vez que hayas añadido las líneas de código relevantes, inicia el entrenamiento desde un script o notebook como Colaboratory.
### Entrenar con un script
Si estás corriendo tu entrenamiento desde un script ejecuta el siguiente comando para crear y guardar un archivo de configuración:
```bash
accelerate config
```
Comienza el entrenamiento con:
```bash
accelerate launch train.py
```
### Entrenar con un notebook
🤗 Accelerate puede correr en un notebook si, por ejemplo, estás planeando utilizar las TPUs de Colaboratory. Encierra el código responsable del entrenamiento en una función y pásalo a `notebook_launcher`:
```py
>>> from accelerate import notebook_launcher
>>> notebook_launcher(training_function)
```
Para obtener más información sobre 🤗 Accelerate y sus numerosas funciones, consulta la [documentación](https://huggingface.co/docs/accelerate).
| transformers/docs/source/es/accelerate.md/0 | {
"file_path": "transformers/docs/source/es/accelerate.md",
"repo_id": "transformers",
"token_count": 1889
} |
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# Compartir un modelo
Los últimos dos tutoriales mostraron cómo puedes realizar fine-tunning a un modelo con PyTorch, Keras y 🤗 Accelerate para configuraciones distribuidas. ¡El siguiente paso es compartir tu modelo con la comunidad! En Hugging Face creemos en compartir abiertamente a todos el conocimiento y los recursos para democratizar la inteligencia artificial. En este sentido, te animamos a considerar compartir tu modelo con la comunidad, de esta forma ayudas a otros ahorrando tiempo y recursos.
En este tutorial aprenderás dos métodos para compartir un modelo trained o fine-tuned en el [Model Hub](https://huggingface.co/models):
- Mediante Código, enviando (push) tus archivos al Hub.
- Con la interfaz Web, con Drag-and-drop de tus archivos al Hub.
<iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
<Tip>
Para compartir un modelo con la comunidad necesitas una cuenta en [huggingface.co](https://huggingface.co/join). También puedes unirte a una organización existente o crear una nueva.
</Tip>
## Características de los repositorios
Cada repositorio en el Model Hub se comporta como cualquier otro repositorio en GitHub. Nuestros repositorios ofrecen versioning, commit history, y la habilidad para visualizar diferencias.
El versioning desarrollado dentro del Model Hub es basado en git y [git-lfs](https://git-lfs.github.com/). En otras palabras, puedes tratar un modelo como un repositorio, brindando un mejor control de acceso y escalabilidad. Version control permite *revisions*, un método para apuntar a una versión específica de un modelo utilizando un commit hash, tag o branch.
Como resultado, puedes cargar una versión específica del modelo con el parámetro `revision`:
```py
>>> model = AutoModel.from_pretrained(
... "julien-c/EsperBERTo-small", revision="4c77982" # tag name, or branch name, or commit hash
... )
```
Los archivos son editados fácilmente dentro de un repositorio. Incluso puedes observar el commit history y las diferencias:
## Configuración inicial
Antes de compartir un modelo al Hub necesitarás tus credenciales de Hugging Face. Si tienes acceso a una terminal ejecuta el siguiente comando en el entorno virtual donde 🤗 Transformers esté instalado. Esto guardará tu token de acceso dentro de tu carpeta cache de Hugging Face (~/.cache/ by default):
```bash
huggingface-cli login
```
Si usas un notebook como Jupyter o Colaboratory, asegúrate de tener instalada la biblioteca [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library). Esta biblioteca te permitirá interactuar por código con el Hub.
```bash
pip install huggingface_hub
```
Luego usa `notebook_login` para iniciar sesión al Hub, y sigue el link [aquí](https://huggingface.co/settings/token) para generar un token con el que iniciaremos sesión:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Convertir un modelo para todos los Frameworks
Para asegurarnos que tu modelo pueda ser usado por alguien que esté trabajando con un framework diferente, te recomendamos convertir y subir tu modelo con checkpoints de pytorch y tensorflow. Aunque los usuarios aún son capaces de cargar su modelo desde un framework diferente, si se omite este paso será más lento debido a que 🤗 Transformers necesitará convertir el checkpoint sobre-la-marcha.
Convertir un checkpoint para otro framework es fácil. Asegúrate tener Pytorch y TensorFlow instalado (Véase [aquí](installation) para instrucciones de instalación), y luego encuentra el modelo específico para tu tarea en el otro Framework.
Por ejemplo, supongamos que has entrenado DistilBert para clasificación de secuencias en PyTorch y quieres convertirlo a su equivalente en TensorFlow. Cargas el equivalente en TensorFlow de tu modelo para tu tarea y especificas `from_pt=True` así 🤗 Transformers convertirá el Pytorch checkpoint a un TensorFlow Checkpoint:
```py
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
```
Luego guardas tu nuevo modelo TensorFlow con su nuevo checkpoint:
```py
>>> tf_model.save_pretrained("path/to/awesome-name-you-picked")
```
De manera similar, especificas `from_tf=True` para convertir un checkpoint de TensorFlow a Pytorch:
```py
>>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
>>> pt_model.save_pretrained("path/to/awesome-name-you-picked")
```
Si algún modelo está disponible en Flax, también puedes convertir un checkpoint de Pytorch a Flax:
```py
>>> flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
... "path/to/awesome-name-you-picked", from_pt=True
... )
```
## Compartir un modelo con `Trainer`
<Youtube id="Z1-XMy-GNLQ"/>
Compartir un modelo al Hub es tan simple como añadir un parámetro extra o un callback. Si recuerdas del tutorial de [fine-tuning tutorial](training), la clase [`TrainingArguments`] es donde especificas los Hiperparámetros y opciones de entrenamiento adicionales. Una de estas opciones incluye la habilidad de compartir un modelo directamente al Hub. Para ello configuras `push_to_hub=True` dentro de [`TrainingArguments`]:
```py
>>> training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True)
```
A continuación, como usualmente, pasa tus argumentos de entrenamiento a [`Trainer`]:
```py
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=small_train_dataset,
... eval_dataset=small_eval_dataset,
... compute_metrics=compute_metrics,
... )
```
Luego que realizas fine-tune a tu modelo, llamas [`~transformers.Trainer.push_to_hub`] en [`Trainer`] para enviar el modelo al Hub!🤗 Transformers incluso añadirá automáticamente los Hiperparámetros de entrenamiento, resultados de entrenamiento y versiones del Framework a tu model card!
```py
>>> trainer.push_to_hub()
```
## Compartir un modelo con `PushToHubCallback`
Los usuarios de TensorFlow pueden activar la misma funcionalidad con [`PushToHubCallback`]. En la funcion [`PushToHubCallback`], agrega:
- Un directorio de salida para tu modelo.
- Un tokenizador.
- El `hub_model_id`, el cual es tu usuario Hub y el nombre del modelo.
```py
>>> from transformers import PushToHubCallback
>>> push_to_hub_callback = PushToHubCallback(
... output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model"
... )
```
Agregamos el callback a [`fit`](https://keras.io/api/models/model_training_apis/), y 🤗 Transformers enviará el modelo entrenado al Hub:
```py
>>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback)
```
## Usando la función `push_to_hub`
Puedes llamar la función `push_to_hub` directamente en tu modelo para subirlo al Hub.
Especifica el nombre del modelo en `push_to_hub`:
```py
>>> pt_model.push_to_hub("my-awesome-model")
```
Esto creará un repositorio bajo tu usuario con el nombre del modelo `my-awesome-model`. Ahora los usuarios pueden cargar tu modelo con la función `from_pretrained`:
```py
>>> from transformers import AutoModel
>>> model = AutoModel.from_pretrained("your_username/my-awesome-model")
```
Si perteneces a una organización y quieres compartir tu modelo bajo el nombre de la organización, añade el parámetro `organization`:
```py
>>> pt_model.push_to_hub("my-awesome-model", organization="my-awesome-org")
```
La función `push_to_hub` también puede ser usada para añadir archivos al repositorio del modelo. Por ejemplo, añade un tokenizador al repositorio:
```py
>>> tokenizer.push_to_hub("my-awesome-model")
```
O quizás te gustaría añadir la versión de TensorFlow de tu modelo fine-tuned en Pytorch:
```py
>>> tf_model.push_to_hub("my-awesome-model")
```
Ahora, cuando navegues a tu perfil en Hugging Face, deberías observar el repositorio de tu modelo creado recientemente. Si das click en el tab **Files** observarás todos los archivos que has subido al repositorio.
Para más detalles sobre cómo crear y subir archivos al repositorio, consulta la [documentación del Hub](https://huggingface.co/docs/hub/how-to-upstream).
## Compartir con la interfaz web
Los usuarios que prefieran un enfoque no-code tienen la opción de cargar su modelo a través de la interfaz gráfica del Hub. Visita la página [huggingface.co/new](https://huggingface.co/new) para crear un nuevo repositorio:
Desde aquí, añade información acerca del modelo:
- Selecciona el **owner** (la persona propietaria) del repositorio. Puedes ser tú o cualquier organización a la que pertenezcas.
- Escoge un nombre para tu modelo. También será el nombre del repositorio.
- Elige si tu modelo es público o privado.
- Especifica la licencia que usará tu modelo.
Ahora puedes hacer click en el tab **Files** y luego en el botón **Add file** para subir un nuevo archivo a tu repositorio. Luego arrastra y suelta un archivo a subir y le añades un mensaje al commit.
## Añadiendo una tarjeta de modelo
Para asegurarnos que los usuarios entiendan las capacidades de tu modelo, sus limitaciones, posibles sesgos y consideraciones éticas, por favor añade una tarjeta (como una tarjeta de presentación) al repositorio del modelo. La tarjeta de modelo es definida en el archivo `README.md`. Puedes agregar una de la siguiente manera:
* Elaborando y subiendo manualmente el archivo`README.md`.
* Dando click en el botón **Edit model card** dentro del repositorio.
Toma un momento para ver la [tarjeta de modelo](https://huggingface.co/distilbert/distilbert-base-uncased) de DistilBert para que tengas un buen ejemplo del tipo de información que debería incluir. Consulta [la documentación](https://huggingface.co/docs/hub/models-cards) para más detalles acerca de otras opciones que puedes controlar dentro del archivo `README.md` como la huella de carbono del modelo o ejemplos de widgets. Consulta la documentación [aquí](https://huggingface.co/docs/hub/models-cards).
| transformers/docs/source/es/model_sharing.md/0 | {
"file_path": "transformers/docs/source/es/model_sharing.md",
"repo_id": "transformers",
"token_count": 3984
} |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
# Subtítulos de Imágenes
[[open-in-colab]]
Los subtítulos de imágenes es la tarea de predecir un subtítulo para una imagen dada. Las aplicaciones comunes en el mundo real incluyen
ayudar a personas con discapacidad visual que les puede ayudar a navegar a través de diferentes situaciones. Por lo tanto, los subtítulos de imágenes
ayuda a mejorar la accesibilidad del contenido para las personas describiéndoles imágenes.
Esta guía te mostrará cómo:
* Ajustar un modelo de subtítulos de imágenes.
* Usar el modelo ajustado para inferencia.
Antes de comenzar, asegúrate de tener todas las bibliotecas necesarias instaladas:
```bash
pip install transformers datasets evaluate -q
pip install jiwer -q
```
Te animamos a que inicies sesión en tu cuenta de Hugging Face para que puedas subir y compartir tu modelo con la comunidad. Cuando se te solicite, ingresa tu token para iniciar sesión:
```python
from huggingface_hub import notebook_login
notebook_login()
```
## Cargar el conjunto de datos de subtítulos BLIP de Pokémon
Utiliza la biblioteca 🤗 Dataset para cargar un conjunto de datos que consiste en pares {image-caption}. Para crear tu propio conjunto de datos de subtítulos de imágenes
en PyTorch, puedes seguir [este cuaderno](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/GIT/Fine_tune_GIT_on_an_image_captioning_dataset.ipynb).
```python
from datasets import load_dataset
ds = load_dataset("lambdalabs/pokemon-blip-captions")
ds
```
```bash
DatasetDict({
train: Dataset({
features: ['image', 'text'],
num_rows: 833
})
})
```
El conjunto de datos tiene dos características, `image` y `text`.
<Tip>
Muchos conjuntos de datos de subtítulos de imágenes contienen múltiples subtítulos por imagen. En esos casos, una estrategia común es muestrear aleatoriamente un subtítulo entre los disponibles durante el entrenamiento.
</Tip>
Divide el conjunto de entrenamiento del conjunto de datos en un conjunto de entrenamiento y de prueba con el método [`~datasets.Dataset.train_test_split`]:
```python
ds = ds["train"].train_test_split(test_size=0.1)
train_ds = ds["train"]
test_ds = ds["test"]
```
Vamos a visualizar un par de muestras del conjunto de entrenamiento.
```python
from textwrap import wrap
import matplotlib.pyplot as plt
import numpy as np
def plot_images(images, captions):
plt.figure(figsize=(20, 20))
for i in range(len(images)):
ax = plt.subplot(1, len(images), i + 1)
caption = captions[i]
caption = "\n".join(wrap(caption, 12))
plt.title(caption)
plt.imshow(images[i])
plt.axis("off")
sample_images_to_visualize = [np.array(train_ds[i]["image"]) for i in range(5)]
sample_captions = [train_ds[i]["text"] for i in range(5)]
plot_images(sample_images_to_visualize, sample_captions)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/sample_training_images_image_cap.png" alt="Sample training images"/>
</div>
## Preprocesar el conjunto de datos
Dado que el conjunto de datos tiene dos modalidades (imagen y texto), el proceso de preprocesamiento preprocesará las imágenes y los subtítulos.
Para hacerlo, carga la clase de procesador asociada con el modelo que estás a punto de ajustar.
```python
from transformers import AutoProcessor
checkpoint = "microsoft/git-base"
processor = AutoProcessor.from_pretrained(checkpoint)
```
El procesador preprocesará internamente la imagen (lo que incluye el cambio de tamaño y la escala de píxeles) y tokenizará el subtítulo.
```python
def transforms(example_batch):
images = [x for x in example_batch["image"]]
captions = [x for x in example_batch["text"]]
inputs = processor(images=images, text=captions, padding="max_length")
inputs.update({"labels": inputs["input_ids"]})
return inputs
train_ds.set_transform(transforms)
test_ds.set_transform(transforms)
```
Con el conjunto de datos listo, ahora puedes configurar el modelo para el ajuste fino.
## Cargar un modelo base
Carga ["microsoft/git-base"](https://huggingface.co/microsoft/git-base) en un objeto [`AutoModelForCausalLM`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM).
```python
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(checkpoint)
```
## Evaluar
Los modelos de subtítulos de imágenes se evalúan típicamente con el [Rouge Score](https://huggingface.co/spaces/evaluate-metric/rouge) o Tasa de Error de Palabra ([Word Error Rate](https://huggingface.co/spaces/evaluate-metric/wer), por sus siglas en inglés). Para esta guía, utilizarás la Tasa de Error de Palabra (WER).
Usamos la biblioteca 🤗 Evaluate para hacerlo. Para conocer las limitaciones potenciales y otros problemas del WER, consulta [esta guía](https://huggingface.co/spaces/evaluate-metric/wer).
```python
from evaluate import load
import torch
wer = load("wer")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predicted = logits.argmax(-1)
decoded_labels = processor.batch_decode(labels, skip_special_tokens=True)
decoded_predictions = processor.batch_decode(predicted, skip_special_tokens=True)
wer_score = wer.compute(predictions=decoded_predictions, references=decoded_labels)
return {"wer_score": wer_score}
```
## ¡Entrenar!
Ahora, estás listo para comenzar a ajustar el modelo. Utilizarás el 🤗 [`Trainer`] para esto.
Primero, define los argumentos de entrenamiento usando [`TrainingArguments`].
```python
from transformers import TrainingArguments, Trainer
model_name = checkpoint.split("/")[1]
training_args = TrainingArguments(
output_dir=f"{model_name}-pokemon",
learning_rate=5e-5,
num_train_epochs=50,
fp16=True,
per_device_train_batch_size=32,
per_device_eval_batch_size=32,
gradient_accumulation_steps=2,
save_total_limit=3,
eval_strategy="steps",
eval_steps=50,
save_strategy="steps",
save_steps=50,
logging_steps=50,
remove_unused_columns=False,
push_to_hub=True,
label_names=["labels"],
load_best_model_at_end=True,
)
```
Luego pásalos junto con los conjuntos de datos y el modelo al 🤗 Trainer.
```python
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset=test_ds,
compute_metrics=compute_metrics,
)
```
Para comenzar el entrenamiento, simplemente llama a [`~Trainer.train`] en el objeto [`Trainer`].
```python
trainer.train()
```
Deberías ver cómo disminuye suavemente la pérdida de entrenamiento a medida que avanza el entrenamiento.
Una vez completado el entrenamiento, comparte tu modelo en el Hub con el método [`~Trainer.push_to_hub`] para que todos puedan usar tu modelo:
```python
trainer.push_to_hub()
```
## Inferencia
Toma una imagen de muestra de test_ds para probar el modelo.
```python
from PIL import Image
import requests
url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
image = Image.open(requests.get(url, stream=True).raw)
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/test_image_image_cap.png" alt="Test image"/>
</div>
Prepara la imagen para el modelo.
```python
device = "cuda" if torch.cuda.is_available() else "cpu"
inputs = processor(images=image, return_tensors="pt").to(device)
pixel_values = inputs.pixel_values
```
Llama a [`generate`] y decodifica las predicciones.
```python
generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_caption)
```
```bash
a drawing of a pink and blue pokemon
```
¡Parece que el modelo ajustado generó un subtítulo bastante bueno! | transformers/docs/source/es/tasks/image_captioning.md/0 | {
"file_path": "transformers/docs/source/es/tasks/image_captioning.md",
"repo_id": "transformers",
"token_count": 3231
} |
<!---
Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Installation
Installez 🤗 Transformers pour n'importe quelle librairie d'apprentissage profond avec laquelle vous avez l'habitude de travaillez, configurez votre cache et configurez 🤗 Transformers pour un usage hors ligne (facultatif).
🤗 Transformers est testé avec Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+ et Flax.
Consulter les instructions d'installation ci-dessous pour la librairie d'apprentissage profond que vous utilisez:
* Instructions d'installation pour [PyTorch](https://pytorch.org/get-started/locally/).
* Instructions d'installation pour [TensorFlow 2.0](https://www.tensorflow.org/install/pip).
* Instructions d'installation pour [Flax](https://flax.readthedocs.io/en/latest/).
## Installation avec pip
Vous devriez installer 🤗 Transformers dans un [environnement virtuel](https://docs.python.org/3/library/venv.html).
Si vous n'êtes pas à l'aise avec les environnements virtuels, consultez ce [guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
Utiliser un environnement virtuel permet de facilement gérer différents projets et d'éviter des erreurs de compatibilité entre les différentes dépendances.
Commencez par créer un environnement virtuel dans l'espace de travail de votre projet :
```bash
python -m venv .env
```
Activez l'environnement virtuel. Sur Linux ou MacOs :
```bash
source .env/bin/activate
```
Activez l'environnement virtuel sur Windows :
```bash
.env/Scripts/activate
```
Maintenant, 🤗 Transformers peut être installé avec la commande suivante :
```bash
pip install transformers
```
Pour une utilisation avec CPU seulement, 🤗 Transformers et la librairie d'apprentissage profond de votre choix peuvent être installés en une seule ligne.
Par exemple, installez 🤗 Transformers et PyTorch avec la commande suivante :
```bash
pip install 'transformers[torch]'
```
🤗 Transformers et TensorFlow 2.0 :
```bash
pip install 'transformers[tf-cpu]'
```
<Tip warning={true}>
Pour les architectures mac M1 / ARM
Vous devez installer les outils suivants avant d'installer TensorFLow 2.0
```bash
brew install cmake
brew install pkg-config
```
</Tip>
🤗 Transformers et Flax :
```bash
pip install 'transformers[flax]'
```
Vérifiez que 🤗 Transformers a bien été installé avec la commande suivante. La commande va télécharger un modèle pré-entraîné :
```bash
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"
```
Le label et score sont ensuite affichés :
```bash
[{'label': 'POSITIVE', 'score': 0.9998704791069031}]
```
## Installation depuis le code source
Installez 🤗 Transformers depuis le code source avec la commande suivante :
```bash
pip install git+https://github.com/huggingface/transformers
```
Cette commande installe la version depuis la branche `main` au lieu de la dernière version stable. La version de la branche `main` est utile pour avoir les derniers développements. Par exemple, si un bug a été résolu depuis la dernière version stable mais n'a pas encore été publié officiellement. Cependant, cela veut aussi dire que la version de la branche `main` n'est pas toujours stable. Nous nous efforçons de maintenir la version de la branche `main` opérationnelle, et la plupart des problèmes sont généralement résolus en l'espace de quelques heures ou d'un jour. Si vous recontrez un problème, n'hésitez pas à créer une [Issue](https://github.com/huggingface/transformers/issues) pour que l'on puisse trouver une solution au plus vite !
Vérifiez que 🤗 Transformers a bien été installé avec la commande suivante :
```bash
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
```
## Installation modifiable
Vous aurez besoin d'une installation modifiable si vous le souhaitez :
* Utiliser la version de la branche `main` du code source.
* Contribuer à 🤗 Transformers et vouler tester vos modifications du code source.
Clonez le projet et installez 🤗 Transformers avec les commandes suivantes :
```bash
git clone https://github.com/huggingface/transformers.git
cd transformers
pip install -e .
```
Ces commandes créent des liens entre le dossier où le projet a été cloné et les chemins de vos librairies Python. Python regardera maintenant dans le dossier que vous avez cloné en plus des dossiers où sont installées vos autres librairies. Par exemple, si vos librairies Python sont installées dans `~/anaconda3/envs/main/lib/python3.7/site-packages/`, Python cherchera aussi dans le dossier où vous avez cloné : `~/transformers/`.
<Tip warning={true}>
Vous devez garder le dossier `transformers` si vous voulez continuer d'utiliser la librairie.
</Tip>
Maintenant, vous pouvez facilement mettre à jour votre clone avec la dernière version de 🤗 Transformers en utilisant la commande suivante :
```bash
cd ~/transformers/
git pull
```
Votre environnement Python utilisera la version de la branche `main` lors de la prochaine exécution.
## Installation avec conda
Installation via le canal `conda-forge` de conda :
```bash
conda install conda-forge::transformers
```
## Configuration du cache
Les modèles pré-entraînés sont téléchargés et mis en cache localement dans le dossier suivant : `~/.cache/huggingface/hub`. C'est le dossier par défaut donné par la variable d'environnement `TRANSFORMERS_CACHE`. Sur Windows, le dossier par défaut est `C:\Users\nom_utilisateur\.cache\huggingface\hub`. Vous pouvez modifier les variables d'environnement indiquées ci-dessous - par ordre de priorité - pour spécifier un dossier de cache différent :
1. Variable d'environnement (par défaut) : `HF_HUB_CACHE` ou `TRANSFORMERS_CACHE`.
2. Variable d'environnement : `HF_HOME`.
3. Variable d'environnement : `XDG_CACHE_HOME` + `/huggingface`.
<Tip>
🤗 Transformers utilisera les variables d'environnement `PYTORCH_TRANSFORMERS_CACHE` ou `PYTORCH_PRETRAINED_BERT_CACHE` si vous utilisez une version précédente de cette librairie et avez défini ces variables d'environnement, sauf si vous spécifiez la variable d'environnement `TRANSFORMERS_CACHE`.
</Tip>
## Mode hors ligne
🤗 Transformers peut fonctionner dans un environnement cloisonné ou hors ligne en n'utilisant que des fichiers locaux. Définissez la variable d'environnement `HF_HUB_OFFLINE=1` pour activer ce mode.
<Tip>
Ajoutez [🤗 Datasets](https://huggingface.co/docs/datasets/) à votre processus d'entraînement hors ligne en définissant la variable d'environnement `HF_DATASETS_OFFLINE=1`.
</Tip>
```bash
HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
```
Le script devrait maintenant s'exécuter sans rester en attente ou attendre une expiration, car il n'essaiera pas de télécharger des modèle sur le Hub.
Vous pouvez aussi éviter de télécharger un modèle à chaque appel de la fonction [`~PreTrainedModel.from_pretrained`] en utilisant le paramètre [local_files_only]. Seuls les fichiers locaux sont chargés lorsque ce paramètre est activé (c.-à-d. `local_files_only=True`) :
```py
from transformers import T5Model
model = T5Model.from_pretrained("./path/to/local/directory", local_files_only=True)
```
### Récupérer des modèles et des tokenizers pour une utilisation hors ligne
Une autre option pour utiliser 🤗 Transformers hors ligne est de télécharger les fichiers à l'avance, puis d'utiliser les chemins locaux lorsque vous en avez besoin en mode hors ligne. Il existe trois façons de faire cela :
* Téléchargez un fichier via l'interface utilisateur sur le [Model Hub](https://huggingface.co/models) en cliquant sur l'icône ↓.
* Utilisez les fonctions [`PreTrainedModel.from_pretrained`] et [`PreTrainedModel.save_pretrained`] :
1. Téléchargez vos fichiers à l'avance avec [`PreTrainedModel.from_pretrained`]:
```py
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("bigscience/T0_3B")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0_3B")
```
2. Sauvegardez les fichiers dans un dossier de votre choix avec [`PreTrainedModel.save_pretrained`]:
```py
>>> tokenizer.save_pretrained("./your/path/bigscience_t0")
>>> model.save_pretrained("./your/path/bigscience_t0")
```
3. Maintenant, lorsque vous êtes hors ligne, rechargez vos fichiers avec [`PreTrainedModel.from_pretrained`] depuis le dossier où vous les avez sauvegardés :
```py
>>> tokenizer = AutoTokenizer.from_pretrained("./your/path/bigscience_t0")
>>> model = AutoModel.from_pretrained("./your/path/bigscience_t0")
```
* Téléchargez des fichiers de manière automatique avec la librairie [huggingface_hub](https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub) :
1. Installez la librairie `huggingface_hub` dans votre environnement virtuel :
```bash
python -m pip install huggingface_hub
```
2. Utilisez la fonction [`hf_hub_download`](https://huggingface.co/docs/hub/adding-a-library#download-files-from-the-hub) pour télécharger un fichier vers un chemin de votre choix. Par exemple, la commande suivante télécharge le fichier `config.json` du modèle [T0](https://huggingface.co/bigscience/T0_3B) vers le chemin de votre choix :
```py
>>> from huggingface_hub import hf_hub_download
>>> hf_hub_download(repo_id="bigscience/T0_3B", filename="config.json", cache_dir="./your/path/bigscience_t0")
```
Une fois que votre fichier est téléchargé et caché localement, spécifiez son chemin local pour le charger et l'utiliser :
```py
>>> from transformers import AutoConfig
>>> config = AutoConfig.from_pretrained("./your/path/bigscience_t0/config.json")
```
<Tip>
Consultez la section [How to download files from the Hub (Comment télécharger des fichiers depuis le Hub)](https://huggingface.co/docs/hub/how-to-downstream) pour plus de détails sur le téléchargement de fichiers stockés sur le Hub.
</Tip>
| transformers/docs/source/fr/installation.md/0 | {
"file_path": "transformers/docs/source/fr/installation.md",
"repo_id": "transformers",
"token_count": 3846
} |
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# Istanziare un big model
Quando vuoi utilizzare un modello preaddestrato (pretrained) molto grande, una sfida è minimizzare l'uso della RAM. Il workflow classico
in PyTorch è:
1. Crea il tuo modello con pesi casuali (random weights).
2. Carica i tuoi pesi preaddestrati.
3. Inserisci i pesi preaddestrati nel tuo modello casuale.
I passi 1 e 2 una versione completa del modello in memoria, in molti casi non è un problema, ma se il modello inizia a pesare diversi GigaBytes, queste due copie possono sturare la nostra RAM. Ancora peggio, se stai usando `torch.distributed` per seguire l'addestramento (training) in distribuito, ogni processo caricherà il modello preaddestrato e memorizzerà queste due copie nella RAM.
<Tip>
Nota che il modello creato casualmente è inizializzato con tensori "vuoti", che occupano spazio in memoria ma senza riempirlo (quindi i valori casuali sono quelli che si trovavano in questa porzione di memoria in un determinato momento). L'inizializzazione casuale che segue la distribuzione appropriata per il tipo di modello/parametri istanziato (come la distribuzione normale per le istanze) è eseguito solo dopo il passaggio 3 sui pesi non inizializzati, per essere più rapido possibile!
</Tip>
In questa guida, esploreremo le soluzioni che Transformers offre per affrontare questo problema. C'è da tenere in conto che questa è un'area in cui si sta attualmente sviluppando, quindi le API spiegate qui possono variare velocemente in futuro.
## Checkpoints condivisi
Dalla versione 4.18.0, i checkpoints dei modelli che occupano più di 10GB di spazio vengono automaticamente frammentati in più parti. Per quanto riguarda la possibilità di avere un unico checkpoint quando si utilizza `model.save_pretrained(save_dir)`, si hanno diversi checkpoint parziali (ognuno con dimensione < 10GB) e un indice che mappa i nomi dei parametri ai file in cui sono memorizzati.
Puoi controllare la dimensione massima dopo la frammentazione con il parametro `max_shard_size`, nel prossimo esempio, useremo modelli di dimensioni normali con frammenti di piccoli dimensioni: prendiamo un modello BERT classico.
```py
from transformers import AutoModel
model = AutoModel.from_pretrained("google-bert/bert-base-cased")
```
Se tu salvi usando [`~PreTrainedModel.save_pretrained`], avrai una nuova cartella con due file: il config del modello e i suoi pesi:
```py
>>> import os
>>> import tempfile
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir)
... print(sorted(os.listdir(tmp_dir)))
['config.json', 'pytorch_model.bin']
```
Adesso usiamo una dimensione massima di frammentazione di 200MB:
```py
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... print(sorted(os.listdir(tmp_dir)))
['config.json', 'pytorch_model-00001-of-00003.bin', 'pytorch_model-00002-of-00003.bin', 'pytorch_model-00003-of-00003.bin', 'pytorch_model.bin.index.json']
```
In aggiunta alla configurazione del modello, vediamo tre differenti file dei pesi, e un file `index.json` che è il nostro indice. Un checkpoint può essere ricaricato totalmente usando il metodo [`~PreTrainedModel.from_pretrained`]:
```py
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... new_model = AutoModel.from_pretrained(tmp_dir)
```
Il vantaggio principale di applicare questo metodo per modelli grandi è che durante il passo 2 del workflow illustrato in precedenza, ogni frammento del checkpoint viene caricato dopo il precedente, limitando l'utilizzo della RAM alla dimensione del modello più la dimensione del frammento più grande.
Dietro le quinte, il file indice è utilizzato per determinare quali chiavi sono nel checkpoint, e dove i corrispondenti pesi sono memorizzati. Possiamo caricare l'indice come un qualsiasi json e ottenere un dizionario:
```py
>>> import json
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... with open(os.path.join(tmp_dir, "pytorch_model.bin.index.json"), "r") as f:
... index = json.load(f)
>>> print(index.keys())
dict_keys(['metadata', 'weight_map'])
```
I metadati consistono solo nella dimensione totale del modello per ora. Abbiamo in programma di aggiungere altre informazioni in futuro:
```py
>>> index["metadata"]
{'total_size': 433245184}
```
La mappa dei pesi è la parte principale di questo indice, che mappa ogni nome dei parametri (si trova solitamente nei modelli PyTorch come `state_dict`) al file in cui è memorizzato:
```py
>>> index["weight_map"]
{'embeddings.LayerNorm.bias': 'pytorch_model-00001-of-00003.bin',
'embeddings.LayerNorm.weight': 'pytorch_model-00001-of-00003.bin',
...
```
Se vuoi caricare direttamente un checkpoint frammentato in un modello senza usare [`~PreTrainedModel.from_pretrained`] (come si farebbe con `model.load_state_dict()` per un checkpoint completo) devi usare [`~modeling_utils.load_sharded_checkpoint`]:
```py
>>> from transformers.modeling_utils import load_sharded_checkpoint
>>> with tempfile.TemporaryDirectory() as tmp_dir:
... model.save_pretrained(tmp_dir, max_shard_size="200MB")
... load_sharded_checkpoint(model, tmp_dir)
```
## Caricamento low memory
Frammentare i checkpoint l'utilizzo di memoria al passo 2 del workflow citato in precedenza, ma per utilizzare questo modello in un ambiente con poca memoria, consigliamo di utilizzare i nostri strumenti basati sulla libreria Accelerate.
Per ulteriori informazioni, leggere la seguente guida: [Large model loading using Accelerate](./main_classes/model#large-model-loading) | transformers/docs/source/it/big_models.md/0 | {
"file_path": "transformers/docs/source/it/big_models.md",
"repo_id": "transformers",
"token_count": 2214
} |
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# Addestramento efficiente su CPU
Questa guida si concentra su come addestrare in maniera efficiente grandi modelli su CPU.
## Mixed precision con IPEX
IPEX è ottimizzato per CPU con AVX-512 o superiore, e funziona per le CPU con solo AVX2. Pertanto, si prevede che le prestazioni saranno più vantaggiose per le le CPU Intel con AVX-512 o superiori, mentre le CPU con solo AVX2 (ad esempio, le CPU AMD o le CPU Intel più vecchie) potrebbero ottenere prestazioni migliori con IPEX, ma non sono garantite. IPEX offre ottimizzazioni delle prestazioni per l'addestramento della CPU sia con Float32 che con BFloat16. L'uso di BFloat16 è l'argomento principale delle seguenti sezioni.
Il tipo di dati a bassa precisione BFloat16 è stato supportato in modo nativo su 3rd Generation Xeon® Scalable Processors (aka Cooper Lake) con AVX512 e sarà supportata dalla prossima generazione di Intel® Xeon® Scalable Processors con Intel® Advanced Matrix Extensions (Intel® AMX) instruction set con prestazioni ulteriormente migliorate. L'Auto Mixed Precision per il backende della CPU è stato abilitato da PyTorch-1.10. allo stesso tempo, il supporto di Auto Mixed Precision con BFloat16 per CPU e l'ottimizzazione degli operatori BFloat16 è stata abilitata in modo massiccio in Intel® Extension per PyTorch, and parzialmente aggiornato al branch master di PyTorch. Gli utenti possono ottenere prestazioni migliori ed users experience con IPEX Auto Mixed Precision..
Vedi informazioni più dettagliate su [Auto Mixed Precision](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/amp.html).
### Installazione di IPEX:
Il rilascio di IPEX segue quello di PyTorch, da installare via pip:
| PyTorch Version | IPEX version |
| :---------------: | :----------: |
| 1.13 | 1.13.0+cpu |
| 1.12 | 1.12.300+cpu |
| 1.11 | 1.11.200+cpu |
| 1.10 | 1.10.100+cpu |
```bash
pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
```
Vedi altri approcci per [IPEX installation](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/installation.html).
### Utilizzo nel Trainer
Per abilitare la auto mixed precision con IPEX in Trainer, l'utende dovrebbe aggiungere `use_ipex`, `bf16` e `no_cuda` negli argomenti del comando di addestramento.
Vedi un sempio di un caso d'uso [Transformers question-answering](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering)
- Training with IPEX using BF16 auto mixed precision on CPU:
<pre> python run_qa.py \
--model_name_or_path google-bert/bert-base-uncased \
--dataset_name squad \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/ \
<b>--use_ipex \</b>
<b>--bf16 --no_cuda</b></pre>
### Esempi pratici
Blog: [Accelerating PyTorch Transformers with Intel Sapphire Rapids](https://huggingface.co/blog/intel-sapphire-rapids)
| transformers/docs/source/it/perf_train_cpu.md/0 | {
"file_path": "transformers/docs/source/it/perf_train_cpu.md",
"repo_id": "transformers",
"token_count": 1310
} |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# BERTology
大規模なトランスフォーマー、例えばBERTの内部動作を調査する研究領域が急成長しています（これを「BERTology」とも呼びます）。この分野の良い例は以下です：
- BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick:
[論文リンク](https://arxiv.org/abs/1905.05950)
- Are Sixteen Heads Really Better than One? by Paul Michel, Omer Levy, Graham Neubig: [論文リンク](https://arxiv.org/abs/1905.10650)
- What Does BERT Look At? An Analysis of BERT's Attention by Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning: [論文リンク](https://arxiv.org/abs/1906.04341)
- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: [論文リンク](https://arxiv.org/abs/2210.04633)
この新しい分野の発展を支援するために、BERT/GPT/GPT-2モデルにいくつかの追加機能を組み込み、人々が内部表現にアクセスできるようにしました。これらの機能は、主にPaul Michel氏の優れた研究（[論文リンク](https://arxiv.org/abs/1905.10650)）に基づいています。具体的には、以下の機能が含まれています：
- BERT/GPT/GPT-2のすべての隠れ状態にアクセスすることができます。
- BERT/GPT/GPT-2の各ヘッドの注意重みにアクセスできます。
- ヘッドの出力値と勾配を取得し、ヘッドの重要性スコアを計算し、[論文リンク](https://arxiv.org/abs/1905.10650)で説明されているようにヘッドを削減できます。
これらの機能を理解し、使用するのを支援するために、特定のサンプルスクリプト「[bertology.py](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py)」を追加しました。このスクリプトは、GLUEで事前トレーニングされたモデルから情報を抽出し、ヘッドを削減する役割を果たします。
| transformers/docs/source/ja/bertology.md/0 | {
"file_path": "transformers/docs/source/ja/bertology.md",
"repo_id": "transformers",
"token_count": 1077
} |
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# Models
ベースクラスである [`PreTrainedModel`]、[`TFPreTrainedModel`]、[`FlaxPreTrainedModel`] は、モデルの読み込みと保存に関する共通のメソッドを実装しており、これはローカルのファイルやディレクトリから、またはライブラリが提供する事前学習モデル構成（HuggingFaceのAWS S3リポジトリからダウンロード）からモデルを読み込むために使用できます。
[`PreTrainedModel`] と [`TFPreTrainedModel`] は、次の共通のメソッドも実装しています：
- 語彙に新しいトークンが追加された場合に、入力トークン埋め込みのリサイズを行う
- モデルのアテンションヘッドを刈り込む
各モデルに共通するその他のメソッドは、[`~modeling_utils.ModuleUtilsMixin`]（PyTorchモデル用）および[`~modeling_tf_utils.TFModuleUtilsMixin`]（TensorFlowモデル用）で定義されており、テキスト生成の場合、[`~generation.GenerationMixin`]（PyTorchモデル用）、[`~generation.TFGenerationMixin`]（TensorFlowモデル用）、および[`~generation.FlaxGenerationMixin`]（Flax/JAXモデル用）もあります。
## PreTrainedModel
[[autodoc]] PreTrainedModel
- push_to_hub
- all
<a id='from_pretrained-torch-dtype'></a>
### 大規模モデルの読み込み
Transformers 4.20.0では、[`~PreTrainedModel.from_pretrained`] メソッドが再設計され、[Accelerate](https://huggingface.co/docs/accelerate/big_modeling) を使用して大規模モデルを扱うことが可能になりました。これには Accelerate >= 0.9.0 と PyTorch >= 1.9.0 が必要です。以前の方法でフルモデルを作成し、その後事前学習の重みを読み込む代わりに（これにはメモリ内のモデルサイズが2倍必要で、ランダムに初期化されたモデル用と重み用の2つが必要でした）、モデルを空の外殻として作成し、事前学習の重みが読み込まれるときにパラメーターを実体化するオプションが追加されました。
このオプションは `low_cpu_mem_usage=True` で有効にできます。モデルはまず空の重みを持つメタデバイス上に作成され、その後状態辞書が内部に読み込まれます（シャードされたチェックポイントの場合、シャードごとに読み込まれます）。この方法で使用される最大RAMは、モデルの完全なサイズだけです。
```py
from transformers import AutoModelForSeq2SeqLM
t0pp = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", low_cpu_mem_usage=True)
```
さらに、モデルが完全にRAMに収まらない場合（現時点では推論のみ有効）、異なるデバイスにモデルを直接配置できます。`device_map="auto"` を使用すると、Accelerateは各レイヤーをどのデバイスに配置するかを決定し、最速のデバイス（GPU）を最大限に活用し、残りの部分をCPU、あるいはGPU RAMが不足している場合はハードドライブにオフロードします。モデルが複数のデバイスに分割されていても、通常どおり実行されます。
`device_map` を渡す際、`low_cpu_mem_usage` は自動的に `True` に設定されるため、それを指定する必要はありません。
```py
from transformers import AutoModelForSeq2SeqLM
t0pp = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", device_map="auto")
```
モデルがデバイス間でどのように分割されたかは、その `hf_device_map` 属性を見ることで確認できます:
```py
t0pp.hf_device_map
```
```python out
{'shared': 0,
'decoder.embed_tokens': 0,
'encoder': 0,
'decoder.block.0': 0,
'decoder.block.1': 1,
'decoder.block.2': 1,
'decoder.block.3': 1,
'decoder.block.4': 1,
'decoder.block.5': 1,
'decoder.block.6': 1,
'decoder.block.7': 1,
'decoder.block.8': 1,
'decoder.block.9': 1,
'decoder.block.10': 1,
'decoder.block.11': 1,
'decoder.block.12': 1,
'decoder.block.13': 1,
'decoder.block.14': 1,
'decoder.block.15': 1,
'decoder.block.16': 1,
'decoder.block.17': 1,
'decoder.block.18': 1,
'decoder.block.19': 1,
'decoder.block.20': 1,
'decoder.block.21': 1,
'decoder.block.22': 'cpu',
'decoder.block.23': 'cpu',
'decoder.final_layer_norm': 'cpu',
'decoder.dropout': 'cpu',
'lm_head': 'cpu'}
```
同じフォーマットに従って、独自のデバイスマップを作成することもできます（レイヤー名からデバイスへの辞書です）。モデルのすべてのパラメータを指定されたデバイスにマップする必要がありますが、1つのレイヤーが完全に同じデバイスにある場合、そのレイヤーのサブモジュールのすべてがどこに行くかの詳細を示す必要はありません。例えば、次のデバイスマップはT0ppに適しています（GPUメモリがある場合）:
```python
device_map = {"shared": 0, "encoder": 0, "decoder": 1, "lm_head": 1}
```
モデルのメモリへの影響を最小限に抑えるもう 1 つの方法は、低精度の dtype (`torch.float16` など) でモデルをインスタンス化するか、以下で説明する直接量子化手法を使用することです。
### Model Instantiation dtype
Pytorch では、モデルは通常 `torch.float32` 形式でインスタンス化されます。これは、しようとすると問題になる可能性があります
重みが fp16 にあるモデルをロードすると、2 倍のメモリが必要になるためです。この制限を克服するには、次のことができます。
`torch_dtype` 引数を使用して、目的の `dtype` を明示的に渡します。
```python
model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype=torch.float16)
```
または、モデルを常に最適なメモリ パターンでロードしたい場合は、特別な値 `"auto"` を使用できます。
そして、`dtype` はモデルの重みから自動的に導出されます。
```python
model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype="auto")
```
スクラッチからインスタンス化されたモデルには、どの `dtype` を使用するかを指示することもできます。
```python
config = T5Config.from_pretrained("t5")
model = AutoModel.from_config(config)
```
Pytorch の設計により、この機能は浮動小数点 dtype でのみ使用できます。
## ModuleUtilsMixin
[[autodoc]] modeling_utils.ModuleUtilsMixin
## TFPreTrainedModel
[[autodoc]] TFPreTrainedModel
- push_to_hub
- all
## TFModelUtilsMixin
[[autodoc]] modeling_tf_utils.TFModelUtilsMixin
## FlaxPreTrainedModel
[[autodoc]] FlaxPreTrainedModel
- push_to_hub
- all
## Pushing to the Hub
[[autodoc]] utils.PushToHubMixin
## Sharded checkpoints
[[autodoc]] modeling_utils.load_sharded_checkpoint
| transformers/docs/source/ja/main_classes/model.md/0 | {
"file_path": "transformers/docs/source/ja/main_classes/model.md",
"repo_id": "transformers",
"token_count": 3297
} |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Bark
## Overview
Bark は、[suno-ai/bark](https://github.com/suno-ai/bark) で Suno AI によって提案されたトランスフォーマーベースのテキスト読み上げモデルです。
Bark は 4 つの主要なモデルで構成されています。
- [`BarkSemanticModel`] ('テキスト'モデルとも呼ばれる): トークン化されたテキストを入力として受け取り、テキストの意味を捉えるセマンティック テキスト トークンを予測する因果的自己回帰変換モデル。
- [`BarkCoarseModel`] ('粗い音響' モデルとも呼ばれる): [`BarkSemanticModel`] モデルの結果を入力として受け取る因果的自己回帰変換器。 EnCodec に必要な最初の 2 つのオーディオ コードブックを予測することを目的としています。
- [`BarkFineModel`] ('微細音響' モデル)、今回は非因果的オートエンコーダー トランスフォーマーで、以前のコードブック埋め込みの合計に基づいて最後のコードブックを繰り返し予測します。
- [`EncodecModel`] からすべてのコードブック チャネルを予測したので、Bark はそれを使用して出力オーディオ配列をデコードします。
最初の 3 つのモジュールはそれぞれ、特定の事前定義された音声に従って出力サウンドを調整するための条件付きスピーカー埋め込みをサポートできることに注意してください。
### Optimizing Bark
Bark は、コードを数行追加するだけで最適化でき、**メモリ フットプリントが大幅に削減**され、**推論が高速化**されます。
#### Using half-precision
モデルを半精度でロードするだけで、推論を高速化し、メモリ使用量を 50% 削減できます。
```python
from transformers import BarkModel
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model = BarkModel.from_pretrained("suno/bark-small", torch_dtype=torch.float16).to(device)
```
#### Using 🤗 Better Transformer
Better Transformer は、内部でカーネル融合を実行する 🤗 最適な機能です。パフォーマンスを低下させることなく、速度を 20% ～ 30% 向上させることができます。モデルを 🤗 Better Transformer にエクスポートするのに必要なコードは 1 行だけです。
```python
model = model.to_bettertransformer()
```
この機能を使用する前に 🤗 Optimum をインストールする必要があることに注意してください。 [インストール方法はこちら](https://huggingface.co/docs/optimum/installation)
#### Using CPU offload
前述したように、Bark は 4 つのサブモデルで構成されており、オーディオ生成中に順番に呼び出されます。言い換えれば、1 つのサブモデルが使用されている間、他のサブモデルはアイドル状態になります。
CUDA デバイスを使用している場合、メモリ フットプリントの 80% 削減による恩恵を受ける簡単な解決策は、アイドル状態の GPU のサブモデルをオフロードすることです。この操作は CPU オフロードと呼ばれます。 1行のコードで使用できます。
```python
model.enable_cpu_offload()
```
この機能を使用する前に、🤗 Accelerate をインストールする必要があることに注意してください。 [インストール方法はこちら](https://huggingface.co/docs/accelerate/basic_tutorials/install)
#### Combining optimization techniques
最適化手法を組み合わせて、CPU オフロード、半精度、🤗 Better Transformer をすべて一度に使用できます。
```python
from transformers import BarkModel
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
# load in fp16
model = BarkModel.from_pretrained("suno/bark-small", torch_dtype=torch.float16).to(device)
# convert to bettertransformer
model = BetterTransformer.transform(model, keep_original_model=False)
# enable CPU offload
model.enable_cpu_offload()
```
推論最適化手法の詳細については、[こちら](https://huggingface.co/docs/transformers/perf_infer_gpu_one) をご覧ください。
### Tips
Suno は、多くの言語で音声プリセットのライブラリを提供しています [こちら](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c)。
これらのプリセットは、ハブ [こちら](https://huggingface.co/suno/bark-small/tree/main/speaker_embeddings) または [こちら](https://huggingface.co/suno/bark/tree/main/speaker_embeddings)。
```python
>>> from transformers import AutoProcessor, BarkModel
>>> processor = AutoProcessor.from_pretrained("suno/bark")
>>> model = BarkModel.from_pretrained("suno/bark")
>>> voice_preset = "v2/en_speaker_6"
>>> inputs = processor("Hello, my dog is cute", voice_preset=voice_preset)
>>> audio_array = model.generate(**inputs)
>>> audio_array = audio_array.cpu().numpy().squeeze()
```
Bark は、非常にリアルな **多言語** 音声だけでなく、音楽、背景ノイズ、単純な効果音などの他の音声も生成できます。
```python
>>> # Multilingual speech - simplified Chinese
>>> inputs = processor("惊人的！我会说中文")
>>> # Multilingual speech - French - let's use a voice_preset as well
>>> inputs = processor("Incroyable! Je peux générer du son.", voice_preset="fr_speaker_5")
>>> # Bark can also generate music. You can help it out by adding music notes around your lyrics.
>>> inputs = processor("♪ Hello, my dog is cute ♪")
>>> audio_array = model.generate(**inputs)
>>> audio_array = audio_array.cpu().numpy().squeeze()
```
このモデルは、笑う、ため息、泣くなどの**非言語コミュニケーション**を生成することもできます。
```python
>>> # Adding non-speech cues to the input text
>>> inputs = processor("Hello uh ... [clears throat], my dog is cute [laughter]")
>>> audio_array = model.generate(**inputs)
>>> audio_array = audio_array.cpu().numpy().squeeze()
```
オーディオを保存するには、モデル設定と scipy ユーティリティからサンプル レートを取得するだけです。
```python
>>> from scipy.io.wavfile import write as write_wav
>>> # save audio to disk, but first take the sample rate from the model config
>>> sample_rate = model.generation_config.sample_rate
>>> write_wav("bark_generation.wav", sample_rate, audio_array)
```
このモデルは、[Yoach Lacombe (ylacombe)](https://huggingface.co/ylacombe) および [Sanchit Gandhi (sanchit-gandhi)](https://github.com/sanchit-gandhi) によって提供されました。
元のコードは [ここ](https://github.com/suno-ai/bark) にあります。
## BarkConfig
[[autodoc]] BarkConfig
- all
## BarkProcessor
[[autodoc]] BarkProcessor
- all
- __call__
## BarkModel
[[autodoc]] BarkModel
- generate
- enable_cpu_offload
## BarkSemanticModel
[[autodoc]] BarkSemanticModel
- forward
## BarkCoarseModel
[[autodoc]] BarkCoarseModel
- forward
## BarkFineModel
[[autodoc]] BarkFineModel
- forward
## BarkCausalModel
[[autodoc]] BarkCausalModel
- forward
## BarkCoarseConfig
[[autodoc]] BarkCoarseConfig
- all
## BarkFineConfig
[[autodoc]] BarkFineConfig
- all
## BarkSemanticConfig
[[autodoc]] BarkSemanticConfig
- all
| transformers/docs/source/ja/model_doc/bark.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/bark.md",
"repo_id": "transformers",
"token_count": 3181
} |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# BLIP
## Overview
BLIP モデルは、[BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) で Junnan Li、Dongxu Li、Caiming Xiong、Steven Hoi によって提案されました。 。
BLIP は、次のようなさまざまなマルチモーダル タスクを実行できるモデルです。
- 視覚的な質問応答
- 画像とテキストの検索（画像とテキストのマッチング）
- 画像キャプション
論文の要約は次のとおりです。
*視覚言語事前トレーニング (VLP) により、多くの視覚言語タスクのパフォーマンスが向上しました。
ただし、既存の事前トレーニング済みモデルのほとんどは、理解ベースのタスクまたは世代ベースのタスクのいずれかでのみ優れています。さらに、最適ではない監視ソースである Web から収集されたノイズの多い画像とテキストのペアを使用してデータセットをスケールアップすることで、パフォーマンスの向上が大幅に達成されました。この論文では、視覚言語の理解と生成タスクの両方に柔軟に移行する新しい VLP フレームワークである BLIP を提案します。 BLIP は、キャプションをブートストラップすることでノイズの多い Web データを効果的に利用します。キャプショナーが合成キャプションを生成し、フィルターがノイズの多いキャプションを除去します。画像テキスト検索 (平均再現率 +2.7%@1)、画像キャプション作成 (CIDEr で +2.8%)、VQA ( VQA スコアは +1.6%)。 BLIP は、ゼロショット方式でビデオ言語タスクに直接転送した場合にも、強力な一般化能力を発揮します。コード、モデル、データセットがリリースされています。*
このモデルは [ybelkada](https://huggingface.co/ybelkada) によって提供されました。
元のコードは [ここ](https://github.com/salesforce/BLIP) にあります。
## Resources
- [Jupyter ノートブック](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) カスタム データセットの画像キャプション用に BLIP を微調整する方法
## BlipConfig
[[autodoc]] BlipConfig
- from_text_vision_configs
## BlipTextConfig
[[autodoc]] BlipTextConfig
## BlipVisionConfig
[[autodoc]] BlipVisionConfig
## BlipProcessor
[[autodoc]] BlipProcessor
## BlipImageProcessor
[[autodoc]] BlipImageProcessor
- preprocess
## BlipImageProcessorFast
[[autodoc]] BlipImageProcessorFast
- preprocess
<frameworkcontent>
<pt>
## BlipModel
[[autodoc]] BlipModel
- forward
- get_text_features
- get_image_features
## BlipTextModel
[[autodoc]] BlipTextModel
- forward
## BlipVisionModel
[[autodoc]] BlipVisionModel
- forward
## BlipForConditionalGeneration
[[autodoc]] BlipForConditionalGeneration
- forward
## BlipForImageTextRetrieval
[[autodoc]] BlipForImageTextRetrieval
- forward
## BlipForQuestionAnswering
[[autodoc]] BlipForQuestionAnswering
- forward
</pt>
<tf>
## TFBlipModel
[[autodoc]] TFBlipModel
- call
- get_text_features
- get_image_features
## TFBlipTextModel
[[autodoc]] TFBlipTextModel
- call
## TFBlipVisionModel
[[autodoc]] TFBlipVisionModel
- call
## TFBlipForConditionalGeneration
[[autodoc]] TFBlipForConditionalGeneration
- call
## TFBlipForImageTextRetrieval
[[autodoc]] TFBlipForImageTextRetrieval
- call
## TFBlipForQuestionAnswering
[[autodoc]] TFBlipForQuestionAnswering
- call
</tf>
</frameworkcontent> | transformers/docs/source/ja/model_doc/blip.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/blip.md",
"repo_id": "transformers",
"token_count": 1814
} |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# ConvBERT
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=convbert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-convbert-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/conv-bert-base">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
</div>
## Overview
ConvBERT モデルは、[ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) で Zihang Jiang、Weihao Yu、Daquan Zhou、Yunpeng Chen、Jiashi Feng、Shuicheng Yan によって提案されました。
やん。
論文の要約は次のとおりです。
*BERT やそのバリアントなどの事前トレーニング済み言語モデルは、最近、さまざまな環境で目覚ましいパフォーマンスを達成しています。
自然言語理解タスク。ただし、BERT はグローバルな自己注意ブロックに大きく依存しているため、問題が発生します。
メモリ使用量と計算コストが大きくなります。すべての注意が入力シーケンス全体に対してクエリを実行しますが、
グローバルな観点からアテンション マップを生成すると、一部のヘッドはローカルな依存関係のみを学習する必要があることがわかります。
これは、計算の冗長性が存在することを意味します。したがって、我々は、新しいスパンベースの動的畳み込みを提案します。
これらのセルフアテンション ヘッドを置き換えて、ローカルの依存関係を直接モデル化します。新しいコンボリューションヘッドと、
自己注意の頭を休め、グローバルとローカルの両方の状況でより効率的な新しい混合注意ブロックを形成します
学ぶ。この混合注意設計を BERT に装備し、ConvBERT モデルを構築します。実験でわかったことは、
ConvBERT は、トレーニング コストが低く、さまざまな下流タスクにおいて BERT およびその亜種よりも大幅に優れたパフォーマンスを発揮します。
モデルパラメータが少なくなります。注目すべきことに、ConvBERTbase モデルは 86.4 GLUE スコアを達成し、ELECTRAbase よりも 0.7 高いのに対し、
トレーニングコストは 1/4 未満です。コードと事前トレーニングされたモデルがリリースされます。*
このモデルは、[abhishek](https://huggingface.co/abhishek) によって提供されました。オリジナルの実装が見つかります
ここ: https://github.com/yitu-opensource/ConvBert
## Usage tips
ConvBERT トレーニングのヒントは BERT のヒントと似ています。使用上のヒントについては、[BERT ドキュメント](bert) を参照してください。
## Resources
- [テキスト分類タスクガイド](../tasks/sequence_classification)
- [トークン分類タスクガイド](../tasks/token_classification)
- [質問回答タスク ガイド](../tasks/question_answering)
- [マスクされた言語モデリング タスク ガイド](../tasks/masked_lang_modeling)
- [多肢選択タスク ガイド](../tasks/multiple_choice)
## ConvBertConfig
[[autodoc]] ConvBertConfig
## ConvBertTokenizer
[[autodoc]] ConvBertTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## ConvBertTokenizerFast
[[autodoc]] ConvBertTokenizerFast
<frameworkcontent>
<pt>
## ConvBertModel
[[autodoc]] ConvBertModel
- forward
## ConvBertForMaskedLM
[[autodoc]] ConvBertForMaskedLM
- forward
## ConvBertForSequenceClassification
[[autodoc]] ConvBertForSequenceClassification
- forward
## ConvBertForMultipleChoice
[[autodoc]] ConvBertForMultipleChoice
- forward
## ConvBertForTokenClassification
[[autodoc]] ConvBertForTokenClassification
- forward
## ConvBertForQuestionAnswering
[[autodoc]] ConvBertForQuestionAnswering
- forward
</pt>
<tf>
## TFConvBertModel
[[autodoc]] TFConvBertModel
- call
## TFConvBertForMaskedLM
[[autodoc]] TFConvBertForMaskedLM
- call
## TFConvBertForSequenceClassification
[[autodoc]] TFConvBertForSequenceClassification
- call
## TFConvBertForMultipleChoice
[[autodoc]] TFConvBertForMultipleChoice
- call
## TFConvBertForTokenClassification
[[autodoc]] TFConvBertForTokenClassification
- call
## TFConvBertForQuestionAnswering
[[autodoc]] TFConvBertForQuestionAnswering
- call
</tf>
</frameworkcontent>
| transformers/docs/source/ja/model_doc/convbert.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/convbert.md",
"repo_id": "transformers",
"token_count": 2155
} |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
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# DialoGPT
## Overview
DialoGPT は、[DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) で Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao,
Jianfeng Gao, Jingjing Liu, Bill Dolan.これは、から抽出された 147M 万の会話のようなやりとりでトレーニングされた GPT2 モデルです。
レディット。
論文の要約は次のとおりです。
*私たちは、大規模で調整可能なニューラル会話応答生成モデル DialoGPT (対話生成事前トレーニング済み) を紹介します。
変成器）。 Reddit のコメント チェーンから抽出された 1 億 4,700 万件の会話のようなやり取りを対象にトレーニングされました。
2005 年から 2017 年にかけて、DialoGPT は人間に近いパフォーマンスを達成するために Hugging Face PyTorch トランスフォーマーを拡張しました。
シングルターンダイアログ設定における自動評価と人間による評価の両方。会話システムが
DialoGPT を活用すると、強力なベースラインよりも関連性が高く、内容が充実し、コンテキストに一貫性のある応答が生成されます。
システム。神経反応の研究を促進するために、事前トレーニングされたモデルとトレーニング パイプラインが公開されています。
よりインテリジェントなオープンドメイン対話システムの生成と開発。*
元のコードは [ここ](https://github.com/microsoft/DialoGPT) にあります。
## Usage tips
- DialoGPT は絶対位置埋め込みを備えたモデルであるため、通常は入力を右側にパディングすることをお勧めします。
左よりも。
- DialoGPT は、会話データの因果言語モデリング (CLM) 目標に基づいてトレーニングされているため、強力です
オープンドメイン対話システムにおける応答生成時。
- DialoGPT を使用すると、[DialoGPT's model card](https://huggingface.co/microsoft/DialoGPT-medium) に示されているように、ユーザーはわずか 10 行のコードでチャット ボットを作成できます。
トレーニング：
DialoGPT をトレーニングまたは微調整するには、因果言語モデリング トレーニングを使用できます。公式論文を引用すると： *私たちは
OpenAI GPT-2に従って、マルチターン対話セッションを長いテキストとしてモデル化し、生成タスクを言語としてフレーム化します
モデリング。まず、ダイアログ セッション内のすべてのダイアログ ターンを長いテキスト x_1,..., x_N に連結します (N は
* 詳細については、元の論文を参照してください。
<Tip>
DialoGPT のアーキテクチャは GPT2 モデルに基づいています。API リファレンスと例については、[GPT2 のドキュメント ページ](openai-community/gpt2) を参照してください。
</Tip>
| transformers/docs/source/ja/model_doc/dialogpt.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/dialogpt.md",
"repo_id": "transformers",
"token_count": 1576
} |
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# Efficient Training on Multiple GPUs
単一のGPUでのトレーニングが遅すぎる場合や、モデルの重みが単一のGPUのメモリに収まらない場合、複数のGPUを使用したセットアップが必要となります。単一のGPUから複数のGPUへの切り替えには、ワークロードを分散するためのある種の並列処理が必要です。データ、テンソル、またはパイプラインの並列処理など、さまざまな並列処理技術があります。ただし、すべてに適した一つの解決策は存在せず、最適な設定は使用するハードウェアに依存します。この記事は、おそらく他のフレームワークにも適用される主要な概念に焦点を当てつつ、PyTorchベースの実装に焦点を当てています。
<Tip>
**注意**: [単一GPUセクション](perf_train_gpu_one) で紹介された多くの戦略（混合精度トレーニングや勾配蓄積など）は一般的であり、モデルのトレーニングに一般的に適用されます。したがって、マルチGPUやCPUトレーニングなどの次のセクションに入る前に、それを確認してください。
</Tip>
まず、さまざまな1D並列処理技術とその利点および欠点について詳しく説明し、それらを2Dおよび3D並列処理に組み合わせてさらに高速なトレーニングを実現し、より大きなモデルをサポートする方法を検討します。さまざまな他の強力な代替手法も紹介されます。
## Concepts
以下は、この文書で後で詳しく説明される主要な概念の簡単な説明です。
1. **DataParallel (DP)** - 同じセットアップが複数回複製され、各セットアップにデータのスライスが供給されます。処理は並行して行われ、各セットアップはトレーニングステップの最後に同期されます。
2. **TensorParallel (TP)** - 各テンソルは複数のチャンクに分割され、単一のGPUにテンソル全体が存在するのではなく、テンソルの各シャードが指定されたGPUに存在します。処理中に、各シャードは別々に並行して処理され、異なるGPUで同期され、ステップの最後に結果が同期されます。これは水平並列処理と呼ばれるもので、分割は水平レベルで行われます。
3. **PipelineParallel (PP)** - モデルは垂直（レイヤーレベル）に複数のGPUに分割され、モデルの単一または複数のレイヤーが単一のGPUに配置されます。各GPUはパイプラインの異なるステージを並行して処理し、バッチの小さなチャンクで作業します。
4. **Zero Redundancy Optimizer (ZeRO)** - TPといくらか似たようなテンソルのシャーディングを実行しますが、前向きまたは後向きの計算のためにテンソル全体が再構築されるため、モデルを変更する必要はありません。また、GPUメモリが制限されている場合に補償するためのさまざまなオフロード技術をサポートします。
5. **Sharded DDP** - Sharded DDPは、さまざまなZeRO実装で使用される基本的なZeROコンセプトの別名です。
各コンセプトの詳細に深入りする前に、大規模なインフラストラクチャで大規模なモデルをトレーニングする際の大まかな決定プロセスを見てみましょう。
## Scalability Strategy
**⇨ シングルノード / マルチGPU**
* モデルが単一のGPUに収まる場合：
1. DDP - 分散データ並列
2. ZeRO - 状況と使用される構成に応じて速いかどうかが異なります
* モデルが単一のGPUに収まらない場合：
1. PP
2. ZeRO
3. TP
非常に高速なノード内接続（NVLINKまたはNVSwitchなど）があれば、これらの3つはほぼ同じ速度になるはずで、これらがない場合、PPはTPまたはZeROよりも速くなります。TPの程度も差を生じるかもしれません。特定のセットアップでの勝者を見つけるために実験することが最善です。
TPはほとんどの場合、単一ノード内で使用されます。つまり、TPサイズ <= ノードごとのGPU数です。
* 最大のレイヤーが単一のGPUに収まらない場合：
1. ZeROを使用しない場合 - TPを使用する必要があります。PP単独では収まらないでしょう。
2. ZeROを使用する場合 - "シングルGPU"のエントリと同じものを参照してください
**⇨ マルチノード / マルチGPU**
* ノード間の高速接続がある場合：
1. ZeRO - モデルへのほとんどの変更が不要です
2. PP+TP+DP - 通信が少なく、モデルへの大規模な変更が必要です
* ノード間の接続が遅く、GPUメモリがまだ不足している場合：
1. DP+PP+TP+ZeRO-1
## Data Parallelism
2つのGPUを持つほとんどのユーザーは、`DataParallel`（DP）と`DistributedDataParallel`（DDP）によって提供されるトレーニング速度の向上をすでに享受しています。これらはほぼ自明に使用できるPyTorchの組み込み機能です。一般的に、すべてのモデルで動作するDDPを使用することをお勧めします。DPは一部のモデルで失敗する可能性があるためです。[PyTorchのドキュメンテーション](https://pytorch.org/docs/master/generated/torch.nn.DataParallel.html)自体もDDPの使用を推奨しています。
### DP vs DDP
`DistributedDataParallel`（DDP）は通常、`DataParallel`（DP）よりも高速ですが、常にそうとは限りません：
* DPはPythonスレッドベースですが、DDPはマルチプロセスベースです。そのため、GIL（Global Interpreter Lock）などのPythonスレッドの制約がないためです。
* 一方、GPUカード間の遅い相互接続性は、DDPの場合に実際には遅い結果をもたらす可能性があります。
以下は、2つのモード間のGPU間通信の主な違いです：
[DDP](https://pytorch.org/docs/master/notes/ddp.html):
- 開始時、メインプロセスはモデルをGPU 0から他のGPUに複製します。
- それから各バッチごとに:
1. 各GPUは各自のミニバッチのデータを直接消費します。
2. `backward`中、ローカル勾配が準備できると、それらはすべてのプロセスで平均化されます。
[DP](https://pytorch.org/docs/master/generated/torch.nn.DataParallel.html):
各バッチごとに:
1. GPU 0はデータバッチを読み取り、それから各GPUにミニバッチを送信します。
2. GPU 0から各GPUに最新のモデルを複製します。
3. `forward`を実行し、各GPUからGPU 0に出力を送信し、損失を計算します。
4. GPU 0からすべてのGPUに損失を分散し、`backward`を実行します。
5. 各GPUからGPU 0に勾配を送信し、それらを平均化します。
DDPはバッチごとに行う通信は勾配の送信のみであり、一方、DPはバッチごとに5つの異なるデータ交換を行います。
DPはプロセス内でデータをPythonスレッドを介してコピーしますが、DDPは[torch.distributed](https://pytorch.org/docs/master/distributed.html)を介してデータをコピーします。
DPではGPU 0は他のGPUよりもはるかに多くの作業を行うため、GPUの未使用率が高くなります。
DDPは複数のマシン間で使用できますが、DPの場合はそうではありません。
DPとDDPの他にも違いがありますが、この議論には関係ありません。
これら2つのモードを深く理解したい場合、この[記事](https://www.telesens.co/2019/04/04/distributed-data-parallel-training-using-pytorch-on-aws/)を強くお勧めします。素晴らしいダイアグラムを含み、さまざまなハードウェアでの複数のベンチマークとプロファイラの出力を示し、知っておく必要があるすべての微妙なニュアンスを説明しています。
実際のベンチマークを見てみましょう：
| Type | NVlink | Time |
| :----- | ----- | ---: |
| 2:DP | Y | 110s |
| 2:DDP | Y | 101s |
| 2:DDP | N | 131s |
解析：
ここで、DPはNVlinkを使用したDDPに比べて約10％遅く、NVlinkを使用しないDDPに比べて約15％高速であることが示されています。
実際の違いは、各GPUが他のGPUと同期する必要があるデータの量に依存します。同期するデータが多いほど、遅いリンクが合計の実行時間を遅くする可能性が高くなります。
以下は完全なベンチマークコードと出力です：
`NCCL_P2P_DISABLE=1`を使用して、対応するベンチマークでNVLink機能を無効にしました。
```bash
# DP
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
python examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 110.5948, 'train_samples_per_second': 1.808, 'epoch': 0.69}
# DDP w/ NVlink
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 101.9003, 'train_samples_per_second': 1.963, 'epoch': 0.69}
# DDP w/o NVlink
rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \
torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 131.4367, 'train_samples_per_second': 1.522, 'epoch': 0.69}
```
ハードウェア: 2x TITAN RTX、各24GB + 2つのNVLink（`nvidia-smi topo -m`で `NV2`）
ソフトウェア: `pytorch-1.8-to-be` + `cuda-11.0` / `transformers==4.3.0.dev0`
## ZeRO Data Parallelism
ZeROパワードデータ並列処理（ZeRO-DP）は、次の[ブログ投稿](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/)のダイアグラムで説明されています。
これは理解が難しいかもしれませんが、実際にはこの概念は非常にシンプルです。これは通常の`DataParallel`（DP）ですが、完全なモデルパラメータ、勾配、およびオプティマイザの状態を複製する代わりに、各GPUはそれぞれのスライスのみを保存します。そして、実行時に、特定のレイヤーに必要な完全なレイヤーパラメータが必要な場合、すべてのGPUが同期して、お互いに不足している部分を提供します。それがすべてです。
3つのレイヤーからなる単純なモデルを考えてみましょう。各レイヤーには3つのパラメータがあります：
```
La | Lb | Lc
---|----|---
a0 | b0 | c0
a1 | b1 | c1
a2 | b2 | c2
```
レイヤーLaには、重みa0、a1、およびa2があります。
3つのGPUがある場合、Sharded DDP（= Zero-DP）はモデルを3つのGPUに次のように分割します：
```
GPU0:
La | Lb | Lc
---|----|---
a0 | b0 | c0
GPU1:
La | Lb | Lc
---|----|---
a1 | b1 | c1
GPU2:
La | Lb | Lc
---|----|---
a2 | b2 | c2
```
これは、典型的なディープニューラルネットワーク（DNN）のダイアグラムを想像すると、テンソル並列処理と同様の水平スライスであるようなものです。垂直スライスは、異なるGPUに完全な層グループを配置する方法です。しかし、これは単なる出発点に過ぎません。
これから、各GPUは通常のデータ並列処理（DP）と同様に、通常のミニバッチを受け取ります：
```
x0 => GPU0
x1 => GPU1
x2 => GPU2
```
最初に、入力データはレイヤーLaに適用されます。
GPU0に焦点を当てましょう：x0は、その前向きパスを実行するためにa0、a1、a2のパラメータが必要ですが、GPU0にはa0しかありません。GPU1からa1を、GPU2からa2を受け取り、モデルの各部分をまとめます。
同様に、GPU1はミニバッチx1を受け取り、a1しか持っていませんが、a0とa2のパラメータが必要です。これらはGPU0とGPU2から取得します。
GPU2もx2を受け取ります。a0とa1はGPU0とGPU1から受け取り、a2とともに完全なテンソルを再構築します。
3つのGPUは完全なテンソルを再構築し、前向き計算が行われます。
計算が完了すると、不要になったデータは削除されます。計算中だけ使用され、再構築は事前にフェッチを使用して効率的に行われます。
そして、このプロセス全体がレイヤーLb、次に前向きでLc、そして逆方向でLc -> Lb -> Laに対して繰り返されます。
私にとって、これは効率的なグループでの重みの分散戦略のように聞こえます：
1. 人Aはテントを持っています。
2. 人Bはストーブを持っています。
3. 人Cは斧を持っています。
今、彼らは毎晩持っているものを共有し、他の人から持っていないものをもらい、朝には割り当てられたタイプのギアを詰めて旅を続けます。これがSharded DDP / Zero DPです。
この戦略を、各人が独自のテント、ストーブ、斧を持って運ばなければならないシンプルな戦略と比較してみてください。これがPyTorchのDataParallel（DPおよびDDP）です。
このトピックの文献を読む際に、以下の類義語に出会うかもしれません：Sharded、Partitioned。
ZeROがモデルの重みを分割する方法に注意を払うと、これはテンソルパラレリズムと非常に似ているように見えます。これは後で議論される垂直モデルパラレリズムとは異なり、各レイヤーの重みをパーティション/シャーディングします。
Implementations:
- [DeepSpeed](https://www.deepspeed.ai/tutorials/zero/) ZeRO-DP stages 1+2+3
- [`transformers` integration](main_classes/trainer#trainer-integrations)
## Naive Model Parallelism (Vertical) and Pipeline Parallelism
ナイーブモデルパラレリズム（MP）は、モデルの層を複数のGPUに分散させる方法です。このメカニズムは比較的単純で、希望する層を`.to()`メソッドを使用して特定のデバイスに切り替えるだけです。これにより、データがこれらの層を通過するたびに、データも層と同じデバイスに切り替えられ、残りの部分は変更されません。
私たちはこれを「垂直MP」と呼びます。なぜなら、ほとんどのモデルがどのように描かれるかを思い出すと、層を垂直にスライスするからです。たとえば、以下の図は8層のモデルを示しています：
```
=================== ===================
| 0 | 1 | 2 | 3 | | 4 | 5 | 6 | 7 |
=================== ===================
gpu0 gpu1
```
我々は、モデルを垂直に2つに分割し、レイヤー0から3をGPU0に配置し、レイヤー4から7をGPU1に配置しました。
データがレイヤー0から1、1から2、2から3に移動する間は通常のモデルと同じです。しかし、データがレイヤー3からレイヤー4に移動する必要がある場合、GPU0からGPU1への移動が発生し、通信のオーバーヘッドが発生します。参加しているGPUが同じコンピュートノード（例：同じ物理マシン）にある場合、このコピーは非常に高速ですが、異なるコンピュートノード（例：複数のマシン）にある場合、通信のオーバーヘッドは大幅に増加する可能性があります。
その後、レイヤー4から5、6から7までは通常のモデルと同様に動作し、7番目のレイヤーが完了すると、データをしばしばレイヤー0に戻す必要があります（またはラベルを最後のレイヤーに送信します）。これで損失を計算し、オプティマイザが作業を開始できます。
問題点：
- 主な欠点、およびなぜこれを「単純な」MPと呼ぶのかは、1つを除いてすべてのGPUがどんな瞬間でもアイドル状態であることです。したがって、4つのGPUを使用する場合、単純なMPは、1つのGPUのメモリ容量を4倍にするのとほぼ同じであり、ハードウェアの残りを無視します。さらに、データのコピーのオーバーヘッドがあることを忘れてはいけません。したがって、4枚の6GBのカードは、データのコピーのオーバーヘッドがない1枚の24GBのカードと同じサイズを収容できるでしょうが、後者はトレーニングをより迅速に完了します。ただし、たとえば40GBのカードがあり、45GBのモデルを収める必要がある場合、勾配とオプティマイザの状態のためにほとんど収めることができません。
- 共有の埋め込みは、GPU間でコピーする必要があるかもしれません。
パイプライン並列処理（PP）は、ほぼ単純なMPと同じですが、GPUがアイドル状態になる問題を解決し、入力バッチをマイクロバッチに分割し、パイプラインを人工的に作成することにより、異なるGPUが計算プロセスに同時に参加できるようにします。
以下は、[GPipe論文](https://ai.googleblog.com/2019/03/introducing-gpipe-open-source-library.html)からの図で、上部には単純なMP、下部にはPPが示されています：
この図から、PPがGPUがアイドル状態の領域である「バブル」を少なく持つことがわかります。アイドル状態の部分は「バブル」と呼ばれます。
図の両方の部分は、4つのGPUがパイプラインに参加している4の次元の並列性を示しています。つまり、4つのパイプステージF0、F1、F2、F3のフォワードパスがあり、逆順のバックワードパスB3、B2、B1、B0があります。
PPは調整する新しいハイパーパラメータを導入します。それは `chunks` で、同じパイプステージを通じて連続して送信されるデータのチャンクの数を定義します。たとえば、下の図では `chunks=4` が表示されています。GPU0はチャンク0、1、2、3（F0,0、F0,1、F0,2、F0,3）で同じフォワードパスを実行し、他のGPUが作業を開始し始めるのを待ってから、GPU0はチャンク3、2、1、0（B0,3、B0,2、B0,1、B0,0）で逆順パスを実行します。
注意すべきは、概念的にはこれが勾配蓄積ステップ（GAS）と同じコンセプトであることです。PyTorchは `chunks` を使用し、DeepSpeedは同じハイパーパラメータをGASと呼びます。
`chunks` の導入により、PPはマイクロバッチ（MBS）の概念を導入します。DPはグローバルデータバッチサイズをミニバッチに分割します。したがって、DPの次数が4で、グローバルバッチサイズが1024の場合、4つのミニバッチ（それぞれ256）に分割されます（1024/4）。そして、`chunks`（またはGAS）の数が32である場合、マイクロバッチサイズは8になります（256/32）。各パイプラインステージは1つのマイクロバッチで作業します。
DP + PPセットアップのグローバルバッチサイズを計算するには、`mbs*chunks*dp_degree`（`8*32*4=1024`）を行います。
図に戻りましょう。
`chunks=1` であれば、非効率な単純なMPになります。非常に大きな `chunks` 値を使用すると、非常に小さなマイクロバッチサイズになり、効率があまり高くないかもしれません。したがって、GPUの効率的な利用を最大化する値を見つけるために実験する必要があります。これは、バブルのサイズを最小限にすることに対応する、すべての参加GPUにわたる高い並行GPU利用を可能にするためです。
2つのソリューショングループがあります。従来のパイプラインAPIソリューションと、ユーザーのモデルを大幅に変更する必要があるより現代的なソリューションです。
従来のパイプラインAPIソリューション：
- PyTorch
- DeepSpeed
- Megatron-LM
現代的なソリューション：
- Varuna
- Sagemaker
従来のパイプラインAPIソリューションの問題点：
- モデルをかなり変更する必要があるため、Pipelineはモジュールの通常のフローを`nn.Sequential`シーケンスに再書き込む必要があり、モデルの設計を変更することが必要です。
- 現在、Pipeline APIは非常に制限的です。最初のパイプラインステージに渡されるPython変数のセットがある場合、回避策を見つける必要があります。現在、パイプラインインターフェースでは、唯一のテンソルまたはテンソルのタプルを入力と出力として要求しています。これらのテンソルはバッチサイズを最初の次元として持っている必要があります。パイプラインはミニバッチをマイクロバッチに分割します。可能な改善点については、こちらの議論が行われています：https://github.com/pytorch/pytorch/pull/50693
- パイプステージのレベルでの条件付き制御フローは不可能です。例えば、T5のようなエンコーダーデコーダーモデルは、条件付きエンコーダーステージを処理するために特別な回避策が必要です。
- 各レイヤーを配置する必要があるため、1つのモデルの出力が他のモデルの入力になるようにします。
VarunaとSageMakerとの実験はまだ行っていませんが、彼らの論文によれば、上記で述べた問題のリストを克服し、ユーザーのモデルにははるかに小さな変更しか必要としないと報告されています。
実装：
- [Pytorch](https://pytorch.org/docs/stable/pipeline.html) (initial support in pytorch-1.8, and progressively getting improved in 1.9 and more so in 1.10). Some [examples](https://github.com/pytorch/pytorch/blob/master/benchmarks/distributed/pipeline/pipe.py)
- [DeepSpeed](https://www.deepspeed.ai/tutorials/pipeline/)
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) has an internal implementation - no API.
- [Varuna](https://github.com/microsoft/varuna)
- [SageMaker](https://arxiv.org/abs/2111.05972) - this is a proprietary solution that can only be used on AWS.
- [OSLO](https://github.com/tunib-ai/oslo) - この実装は、Hugging Face Transformersに基づいています。
🤗 Transformersのステータス: この執筆時点では、いずれのモデルも完全なPP（パイプライン並列処理）をサポートしていません。GPT2モデルとT5モデルは単純なMP（モデル並列処理）サポートを持っています。主な障害は、モデルを`nn.Sequential`に変換できず、すべての入力がテンソルである必要があることです。現在のモデルには、変換を非常に複雑にする多くの機能が含まれており、これらを削除する必要があります。
他のアプローチ：
DeepSpeed、Varuna、およびSageMakerは、[交互にパイプラインを実行](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html)するコンセプトを使用しています。ここでは、バックワードパスを優先させてバブル（アイドル時間）をさらに最小限に抑えます。
Varunaは、最適なスケジュールを発見するためにシミュレーションを使用してスケジュールをさらに改善しようとします。
OSLOは、`nn.Sequential`の変換なしでTransformersに基づくパイプライン並列処理を実装しています。
## Tensor Parallelism
テンソル並列処理では、各GPUがテンソルのスライスのみを処理し、全体が必要な操作のためにのみ完全なテンソルを集約します。
このセクションでは、[Megatron-LM](https://github.com/NVIDIA/Megatron-LM)論文からのコンセプトと図を使用します：[GPUクラスタでの効率的な大規模言語モデルトレーニング](https://arxiv.org/abs/2104.04473)。
どのトランスフォーマの主要な構築要素は、完全に接続された`nn.Linear`に続く非線形アクティベーション`GeLU`です。
Megatronの論文の表記法に従って、行列の乗算部分を`Y = GeLU(XA)`と書くことができます。ここで、`X`と`Y`は入力ベクトルと出力ベクトルで、`A`は重み行列です。
行列の計算を行列形式で見ると、行列乗算を複数のGPUで分割できる方法が簡単に理解できます：
重み行列`A`を`N`個のGPUに対して列ごとに分割し、並列で行列乗算`XA_1`から`XA_n`を実行すると、`N`個の出力ベクトル`Y_1、Y_2、...、Y_n`が得られ、それらを独立して`GeLU`に供給できます：
この原理を使用して、最後まで同期が必要ないまま、任意の深さのMLPを更新できます。Megatron-LMの著者はそのための有用なイラストを提供しています：
マルチヘッドアテンションレイヤーを並列化することはさらに簡単です。それらは既に複数の独立したヘッドを持っているため、本質的に並列です！
特別な考慮事項：TPには非常に高速なネットワークが必要であり、したがって1つのノードを超えてTPを実行しないことがお勧めされません。実際には、1つのノードに4つのGPUがある場合、最大のTP度数は4です。TP度数8が必要な場合は、少なくとも8つのGPUを持つノードを使用する必要があります。
このセクションは、元のより詳細な[TPの概要](https://github.com/huggingface/transformers/issues/10321#issuecomment-783543530)に基づいています。
by [@anton-l](https://github.com/anton-l)。
SageMakerは、より効率的な処理のためにTPとDPを組み合わせて使用します。
代替名：
- [DeepSpeed](https://github.com/deepspeedai/DeepSpeed)はこれを「テンソルスライシング」と呼びます。詳細は[DeepSpeedの特徴](https://www.deepspeed.ai/training/#model-parallelism)をご覧ください。
実装例:
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)には、モデル固有の内部実装があります。
- [parallelformers](https://github.com/tunib-ai/parallelformers)（現時点では推論のみ）。
- [SageMaker](https://arxiv.org/abs/2111.05972) - これはAWSでのみ使用できるプロプライエタリなソリューションです。
- [OSLO](https://github.com/tunib-ai/oslo)には、Transformersに基づいたテンソル並列実装があります。
🤗 Transformersの状況:
- コア: まだコアには実装されていません。
- ただし、推論が必要な場合、[parallelformers](https://github.com/tunib-ai/parallelformers)はほとんどのモデルに対してサポートを提供します。これがコアに実装されるまで、これを使用できます。そして、トレーニングモードもサポートされることを期待しています。
- Deepspeed-Inferenceでは、BERT、GPT-2、およびGPT-NeoモデルをCUDAカーネルベースの高速推論モードでサポートしています。詳細は[こちら](https://www.deepspeed.ai/tutorials/inference-tutorial/)をご覧ください。
## DP+PP
DeepSpeedの[パイプラインチュートリアル](https://www.deepspeed.ai/tutorials/pipeline/)からの次の図は、DPをPPと組み合わせる方法を示しています。
ここで重要なのは、DPランク0がGPU2を見えなくし、DPランク1がGPU3を見えなくすることです。DPにとって、存在するのはGPU 0 と 1 のみで、それらの2つのGPUのようにデータを供給します。GPU0はPPを使用してGPU2に一部の負荷を「秘密裏に」オフロードし、GPU1も同様にGPU3を支援に引き入れます。
各次元には少なくとも2つのGPUが必要ですので、ここでは少なくとも4つのGPUが必要です。
実装例:
- [DeepSpeed](https://github.com/deepspeedai/DeepSpeed)
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
- [Varuna](https://github.com/microsoft/varuna)
- [SageMaker](https://arxiv.org/abs/2111.05972)
- [OSLO](https://github.com/tunib-ai/oslo)
🤗 Transformersの状況: まだ実装されていません
## DP+PP+TP
さらに効率的なトレーニングを行うために、3Dパラレリズムを使用し、PPをTPとDPと組み合わせます。これは次の図で示されています。
この図は[3Dパラレリズム：兆パラメータモデルへのスケーリング](https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/)というブログ投稿から取得されたもので、おすすめの読み物です。
各次元には少なくとも2つのGPUが必要ですので、ここでは少なくとも8つのGPUが必要です。
実装例:
- [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) - DeepSpeedには、さらに効率的なDPであるZeRO-DPと呼ばれるものも含まれています。
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
- [Varuna](https://github.com/microsoft/varuna)
- [SageMaker](https://arxiv.org/abs/2111.05972)
- [OSLO](https://github.com/tunib-ai/oslo)
🤗 Transformersの状況: まだ実装されていません。PPとTPがないため。
## ZeRO DP+PP+TP
DeepSpeedの主要な機能の1つはZeROで、これはDPの拡張機能です。これについてはすでに「ZeROデータ並列化」で説明されています。通常、これは単独で動作する機能で、PPやTPは必要ありません。しかし、PPとTPと組み合わせることもできます。
ZeRO-DPがPPと組み合わされる場合、通常はZeROステージ1（オプティマイザーシャーディング）のみが有効になります。
ZeROステージ2（勾配シャーディング）をパイプライン並列化と組み合わせて使用する理論的な可能性はありますが、性能に悪影響を及ぼします。各マイクロバッチごとに勾配をシャーディングする前に、勾配を集約するための追加のリダクションスキャッター集計が必要で、通信オーバーヘッドが発生する可能性があります。パイプライン並列化の性質上、小さなマイクロバッチが使用され、計算の集中度（マイクロバッチサイズ）をバランスにかけ、パイプラインバブル（マイクロバッチ数）を最小限に抑えることに焦点が当てられています。したがって、これらの通信コストは影響を及ぼすでしょう。
さらに、PPには通常よりも少ない層が含まれており、メモリの節約はそれほど大きくありません。PPは既に勾配サイズを「1/PP」に削減するため、勾配シャーディングの節約は純粋なDPよりもはるかに重要ではありません。
ZeROステージ3も同様の理由で適していません - より多くのノード間通信が必要です。
そして、ZeROを持っているので、もう一つの利点はZeRO-Offloadです。これはステージ1オプティマイザーステートをCPUにオフロードできます。
実装例:
- [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed)と[BigScienceからのMegatron-Deepspeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed)は、前者のリポジトリのフォークです。
- [OSLO](https://github.com/tunib-ai/oslo)
重要な論文:
- [DeepSpeedとMegatronを使用したMegatron-Turing NLG 530Bのトレーニング](https://arxiv.org/abs/2201.11990)
🤗 Transformersの状況: まだ実装されていません。PPとTPがないため。
## FlexFlow
[FlexFlow](https://github.com/flexflow/FlexFlow)は、わずかに異なるアプローチで並列化の問題を解決します。
論文: [Zhihao Jia、Matei Zaharia、Alex Aikenによる "Deep Neural Networksのデータとモデルの並列化を超えて"](https://arxiv.org/abs/1807.05358)
FlexFlowは、サンプル-オペレータ-属性-パラメータの4D並列化を行います。
1. サンプル = データ並列化（サンプル単位の並列化）
2. オペレータ = 単一の操作をいくつかのサブ操作に並列化
3. 属性 = データ並列化（長さ方向の並列化）
4. パラメータ = モデル並列化（次元に関係なく、水平または垂直）
例:
* サンプル
シーケンス長512の10バッチを考えてみましょう。これらをサンプル次元で2つのデバイスに並列化すると、10 x 512が5 x 2 x 512になります。
* オペレータ
層正規化を行う場合、まずstdを計算し、次にmeanを計算し、データを正規化できます。オペレータの並列化により、stdとmeanを並列に計算できます。したがって、オペレータ次元で2つのデバイス（cuda:0、cuda:1）に並列化すると、最初に入力データを両方のデバイスにコピーし、cuda:0でstdを計算し、cuda:1でmeanを同時に計算します。
* 属性
10バッチの512長があります。これらを属性次元で2つのデバイスに並列化すると、10 x 512が10 x 2 x 256になります。
* パラメータ
これはテンソルモデルの並列化または単純な層ごとのモデルの並列化と似ています。
このフレームワークの重要性は、（1）GPU/TPU/CPU対（2）RAM/DRAM対（3）高速内部接続/低速外部接続などのリソースを取り、これらすべてをアルゴリズムによって自動的に最適化することです。どの並列化をどこで使用するかをアルゴリズム的に決定します。
非常に重要な側面の1つは、FlexFlowは静的で固定のワークロードを持つモデルのために設計されており、動的な動作を持つモデルはイテレーションごとに異なる並列化戦略を好む場合があることです。
したがって、このフレームワークの約束は非常に魅力的です。選択したクラスタで30分間のシミュレーションを実行し、この特定の環境を最適に利用するための最良の戦略を提供します。部分を追加/削除/置換すると、それに対して実行して再最適化プランを作成します。その後、トレーニングできます。異なるセットアップには独自の最適化があります。
🤗 Transformersの現在の状況: まだ統合されていません。すでに[transformers.utils.fx](https://github.com/huggingface/transformers/blob/master/src/transformers/utils/fx.py)を使用してモデルがFXトレース可能であるため、FlexFlowを動作させるために必要な手順を誰かが見つける必要があります。
## Which Strategy To Use When
ここでは、どの並列化戦略をいつ使用するかの非常におおまかなアウトラインを示します。各リストの最初が通常よりも速いことが一般的です。
**⇨ 単一GPU**
* モデルが単一GPUに収まる場合：
1. 通常の使用
* モデルが単一GPUに収まらない場合：
1. ZeRO + CPUをオフロードし、オプションでNVMeをオフロード
2. 上記に加えて、最大のレイヤーが単一GPUに収まらない場合、[Memory Centric Tiling](https://deepspeed.readthedocs.io/en/latest/zero3.html#memory-centric-tiling)（詳細は以下参照）を有効化
* 最大のレイヤーが単一GPUに収まらない場合：
1. ZeROを使用しない場合 - TPを有効化する必要があります。なぜなら、PPだけでは収めることができないからです。
2. ZeROを使用する場合は、上記の「単一GPU」のエントリと同じものを参照してください
**⇨ 単一ノード/マルチGPU**
* モデルが単一GPUに収まる場合：
1. DDP - 分散データ並列
2. ZeRO - 状況と使用される構成に依存して速いかどうかが異なることがあります
* モデルが単一GPUに収まらない場合：
1. PP
2. ZeRO
3. TP
非常に高速なノード内接続がNVLINKまたはNVSwitchである場合、これらのすべてはほとんど同等の性能です。これらがない場合、PPはTPまたはZeROよりも速くなります。TPの度合いも違いを生じるかもしれません。特定のセットアップで勝者を見つけるために実験するのが最善です。
TPはほとんど常に単一ノード内で使用されます。つまり、TPサイズ <= ノードあたりのGPUです。
* 最大のレイヤーが単一GPUに収まらない場合：
1. ZeROを使用しない場合 - TPを使用する必要があります。なぜなら、PPだけでは収めることができないからです。
2. ZeROを使用する場合は、上記の「単一GPU」のエントリと同じものを参照してください
**⇨ マルチノード/マルチGPU**
* 高速なノード間接続がある場合：
1. ZeRO - モデルへのほとんどの変更が不要です
2. PP+TP+DP - 通信が少なく、モデルに大規模な変更が必要です
* 遅いノード間接続があり、GPUメモリが少ない場合：
1. DP+PP+TP+ZeRO-1
| transformers/docs/source/ja/perf_train_gpu_many.md/0 | {
"file_path": "transformers/docs/source/ja/perf_train_gpu_many.md",
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# Automatic speech recognition
[[open-in-colab]]
<Youtube id="TksaY_FDgnk"/>
自動音声認識 (ASR) は音声信号をテキストに変換し、一連の音声入力をテキスト出力にマッピングします。 Siri や Alexa などの仮想アシスタントは ASR モデルを使用してユーザーを日常的に支援しており、ライブキャプションや会議中のメモ取りなど、他にも便利なユーザー向けアプリケーションが数多くあります。
このガイドでは、次の方法を説明します。
1. [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) データセットの [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) を微調整して、音声をテキストに書き起こします。
2. 微調整したモデルを推論に使用します。
<Tip>
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/automatic-speech-recognition) を確認することをお勧めします。
</Tip>
始める前に、必要なライブラリがすべてインストールされていることを確認してください。
```bash
pip install transformers datasets evaluate jiwer
```
モデルをアップロードしてコミュニティと共有できるように、Hugging Face アカウントにログインすることをお勧めします。プロンプトが表示されたら、トークンを入力してログインします。
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load MInDS-14 dataset
まず、🤗 データセット ライブラリから [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) データセットの小さいサブセットをロードします。これにより、完全なデータセットのトレーニングにさらに時間を費やす前に、実験してすべてが機能することを確認する機会が得られます。
```py
>>> from datasets import load_dataset, Audio
>>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train[:100]")
```
[`~Dataset.train_test_split`] メソッドを使用して、データセットの `train` 分割をトレイン セットとテスト セットに分割します。
```py
>>> minds = minds.train_test_split(test_size=0.2)
```
次に、データセットを見てみましょう。
```py
>>> minds
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
num_rows: 16
})
test: Dataset({
features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
num_rows: 4
})
})
```
データセットには`lang_id`や`english_transcription`などの多くの有用な情報が含まれていますが、このガイドでは「`audio`」と「`transciption`」に焦点を当てます。 [`~datasets.Dataset.remove_columns`] メソッドを使用して他の列を削除します。
```py
>>> minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"])
```
もう一度例を見てみましょう。
```py
>>> minds["train"][0]
{'audio': {'array': array([-0.00024414, 0. , 0. , ..., 0.00024414,
0.00024414, 0.00024414], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'sampling_rate': 8000},
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
```
次の 2 つのフィールドがあります。
- `audio`: 音声ファイルをロードしてリサンプリングするために呼び出す必要がある音声信号の 1 次元の `array`。
- `transcription`: ターゲットテキスト。
## Preprocess
次のステップでは、Wav2Vec2 プロセッサをロードしてオーディオ信号を処理します。
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")
```
MInDS-14 データセットのサンプリング レートは 8000kHz です (この情報は [データセット カード](https://huggingface.co/datasets/PolyAI/minds14) で確認できます)。つまり、データセットを再サンプリングする必要があります。事前トレーニングされた Wav2Vec2 モデルを使用するには、16000kHz に設定します。
```py
>>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000))
>>> minds["train"][0]
{'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, ...,
2.78103951e-04, 2.38446111e-04, 1.18740834e-04], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'sampling_rate': 16000},
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
```
上の `transcription` でわかるように、テキストには大文字と小文字が混在しています。 Wav2Vec2 トークナイザーは大文字のみでトレーニングされるため、テキストがトークナイザーの語彙と一致することを確認する必要があります。
```py
>>> def uppercase(example):
... return {"transcription": example["transcription"].upper()}
>>> minds = minds.map(uppercase)
```
次に、次の前処理関数を作成します。
1. `audio`列を呼び出して、オーディオ ファイルをロードしてリサンプリングします。
2. オーディオ ファイルから `input_values` を抽出し、プロセッサを使用して `transcription` 列をトークン化します。
```py
>>> def prepare_dataset(batch):
... audio = batch["audio"]
... batch = processor(audio["array"], sampling_rate=audio["sampling_rate"], text=batch["transcription"])
... batch["input_length"] = len(batch["input_values"][0])
... return batch
```
データセット全体に前処理関数を適用するには、🤗 Datasets [`~datasets.Dataset.map`] 関数を使用します。 `num_proc` パラメータを使用してプロセスの数を増やすことで、`map` を高速化できます。 [`~datasets.Dataset.remove_columns`] メソッドを使用して、不要な列を削除します。
```py
>>> encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4)
```
🤗 Transformers には ASR 用のデータ照合器がないため、[`DataCollatorWithPadding`] を調整してサンプルのバッチを作成する必要があります。また、テキストとラベルが (データセット全体ではなく) バッチ内の最も長い要素の長さに合わせて動的に埋め込まれ、均一な長さになります。 `padding=True` を設定すると、`tokenizer` 関数でテキストを埋め込むことができますが、動的な埋め込みの方が効率的です。
他のデータ照合器とは異なり、この特定のデータ照合器は、`input_values`と `labels`」に異なるパディング方法を適用する必要があります。
```py
>>> import torch
>>> from dataclasses import dataclass, field
>>> from typing import Any, Dict, List, Optional, Union
>>> @dataclass
... class DataCollatorCTCWithPadding:
... processor: AutoProcessor
... padding: Union[bool, str] = "longest"
... def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
... # split inputs and labels since they have to be of different lengths and need
... # different padding methods
... input_features = [{"input_values": feature["input_values"][0]} for feature in features]
... label_features = [{"input_ids": feature["labels"]} for feature in features]
... batch = self.processor.pad(input_features, padding=self.padding, return_tensors="pt")
... labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors="pt")
... # replace padding with -100 to ignore loss correctly
... labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
... batch["labels"] = labels
... return batch
```
次に、`DataCollatorForCTCWithPadding` をインスタンス化します。
```py
>>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding="longest")
```
## Evaluate
トレーニング中にメトリクスを含めると、多くの場合、モデルのパフォーマンスを評価するのに役立ちます。 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) ライブラリを使用して、評価メソッドをすばやくロードできます。このタスクでは、[単語エラー率](https://huggingface.co/spaces/evaluate-metric/wer) (WER) メトリクスを読み込みます (🤗 Evaluate [クイック ツアー](https://huggingface.co/docs/evaluate/a_quick_tour) を参照して、メトリクスをロードして計算する方法の詳細を確認してください)。
```py
>>> import evaluate
>>> wer = evaluate.load("wer")
```
次に、予測とラベルを [`~evaluate.EvaluationModule.compute`] に渡して WER を計算する関数を作成します。
```py
>>> import numpy as np
>>> def compute_metrics(pred):
... pred_logits = pred.predictions
... pred_ids = np.argmax(pred_logits, axis=-1)
... pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
... pred_str = processor.batch_decode(pred_ids)
... label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
... wer = wer.compute(predictions=pred_str, references=label_str)
... return {"wer": wer}
```
これで`compute_metrics`関数の準備が整いました。トレーニングをセットアップするときにこの関数に戻ります。
## Train
<frameworkcontent>
<pt>
<Tip>
[`Trainer`] を使用したモデルの微調整に慣れていない場合は、[ここ](../training#train-with-pytorch-trainer) の基本的なチュートリアルをご覧ください。
</Tip>
これでモデルのトレーニングを開始する準備が整いました。 [`AutoModelForCTC`] で Wav2Vec2 をロードします。 `ctc_loss_reduction` パラメータで適用する削減を指定します。多くの場合、デフォルトの合計ではなく平均を使用する方が適切です。
```py
>>> from transformers import AutoModelForCTC, TrainingArguments, Trainer
>>> model = AutoModelForCTC.from_pretrained(
... "facebook/wav2vec2-base",
... ctc_loss_reduction="mean",
... pad_token_id=processor.tokenizer.pad_token_id,
... )
```
この時点で残っている手順は次の 3 つだけです。
1. [`TrainingArguments`] でトレーニング ハイパーパラメータを定義します。唯一の必須パラメータは、モデルの保存場所を指定する `output_dir` です。 `push_to_hub=True`を設定して、このモデルをハブにプッシュします (モデルをアップロードするには、Hugging Face にサインインする必要があります)。各エポックの終了時に、[`トレーナー`] は WER を評価し、トレーニング チェックポイントを保存します。
2. トレーニング引数を、モデル、データセット、トークナイザー、データ照合器、および `compute_metrics` 関数とともに [`Trainer`] に渡します。
3. [`~Trainer.train`] を呼び出してモデルを微調整します。
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_asr_mind_model",
... per_device_train_batch_size=8,
... gradient_accumulation_steps=2,
... learning_rate=1e-5,
... warmup_steps=500,
... max_steps=2000,
... gradient_checkpointing=True,
... fp16=True,
... group_by_length=True,
... eval_strategy="steps",
... per_device_eval_batch_size=8,
... save_steps=1000,
... eval_steps=1000,
... logging_steps=25,
... load_best_model_at_end=True,
... metric_for_best_model="wer",
... greater_is_better=False,
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=encoded_minds["train"],
... eval_dataset=encoded_minds["test"],
... processing_class=processor,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
トレーニングが完了したら、 [`~transformers.Trainer.push_to_hub`] メソッドを使用してモデルをハブに共有し、誰もがモデルを使用できるようにします。
```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<Tip>
自動音声認識用にモデルを微調整する方法のより詳細な例については、英語 ASR および英語のこのブログ [投稿](https://huggingface.co/blog/fine-tune-wav2vec2-english) を参照してください。多言語 ASR については、この [投稿](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) を参照してください。
</Tip>
## Inference
モデルを微調整したので、それを推論に使用できるようになりました。
推論を実行したい音声ファイルをロードします。必要に応じて、オーディオ ファイルのサンプリング レートをモデルのサンプリング レートと一致するようにリサンプリングすることを忘れないでください。
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("PolyAI/minds14", "en-US", split="train")
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> audio_file = dataset[0]["audio"]["path"]
```
推論用に微調整されたモデルを試す最も簡単な方法は、それを [`pipeline`] で使用することです。モデルを使用して自動音声認識用の`pipeline`をインスタンス化し、オーディオ ファイルをそれに渡します。
```py
>>> from transformers import pipeline
>>> transcriber = pipeline("automatic-speech-recognition", model="stevhliu/my_awesome_asr_minds_model")
>>> transcriber(audio_file)
{'text': 'I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'}
```
<Tip>
転写はまあまあですが、もっと良くなる可能性があります。さらに良い結果を得るには、より多くの例でモデルを微調整してみてください。
</Tip>
必要に応じて、「パイプライン」の結果を手動で複製することもできます。
<frameworkcontent>
<pt>
プロセッサをロードしてオーディオ ファイルと文字起こしを前処理し、`input`を PyTorch テンソルとして返します。
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("stevhliu/my_awesome_asr_mind_model")
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
```
Pass your inputs to the model and return the logits:
```py
>>> from transformers import AutoModelForCTC
>>> model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
最も高い確率で予測された `input_ids` を取得し、プロセッサを使用して予測された `input_ids` をデコードしてテキストに戻します。
```py
>>> import torch
>>> predicted_ids = torch.argmax(logits, dim=-1)
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription
['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER']
```
</pt>
</frameworkcontent>
| transformers/docs/source/ja/tasks/asr.md/0 | {
"file_path": "transformers/docs/source/ja/tasks/asr.md",
"repo_id": "transformers",
"token_count": 7051
} |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
# Transformers Agents
<Tip warning={true}>
Transformers Agentsは、いつでも変更される可能性のある実験的なAPIです。エージェントが返す結果は、APIまたは基礎となるモデルが変更される可能性があるため、異なることがあります。
</Tip>
Transformersバージョンv4.29.0は、*ツール*と*エージェント*のコンセプトを基に構築されています。この[colab](https://colab.research.google.com/drive/1c7MHD-T1forUPGcC_jlwsIptOzpG3hSj)で試すことができます。
要するに、これはtransformersの上に自然言語APIを提供するものです：私たちは一連の厳選されたツールを定義し、自然言語を解釈し、これらのツールを使用するエージェントを設計します。これは設計上拡張可能です。私たちはいくつかの関連するツールを厳選しましたが、コミュニティによって開発された任意のツールを使用するためにシステムを簡単に拡張できる方法も示します。
この新しいAPIで何ができるかのいくつかの例から始めましょう。特に多モーダルなタスクに関して強力ですので、画像を生成したりテキストを読み上げたりするのに最適です。
上記のテキストの上に、日本語の翻訳を提供します。
```py
agent.run("Caption the following image", image=image)
```
| **Input** | **Output** |
|-----------------------------------------------------------------------------------------------------------------------------|-----------------------------------|
| <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/beaver.png" width=200> | A beaver is swimming in the water |
---
```py
agent.run("Read the following text out loud", text=text)
```
| **Input** | **Output** |
|-------------------------------------------------------------------------------------------------------------------------|----------------------------------------------|
| A beaver is swimming in the water | <audio controls><source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tts_example.wav" type="audio/wav"> your browser does not support the audio element. </audio>
---
```py
agent.run(
"In the following `document`, where will the TRRF Scientific Advisory Council Meeting take place?",
document=document,
)
```
| **Input** | **Output** |
|-----------------------------------------------------------------------------------------------------------------------------|----------------|
| <img src="https://datasets-server.huggingface.co/assets/hf-internal-testing/example-documents/--/hf-internal-testing--example-documents/test/0/image/image.jpg" width=200> | ballroom foyer |
## Quickstart
`agent.run`を使用する前に、エージェントをインスタンス化する必要があります。エージェントは、大規模な言語モデル（LLM）です。
OpenAIモデルとBigCode、OpenAssistantからのオープンソースの代替モデルをサポートしています。OpenAIモデルはパフォーマンスが優れていますが、OpenAIのAPIキーが必要であり、無料で使用することはできません。一方、Hugging FaceはBigCodeとOpenAssistantモデルのエンドポイントへの無料アクセスを提供しています。
まず、デフォルトの依存関係をすべてインストールするために`agents`のエクストラをインストールしてください。
```bash
pip install transformers[agents]
```
OpenAIモデルを使用するには、`openai`の依存関係をインストールした後、`OpenAiAgent`をインスタンス化します。
```bash
pip install openai
```
```py
from transformers import OpenAiAgent
agent = OpenAiAgent(model="text-davinci-003", api_key="<your_api_key>")
```
BigCodeまたはOpenAssistantを使用するには、まずログインしてInference APIにアクセスしてください。
```py
from huggingface_hub import login
login("<YOUR_TOKEN>")
```
次に、エージェントをインスタンス化してください。
```py
from transformers import HfAgent
# Starcoder
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
# StarcoderBase
# agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoderbase")
# OpenAssistant
# agent = HfAgent(url_endpoint="https://api-inference.huggingface.co/models/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5")
```
これは、Hugging Faceが現在無料で提供している推論APIを使用しています。このモデル（または別のモデル）の独自の推論エンドポイントをお持ちの場合は、上記のURLエンドポイントをご自分のURLエンドポイントで置き換えることができます。
<Tip>
StarCoderとOpenAssistantは無料で利用でき、シンプルなタスクには非常に優れた性能を発揮します。ただし、より複雑なプロンプトを処理する際には、チェックポイントが十分でないことがあります。そのような場合には、現時点ではオープンソースではないものの、パフォーマンスが向上する可能性のあるOpenAIモデルを試してみることをお勧めします。
</Tip>
これで準備が整いました！これから、あなたが利用できる2つのAPIについて詳しく説明します。
### Single execution (run)
単一実行メソッドは、エージェントの [`~Agent.run`] メソッドを使用する場合です。
```py
agent.run("Draw me a picture of rivers and lakes.")
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png" width=200>
これは、実行したいタスクに適したツール（またはツール）を自動的に選択し、適切に実行します。1つまたは複数のタスクを同じ命令で実行することができます（ただし、命令が複雑であるほど、エージェントが失敗する可能性が高くなります）。
```py
agent.run("Draw me a picture of the sea then transform the picture to add an island")
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/sea_and_island.png" width=200>
<br/>
[`~Agent.run`] 操作は独立して実行できますので、異なるタスクで何度も実行することができます。
注意点として、あなたの `agent` は単なる大規模な言語モデルであるため、プロンプトのわずかな変更でも完全に異なる結果が得られる可能性があります。したがって、実行したいタスクをできるだけ明確に説明することが重要です。良いプロンプトの書き方については、[こちら](custom_tools#writing-good-user-inputs) で詳しく説明しています。
実行ごとに状態を保持したり、テキスト以外のオブジェクトをエージェントに渡したりする場合は、エージェントが使用する変数を指定することができます。例えば、最初の川や湖の画像を生成し、その画像に島を追加するようにモデルに指示するには、次のように行うことができます：
```python
picture = agent.run("Generate a picture of rivers and lakes.")
updated_picture = agent.run("Transform the image in `picture` to add an island to it.", picture=picture)
```
<Tip>
これは、モデルがあなたのリクエストを理解できない場合や、ツールを混同する場合に役立つことがあります。例えば：
```py
agent.run("Draw me the picture of a capybara swimming in the sea")
```
ここでは、モデルは2つの方法で解釈できます：
- `text-to-image`に海で泳ぐカピバラを生成させる
- または、`text-to-image`でカピバラを生成し、それを海で泳がせるために`image-transformation`ツールを使用する
最初のシナリオを強制したい場合は、プロンプトを引数として渡すことができます：
```py
agent.run("Draw me a picture of the `prompt`", prompt="a capybara swimming in the sea")
```
</Tip>
### Chat-based execution (チャット)
エージェントは、[`~Agent.chat`] メソッドを使用することで、チャットベースのアプローチも可能です。
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png" width=200>
```py
agent.chat("Transform the picture so that there is a rock in there")
```
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes_and_beaver.png" width=200>
<br/>
これは、指示をまたいで状態を保持したい場合に便利なアプローチで、単一の指示に比べて複雑な指示を処理するのは難しいかもしれません（その場合は [`~Agent.run`] メソッドの方が適しています）。
このメソッドは、非テキスト型の引数や特定のプロンプトを渡したい場合にも使用できます。
### ⚠️ Remote execution
デモンストレーションの目的やすべてのセットアップで使用できるように、リリースのためにいくつかのデフォルトツール用のリモート実行ツールも作成しました。これらは [推論エンドポイント](https://huggingface.co/inference-endpoints) を使用して作成されます。
これらは現在オフになっていますが、リモート実行ツールを自分で設定する方法については、[カスタムツールガイド](./custom_tools) を読むことをお勧めします。
### What's happening here? What are tools, and what are agents?
#### Agents
ここでの「エージェント」とは、大規模な言語モデルのことであり、特定の一連のツールにアクセスできるようにプロンプトを設定しています。
LLM（大規模言語モデル）は、コードの小さなサンプルを生成するのにかなり優れており、このAPIは、エージェントに特定のツールセットを使用してタスクを実行するコードの小さなサンプルを生成させることに利用しています。このプロンプトは、エージェントにタスクとツールの説明を提供することで、エージェントが使用しているツールのドキュメントにアクセスし、関連するコードを生成できるようになります。
#### Tools
ツールは非常に単純で、名前と説明からなる単一の関数です。それから、これらのツールの説明を使用してエージェントをプロンプトします。プロンプトを通じて、エージェントに、ツールを使用してクエリで要求されたタスクをどのように実行するかを示します。特に、ツールの期待される入力と出力を示します。
これは新しいツールを使用しており、パイプラインではなくツールを使用しています。なぜなら、エージェントは非常に原子的なツールでより良いコードを生成するからです。パイプラインはよりリファクタリングされ、しばしば複数のタスクを組み合わせています。ツールは非常に単純なタスクに焦点を当てることを意図しています。
#### Code-execution?!
このコードは、ツールとツールと一緒に渡される入力のセットで、当社の小規模なPythonインタープリタで実行されます。すでに提供されたツールとprint関数しか呼び出すことができないため、実行できることはすでに制限されています。Hugging Faceのツールに制限されているため、安全だと考えても問題ありません。
さらに、属性の検索やインポートは許可しておらず（それらは渡された入力/出力を処理するためには必要ないはずです）、最も明らかな攻撃は問題ありません（エージェントにそれらを出力するようにプロンプトする必要があります）。超安全な側に立ちたい場合は、追加の引数 return_code=True を指定して run() メソッドを実行できます。その場合、エージェントは実行するコードを返すだけで、実行するかどうかはあなた次第です。
実行は、違法な操作を試みる行またはエージェントが生成したコードに通常のPythonエラーがある場合に停止します。
### A curated set of tools
私たちは、このようなエージェントを強化できるツールのセットを特定します。以下は、`transformers`に統合されたツールの更新されたリストです：
- **ドキュメント質問応答**: 画像形式のドキュメント（PDFなど）が与えられた場合、このドキュメントに関する質問に回答します（[Donut](./model_doc/donut)）
- **テキスト質問応答**: 長いテキストと質問が与えられた場合、テキスト内の質問に回答します（[Flan-T5](./model_doc/flan-t5)）
- **無条件の画像キャプション**: 画像にキャプションを付けます！（[BLIP](./model_doc/blip)）
- **画像質問応答**: 画像が与えられた場合、その画像に関する質問に回答します（[VILT](./model_doc/vilt)）
- **画像セグメンテーション**: 画像とプロンプトが与えられた場合、そのプロンプトのセグメンテーションマスクを出力します（[CLIPSeg](./model_doc/clipseg)）
- **音声からテキストへの変換**: 人の話し声のオーディオ録音が与えられた場合、その音声をテキストに転記します（[Whisper](./model_doc/whisper)）
- **テキストから音声への変換**: テキストを音声に変換します（[SpeechT5](./model_doc/speecht5)）
- **ゼロショットテキスト分類**: テキストとラベルのリストが与えられた場合、テキストが最も対応するラベルを識別します（[BART](./model_doc/bart)）
- **テキスト要約**: 長いテキストを1つまたは数文に要約します（[BART](./model_doc/bart)）
- **翻訳**: テキストを指定された言語に翻訳します（[NLLB](./model_doc/nllb)）
これらのツールはtransformersに統合されており、手動でも使用できます。たとえば、次のように使用できます：
```py
from transformers import load_tool
tool = load_tool("text-to-speech")
audio = tool("This is a text to speech tool")
```
### Custom tools
私たちは、厳選されたツールのセットを特定する一方、この実装が提供する主要な価値は、カスタムツールを迅速に作成して共有できる能力だと強く信じています。
ツールのコードをHugging Face Spaceまたはモデルリポジトリにプッシュすることで、エージェントと直接連携してツールを活用できます。[`huggingface-tools` organization](https://huggingface.co/huggingface-tools)には、**transformers非依存**のいくつかのツールが追加されました：
- **テキストダウンローダー**: ウェブURLからテキストをダウンロードするためのツール
- **テキストから画像へ**: プロンプトに従って画像を生成するためのツール。安定した拡散を活用します
- **画像変換**: 初期画像とプロンプトを指定して画像を変更するためのツール。instruct pix2pixの安定した拡散を活用します
- **テキストからビデオへ**: プロンプトに従って小さなビデオを生成するためのツール。damo-vilabを活用します
最初から使用しているテキストから画像へのツールは、[*huggingface-tools/text-to-image*](https://huggingface.co/spaces/huggingface-tools/text-to-image)にあるリモートツールです！今後も、この組織および他の組織にさらにこのようなツールをリリースし、この実装をさらに強化していきます。
エージェントはデフォルトで[`huggingface-tools`](https://huggingface.co/huggingface-tools)にあるツールにアクセスできます。
ツールの作成と共有方法、またHubに存在するカスタムツールを活用する方法についての詳細は、[次のガイド](custom_tools)で説明しています。
### Code generation
これまで、エージェントを使用してあなたのためにアクションを実行する方法を示しました。ただし、エージェントはコードを生成するだけで、非常に制限されたPythonインタープリタを使用して実行します。生成されたコードを異なる環境で使用したい場合、エージェントにコードを返すように指示できます。ツールの定義と正確なインポートも含めて。
例えば、以下の命令：
```python
agent.run("Draw me a picture of rivers and lakes", return_code=True)
```
次のコードを返します
```python
from transformers import load_tool
image_generator = load_tool("huggingface-tools/text-to-image")
image = image_generator(prompt="rivers and lakes")
```
その後、自分で変更して実行できます。
| transformers/docs/source/ja/transformers_agents.md/0 | {
"file_path": "transformers/docs/source/ja/transformers_agents.md",
"repo_id": "transformers",
"token_count": 7879
} |
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# 사용자 정의 모델 공유하기[[sharing-custom-models]]
🤗 Transformers 라이브러리는 쉽게 확장할 수 있도록 설계되었습니다.
모든 모델은 추상화 없이 저장소의 지정된 하위 폴더에 완전히 코딩되어 있으므로, 손쉽게 모델링 파일을 복사하고 필요에 따라 조정할 수 있습니다.
완전히 새로운 모델을 만드는 경우에는 처음부터 시작하는 것이 더 쉬울 수 있습니다.
이 튜토리얼에서는 Transformers 내에서 사용할 수 있도록 사용자 정의 모델과 구성을 작성하는 방법과
🤗 Transformers 라이브러리에 없는 경우에도 누구나 사용할 수 있도록 (의존성과 함께) 커뮤니티에 공유하는 방법을 배울 수 있습니다.
[timm 라이브러리](https://github.com/rwightman/pytorch-image-models)의 ResNet 클래스를 [`PreTrainedModel`]로 래핑한 ResNet 모델을 예로 모든 것을 설명합니다.
## 사용자 정의 구성 작성하기[[writing-a-custom-configuration]]
모델에 들어가기 전에 먼저 구성을 작성해보도록 하겠습니다.
모델의 `configuration`은 모델을 만들기 위해 필요한 모든 중요한 것들을 포함하고 있는 객체입니다.
다음 섹션에서 볼 수 있듯이, 모델은 `config`를 사용해서만 초기화할 수 있기 때문에 완벽한 구성이 필요합니다.
아래 예시에서는 ResNet 클래스의 인수(argument)를 조정해보겠습니다.
다른 구성은 가능한 ResNet 중 다른 유형을 제공합니다.
그런 다음 몇 가지 유효성을 확인한 후 해당 인수를 저장합니다.
```python
from transformers import PretrainedConfig
from typing import List
class ResnetConfig(PretrainedConfig):
model_type = "resnet"
def __init__(
self,
block_type="bottleneck",
layers: List[int] = [3, 4, 6, 3],
num_classes: int = 1000,
input_channels: int = 3,
cardinality: int = 1,
base_width: int = 64,
stem_width: int = 64,
stem_type: str = "",
avg_down: bool = False,
**kwargs,
):
if block_type not in ["basic", "bottleneck"]:
raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
if stem_type not in ["", "deep", "deep-tiered"]:
raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
self.block_type = block_type
self.layers = layers
self.num_classes = num_classes
self.input_channels = input_channels
self.cardinality = cardinality
self.base_width = base_width
self.stem_width = stem_width
self.stem_type = stem_type
self.avg_down = avg_down
super().__init__(**kwargs)
```
사용자 정의 `configuration`을 작성할 때 기억해야 할 세 가지 중요한 사항은 다음과 같습니다:
- `PretrainedConfig`을 상속해야 합니다.
- `PretrainedConfig`의 `__init__`은 모든 kwargs를 허용해야 하고,
- 이러한 `kwargs`는 상위 클래스 `__init__`에 전달되어야 합니다.
상속은 🤗 Transformers 라이브러리에서 모든 기능을 가져오는 것입니다.
이러한 점으로부터 비롯되는 두 가지 제약 조건은 `PretrainedConfig`에 설정하는 것보다 더 많은 필드가 있습니다.
`from_pretrained` 메서드로 구성을 다시 로드할 때 해당 필드는 구성에서 수락한 후 상위 클래스로 보내야 합니다.
모델을 auto 클래스에 등록하지 않는 한, `configuration`에서 `model_type`을 정의(여기서 `model_type="resnet"`)하는 것은 필수 사항이 아닙니다 (마지막 섹션 참조).
이렇게 하면 라이브러리의 다른 모델 구성과 마찬가지로 구성을 쉽게 만들고 저장할 수 있습니다.
다음은 resnet50d 구성을 생성하고 저장하는 방법입니다:
```py
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d_config.save_pretrained("custom-resnet")
```
이렇게 하면 `custom-resnet` 폴더 안에 `config.json`이라는 파일이 저장됩니다.
그런 다음 `from_pretrained` 메서드를 사용하여 구성을 다시 로드할 수 있습니다.
```py
resnet50d_config = ResnetConfig.from_pretrained("custom-resnet")
```
구성을 Hub에 직접 업로드하기 위해 [`PretrainedConfig`] 클래스의 [`~PretrainedConfig.push_to_hub`]와 같은 다른 메서드를 사용할 수 있습니다.
## 사용자 정의 모델 작성하기[[writing-a-custom-model]]
이제 ResNet 구성이 있으므로 모델을 작성할 수 있습니다.
실제로는 두 개를 작성할 것입니다. 하나는 이미지 배치에서 hidden features를 추출하는 것([`BertModel`]과 같이), 다른 하나는 이미지 분류에 적합한 것입니다([`BertForSequenceClassification`]과 같이).
이전에 언급했듯이 이 예제에서는 단순하게 하기 위해 모델의 느슨한 래퍼(loose wrapper)만 작성할 것입니다.
이 클래스를 작성하기 전에 블록 유형과 실제 블록 클래스 간의 매핑 작업만 하면 됩니다.
그런 다음 `ResNet` 클래스로 전달되어 `configuration`을 통해 모델이 선언됩니다:
```py
from transformers import PreTrainedModel
from timm.models.resnet import BasicBlock, Bottleneck, ResNet
from .configuration_resnet import ResnetConfig
BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}
class ResnetModel(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
)
def forward(self, tensor):
return self.model.forward_features(tensor)
```
이미지 분류 모델을 만들기 위해서는 forward 메소드만 변경하면 됩니다:
```py
import torch
class ResnetModelForImageClassification(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
)
def forward(self, tensor, labels=None):
logits = self.model(tensor)
if labels is not None:
loss = torch.nn.functional.cross_entropy(logits, labels)
return {"loss": loss, "logits": logits}
return {"logits": logits}
```
두 경우 모두 `PreTrainedModel`를 상속받고, `config`를 통해 상위 클래스 초기화를 호출하다는 점을 기억하세요 (일반적인 `torch.nn.Module`을 작성할 때와 비슷함).
모델을 auto 클래스에 등록하고 싶은 경우에는 `config_class`를 설정하는 부분이 필수입니다 (마지막 섹션 참조).
<Tip>
라이브러리에 존재하는 모델과 굉장히 유사하다면, 모델을 생성할 때 구성을 참조해 재사용할 수 있습니다.
</Tip>
원하는 것을 모델이 반환하도록 할 수 있지만, `ResnetModelForImageClassification`에서 했던 것 처럼
레이블을 통과시켰을 때 손실과 함께 사전 형태로 반환하는 것이 [`Trainer`] 클래스 내에서 직접 모델을 사용하기에 유용합니다.
자신만의 학습 루프 또는 다른 학습 라이브러리를 사용할 계획이라면 다른 출력 형식을 사용해도 좋습니다.
이제 모델 클래스가 있으므로 하나 생성해 보겠습니다:
```py
resnet50d = ResnetModelForImageClassification(resnet50d_config)
```
다시 말하지만, [`~PreTrainedModel.save_pretrained`]또는 [`~PreTrainedModel.push_to_hub`]처럼 [`PreTrainedModel`]에 속하는 모든 메소드를 사용할 수 있습니다.
다음 섹션에서 두 번째 메소드를 사용해 모델 코드와 모델 가중치를 업로드하는 방법을 살펴보겠습니다.
먼저, 모델 내부에 사전 훈련된 가중치를 로드해 보겠습니다.
이 예제를 활용할 때는, 사용자 정의 모델을 자신만의 데이터로 학습시킬 것입니다.
이 튜토리얼에서는 빠르게 진행하기 위해 사전 훈련된 resnet50d를 사용하겠습니다.
아래 모델은 resnet50d의 래퍼이기 때문에, 가중치를 쉽게 로드할 수 있습니다.
```py
import timm
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())
```
이제 [`~PreTrainedModel.save_pretrained`] 또는 [`~PreTrainedModel.push_to_hub`]를 사용할 때 모델 코드가 저장되는지 확인해봅시다.
## Hub로 코드 업로드하기[[sending-the-code-to-the-hub]]
<Tip warning={true}>
이 API는 실험적이며 다음 릴리스에서 약간의 변경 사항이 있을 수 있습니다.
</Tip>
먼저 모델이 `.py` 파일에 완전히 정의되어 있는지 확인하세요.
모든 파일이 동일한 작업 경로에 있기 때문에 상대경로 임포트(relative import)에 의존할 수 있습니다 (transformers에서는 이 기능에 대한 하위 모듈을 지원하지 않습니다).
이 예시에서는 작업 경로 안의 `resnet_model`에서 `modeling_resnet.py` 파일과 `configuration_resnet.py` 파일을 정의합니다.
구성 파일에는 `ResnetConfig`에 대한 코드가 있고 모델링 파일에는 `ResnetModel` 및 `ResnetModelForImageClassification`에 대한 코드가 있습니다.
```
.
└── resnet_model
├── __init__.py
├── configuration_resnet.py
└── modeling_resnet.py
```
Python이 `resnet_model`을 모듈로 사용할 수 있도록 감지하는 목적이기 때문에 `__init__.py`는 비어 있을 수 있습니다.
<Tip warning={true}>
라이브러리에서 모델링 파일을 복사하는 경우,
모든 파일 상단에 있는 상대 경로 임포트(relative import) 부분을 `transformers` 패키지에서 임포트 하도록 변경해야 합니다.
</Tip>
기존 구성이나 모델을 재사용(또는 서브 클래스화)할 수 있습니다.
커뮤니티에 모델을 공유하기 위해서는 다음 단계를 따라야 합니다:
먼저, 새로 만든 파일에 ResNet 모델과 구성을 임포트합니다:
```py
from resnet_model.configuration_resnet import ResnetConfig
from resnet_model.modeling_resnet import ResnetModel, ResnetModelForImageClassification
```
다음으로 `save_pretrained` 메소드를 사용해 해당 객체의 코드 파일을 복사하고,
복사한 파일을 Auto 클래스로 등록하고(모델인 경우) 실행합니다:
```py
ResnetConfig.register_for_auto_class()
ResnetModel.register_for_auto_class("AutoModel")
ResnetModelForImageClassification.register_for_auto_class("AutoModelForImageClassification")
```
`configuration`에 대한 auto 클래스를 지정할 필요는 없지만(`configuration` 관련 auto 클래스는 AutoConfig 클래스 하나만 있음), 모델의 경우에는 지정해야 합니다.
사용자 지정 모델은 다양한 작업에 적합할 수 있으므로, 모델에 맞는 auto 클래스를 지정해야 합니다.
다음으로, 이전에 작업했던 것과 마찬가지로 구성과 모델을 작성합니다:
```py
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d = ResnetModelForImageClassification(resnet50d_config)
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())
```
이제 모델을 Hub로 업로드하기 위해 로그인 상태인지 확인하세요.
터미널에서 다음 코드를 실행해 확인할 수 있습니다:
```bash
huggingface-cli login
```
주피터 노트북의 경우에는 다음과 같습니다:
```py
from huggingface_hub import notebook_login
notebook_login()
```
그런 다음 이렇게 자신의 네임스페이스(또는 자신이 속한 조직)에 업로드할 수 있습니다:
```py
resnet50d.push_to_hub("custom-resnet50d")
```
On top of the modeling weights and the configuration in json format, this also copied the modeling and
configuration `.py` files in the folder `custom-resnet50d` and uploaded the result to the Hub. You can check the result
in this [model repo](https://huggingface.co/sgugger/custom-resnet50d).
json 형식의 모델링 가중치와 구성 외에도 `custom-resnet50d` 폴더 안의 모델링과 구성 `.py` 파일을 복사하해 Hub에 업로드합니다.
[모델 저장소](https://huggingface.co/sgugger/custom-resnet50d)에서 결과를 확인할 수 있습니다.
[sharing tutorial](model_sharing) 문서의 `push_to_hub` 메소드에서 자세한 내용을 확인할 수 있습니다.
## 사용자 정의 코드로 모델 사용하기[[using-a-model-with-custom-code]]
auto 클래스와 `from_pretrained` 메소드를 사용하여 사용자 지정 코드 파일과 함께 모든 구성, 모델, 토크나이저를 사용할 수 있습니다.
Hub에 업로드된 모든 파일 및 코드는 멜웨어가 있는지 검사되지만 (자세한 내용은 [Hub 보안](https://huggingface.co/docs/hub/security#malware-scanning) 설명 참조),
자신의 컴퓨터에서 모델 코드와 작성자가 악성 코드를 실행하지 않는지 확인해야 합니다.
사용자 정의 코드로 모델을 사용하려면 `trust_remote_code=True`로 설정하세요:
```py
from transformers import AutoModelForImageClassification
model = AutoModelForImageClassification.from_pretrained("sgugger/custom-resnet50d", trust_remote_code=True)
```
모델 작성자가 악의적으로 코드를 업데이트하지 않았다는 점을 확인하기 위해, 커밋 해시(commit hash)를 `revision`으로 전달하는 것도 강력히 권장됩니다 (모델 작성자를 완전히 신뢰하지 않는 경우).
```py
commit_hash = "ed94a7c6247d8aedce4647f00f20de6875b5b292"
model = AutoModelForImageClassification.from_pretrained(
"sgugger/custom-resnet50d", trust_remote_code=True, revision=commit_hash
)
```
Hub에서 모델 저장소의 커밋 기록을 찾아볼 때, 모든 커밋의 커밋 해시를 쉽게 복사할 수 있는 버튼이 있습니다.
## 사용자 정의 코드로 만든 모델을 auto 클래스로 등록하기[[registering-a-model-with-custom-code-to-the-auto-classes]]
🤗 Transformers를 상속하는 라이브러리를 작성하는 경우 사용자 정의 모델을 auto 클래스에 추가할 수 있습니다.
사용자 정의 모델을 사용하기 위해 해당 라이브러리를 임포트해야 하기 때문에, 이는 Hub로 코드를 업로드하는 것과 다릅니다 (Hub에서 자동적으로 모델 코드를 다운로드 하는 것과 반대).
구성에 기존 모델 유형과 다른 `model_type` 속성이 있고 모델 클래스에 올바른 `config_class` 속성이 있는 한,
다음과 같이 auto 클래스에 추가할 수 있습니다:
```py
from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
AutoConfig.register("resnet", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification)
```
사용자 정의 구성을 [`AutoConfig`]에 등록할 때 사용되는 첫 번째 인수는 사용자 정의 구성의 `model_type`과 일치해야 합니다.
또한, 사용자 정의 모델을 auto 클래스에 등록할 때 사용되는 첫 번째 인수는 해당 모델의 `config_class`와 일치해야 합니다. | transformers/docs/source/ko/custom_models.md/0 | {
"file_path": "transformers/docs/source/ko/custom_models.md",
"repo_id": "transformers",
"token_count": 10731
} |
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# BLIP[[blip]]
## 개요[[overview]]
BLIP 모델은 Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi의 [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) 논문에서 제안되었습니다.
BLIP은 여러 멀티모달 작업을 수행할 수 있는 모델입니다:
- 시각 질문 응답 (Visual Question Answering, VQA)
- 이미지-텍스트 검색 (이미지-텍스트 매칭)
- 이미지 캡셔닝
논문의 초록은 다음과 같습니다:
*비전-언어 사전 학습(Vision-Language Pre-training, VLP)은 다양한 비전-언어 작업의 성능을 크게 향상시켰습니다. 하지만, 대부분의 기존 사전 학습 모델들은 이해 기반 작업이나 생성 기반 작업 중 하나에서만 뛰어난 성능을 발휘합니다. 또한 성능 향상은 주로 웹에서 수집한 노이즈가 많은 이미지-텍스트 쌍으로 데이터셋의 규모를 키우는 방식으로 이루어졌는데, 이는 최적의 지도 학습 방식이라고 보기 어렵습니다. 본 논문에서는 BLIP이라는 새로운 VLP 프레임워크를 제안합니다. 이 프레임워크는 비전-언어 이해 및 생성 작업 모두에 유연하게 적용될 수 있습니다. BLIP는 캡셔너가 합성 캡션을 생성하고 필터가 노이즈 캡션을 제거하는 부트스트래핑 방법을 통해 웹 데이터의 노이즈를 효과적으로 활용합니다. 우리는 이미지-텍스트 검색(Recall@1에서 +2.7%), 이미지 캡셔닝(CIDEr에서 +2.8%), 그리고 VQA(VQA 점수에서 +1.6%)와 같은 다양한 비전-언어 작업에서 최신 성과를 달성했습니다. 또한 BLIP은 제로샷 방식으로 비디오-언어 작업에 직접 전이될 때도 강력한 일반화 능력을 보여줍니다. 이 논문의 코드, 모델, 데이터셋은 공개되었습니다.*
이 모델은 [ybelkada](https://huggingface.co/ybelkada)가 기여했습니다.
원본 코드는 [여기](https://github.com/salesforce/BLIP)에서 찾을 수 있습니다.
## 자료[[resources]]
- [Jupyter notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb): 사용자 정의 데이터셋에서 BLIP를 이미지 캡셔닝으로 미세 조정하는 방법
## BlipConfig[[transformers.BlipConfig]]
[[autodoc]] BlipConfig
- from_text_vision_configs
## BlipTextConfig[[transformers.BlipTextConfig]]
[[autodoc]] BlipTextConfig
## BlipVisionConfig[[transformers.BlipVisionConfig]]
[[autodoc]] BlipVisionConfig
## BlipProcessor[[transformers.BlipProcessor]]
[[autodoc]] BlipProcessor
## BlipImageProcessor[[transformers.BlipImageProcessor]]
[[autodoc]] BlipImageProcessor
- preprocess
<frameworkcontent>
<pt>
## BlipModel[[transformers.BlipModel]]
`BlipModel`은 향후 버전에서 더 이상 지원되지 않을 예정입니다. 목적에 따라 `BlipForConditionalGeneration`, `BlipForImageTextRetrieval` 또는 `BlipForQuestionAnswering`을 사용하십시오.
[[autodoc]] BlipModel
- forward
- get_text_features
- get_image_features
## BlipTextModel[[transformers.BlipTextModel]]
[[autodoc]] BlipTextModel
- forward
## BlipVisionModel[[transformers.BlipVisionModel]]
[[autodoc]] BlipVisionModel
- forward
## BlipForConditionalGeneration[[transformers.BlipForConditionalGeneration]]
[[autodoc]] BlipForConditionalGeneration
- forward
## BlipForImageTextRetrieval[[transformers.BlipForImageTextRetrieval]]
[[autodoc]] BlipForImageTextRetrieval
- forward
## BlipForQuestionAnswering[[transformers.BlipForQuestionAnswering]]
[[autodoc]] BlipForQuestionAnswering
- forward
</pt>
<tf>
## TFBlipModel[[transformers.TFBlipModel]]
[[autodoc]] TFBlipModel
- call
- get_text_features
- get_image_features
## TFBlipTextModel[[transformers.TFBlipTextModel]]
[[autodoc]] TFBlipTextModel
- call
## TFBlipVisionModel[[transformers.TFBlipVisionModel]]
[[autodoc]] TFBlipVisionModel
- call
## TFBlipForConditionalGeneration[[transformers.TFBlipForConditionalGeneration]]
[[autodoc]] TFBlipForConditionalGeneration
- call
## TFBlipForImageTextRetrieval[[transformers.TFBlipForImageTextRetrieval]]
[[autodoc]] TFBlipForImageTextRetrieval
- call
## TFBlipForQuestionAnswering[[transformers.TFBlipForQuestionAnswering]]
[[autodoc]] TFBlipForQuestionAnswering
- call
</tf>
</frameworkcontent>
| transformers/docs/source/ko/model_doc/blip.md/0 | {
"file_path": "transformers/docs/source/ko/model_doc/blip.md",
"repo_id": "transformers",
"token_count": 2800
} |
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specific language governing permissions and limitations under the License.
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# 궤적 트랜스포머[[trajectory-transformer]]
<Tip warning={true}>
이 모델은 유지 보수 모드로만 운영되며, 코드를 변경하는 새로운 PR(Pull Request)은 받지 않습니다.
이 모델을 실행하는 데 문제가 발생한다면, 이 모델을 지원하는 마지막 버전인 v4.30.0를 다시 설치해 주세요. 다음 명령어를 실행하여 재설치할 수 있습니다: `pip install -U transformers==4.30.0`.
</Tip>
## 개요[[overview]]
Trajectory Transformer 모델은 Michael Janner, Qiyang Li, Sergey Levine이 제안한 [하나의 커다란 시퀀스 모델링 문제로서의 오프라인 강화학습](https://arxiv.org/abs/2106.02039)라는 논문에서 소개되었습니다.
해당 논문의 초록입니다:
*강화학습(RL)은 일반적으로 마르코프 속성을 활용하여 시간에 따라 문제를 인수분해하면서 정적 정책이나 단일 단계 모델을 추정하는 데 중점을 둡니다. 하지만 우리는 RL을 높은 보상 시퀀스로 이어지는 행동 시퀀스를 생성하는 것을 목표로 하는 일반적인 시퀀스 모델링 문제로 볼 수도 있습니다. 이러한 관점에서, 자연어 처리와 같은 다른 도메인에서 잘 작동하는 고용량 시퀀스 예측 모델이 RL 문제에도 효과적인 해결책을 제공할 수 있는지 고려해 볼 만합니다. 이를 위해 우리는 RL을 시퀀스 모델링의 도구로 어떻게 다룰 수 있는지 탐구하며, 트랜스포머 아키텍처를 사용하여 궤적에 대한 분포를 모델링하고 빔 서치를 계획 알고리즘으로 재활용합니다. RL을 시퀀스 모델링 문제로 프레임화하면 다양한 설계 결정이 단순화되어, 오프라인 RL 알고리즘에서 흔히 볼 수 있는 많은 구성 요소를 제거할 수 있습니다. 우리는 이 접근 방식의 유연성을 장기 동역학 예측, 모방 학습, 목표 조건부 RL, 오프라인 RL에 걸쳐 입증합니다. 더 나아가, 이 접근 방식을 기존의 모델 프리 알고리즘과 결합하여 희소 보상, 장기 과제에서 최신 계획기(planner)를 얻을 수 있음을 보여줍니다.*
이 모델은 [CarlCochet](https://huggingface.co/CarlCochet)에 의해 기여되었습니다.
원본 코드는 [이곳](https://github.com/jannerm/trajectory-transformer)에서 확인할 수 있습니다.
## 사용 팁[[usage-tips]]
이 트랜스포머는 심층 강화학습에 사용됩니다. 사용하려면 이전의 모든 타임스텝에서의 행동, 상태, 보상으로부터 시퀀스를 생성해야 합니다. 이 모델은 이 모든 요소를 함께 하나의 큰 시퀀스(궤적)로 취급합니다.
## TrajectoryTransformerConfig[[transformers.TrajectoryTransformerConfig]]
[[autodoc]] TrajectoryTransformerConfig
## TrajectoryTransformerModel[[transformers.TrajectoryTransformerModel]]
[[autodoc]] TrajectoryTransformerModel
- forward
| transformers/docs/source/ko/model_doc/trajectory_transformer.md/0 | {
"file_path": "transformers/docs/source/ko/model_doc/trajectory_transformer.md",
"repo_id": "transformers",
"token_count": 2410
} |
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# 다중 GPU에서 효율적인 훈련 [[efficient-training-on-multiple-gpus]]
단일 GPU에서의 훈련이 너무 느리거나 모델 가중치가 단일 GPU의 메모리에 맞지 않는 경우, 다중-GPU 설정을 사용합니다. 단일 GPU에서 다중 GPU로 전환하기 위해서는 작업을 분산해야 합니다. 데이터, 텐서 또는 파이프라인과 같은 병렬화 기법을 사용하여 작업을 병렬로 처리할 수 있습니다. 그러나 이러한 설정을 모두에게 적용할 수 있는 완벽한 해결책은 없으며, 어떤 설정이 가장 적합한지는 사용하는 하드웨어에 따라 달라집니다. 이 문서는 주로 PyTorch 기반의 구현을 중심으로 설명하며, 대부분의 개념은 다른 프레임워크에도 적용될 수 있을 것으로 예상됩니다.
<Tip>
참고: [단일 GPU 섹션](perf_train_gpu_one)에서 소개된 전략(혼합 정밀도 훈련 또는 그래디언트 누적 등)은 일반적으로 모델 훈련에 적용되며, 다중-GPU 또는 CPU 훈련과 같은 다음 섹션으로 진입하기 전에 해당 섹션을 참고하는 것이 좋습니다.
</Tip>
먼저 1D 병렬화 기술에 대해 자세히 논의한 후, 이러한 기술을 결합하여 2D 및 3D 병렬화를 구현하여 더 빠른 훈련과 더 큰 모델을 지원하는 방법을 살펴볼 것입니다. 또한 다른 효과적인 대안 방식도 소개될 예정입니다.
## 개념 [[concepts]]
다음은 이 문서에서 자세히 설명될 주요 개념에 대한 간단한 설명입니다.
1. **DataParallel (DP)** - 동일한 설정이 여러 번 복제되고, 각 설정에 데이터 일부를 받습니다. 처리는 병렬로 수행되며 모든 설정은 각 훈련 단계의 끝날 때 동기화됩니다.
2. **TensorParallel (TP)** - 각 텐서는 여러 개의 묶음으로 분할되기에, 전체 텐서가 단일 GPU에 상주하는 대신 텐서의 각 샤드가 지정된 GPU에 상주합니다. 처리하는 동안 각 샤드는 서로 다른 GPU에서 개별적으로 병렬 처리되며 결과는 단계가 끝날 때 동기화됩니다. 분할이 수평 수준에서 이루어지기 때문에 이를 수평 병렬 처리라고 부를 수 있습니다.
3. **PipelineParallel (PP)** - 모델이 수직으로 (레이어 수준) 여러 GPU에 분할되어 모델의 단일 GPU에는 하나 또는 여러 레이어가 배치됩니다. 각 GPU는 파이프라인의 서로 다른 단계를 병렬로 처리하며 작은 배치 묶음에서 작동합니다.
4. **Zero Redundancy Optimizer (ZeRO)** - TP와 유사하게 텐서를 샤딩하지만, 전체 텐서는 순방향 또는 역방향 계산을 위해 재구성되므로 모델을 수정할 필요가 없습니다. 또한 제한된 GPU 메모리를 보완하기 위해 다양한 오프로드 기술을 지원합니다.
5. **Sharded DDP** - ZeRO의 기본 개념으로 다른 ZeRO 구현에서도 사용되는 용어입니다.
각 개념의 구체적인 내용에 대해 자세히 들어가기 전에 대규모 인프라에서 대규모 모델을 훈련하는 경우의 대략적인 결정 과정을 살펴보겠습니다.
## 확장성 전략 [[scalability-strategy]]
**⇨ 단일 노드 / 다중-GPU**
* 모델이 단일 GPU에 맞는 경우:
1. DDP - 분산 DP
2. ZeRO - 상황과 구성에 따라 더 빠를 수도 있고 그렇지 않을 수도 있음
* 모델이 단일 GPU에 맞지 않는 경우:
1. PP
2. ZeRO
3. TP
노드 내 연결 속도가 매우 빠른 NVLINK 또는 NVSwitch의 경우 세 가지 방법은 대부분 비슷한 성능을 보여야 하며, PP가 없는 경우 TP 또는 ZeRO보다 빠를 것입니다. TP의 정도도 차이를 만들 수 있습니다. 특정 설정에서 승자를 찾기 위해 실험하는 것이 가장 좋습니다.
TP는 거의 항상 단일 노드 내에서 사용됩니다. 즉, TP 크기 <= 노드당 GPU 수입니다.
* 가장 큰 레이어가 단일 GPU에 맞지 않는 경우:
1. ZeRO를 사용하지 않는 경우 - PP만으로는 맞지 않으므로 TP를 반드시 사용해야 함
2. ZeRO를 사용하는 경우에는 위의 "단일 GPU" 항목과 동일
**⇨ 다중 노드 / 다중 GPU**
* 노드 간 연결 속도가 빠른 경우:
1. ZeRO - 모델에 대부분의 수정을 필요로 하지 않음
2. PP+TP+DP - 통신이 적지만 모델에 대대적인 변경이 필요함
* 노드 간 연결 속도가 느리며, GPU 메모리가 여전히 부족한 경우:
1. DP+PP+TP+ZeRO-1
## 데이터 병렬화 [[data-parallelism]]
2개의 GPU만으로도 대부분의 사용자들은 `DataParallel` (DP)과 `DistributedDataParallel` (DDP)을 통해 향상된 훈련 속도를 누릴 수 있습니다. 이는 PyTorch의 내장 기능입니다. 일반적으로 DDP를 사용하는 것이 좋으며, DP는 일부 모델에서 작동하지 않을 수 있으므로 주의해야 합니다. [PyTorch 문서](https://pytorch.org/docs/master/generated/torch.nn.DataParallel.html)에서도 DDP의 사용을 권장합니다.
### DP vs DDP [[dp-vs-ddp]]
`DistributedDataParallel` (DDP)은 일반적으로 `DataParallel` (DP)보다 빠르지만, 항상 그렇지는 않습니다:
* DP는 파이썬 스레드 기반인 반면, DDP는 다중 프로세스 기반이기 때문에 GIL과 같은 파이썬 스레드 제한이 없습니다.
* 그러나 GPU 카드 간의 느린 상호 연결성은 DDP로 인해 실제로 느린 결과를 낼 수 있습니다.
이 두 모드 간의 GPU 간 통신 오버헤드의 주요 차이점은 다음과 같습니다:
[DDP](https://pytorch.org/docs/master/notes/ddp.html):
- 시작할 때, 주 프로세스가 모델을 gpu 0에서 다른 모든 gpu로 복제합니다.
- 그런 다음 각 배치에 대해:
1. 각 gpu는 자체 미니 배치 데이터를 직접 사용합니다.
2. `backward` 동안 로컬 그래디언트가 준비되면, 모든 프로세스에 평균화됩니다.
[DP](https://pytorch.org/docs/master/generated/torch.nn.DataParallel.html):
각 배치에 대해:
1. gpu 0은 데이터 배치를 읽고 각 gpu에 미니 배치를 보냅니다.
2. 업데이트된 모델을 gpu 0에서 각 gpu로 복제합니다.
3. `forward`를 실행하고 각 gpu의 출력을 gpu 0으로 보내고 손실을 계산합니다.
4. gpu 0에서 모든 gpu로 손실을 분산하고 `backward`를 실행합니다.
5. 각 gpu에서 그래디언트를 gpu 0으로 보내고 이를 평균화합니다.
DDP는 각 배치마다 그래디언트를 보내는 통신만을 수행하며, DP는 배치마다 5개의 다른 데이터 교환을 수행합니다.
DP는 파이썬 스레드를 통해 프로세스 내에서 데이터를 복제하며, DDP는 [torch.distributed](https://pytorch.org/docs/master/distributed.html)를 통해 데이터를 복제합니다.
DP에서는 gpu 0이 다른 gpu보다 훨씬 더 많은 작업을 수행하므로, gpu의 활용도가 낮아집니다.
DDP는 여러 대의 컴퓨터에서 사용할 수 있지만, DP의 경우는 그렇지 않습니다.
DP와 DDP 사이에는 다른 차이점이 있지만, 이 토론과는 관련이 없습니다.
이 2가지 모드를 깊게 이해하고 싶다면, [이 문서](https://www.telesens.co/2019/04/04/distributed-data-parallel-training-using-pytorch-on-aws/)를 강력히 추천합니다. 이 문서는 멋진 다이어그램을 포함하고 있으며, 다양한 하드웨어에서 여러 벤치마크와 프로파일러 출력을 설명하여 필요한 세부 사항을 모두 설명합니다.
실제 벤치마크를 살펴보겠습니다:
| Type | NVlink | Time |
| :----- | ----- | ---: |
| 2:DP | Y | 110s |
| 2:DDP | Y | 101s |
| 2:DDP | N | 131s |
분석:
여기서 DP는 NVlink가 있는 DDP보다 약 10% 느립니다. 그러나 NVlink가 없는 DDP보다 약 15% 빠릅니다.
실제 차이는 각 GPU가 다른 GPU와 동기화해야 하는 데이터 양에 따라 달라질 것입니다. 동기화할 데이터가 많을수록 느린 링크가 총 실행 시간을 늦출 수 있습니다.
다음은 전체 벤치마크 코드와 출력입니다:
해당 벤치마크에서 `NCCL_P2P_DISABLE=1`을 사용하여 NVLink 기능을 비활성화했습니다.
```bash
# DP
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
python examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 110.5948, 'train_samples_per_second': 1.808, 'epoch': 0.69}
# DDP w/ NVlink
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 101.9003, 'train_samples_per_second': 1.963, 'epoch': 0.69}
# DDP w/o NVlink
rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \
torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
{'train_runtime': 131.4367, 'train_samples_per_second': 1.522, 'epoch': 0.69}
```
하드웨어: 각각 24GB의 TITAN RTX 2개 + NVlink과 2개의 NVLink (`nvidia-smi topo -m`에서 `NV2`입니다.)
소프트웨어: `pytorch-1.8-to-be` + `cuda-11.0` / `transformers==4.3.0.dev0`
## ZeRO 데이터 병렬화 [[zero-data-parallelism]]
ZeRO를 기반으로 한 데이터 병렬화 (ZeRO-DP)는 다음 [블로그 글](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/)의 다음 다이어그램에서 설명되고 있습니다.
이 개념은 이해하기 어려울 수 있지만, 실제로는 매우 간단한 개념입니다. 이는 일반적인 `DataParallel` (DP)과 동일하지만, 전체 모델 매개변수, 그래디언트 및 옵티마이저 상태를 복제하는 대신 각 GPU는 그 중 일부만 저장합니다. 그리고 실행 시간에는 주어진 레이어에 대해 전체 레이어 매개변수가 필요할 때 각 GPU가 서로에게 필요한 부분을 제공하기 위해 동기화됩니다 - 그게 전부입니다.
각각 3개의 레이어와 3개의 매개변수가 있는 간단한 모델을 생각해 봅시다:
```
La | Lb | Lc
---|----|---
a0 | b0 | c0
a1 | b1 | c1
a2 | b2 | c2
```
레이어 La에는 가중치 a0, a1 및 a2가 있습니다.
3개의 GPU가 있는 경우, Sharded DDP (= Zero-DP)는 다음과 같이 모델을 3개의 GPU에 분할합니다:
```
GPU0:
La | Lb | Lc
---|----|---
a0 | b0 | c0
GPU1:
La | Lb | Lc
---|----|---
a1 | b1 | c1
GPU2:
La | Lb | Lc
---|----|---
a2 | b2 | c2
```
일반적인 DNN 다이어그램을 상상해보면 이는 텐서 병렬 처리와 같은 수평 슬라이싱입니다. 수직 슬라이싱은 전체 레이어 그룹을 다른 GPU에 배치하는 것입니다. 이는 시작에 불과합니다.
이제 이러한 각각의 GPU는 DP에서 작동하는 것과 마찬가지로 일반적인 미니 배치를 받습니다:
```
x0 => GPU0
x1 => GPU1
x2 => GPU2
```
입력은 수정되지 않은 상태로 일반 모델에 의해 처리될 것으로 간주합니다.
먼저, 입력은 레이어 La에 도달합니다.
GPU0에만 집중해 보겠습니다. x0은 순방향 경로를 수행하기 위해 a0, a1, a2 파라미터가 필요하지만 GPU0에는 a0만 있습니다. GPU1에서 a1을, GPU2에서 a2를 전송받아 모델의 모든 조각을 하나로 모읍니다.
병렬적으로, GPU1은 미니 배치 x1을 받고 a1만 가지고 있지만, a0 및 a2 매개변수가 필요합니다. 따라서 GPU0 및 GPU2에서 이를 가져옵니다.
GPU2도 동일한 작업을 수행합니다. 입력 x2를 받고 GPU0 및 GPU1에서 각각 a0과 a1을, 그리고 자신의 a2와 함께 전체 텐서를 복원합니다.
3개의 GPU는 복원된 전체 텐서를 받고 forward가 수행됩니다.
계산이 완료되면 더 이상 필요하지 않은 데이터는 삭제되고, 해당 데이터는 계산 중에만 사용됩니다. 복원은 사전 패치를 통해 효율적으로 수행됩니다.
그리고 전체 프로세스는 레이어 Lb에 대해 반복되고, 그 다음 Lc로 순방향으로, 그다음은 역방향으로 Lc -> Lb -> La로 반복됩니다.
개인적으로 이것은 효율적인 그룹 배낭 여행자의 중량 분배 전략처럼 들립니다:
1. 사람 A가 텐트를 운반합니다.
2. 사람 B가 난로를 운반합니다.
3. 사람 C가 도끼를 운반합니다.
이제 매일 밤 각자 가진 것을 다른 사람들과 공유하고, 가지지 않은 것은 다른 사람들로부터 받고, 아침에는 할당된 유형의 장비를 싸고 계속해서 여행을 진행합니다. 이것이 Sharded DDP / Zero DP입니다.
이 전략을 각각 자신의 텐트, 난로 및 도끼를 개별적으로 운반해야 하는 단순한 전략과 비교해보면 훨씬 비효율적일 것입니다. 이것이 Pytorch의 DataParallel (DP 및 DDP)입니다.
이 주제에 대해 논문을 읽을 때 다음 동의어를 만날 수 있습니다: Sharded, Partitioned.
ZeRO가 모델 가중치를 분할하는 방식을 자세히 살펴보면, 텐서 병렬화와 매우 유사한 것을 알 수 있습니다. 이는 이후에 설명될 수직 모델 병렬화와는 달리 각 레이어의 가중치를 분할/분할하기 때문입니다.
구현:
- [DeepSpeed](https://www.deepspeed.ai/tutorials/zero/)는 1단계 + 2단계 + 3단계의 ZeRO-DP를 제공합니다.
- [Fairscale](https://github.com/facebookresearch/fairscale/#optimizer-state-sharding-zero)은 1단계 + 2단계 + 3단계의 ZeRO-DP를 제공합니다.
- [`transformers` 통합](main_classes/trainer#trainer-integrations)
## 네이티브 모델 병렬 처리(수직적) 및 파이프라인 병렬 처리[[naive-model-parallelism-vertical-and-pipeline-parallelism]]
Naive Model Parallelism (MP)은 모델 레이어 그룹을 다중 GPU에 분산하는 방식입니다. 메커니즘은 상대적으로 간단합니다. 원하는 레이어를 `.to()`를 사용하여 원하는 장치로 전환하면 데이터가 해당 레이어로 들어오고 나갈 때 데이터도 레이어와 동일한 장치로 전환되고 나머지는 수정되지 않습니다.
대부분의 모델이 그려지는 방식이 레이어를 세로로 슬라이스하기 때문에 이를 수직 모델 병렬화라고 부릅니다. 예를 들어 다음 다이어그램은 8레이어 모델을 보여줍니다:
```
=================== ===================
| 0 | 1 | 2 | 3 | | 4 | 5 | 6 | 7 |
=================== ===================
gpu0 gpu1
```
우리는 모델을 수직으로 2개로 분할하여 레이어 0-3을 GPU0에 배치하고 레이어 4-7을 GPU1에 배치했습니다.
이제 데이터가 레이어 0에서 1로, 1에서 2로, 2에서 3으로 이동하는 동안에는 일반적인 모델입니다. 그러나 데이터가 레이어 3에서 레이어 4로 전달되어야 할 때는 GPU0에서 GPU1로 이동해야 하므로 통신 오버헤드가 발생합니다. 참여하는 GPU가 동일한 컴퓨팅 노드(예: 동일한 물리적인 기계)에 있는 경우 이 복사는 매우 빠릅니다. 그러나 GPU가 서로 다른 컴퓨팅 노드(예: 여러 기계)에 위치한 경우 통신 오버헤드는 상당히 크게 될 수 있습니다.
그런 다음 레이어 4부터 5로, 6으로, 7로 진행되는 것은 일반적인 모델과 동일하게 진행되고, 7번째 레이어가 완료되면 데이터를 다시 레이어 0으로 보내거나 또는 레이블을 마지막 레이어로 보내야 할 필요가 있습니다. 이제 손실을 계산하고 옵티마이저가 작동할 수 있습니다.
문제점:
- 이 방식을 "naive" MP라고 부르는 이유는 주어진 상황에 하나의 GPU를 제외한 모든 GPU가 유휴 상태라는 점입니다. 따라서 4개의 GPU를 사용하는 경우 단일 GPU의 메모리 양을 4배로 늘리고 나머지 하드웨어는 무시하는 것과 거의 동일합니다. 또한 장치 간 데이터 복사의 오버헤드도 있습니다. 따라서 4개의 6GB 카드는 naive MP를 사용하여 1개의 24GB 카드와 동일한 크기를 수용할 수 있지만, 후자는 데이터 복사의 오버헤드가 없으므로 훈련을 더 빨리 완료합니다. 그러나 예를 들어 40GB 카드가 있고 45GB 모델을 맞추어야 할 경우 4개의 40GB 카드로 맞출 수 있습니다 (하지만 그래디언트와 옵티마이저 상태 때문에 가까스로 가능합니다).
- 공유 임베딩은 GPU 간에 복사해야 할 수도 있습니다.
파이프라인 병렬화 (PP)은 거의 naive MP와 동일하지만 GPU 유휴 상태 문제를 해결하기 위해 들어오는 배치를 마이크로 배치로 나누고 인공적으로 파이프라인을 생성하여 서로 다른 GPU가 동시에 계산에 참여할 수 있게 합니다.
[GPipe 논문](https://ai.googleblog.com/2019/03/introducing-gpipe-open-source-library.html)에서 가져온 그림은 상단에 naive MP를, 하단에는 PP를 보여줍니다:
하단 다이어그램에서 PP가 유휴 영역이 적은 것을 쉽게 볼 수 있습니다. 유휴 부분을 "bubble"이라고 합니다.
다이어그램의 양쪽 부분은 참여하는 GPU가 4개인 병렬성을 보여줍니다. 즉, 4개의 GPU가 파이프라인에 참여합니다. 따라서 4개의 파이프 단계 F0, F1, F2 및 F3의 순방향 경로와 B3, B2, B1 및 B0의 역방향 경로가 있습니다.
PP는 조정해야 할 새로운 하이퍼파라미터인 `chunks`를 도입합니다. 이는 동일한 파이프 단계를 통해 일련의 데이터를 묶어서 보내는 방식을 정의합니다. 예를 들어, 아래 다이어그램에서 `chunks=4`를 볼 수 있습니다. GPU0은 0, 1, 2 및 3 (F0,0, F0,1, F0,2, F0,3) 묶음에서 동일한 순방향 경로를 수행하고, 다른 GPU가 작업을 수행하기 시작하고 완료가 시작될 때만 GPU0이 묶음의 역순으로 3, 2, 1 및 0 (B0,3, B0,2, B0,1, B0,0) 경로를 수행합니다.
개념적으로 이는 그래디언트 누적 단계 (GAS)와 동일한 개념입니다. 파이토치에서는 `chunks`를 사용하고 DeepSpeed에서는 동일한 하이퍼파라미터를 GAS로 참조합니다.
묶음으로 인해 PP는 마이크로 배치 (MBS)의 개념을 도입합니다. DP는 전역 데이터 배치 크기를 미니 배치로 나눕니다. 따라서 DP 차수가 4이고 전역 배치 크기가 1024이면 256씩 4개의 미니 배치로 분할됩니다 (1024/4). 그리고 `chunks` (또는 GAS)의 수가 32이면 마이크로 배치 크기는 8이 됩니다 (256/32). 각 파이프라인 단계는 한 번에 하나의 마이크로 배치와 함께 작동합니다.
DP + PP 설정의 전역 배치 크기를 계산하려면 `mbs*chunks*dp_degree` (`8*32*4=1024`)를 수행합니다.
다이어그램으로 돌아가 보겠습니다.
`chunks=1`로 설정하면 매우 비효율적인 naive MP가 생성되며, 매우 큰 `chunks` 값으로 설정하면 아주 작은 마이크로 배치 크기가 생성되어 효율적이지 않을 수 있습니다. 따라서 가장 효율적인 GPU 활용을 위해 어떤 값이 가장 적절한지 실험을 해야 합니다.
다이어그램에서 보이는 것처럼 "dead" 시간의 버블이 존재하여 마지막 `forward` 단계가 `backward` 단계가 파이프라인을 완료하기를 기다려야 하는 상황이 발생하지만, `chunks`의 가장 적절한 값을 찾는 것의 목적은 모든 참여하는 GPU에서 동시에 고도로 활용되는 GPU 활용을 가능하게 하여 버블의 크기를 최소화하는 것입니다.
해결책은 전통적인 파이프라인 API와 더 현대적인 솔루션으로 나뉩니다. 전통적인 파이프라인 API 솔루션과 현대적인 솔루션에 대해 알아보겠습니다.
전통적인 파이프라인 API 솔루션:
- 파이토치
- FairScale
- DeepSpeed
- Megatron-LM
현대적인 솔루션:
- Varuna
- Sagemaker
전통적인 파이프라인 API 솔루션의 문제점:
- 모델을 상당히 수정해야 한다는 점이 문제입니다. 파이프라인은 모듈의 정상적인 흐름을 `nn.Sequential` 시퀀스로 다시 작성해야 하므로 모델의 설계를 변경해야 할 수 있습니다.
- 현재 파이프라인 API는 매우 제한적입니다. 파이프라인의 매우 첫 번째 단계에서 전달되는 많은 파이썬 변수가 있는 경우 이를 해결해야 합니다. 현재 파이프라인 인터페이스는 하나의 텐서 또는 텐서의 튜플을 유일한 입력 및 출력으로 요구합니다. 이러한 텐서는 마이크로 배치로 미니 배치로 묶을 것이므로 첫 번째 차원으로 배치 크기가 있어야 합니다. 가능한 개선 사항은 여기에서 논의되고 있습니다. https://github.com/pytorch/pytorch/pull/50693
- 파이프 단계 수준에서 조건부 제어 흐름은 불가능합니다. 예를 들어, T5와 같은 인코더-디코더 모델은 조건부 인코더 단계를 처리하기 위해 특별한 해결책이 필요합니다.
- 각 레이어를 정렬하여 하나의 모델의 출력이 다른 모델의 입력이 되도록해야 합니다.
우리는 아직 Varuna와 SageMaker로 실험하지 않았지만, 해당 논문들은 위에서 언급한 문제들의 목록을 극복했고 사용자의 모델에 대한 변경 사항이 훨씬 적게 필요하다고 보고하고 있습니다.
구현:
- [파이토치](https://pytorch.org/docs/stable/pipeline.html) (파이토치-1.8에서 초기 지원, 1.9에서 점진적으로 개선되고 1.10에서 더 개선됨). [예제](https://github.com/pytorch/pytorch/blob/master/benchmarks/distributed/pipeline/pipe.py)도 참고하세요.
- [FairScale](https://fairscale.readthedocs.io/en/latest/tutorials/pipe.html)
- [DeepSpeed](https://www.deepspeed.ai/tutorials/pipeline/)
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)은 내부 구현을 가지고 있습니다 - API 없음.
- [Varuna](https://github.com/microsoft/varuna)
- [SageMaker](https://arxiv.org/abs/2111.05972) - 이는 AWS에서만 사용할 수 있는 소유 솔루션입니다.
- [OSLO](https://github.com/tunib-ai/oslo) - 이는 Hugging Face Transformers를 기반으로 구현된 파이프라인 병렬화입니다.
🤗 Transformers 상태: 이 작성 시점에서 모델 중 어느 것도 완전한 PP를 지원하지 않습니다. GPT2와 T5 모델은 naive MP를 지원합니다. 주요 장애물은 모델을 `nn.Sequential`로 변환하고 모든 입력을 텐서로 가져와야 하는 것을 처리할 수 없기 때문입니다. 현재 모델에는 이러한 변환을 매우 복잡하게 만드는 많은 기능이 포함되어 있어 제거해야 합니다.
기타 접근 방법:
DeepSpeed, Varuna 및 SageMaker는 [교차 파이프라인(Interleaved Pipeline)](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html) 개념을 사용합니다.
여기서는 버블(유휴 시간)을 역방향 패스에 우선순위를 부여하여 최소화합니다.
Varuna는 가장 효율적인 스케줄링을 찾기 위해 시뮬레이션을 사용하여 스케줄을 개선하려고 합니다.
OSLO는 `nn.Sequential`로 변환하지 않고 Transformers를 기반으로 한 파이프라인 병렬화를 구현했습니다.
## 텐서 병렬 처리 [[tensor-parallelism]]
텐서 병렬 처리에서는 각 GPU가 텐서의 일부분만 처리하고 전체 텐서가 필요한 연산에 대해서만 전체 텐서를 집계합니다.
이 섹션에서는 [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) 논문인 [Efficient Large-Scale Language Model Training on GPU Clusters](https://arxiv.org/abs/2104.04473)에서의 개념과 다이어그램을 사용합니다.
Transformer의 주요 구성 요소는 fully connected `nn.Linear`와 비선형 활성화 함수인 `GeLU`입니다.
Megatron 논문의 표기법을 따라 행렬의 점곱 부분을 `Y = GeLU(XA)`로 표현할 수 있습니다. 여기서 `X`와 `Y`는 입력 및 출력 벡터이고 `A`는 가중치 행렬입니다.
행렬 형태로 계산을 살펴보면, 행렬 곱셈을 다중 GPU로 분할할 수 있는 방법을 쉽게 알 수 있습니다:
가중치 행렬 `A`를 `N`개의 GPU에 대해 열별로 분할하고 병렬로 행렬 곱셈 `XA_1`에서 `XA_n`까지 수행하면 `N`개의 출력 벡터 `Y_1, Y_2, ..., Y_n`가 생성되며 독립적으로 `GeLU`에 전달될 수 있습니다:
이 원리를 사용하여 동기화가 필요하지 않은 GPU 간의 임의 깊이의 MLP를 업데이트할 수 있습니다. 그러나 결과 벡터를 샤드로부터 재구성해야 하는 마지막 단계까지는 GPU 간의 동기화가 필요합니다. Megatron-LM 논문의 저자들은 이에 대한 유용한 그림을 제공합니다:
다중 헤드 어텐션 레이어의 병렬화는 더욱 간단합니다. 이미 독립적인 다중 헤드를 가지고 있기 때문에 이미 병렬화되어 있습니다!
특별 고려사항: TP는 매우 빠른 네트워크가 필요하므로 한 개 이상의 노드에서 TP를 수행하는 것은 권장되지 않습니다. 실제로 노드에 4개의 GPU가 있는 경우 TP의 최대 차수는 4입니다. TP 차수가 8인 경우 최소한 8개의 GPU가 있는 노드를 사용해야 합니다.
이 섹션은 원래의 [더 자세한 TP 개요](https://github.com/huggingface/transformers/issues/10321#issuecomment-783543530)를 기반으로 합니다.
작성자는 [@anton-l](https://github.com/anton-l)입니다.
SageMaker는 더 효율적인 처리를 위해 TP와 DP를 결합합니다.
대체 이름:
- DeepSpeed는 이를 [텐서 슬라이싱](https://www.deepspeed.ai/training/#model-parallelism)이라고 부릅니다.
구현:
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)은 내부 구현을 가지고 있으므로 모델에 매우 특화되어 있습니다.
- [parallelformers](https://github.com/tunib-ai/parallelformers) (현재는 추론에만 해당)
- [SageMaker](https://arxiv.org/abs/2111.05972) - 이는 AWS에서만 사용할 수 있는 소유 솔루션입니다.
- [OSLO](https://github.com/tunib-ai/oslo)은 Transformers를 기반으로 한 텐서 병렬 처리 구현을 가지고 있습니다.
🤗 Transformers 현황:
- core: 아직 핵심 부분에 구현되지 않음
- 그러나 추론을 하려면 [parallelformers](https://github.com/tunib-ai/parallelformers)가 대부분의 모델을 지원합니다. 따라서 핵심 부분에 구현되기 전까지 그들의 것을 사용할 수 있습니다. 그리고 훈련 모드도 지원될 예정입니다.
- Deepspeed-Inference는 CUDA 커널을 기반으로 하는 매우 빠른 추론 모드에서 BERT, GPT-2 및 GPT-Neo 모델을 지원합니다. 자세한 내용은 [여기](https://www.deepspeed.ai/tutorials/inference-tutorial/)를 참조하세요.
## DP+PP [[dppp]]
DeepSpeed [pipeline tutorial](https://www.deepspeed.ai/tutorials/pipeline/)에서 다음 다이어그램은 DP와 PP를 결합하는 방법을 보여줍니다.
여기서 DP 랭크 0은 GPU2를 보지 못하고, DP 랭크 1은 GPU3을 보지 못하는 것이 중요합니다. DP에게는 딱 2개의 GPU인 것처럼 데이터를 공급합니다. GPU0은 PP를 사용하여 GPU2에게 일부 작업을 "비밀리에" 할당합니다. 그리고 GPU1도 GPU3을 도움으로 삼아 같은 방식으로 작업합니다.
각 차원마다 적어도 2개의 GPU가 필요하므로 최소한 4개의 GPU가 필요합니다.
구현:
- [DeepSpeed](https://github.com/deepspeedai/DeepSpeed)
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
- [Varuna](https://github.com/microsoft/varuna)
- [SageMaker](https://arxiv.org/abs/2111.05972)
- [OSLO](https://github.com/tunib-ai/oslo)
🤗 Transformers 현황: 아직 구현되지 않음
## DP+PP+TP [[dppptp]]
더 효율적인 훈련을 위해 PP와 TP 및 DP를 결합하여 3D 병렬 처리를 사용합니다. 다음 다이어그램에서 이를 확인할 수 있습니다.
이 다이어그램은 [3D parallelism: Scaling to trillion-parameter models](https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/)이라는 블로그 글에서 확인할 수 있습니다.
각 차원마다 적어도 2개의 GPU가 필요하므로 최소한 8개의 GPU가 필요합니다.
구현:
- [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) - DeepSpeed는 더욱 효율적인 DP인 ZeRO-DP라고도 부릅니다.
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
- [Varuna](https://github.com/microsoft/varuna)
- [SageMaker](https://arxiv.org/abs/2111.05972)
- [OSLO](https://github.com/tunib-ai/oslo)
🤗 Transformers 현황: 아직 구현되지 않음. PP와 TP가 없기 때문입니다.
## ZeRO DP+PP+TP [[zero-dppptp]]
DeepSpeed의 주요 기능 중 하나는 DP의 확장인 ZeRO입니다. ZeRO-DP에 대해 이미 [ZeRO Data Parallelism](#zero-data-parallelism)에서 논의되었습니다. 일반적으로 이는 PP나 TP를 필요로하지 않는 독립적인 기능입니다. 그러나 PP와 TP와 결합할 수도 있습니다.
ZeRO-DP가 PP와 (선택적으로 TP와) 결합되면 일반적으로 ZeRO 단계 1(옵티마이저 분할)만 활성화됩니다.
이론적으로는 ZeRO 단계 2(그라디언트 분할)를 파이프라인 병렬 처리와 함께 사용할 수도 있지만, 이는 성능에 나쁜 영향을 미칠 것입니다. 각 마이크로 배치마다 그라디언트를 샤딩하기 전에 추가적인 리듀스-스캐터 컬렉티브가 필요하며, 이는 잠재적으로 상당한 통신 오버헤드를 추가합니다. 파이프라인 병렬 처리의 특성상 작은 마이크로 배치가 사용되며, 산술 연산 강도(마이크로 배치 크기)를 균형 있게 유지하면서 파이프라인 버블(마이크로 배치 수)을 최소화하는 것에 중점을 둡니다. 따라서 해당 통신 비용은 문제가 될 것입니다.
또한, PP로 인해 정상보다 적은 수의 레이어가 있으므로 메모리 절약은 크지 않을 것입니다. PP는 이미 그래디언트 크기를 ``1/PP``로 줄이기 때문에 그래디언트 샤딩의 절약 효과는 순수 DP보다는 미미합니다.
ZeRO 단계 3도 같은 이유로 좋은 선택이 아닙니다 - 더 많은 노드 간 통신이 필요합니다.
그리고 ZeRO가 있기 때문에 다른 이점은 ZeRO-Offload입니다. 이는 단계 1이므로 옵티마이저 상태를 CPU로 오프로드할 수 있습니다.
구현:
- [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed) 및 [BigScience의 Megatron-Deepspeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed), 이전 저장소의 포크입니다.
- [OSLO](https://github.com/tunib-ai/oslo)
중요한 논문:
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](
https://arxiv.org/abs/2201.11990)
🤗 Transformers 현황: 아직 구현되지 않음, PP와 TP가 없기 때문입니다.
## FlexFlow [[flexflow]]
[FlexFlow](https://github.com/flexflow/FlexFlow)는 약간 다른 방식으로 병렬화 문제를 해결합니다.
논문: ["Beyond Data and Model Parallelism for Deep Neural Networks" by Zhihao Jia, Matei Zaharia, Alex Aiken](https://arxiv.org/abs/1807.05358)
이는 Sample-Operator-Attribute-Parameter를 기반으로 하는 일종의 4D 병렬화를 수행합니다.
1. Sample = 데이터 병렬화 (샘플별 병렬)
2. Operator = 단일 연산을 여러 하위 연산으로 병렬화
3. Attribute = 데이터 병렬화 (길이별 병렬)
4. Parameter = 모델 병렬화 (수평 또는 수직과 관계없이)
예시:
* Sample
512 길이의 10개의 배치를 가정해 봅시다. 이를 sample 차원으로 2개의 장치에 병렬화하면, 10 x 512는 5 x 2 x 512가 됩니다.
* Operator
레이어 정규화를 수행한다면, 우선 std를 계산하고 두 번째로 mean을 계산한 다음 데이터를 정규화할 수 있습니다. Operator 병렬화는 std와 mean을 병렬로 계산할 수 있도록 합니다. 따라서 operator 차원으로 2개의 장치 (cuda:0, cuda:1)에 병렬화하면, 먼저 입력 데이터를 두 장치로 복사한 다음 cuda:0에서 std를 계산하고 cuda:1에서 동시에 mean을 계산합니다.
* Attribute
512 길이의 10개의 배치가 있습니다. 이를 attribute 차원으로 2개의 장치에 병렬화하면, 10 x 512는 10 x 2 x 256이 됩니다.
* Parameter
이는 tensor 모델 병렬화 또는 naive layer-wise 모델 병렬화와 유사합니다.
이 프레임워크의 중요한 점은 (1) GPU/TPU/CPU 대 (2) RAM/DRAM 대 (3) 빠른 인트라-커넥트 대 느린 인터-커넥트와 같은 리소스를 고려하여 어디에서 어떤 병렬화를 사용할지를 알고리즘적으로 자동으로 최적화한다는 것입니다.
하나 매우 중요한 측면은 FlexFlow가 정적이고 고정된 워크로드를 가진 모델에 대한 DNN 병렬화를 최적화하기 위해 설계되었다는 것입니다. 동적인 동작을 가진 모델은 반복마다 다른 병렬화 전략을 선호할 수 있습니다.
따라서 이 프레임워크의 장점은 선택한 클러스터에서 30분 동안 시뮬레이션을 실행하고 이 특정 환경을 최적으로 활용하기 위한 최상의 전략을 제안한다는 것입니다. 부품을 추가/제거/교체하면 실행하고 그에 대한 계획을 다시 최적화한 후 훈련할 수 있습니다. 다른 설정은 자체적인 사용자 정의 최적화를 가질 수 있습니다.
🤗 Transformers 현황: 아직 통합되지 않음. 이미 [transformers.utils.fx](https://github.com/huggingface/transformers/blob/master/src/transformers/utils/fx.py)를 통해 모델을 FX-추적할 수 있으며, 이는 FlexFlow의 선행 조건입니다. 따라서 어떤 작업을 수행해야 FlexFlow가 우리의 모델과 함께 작동할 수 있는지 파악해야 합니다.
## 어떤 전략을 사용해야 할까요? [[which-strategy-to-use-when]]
다음은 어떤 병렬화 전략을 언제 사용해야 하는지에 대한 매우 대략적인 개요입니다. 각 목록의 첫 번째 전략이 일반적으로 더 빠릅니다.
**⇨ 단일 GPU**
* 모델이 단일 GPU에 맞는 경우:
1. 일반적인 사용
* 모델이 단일 GPU에 맞지 않는 경우:
1. ZeRO + CPU 및 옵션으로 NVMe 언로드
2. 위와 동일하게 사용하되, 가장 큰 레이어가 단일 GPU에 맞지 않는 경우 Memory Centric Tiling(자세한 내용은 아래 참조)을 추가적으로 사용
* 가장 큰 레이어가 단일 GPU에 맞지 않는 경우:
1. ZeRO - [Memory Centric Tiling](https://deepspeed.readthedocs.io/en/latest/zero3.html#memory-centric-tiling) (MCT) 활성화. 이를 통해 크기가 매우 큰 레이어를 임의로 분할하여 순차적으로 실행할 수 있습니다. MCT는 GPU에 활성화된 매개변수의 수를 줄이지만 활성화 메모리에는 영향을 주지 않습니다. 현재 작성 기준으로 이 요구사항은 매우 드물기 때문에 사용자가 `torch.nn.Linear`를 수동으로 수정해야 합니다.
**⇨ 단일 노드 / 다중 GPU**
* 모델이 단일 GPU에 맞는 경우:
1. DDP - 분산 DP
2. ZeRO - 상황과 구성에 따라 빠를 수도 있고 그렇지 않을 수도 있습니다.
* 모델이 단일 GPU에 맞지 않는 경우:
1. PP
2. ZeRO
3. TP
NVLINK 또는 NVSwitch를 통한 매우 빠른 인트라-노드 연결이 있는 경우 이 세 가지 방법은 거의 동등할 것이며, 이러한 연결이 없는 경우 PP가 TP나 ZeRO보다 빠를 것입니다. 또한 TP의 차수도 영향을 줄 수 있습니다. 특정 설정에서 우승자를 찾기 위해 실험하는 것이 가장 좋습니다.
TP는 거의 항상 단일 노드 내에서 사용됩니다. 즉, TP 크기 <= 노드당 GPU 수입니다.
* 가장 큰 레이어가 단일 GPU에 맞지 않는 경우:
1. ZeRO를 사용하지 않을 경우 - PP만 사용할 수 없으므로 TP를 사용해야 합니다.
2. ZeRO를 사용할 경우, "단일 GPU"의 항목과 동일한 항목 참조
**⇨ 다중 노드 / 다중 GPU**
* 빠른 노드 간 연결이 있는 경우:
1. ZeRO - 모델에 대한 수정이 거의 필요하지 않습니다.
2. PP+TP+DP - 통신이 적지만 모델에 대한 대규모 변경이 필요합니다.
* 느린 노드 간 연결 및 GPU 메모리 부족한 경우:
1. DP+PP+TP+ZeRO-1
| transformers/docs/source/ko/perf_train_gpu_many.md/0 | {
"file_path": "transformers/docs/source/ko/perf_train_gpu_many.md",
"repo_id": "transformers",
"token_count": 28503
} |
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# 스크립트로 실행하기[[train-with-a-script]]
🤗 Transformers 노트북과 함께 [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch), [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow), 또는 [JAX/Flax](https://github.com/huggingface/transformers/tree/main/examples/flax)를 사용해 특정 태스크에 대한 모델을 훈련하는 방법을 보여주는 예제 스크립트도 있습니다.
또한 [연구 프로젝트](https://github.com/huggingface/transformers/tree/main/examples/research_projects) 및 [레거시 예제](https://github.com/huggingface/transformers/tree/main/examples/legacy)에서 대부분 커뮤니티에서 제공한 스크립트를 찾을 수 있습니다.
이러한 스크립트는 적극적으로 유지 관리되지 않으며 최신 버전의 라이브러리와 호환되지 않을 가능성이 높은 특정 버전의 🤗 Transformers를 필요로 합니다.
예제 스크립트가 모든 문제에서 바로 작동하는 것은 아니며, 해결하려는 문제에 맞게 스크립트를 변경해야 할 수도 있습니다.
이를 위해 대부분의 스크립트에는 데이터 전처리 방법이 나와있어 필요에 따라 수정할 수 있습니다.
예제 스크립트에 구현하고 싶은 기능이 있으면 pull request를 제출하기 전에 [포럼](https://discuss.huggingface.co/) 또는 [이슈](https://github.com/huggingface/transformers/issues)에서 논의해 주세요.
버그 수정은 환영하지만 가독성을 희생하면서까지 더 많은 기능을 추가하는 pull request는 병합(merge)하지 않을 가능성이 높습니다.
이 가이드에서는 [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) 및 [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization)에서 요약 훈련하는
스크립트 예제를 실행하는 방법을 설명합니다.
특별한 설명이 없는 한 모든 예제는 두 프레임워크 모두에서 작동할 것으로 예상됩니다.
## 설정하기[[setup]]
최신 버전의 예제 스크립트를 성공적으로 실행하려면 새 가상 환경에서 **소스로부터 🤗 Transformers를 설치**해야 합니다:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
pip install .
```
이전 버전의 예제 스크립트를 보려면 아래 토글을 클릭하세요:
<details>
<summary>이전 버전의 🤗 Transformers 예제</summary>
<ul>
<li><a href="https://github.com/huggingface/transformers/tree/v4.5.1/examples">v4.5.1</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v4.4.2/examples">v4.4.2</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v4.3.3/examples">v4.3.3</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v4.2.2/examples">v4.2.2</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v4.1.1/examples">v4.1.1</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v4.0.1/examples">v4.0.1</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v3.5.1/examples">v3.5.1</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v3.4.0/examples">v3.4.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v3.3.1/examples">v3.3.1</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v3.2.0/examples">v3.2.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v3.1.0/examples">v3.1.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v3.0.2/examples">v3.0.2</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.11.0/examples">v2.11.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.10.0/examples">v2.10.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.9.1/examples">v2.9.1</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.8.0/examples">v2.8.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.7.0/examples">v2.7.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.6.0/examples">v2.6.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.5.1/examples">v2.5.1</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.4.0/examples">v2.4.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.3.0/examples">v2.3.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.2.0/examples">v2.2.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.1.0/examples">v2.1.1</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v2.0.0/examples">v2.0.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v1.2.0/examples">v1.2.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v1.1.0/examples">v1.1.0</a></li>
<li><a href="https://github.com/huggingface/transformers/tree/v1.0.0/examples">v1.0.0</a></li>
</ul>
</details>
그리고 다음과 같이 복제(clone)해온 🤗 Transformers 버전을 특정 버전(예: v3.5.1)으로 전환하세요:
```bash
git checkout tags/v3.5.1
```
올바른 라이브러리 버전을 설정한 후 원하는 예제 폴더로 이동하여 예제별로 라이브러리에 대한 요구 사항(requirements)을 설치합니다:
```bash
pip install -r requirements.txt
```
## 스크립트 실행하기[[run-a-script]]
<frameworkcontent>
<pt>
예제 스크립트는 🤗 [Datasets](https://huggingface.co/docs/datasets/) 라이브러리에서 데이터 세트를 다운로드하고 전처리합니다.
그런 다음 스크립트는 요약 기능을 지원하는 아키텍처에서 [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer)를 사용하여 데이터 세트를 미세 조정합니다.
다음 예는 [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) 데이터 세트에서 [T5-small](https://huggingface.co/google-t5/t5-small)을 미세 조정합니다.
T5 모델은 훈련 방식에 따라 추가 `source_prefix` 인수가 필요하며, 이 프롬프트는 요약 작업임을 T5에 알려줍니다.
```bash
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate
```
</pt>
<tf>
예제 스크립트는 🤗 [Datasets](https://huggingface.co/docs/datasets/) 라이브러리에서 데이터 세트를 다운로드하고 전처리합니다.
그런 다음 스크립트는 요약 기능을 지원하는 아키텍처에서 Keras를 사용하여 데이터 세트를 미세 조정합니다.
다음 예는 [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) 데이터 세트에서 [T5-small](https://huggingface.co/google-t5/t5-small)을 미세 조정합니다.
T5 모델은 훈련 방식에 따라 추가 `source_prefix` 인수가 필요하며, 이 프롬프트는 요약 작업임을 T5에 알려줍니다.
```bash
python examples/tensorflow/summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 16 \
--num_train_epochs 3 \
--do_train \
--do_eval
```
</tf>
</frameworkcontent>
## 혼합 정밀도(mixed precision)로 분산 훈련하기[[distributed-training-and-mixed-precision]]
[Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) 클래스는 분산 훈련과 혼합 정밀도(mixed precision)를 지원하므로 스크립트에서도 사용할 수 있습니다.
이 두 가지 기능을 모두 활성화하려면 다음 두 가지를 설정해야 합니다:
- `fp16` 인수를 추가해 혼합 정밀도(mixed precision)를 활성화합니다.
- `nproc_per_node` 인수를 추가해 사용할 GPU 개수를 설정합니다.
```bash
torchrun \
--nproc_per_node 8 pytorch/summarization/run_summarization.py \
--fp16 \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate
```
TensorFlow 스크립트는 분산 훈련을 위해 [`MirroredStrategy`](https://www.tensorflow.org/guide/distributed_training#mirroredstrategy)를 활용하며, 훈련 스크립트에 인수를 추가할 필요가 없습니다.
다중 GPU 환경이라면, TensorFlow 스크립트는 기본적으로 여러 개의 GPU를 사용합니다.
## TPU 위에서 스크립트 실행하기[[run-a-script-on-a-tpu]]
<frameworkcontent>
<pt>
Tensor Processing Units (TPUs)는 성능을 가속화하기 위해 특별히 설계되었습니다.
PyTorch는 [XLA](https://www.tensorflow.org/xla) 딥러닝 컴파일러와 함께 TPU를 지원합니다(자세한 내용은 [여기](https://github.com/pytorch/xla/blob/master/README.md) 참조).
TPU를 사용하려면 `xla_spawn.py` 스크립트를 실행하고 `num_cores` 인수를 사용하여 사용하려는 TPU 코어 수를 설정합니다.
```bash
python xla_spawn.py --num_cores 8 \
summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate
```
</pt>
<tf>
Tensor Processing Units (TPUs)는 성능을 가속화하기 위해 특별히 설계되었습니다.
TensorFlow 스크립트는 TPU를 훈련에 사용하기 위해 [`TPUStrategy`](https://www.tensorflow.org/guide/distributed_training#tpustrategy)를 활용합니다.
TPU를 사용하려면 TPU 리소스의 이름을 `tpu` 인수에 전달합니다.
```bash
python run_summarization.py \
--tpu name_of_tpu_resource \
--model_name_or_path google-t5/t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 16 \
--num_train_epochs 3 \
--do_train \
--do_eval
```
</tf>
</frameworkcontent>
## 🤗 Accelerate로 스크립트 실행하기[[run-a-script-with-accelerate]]
🤗 [Accelerate](https://huggingface.co/docs/accelerate)는 PyTorch 훈련 과정에 대한 완전한 가시성을 유지하면서 여러 유형의 설정(CPU 전용, 다중 GPU, TPU)에서 모델을 훈련할 수 있는 통합 방법을 제공하는 PyTorch 전용 라이브러리입니다.
🤗 Accelerate가 설치되어 있는지 확인하세요:
> 참고: Accelerate는 빠르게 개발 중이므로 스크립트를 실행하려면 accelerate를 설치해야 합니다.
```bash
pip install git+https://github.com/huggingface/accelerate
```
`run_summarization.py` 스크립트 대신 `run_summarization_no_trainer.py` 스크립트를 사용해야 합니다.
🤗 Accelerate 클래스가 지원되는 스크립트는 폴더에 `task_no_trainer.py` 파일이 있습니다.
다음 명령을 실행하여 구성 파일을 생성하고 저장합니다:
```bash
accelerate config
```
설정을 테스트하여 올바르게 구성되었는지 확인합니다:
```bash
accelerate test
```
이제 훈련을 시작할 준비가 되었습니다:
```bash
accelerate launch run_summarization_no_trainer.py \
--model_name_or_path google-t5/t5-small \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir ~/tmp/tst-summarization
```
## 사용자 정의 데이터 세트 사용하기[[use-a-custom-dataset]]
요약 스크립트는 사용자 지정 데이터 세트가 CSV 또는 JSON 파일인 경우 지원합니다.
사용자 지정 데이터 세트를 사용하는 경우에는 몇 가지 추가 인수를 지정해야 합니다:
- `train_file`과 `validation_file`은 훈련 및 검증 파일의 경로를 지정합니다.
- `text_column`은 요약할 입력 텍스트입니다.
- `summary_column`은 출력할 대상 텍스트입니다.
사용자 지정 데이터 세트를 사용하는 요약 스크립트는 다음과 같습니다:
```bash
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--train_file path_to_csv_or_jsonlines_file \
--validation_file path_to_csv_or_jsonlines_file \
--text_column text_column_name \
--summary_column summary_column_name \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--overwrite_output_dir \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--predict_with_generate
```
## 스크립트 테스트하기[[test-a-script]]
전체 데이터 세트를 대상으로 훈련을 완료하는데 꽤 오랜 시간이 걸리기 때문에, 작은 데이터 세트에서 모든 것이 예상대로 실행되는지 확인하는 것이 좋습니다.
다음 인수를 사용하여 데이터 세트를 최대 샘플 수로 잘라냅니다:
- `max_train_samples`
- `max_eval_samples`
- `max_predict_samples`
```bash
python examples/pytorch/summarization/run_summarization.py \
--model_name_or_path google-t5/t5-small \
--max_train_samples 50 \
--max_eval_samples 50 \
--max_predict_samples 50 \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate
```
모든 예제 스크립트가 `max_predict_samples` 인수를 지원하지는 않습니다.
스크립트가 이 인수를 지원하는지 확실하지 않은 경우 `-h` 인수를 추가하여 확인하세요:
```bash
examples/pytorch/summarization/run_summarization.py -h
```
## 체크포인트(checkpoint)에서 훈련 이어서 하기[[resume-training-from-checkpoint]]
또 다른 유용한 옵션은 이전 체크포인트에서 훈련을 재개하는 것입니다.
이렇게 하면 훈련이 중단되더라도 처음부터 다시 시작하지 않고 중단한 부분부터 다시 시작할 수 있습니다.
체크포인트에서 훈련을 재개하는 방법에는 두 가지가 있습니다.
첫 번째는 `output_dir previous_output_dir` 인수를 사용하여 `output_dir`에 저장된 최신 체크포인트부터 훈련을 재개하는 방법입니다.
이 경우 `overwrite_output_dir`을 제거해야 합니다:
```bash
python examples/pytorch/summarization/run_summarization.py
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--output_dir previous_output_dir \
--predict_with_generate
```
두 번째는 `resume_from_checkpoint path_to_specific_checkpoint` 인수를 사용하여 특정 체크포인트 폴더에서 훈련을 재개하는 방법입니다.
```bash
python examples/pytorch/summarization/run_summarization.py
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--resume_from_checkpoint path_to_specific_checkpoint \
--predict_with_generate
```
## 모델 공유하기[[share-your-model]]
모든 스크립트는 최종 모델을 [Model Hub](https://huggingface.co/models)에 업로드할 수 있습니다.
시작하기 전에 Hugging Face에 로그인했는지 확인하세요:
```bash
huggingface-cli login
```
그런 다음 스크립트에 `push_to_hub` 인수를 추가합니다.
이 인수는 Hugging Face 사용자 이름과 `output_dir`에 지정된 폴더 이름으로 저장소를 생성합니다.
저장소에 특정 이름을 지정하려면 `push_to_hub_model_id` 인수를 사용하여 추가합니다.
저장소는 네임스페이스 아래에 자동으로 나열됩니다.
다음 예는 특정 저장소 이름으로 모델을 업로드하는 방법입니다:
```bash
python examples/pytorch/summarization/run_summarization.py
--model_name_or_path google-t5/t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--push_to_hub \
--push_to_hub_model_id finetuned-t5-cnn_dailymail \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate
``` | transformers/docs/source/ko/run_scripts.md/0 | {
"file_path": "transformers/docs/source/ko/run_scripts.md",
"repo_id": "transformers",
"token_count": 10840
} |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# 단일 영상 기반 깊이 추정[[depth-estimation-pipeline]]
단일 영상 기반 깊이 추정은 한 장면의 단일 이미지에서 장면의 깊이 정보를 예측하는 컴퓨터 비전 작업입니다.
즉, 단일 카메라 시점의 장면에 있는 물체의 거리를 예측하는 과정입니다.
단일 영상 기반 깊이 추정은 3D 재구성, 증강 현실, 자율 주행, 로봇 공학 등 다양한 분야에서 응용됩니다.
조명 조건, 가려짐, 텍스처와 같은 요소의 영향을 받을 수 있는 장면 내 물체와 해당 깊이 정보 간의 복잡한 관계를 모델이 이해해야 하므로 까다로운 작업입니다.
<Tip>
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/depth-estimation)를 확인하는 것이 좋습니다.
</Tip>
이번 가이드에서 배울 내용은 다음과 같습니다:
* 깊이 추정 파이프라인 만들기
* 직접 깊이 추정 추론하기
시작하기 전에, 필요한 모든 라이브러리가 설치되어 있는지 확인하세요:
```bash
pip install -q transformers
```
## 깊이 추정 파이프라인[[depth-estimation-inference-by-hand]]
깊이 추정을 추론하는 가장 간단한 방법은 해당 기능을 제공하는 [`pipeline`]을 사용하는 것입니다.
[Hugging Face Hub 체크포인트](https://huggingface.co/models?pipeline_tag=depth-estimation&sort=downloads)에서 파이프라인을 초기화합니다:
```py
>>> from transformers import pipeline
>>> checkpoint = "vinvino02/glpn-nyu"
>>> depth_estimator = pipeline("depth-estimation", model=checkpoint)
```
다음으로, 분석할 이미지를 한 장 선택하세요:
```py
>>> from PIL import Image
>>> import requests
>>> url = "https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-estimation-example.jpg" alt="Photo of a busy street"/>
</div>
이미지를 파이프라인으로 전달합니다.
```py
>>> predictions = depth_estimator(image)
```
파이프라인은 두 개의 항목을 가지는 딕셔너리를 반환합니다.
첫 번째는 `predicted_depth`로 각 픽셀의 깊이를 미터로 표현한 값을 가지는 텐서입니다.
두 번째는 `depth`로 깊이 추정 결과를 시각화하는 PIL 이미지입니다.
이제 시각화한 결과를 살펴보겠습니다:
```py
>>> predictions["depth"]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/>
</div>
## 직접 깊이 추정 추론하기[[depth-estimation-inference-by-hand]]
이제 깊이 추정 파이프라인 사용법을 살펴보았으니 동일한 결과를 복제하는 방법을 살펴보겠습니다.
[Hugging Face Hub 체크포인트](https://huggingface.co/models?pipeline_tag=depth-estimation&sort=downloads)에서 모델과 관련 프로세서를 가져오는 것부터 시작합니다.
여기서 이전에 사용한 체크포인트와 동일한 것을 사용합니다:
```py
>>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation
>>> checkpoint = "vinvino02/glpn-nyu"
>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
>>> model = AutoModelForDepthEstimation.from_pretrained(checkpoint)
```
필요한 이미지 변환을 처리하는 `image_processor`를 사용하여 모델에 대한 이미지 입력을 준비합니다.
`image_processor`는 크기 조정 및 정규화 등 필요한 이미지 변환을 처리합니다:
```py
>>> pixel_values = image_processor(image, return_tensors="pt").pixel_values
```
준비한 입력을 모델로 전달합니다:
```py
>>> import torch
>>> with torch.no_grad():
... outputs = model(pixel_values)
... predicted_depth = outputs.predicted_depth
```
결과를 시각화합니다:
```py
>>> import numpy as np
>>> # 원본 사이즈로 복원
>>> prediction = torch.nn.functional.interpolate(
... predicted_depth.unsqueeze(1),
... size=image.size[::-1],
... mode="bicubic",
... align_corners=False,
... ).squeeze()
>>> output = prediction.numpy()
>>> formatted = (output * 255 / np.max(output)).astype("uint8")
>>> depth = Image.fromarray(formatted)
>>> depth
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/>
</div>
| transformers/docs/source/ko/tasks/monocular_depth_estimation.md/0 | {
"file_path": "transformers/docs/source/ko/tasks/monocular_depth_estimation.md",
"repo_id": "transformers",
"token_count": 3117
} |
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the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# TensorFlow 모델을 위한 XLA 통합 [[xla-integration-for-tensorflow-models]]
[[open-in-colab]]
XLA(Accelerated Linear Algebra)는 TensorFlow 모델의 실행 시간을 가속화하기 위한 컴파일러입니다. [공식 문서](https://www.tensorflow.org/xla)에 따르면 다음과 같습니다:
XLA(Accelerated Linear Algebra)는 선형 대수를 위한 도메인 특화 컴파일러로, TensorFlow 모델을 소스 코드 변경 없이 가속화할 수 있습니다.
TensorFlow에서 XLA를 사용하는 것은 간단합니다. XLA는 `tensorflow` 라이브러리 내에 패키지로 제공되며, [`tf.function`](https://www.tensorflow.org/guide/intro_to_graphs)과 같은 그래프 생성 함수에서 `jit_compile` 인수를 사용하여 활성화할 수 있습니다. `fit()` 및 `predict()`와 같은 Keras 메소드를 사용하는 경우, `jit_compile` 인수를 `model.compile()`에 전달하여 XLA를 간단하게 활성화할 수 있습니다. 그러나 XLA는 이러한 메소드에 국한되지 않고 임의의 `tf.function`을 가속화하는 데에도 사용할 수 있습니다.
🤗 Transformers에서는 [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2), [T5](https://huggingface.co/docs/transformers/model_doc/t5), [OPT](https://huggingface.co/docs/transformers/model_doc/opt)와 같은 모델의 텍스트 생성, 그리고 [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)와 같은 모델의 음성 처리를 포함하여 여러 TensorFlow 메소드가 XLA와 호환되도록 다시 작성되었습니다.
정확한 속도 향상은 모델에 따라 다르지만, 🤗 Transformers 내의 TensorFlow 텍스트 생성 모델의 경우 최대 100배의 속도 향상을 확인했습니다. 이 문서에서는 이러한 모델에 대해 XLA를 사용하여 최대 성능을 얻는 방법을 설명합니다. 또한 XLA 통합의 벤치마크 및 디자인 철학에 대한 추가 자료 링크도 제공할 것입니다.
## XLA를 사용하여 TF 함수 실행하기 [[running-tf-functions-with-xla]]
TensorFlow에서 다음과 같은 모델을 고려해 봅시다:
```py
import tensorflow as tf
model = tf.keras.Sequential(
[tf.keras.layers.Dense(10, input_shape=(10,), activation="relu"), tf.keras.layers.Dense(5, activation="softmax")]
)
```
위 모델은 차원이 `(10, )`인 입력을 받습니다. 다음과 같이 모델을 사용하여 순전파를 실행할 수 있습니다:
```py
# 모델에 대한 임의의 입력을 생성합니다.
batch_size = 16
input_vector_dim = 10
random_inputs = tf.random.normal((batch_size, input_vector_dim))
# 순전파를 실행합니다.
_ = model(random_inputs)
```
XLA로 컴파일된 함수로 순전파를 실행하려면 다음과 같이 해야 합니다:
```py
xla_fn = tf.function(model, jit_compile=True)
_ = xla_fn(random_inputs)
```
`model`의 기본 `call()` 함수는 XLA 그래프를 컴파일하는 데 사용됩니다. 그러나 다른 모델 함수를 XLA로 컴파일하려면 다음과 같이 할 수도 있습니다:
```py
my_xla_fn = tf.function(model.my_xla_fn, jit_compile=True)
```
## 🤗 Transformers에서 XLA를 사용하여 TF 텍스트 생성 모델 실행하기 [[running-a-tf-text-generation-model-with-xla-from-transformers]]
🤗 Transformers에서 XLA로 가속화된 생성을 활성화하려면 최신 버전의 `transformers`가 설치되어 있어야 합니다. 다음과 같이 설치할 수 있습니다:
```bash
pip install transformers --upgrade
```
그리고 다음 코드를 실행할 수 있습니다:
```py
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
# 최소 버전의 Transformers가 설치되어 있지 않다면 오류가 발생합니다.
from transformers.utils import check_min_version
check_min_version("4.21.0")
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
input_string = ["TensorFlow is"]
# XLA 생성 함수를 만들기 위한 한 줄
xla_generate = tf.function(model.generate, jit_compile=True)
tokenized_input = tokenizer(input_string, return_tensors="tf")
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
print(f"Generated -- {decoded_text}")
# Generated -- TensorFlow is an open-source, open-source, distributed-source application # framework for the
```
알 수 있듯이, `generate()`에서 XLA를 활성화하는 것은 단 한 줄의 코드입니다. 코드의 나머지 부분은 변경되지 않습니다. 그러나 위 코드 스니펫에서는 XLA에 특정한 몇 가지 주의할 점이 있습니다. XLA가 가져다줄 속도 향상을 실현하기 위해서는 이를 알고 있어야 합니다. 다음 섹션에서 이에 대해 논의합니다.
## 주의할 점 [[gotchas-to-be-aware-of]]
XLA 활성화 함수(`xla_generate()`와 같은)를 처음 실행할 때 내부적으로 계산 그래프를 추론하려고 하며, 이는 시간이 소요됩니다. 이 과정은 [“추적(tracing)”](https://www.tensorflow.org/guide/intro_to_graphs#when_is_a_function_tracing)이라고 알려져 있습니다.
생성 시간이 빠르지 않다는 것을 알 수 있을 것입니다. `xla_generate()`(또는 다른 XLA 활성화 함수)의 연속 호출은 함수에 전달된 입력이 초기에 구축된 계산 그래프와 동일한 형태를 따른다면, 계산 그래프를 추론할 필요가 없습니다. 이는 입력 형태가 고정된 모달리티(예: 이미지)에는 문제가 되지 않지만, 가변 입력 형태 모달리티(예: 텍스트)를 사용할 때 주의해야 합니다.
`xla_generate()`가 항상 동일한 입력 형태로 동작하도록 하려면, 토크나이저를 호출할 때 `padding` 인수를 지정할 수 있습니다.
```py
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
input_string = ["TensorFlow is"]
xla_generate = tf.function(model.generate, jit_compile=True)
# 여기서, padding 옵션이 있는 토크나이저를 호출합니다.
tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf")
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
decoded_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
print(f"Generated -- {decoded_text}")
```
이렇게 하면 `xla_generate()`에 대한 입력이 항상 추적된 형태로 전달되어 생성 시간이 가속화됩니다. 다음 코드로 이를 확인할 수 있습니다:
```py
import time
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
xla_generate = tf.function(model.generate, jit_compile=True)
for input_string in ["TensorFlow is", "TensorFlow is a", "TFLite is a"]:
tokenized_input = tokenizer(input_string, pad_to_multiple_of=8, padding=True, return_tensors="tf")
start = time.time_ns()
generated_tokens = xla_generate(**tokenized_input, num_beams=2)
end = time.time_ns()
print(f"Execution time -- {(end - start) / 1e6:.1f} ms\n")
```
Tesla T4 GPU에서는 다음과 같은 출력을 예상할 수 있습니다:
```bash
Execution time -- 30819.6 ms
Execution time -- 79.0 ms
Execution time -- 78.9 ms
```
`xla_generate()`의 첫 번째 호출은 추적 때문에 시간이 오래 걸리지만, 연속 호출은 몇 배나 빠릅니다. 생성 옵션에 대한 어떤 변경이든 다시 추적을 유발하므로 생성 시간이 느려질 수 있음을 명심하세요.
이 문서에서는 🤗 Transformers에서 제공하는 모든 텍스트 생성 옵션을 다루지 않았습니다. 고급 사용 사례에 대해 문서를 참조하시기 바랍니다.
## 추가 자료 [[additional-resources]]
여기에 🤗 Transformers와 XLA에 대해 더 자세히 알고 싶은 경우 도움이 될 수 있는 몇 가지 추가 자료를 제공합니다.
* [이 Colab 노트북](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/91_tf_xla_generate.ipynb)은 XLA와 호환되는 인코더-디코더([T5](https://huggingface.co/docs/transformers/model_doc/t5)와 같은) 및 디코더 전용([GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)와 같은) 텍스트 생성 모델을 실험해 볼 수 있는 대화형 데모를 제공합니다.
* [이 블로그 글](https://huggingface.co/blog/tf-xla-generate)은 TensorFlow에서 XLA에 대한 친절한 소개와 함께 XLA와 호환되는 모델의 비교 벤치마크에 대한 개요를 제공합니다.
* [이 블로그 글](https://blog.tensorflow.org/2022/11/how-hugging-face-improved-text-generation-performance-with-xla.html)은 🤗 Transformers의 TensorFlow 모델에 XLA 지원을 추가하는 것에 대한 디자인 철학을 논의합니다.
* XLA와 TensorFlow 그래프에 대해 더 자세히 알고 싶은 경우 추천하는 글:
* [XLA: 기계 학습을 위한 최적화 컴파일러](https://www.tensorflow.org/xla)
* [그래프 및 tf.function 소개](https://www.tensorflow.org/guide/intro_to_graphs)
* [tf.function으로 성능 향상하기](https://www.tensorflow.org/guide/function) | transformers/docs/source/ko/tf_xla.md/0 | {
"file_path": "transformers/docs/source/ko/tf_xla.md",
"repo_id": "transformers",
"token_count": 6235
} |
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# Usando os Tokenizers do 🤗 Tokenizers
O [`PreTrainedTokenizerFast`] depende da biblioteca [🤗 Tokenizers](https://huggingface.co/docs/tokenizers). O Tokenizer obtido da biblioteca 🤗 Tokenizers pode ser carregado facilmente pelo 🤗 Transformers.
Antes de entrar nos detalhes, vamos começar criando um tokenizer fictício em algumas linhas:
```python
>>> from tokenizers import Tokenizer
>>> from tokenizers.models import BPE
>>> from tokenizers.trainers import BpeTrainer
>>> from tokenizers.pre_tokenizers import Whitespace
>>> tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
>>> trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
>>> tokenizer.pre_tokenizer = Whitespace()
>>> files = [...]
>>> tokenizer.train(files, trainer)
```
Agora temos um tokenizer treinado nos arquivos que foram definidos. Nós podemos continuar usando nessa execução ou salvar em um arquivo JSON para re-utilizar no futuro.
## Carregando diretamente de um objeto tokenizer
Vamos ver como aproveitar esse objeto tokenizer na biblioteca 🤗 Transformers. A classe [`PreTrainedTokenizerFast`] permite uma instanciação fácil, aceitando o objeto *tokenizer* instanciado como um argumento:
```python
>>> from transformers import PreTrainedTokenizerFast
>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
```
Esse objeto pode ser utilizado com todos os métodos compartilhados pelos tokenizers dos 🤗 Transformers! Vá para [a página do tokenizer](main_classes/tokenizer) para mais informações.
## Carregando de um arquivo JSON
Para carregar um tokenizer de um arquivo JSON vamos primeiro começar salvando nosso tokenizer:
```python
>>> tokenizer.save("tokenizer.json")
```
A pasta para qual salvamos esse arquivo pode ser passada para o método de inicialização do [`PreTrainedTokenizerFast`] usando o `tokenizer_file` parâmetro:
```python
>>> from transformers import PreTrainedTokenizerFast
>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
```
Esse objeto pode ser utilizado com todos os métodos compartilhados pelos tokenizers dos 🤗 Transformers! Vá para [a página do tokenizer](main_classes/tokenizer) para mais informações. | transformers/docs/source/pt/fast_tokenizers.md/0 | {
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"repo_id": "transformers",
"token_count": 937
} |
- sections:
- local: index
title: 🤗 Transformers 简介
- local: quicktour
title: 快速上手
- local: installation
title: 安装
title: 开始使用
- sections:
- local: pipeline_tutorial
title: 使用pipelines进行推理
- local: autoclass_tutorial
title: 使用AutoClass编写可移植的代码
- local: preprocessing
title: 预处理数据
- local: training
title: 微调预训练模型
- local: run_scripts
title: 通过脚本训练模型
- local: accelerate
title: 使用🤗Accelerate进行分布式训练
- local: peft
title: 使用🤗 PEFT加载和训练adapters
- local: model_sharing
title: 分享您的模型
- local: agents
title: 智能体和工具
- local: agents_advanced
title: 智能体，超强版 - 多智能体、外部工具等
- local: llm_tutorial
title: 使用LLMs进行生成
title: 教程
- sections:
- isExpanded: false
sections:
- local: tasks/asr
title: 自动语音识别
- sections:
- local: fast_tokenizers
title: 使用 🤗 Tokenizers 中的分词器
- local: multilingual
title: 使用多语言模型进行推理
- local: create_a_model
title: 使用特定于模型的 API
- local: custom_models
title: 共享自定义模型
- local: chat_templating
title: 聊天模型的模板
- local: serialization
title: 导出为 ONNX
- local: tflite
title: 导出为 TFLite
- local: torchscript
title: 导出为 TorchScript
- local: gguf
title: 与 GGUF 格式的互操作性
- local: tiktoken
title: 与 Tiktoken 文件的互操作性
- local: community
title: 社区资源
title: 开发者指南
- sections:
- local: performance
title: 综述
- sections:
- local: fsdp
title: 完全分片数据并行
- local: perf_train_special
title: 在 Apple silicon 芯片上进行 PyTorch 训练
- local: perf_infer_gpu_multi
title: 多GPU推理
- local: perf_train_cpu
title: 在CPU上进行高效训练
- local: perf_hardware
title: 用于训练的定制硬件
- local: hpo_train
title: 使用Trainer API 进行超参数搜索
title: 高效训练技术
- local: big_models
title: 实例化大模型
- local: debugging
title: 问题定位及解决
- local: tf_xla
title: TensorFlow模型的XLA集成
- local: perf_torch_compile
title: 使用 `torch.compile()` 优化推理
title: 性能和可扩展性
- sections:
- local: contributing
title: 如何为 🤗 Transformers 做贡献？
- local: add_new_pipeline
title: 如何将流水线添加到 🤗 Transformers？
title: 贡献
- sections:
- local: philosophy
title: Transformers的设计理念
- local: task_summary
title: 🤗Transformers能做什么
- local: tokenizer_summary
title: 分词器的摘要
- local: attention
title: 注意力机制
- local: bertology
title: 基于BERT进行的相关研究
title: 概念指南
- sections:
- sections:
- local: main_classes/agent
title: 智能体和工具
- local: main_classes/callback
title: Callbacks
- local: main_classes/configuration
title: Configuration
- local: main_classes/data_collator
title: Data Collator
- local: main_classes/keras_callbacks
title: Keras callbacks
- local: main_classes/logging
title: Logging
- local: main_classes/model
title: 模型
- local: main_classes/text_generation
title: 文本生成
- local: main_classes/onnx
title: ONNX
- local: main_classes/optimizer_schedules
title: Optimization
- local: main_classes/output
title: 模型输出
- local: main_classes/pipelines
title: Pipelines
- local: main_classes/processors
title: Processors
- local: main_classes/quantization
title: Quantization
- local: main_classes/tokenizer
title: Tokenizer
- local: main_classes/trainer
title: Trainer
- local: main_classes/deepspeed
title: DeepSpeed集成
- local: main_classes/feature_extractor
title: Feature Extractor
- local: main_classes/image_processor
title: Image Processor
title: 主要类
- sections:
- local: internal/modeling_utils
title: 自定义层和工具
- local: internal/pipelines_utils
title: pipelines工具
- local: internal/tokenization_utils
title: Tokenizers工具
- local: internal/trainer_utils
title: 训练器工具
- local: internal/generation_utils
title: 生成工具
- local: internal/image_processing_utils
title: 图像处理工具
- local: internal/audio_utils
title: 音频处理工具
- local: internal/file_utils
title: 通用工具
- local: internal/time_series_utils
title: 时序数据工具
title: 内部辅助工具
title: 应用程序接口 (API)
| transformers/docs/source/zh/_toctree.yml/0 | {
"file_path": "transformers/docs/source/zh/_toctree.yml",
"repo_id": "transformers",
"token_count": 2392
} |
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# 完全分片数据并行
[完全分片数据并行（FSDP）](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/)是一种数据并行方法，
它将模型的参数、梯度和优化器状态在可用 GPU（也称为 Worker 或 *rank*）的数量上进行分片。
与[分布式数据并行（DDP）](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html)不同，
FSDP 减少了内存使用量，因为模型在每个 GPU 上都被复制了一次。这就提高了 GPU 内存效率，
使您能够用较少的 GPU 训练更大的模型。FSDP 已经集成到 Accelerate 中，
这是一个用于在分布式环境中轻松管理训练的库，这意味着可以从 [`Trainer`] 类中调用这个库。
在开始之前，请确保已安装 Accelerate，并且至少使用 PyTorch 2.1.0 或更高版本。
```bash
pip install accelerate
```
## FSDP 配置
首先，运行 [`accelerate config`](https://huggingface.co/docs/accelerate/package_reference/cli#accelerate-config)
命令为您的训练环境创建一个配置文件。Accelerate 使用此配置文件根据您在 `accelerate config`
中选择的训练选项来自动搭建正确的训练环境。
```bash
accelerate config
```
运行 `accelerate config` 时，您将被提示一系列选项来配置训练环境。
本节涵盖了一些最重要的 FSDP 选项。要了解有关其他可用的 FSDP 选项的更多信息，
请查阅 [fsdp_config](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments.fsdp_config) 参数。
### 分片策略
FSDP 提供了多种可选择的分片策略：
- `FULL_SHARD` - 将模型参数、梯度和优化器状态跨 Worker 进行分片；为此选项选择 `1`
- `SHARD_GRAD_OP`- 将梯度和优化器状态跨 Worker 进行分片；为此选项选择 `2`
- `NO_SHARD` - 不分片任何内容（这等同于 DDP）；为此选项选择 `3`
- `HYBRID_SHARD` - 在每个 Worker 中分片模型参数、梯度和优化器状态，其中每个 Worker 也有完整副本；为此选项选择 `4`
- `HYBRID_SHARD_ZERO2` - 在每个 Worker 中分片梯度和优化器状态，其中每个 Worker 也有完整副本；为此选项选择 `5`
这由 `fsdp_sharding_strategy` 标志启用。
### CPU 卸载
当参数和梯度在不使用时可以卸载到 CPU 上，以节省更多 GPU 内存并帮助您适应即使 FSDP 也不足以容纳大型模型的情况。
在运行 `accelerate config` 时，通过设置 `fsdp_offload_params: true` 来启用此功能。
### 包装策略
FSDP 是通过包装网络中的每个层来应用的。通常，包装是以嵌套方式应用的，其中完整的权重在每次前向传递后被丢弃，
以便在下一层使用内存。**自动包装**策略是实现这一点的最简单方法，您不需要更改任何代码。
您应该选择 `fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP` 来包装一个 Transformer 层，
并且 `fsdp_transformer_layer_cls_to_wrap` 来指定要包装的层（例如 `BertLayer`）。
否则，您可以选择基于大小的包装策略，其中如果一层的参数超过一定数量，则应用 FSDP。通过设置
`fsdp_wrap_policy: SIZE_BASED_WRAP` 和 `min_num_param` 来启用此功能，将参数设置为所需的大小阈值。
### 检查点
应该使用 `fsdp_state_dict_type: SHARDED_STATE_DICT` 来保存中间检查点，
因为在排名 0 上保存完整状态字典需要很长时间，通常会导致 `NCCL Timeout` 错误，因为在广播过程中会无限期挂起。
您可以使用 [`~accelerate.Accelerator.load_state`] 方法加载分片状态字典以恢复训练。
```py
# 包含检查点的目录
accelerator.load_state("ckpt")
```
然而，当训练结束时，您希望保存完整状态字典，因为分片状态字典仅与 FSDP 兼容。
```py
if trainer.is_fsdp_enabled:
trainer.accelerator.state.fsdp_plugin.set_state_dict_type("FULL_STATE_DICT")
trainer.save_model(script_args.output_dir)
```
### TPU
[PyTorch XLA](https://pytorch.org/xla/release/2.1/index.html) 支持用于 TPUs 的 FSDP 训练，
可以通过修改由 `accelerate config` 生成的 FSDP 配置文件来启用。除了上面指定的分片策略和包装选项外，
您还可以将以下参数添加到文件中。
```yaml
xla: True # 必须设置为 True 以启用 PyTorch/XLA
xla_fsdp_settings: # XLA 特定的 FSDP 参数
xla_fsdp_grad_ckpt: True # 使用梯度检查点
```
[`xla_fsdp_settings`](https://github.com/pytorch/xla/blob/2e6e183e0724818f137c8135b34ef273dea33318/torch_xla/distributed/fsdp/xla_fully_sharded_data_parallel.py#L128)
允许您配置用于 FSDP 的额外 XLA 特定参数。
## 启动训练
FSDP 配置文件示例如下所示：
```yaml
compute_environment: LOCAL_MACHINE
debug: false
distributed_type: FSDP
downcast_bf16: "no"
fsdp_config:
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_backward_prefetch_policy: BACKWARD_PRE
fsdp_cpu_ram_efficient_loading: true
fsdp_forward_prefetch: false
fsdp_offload_params: true
fsdp_sharding_strategy: 1
fsdp_state_dict_type: SHARDED_STATE_DICT
fsdp_sync_module_states: true
fsdp_transformer_layer_cls_to_wrap: BertLayer
fsdp_use_orig_params: true
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 2
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```
要启动训练，请运行 [`accelerate launch`](https://huggingface.co/docs/accelerate/package_reference/cli#accelerate-launch)
命令，它将自动使用您之前使用 `accelerate config` 创建的配置文件。
```bash
accelerate launch my-trainer-script.py
```
```bash
accelerate launch --fsdp="full shard" --fsdp_config="path/to/fsdp_config/ my-trainer-script.py
```
## 下一步
FSDP 在大规模模型训练方面是一个强大的工具，您可以使用多个 GPU 或 TPU。
通过分片模型参数、优化器和梯度状态，甚至在它们不活动时将其卸载到 CPU 上，
FSDP 可以减少大规模训练的高成本。如果您希望了解更多信息，下面的内容可能会有所帮助：
- 深入参考 Accelerate 指南，了解有关
[FSDP](https://huggingface.co/docs/accelerate/usage_guides/fsdp)的更多信息。
- 阅读[介绍 PyTorch 完全分片数据并行（FSDP）API](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/) 博文。
- 阅读[使用 FSDP 在云 TPU 上扩展 PyTorch 模型](https://pytorch.org/blog/scaling-pytorch-models-on-cloud-tpus-with-fsdp/)博文。
| transformers/docs/source/zh/fsdp.md/0 | {
"file_path": "transformers/docs/source/zh/fsdp.md",
"repo_id": "transformers",
"token_count": 4054
} |
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# Callbacks
Callbacks可以用来自定义PyTorch [Trainer]中训练循环行为的对象（此功能尚未在TensorFlow中实现），该对象可以检查训练循环状态（用于进度报告、在TensorBoard或其他ML平台上记录日志等），并做出决策（例如提前停止）。
Callbacks是“只读”的代码片段，除了它们返回的[TrainerControl]对象外，它们不能更改训练循环中的任何内容。对于需要更改训练循环的自定义，您应该继承[Trainer]并重载您需要的方法（有关示例，请参见[trainer](trainer)）。
默认情况下，`TrainingArguments.report_to` 设置为"all"，然后[Trainer]将使用以下callbacks。
- [`DefaultFlowCallback`]，它处理默认的日志记录、保存和评估行为
- [`PrinterCallback`] 或 [`ProgressCallback`]，用于显示进度和打印日志（如果通过[`TrainingArguments`]停用tqdm，则使用第一个函数；否则使用第二个）。
- [`~integrations.TensorBoardCallback`]，如果TensorBoard可访问（通过PyTorch版本 >= 1.4 或者 tensorboardX）。
- [`~integrations.WandbCallback`]，如果安装了[wandb](https://www.wandb.com/)。
- [`~integrations.CometCallback`]，如果安装了[comet_ml](https://www.comet.com/site/)。
- [`~integrations.MLflowCallback`]，如果安装了[mlflow](https://www.mlflow.org/)。
- [`~integrations.NeptuneCallback`]，如果安装了[neptune](https://neptune.ai/)。
- [`~integrations.AzureMLCallback`]，如果安装了[azureml-sdk](https://pypi.org/project/azureml-sdk/)。
- [`~integrations.CodeCarbonCallback`]，如果安装了[codecarbon](https://pypi.org/project/codecarbon/)。
- [`~integrations.ClearMLCallback`]，如果安装了[clearml](https://github.com/allegroai/clearml)。
- [`~integrations.DagsHubCallback`]，如果安装了[dagshub](https://dagshub.com/)。
- [`~integrations.FlyteCallback`]，如果安装了[flyte](https://flyte.org/)。
- [`~integrations.DVCLiveCallback`]，如果安装了[dvclive](https://dvc.org/doc/dvclive)。
如果安装了一个软件包，但您不希望使用相关的集成，您可以将 `TrainingArguments.report_to` 更改为仅包含您想要使用的集成的列表（例如 `["azure_ml", "wandb"]`）。
实现callbacks的主要类是[`TrainerCallback`]。它获取用于实例化[`Trainer`]的[`TrainingArguments`]，可以通过[`TrainerState`]访问该Trainer的内部状态，并可以通过[`TrainerControl`]对训练循环执行一些操作。
## 可用的Callbacks
这里是库里可用[`TrainerCallback`]的列表：
[[autodoc]] integrations.CometCallback
- setup
[[autodoc]] DefaultFlowCallback
[[autodoc]] PrinterCallback
[[autodoc]] ProgressCallback
[[autodoc]] EarlyStoppingCallback
[[autodoc]] integrations.TensorBoardCallback
[[autodoc]] integrations.WandbCallback
- setup
[[autodoc]] integrations.MLflowCallback
- setup
[[autodoc]] integrations.AzureMLCallback
[[autodoc]] integrations.CodeCarbonCallback
[[autodoc]] integrations.NeptuneCallback
[[autodoc]] integrations.ClearMLCallback
[[autodoc]] integrations.DagsHubCallback
[[autodoc]] integrations.FlyteCallback
[[autodoc]] integrations.DVCLiveCallback
- setup
## TrainerCallback
[[autodoc]] TrainerCallback
以下是如何使用PyTorch注册自定义callback的示例：
[`Trainer`]:
```python
class MyCallback(TrainerCallback):
"A callback that prints a message at the beginning of training"
def on_train_begin(self, args, state, control, **kwargs):
print("Starting training")
trainer = Trainer(
model,
args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
callbacks=[MyCallback], # We can either pass the callback class this way or an instance of it (MyCallback())
)
```
注册callback的另一种方式是调用 `trainer.add_callback()`，如下所示：
```python
trainer = Trainer(...)
trainer.add_callback(MyCallback)
# Alternatively, we can pass an instance of the callback class
trainer.add_callback(MyCallback())
```
## TrainerState
[[autodoc]] TrainerState
## TrainerControl
[[autodoc]] TrainerControl
| transformers/docs/source/zh/main_classes/callback.md/0 | {
"file_path": "transformers/docs/source/zh/main_classes/callback.md",
"repo_id": "transformers",
"token_count": 2183
} |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Tokenizer
tokenizer负责准备输入以供模型使用。该库包含所有模型的tokenizer。大多数tokenizer都有两种版本：一个是完全的 Python 实现，另一个是基于 Rust 库 [🤗 Tokenizers](https://github.com/huggingface/tokenizers) 的“Fast”实现。"Fast" 实现允许：
1. 在批量分词时显著提速
2. 在原始字符串（字符和单词）和token空间之间进行映射的其他方法（例如，获取包含给定字符的token的索引或与给定token对应的字符范围）。
基类 [PreTrainedTokenizer] 和 [PreTrained TokenizerFast] 实现了在模型输入中编码字符串输入的常用方法（见下文），并从本地文件或目录或从库提供的预训练的 tokenizer（从 HuggingFace 的 AWS S3 存储库下载）实例化/保存 python 和“Fast” tokenizer。它们都依赖于包含常用方法的 [`~tokenization_utils_base.PreTrainedTokenizerBase`]和[`~tokenization_utils_base.SpecialTokensMixin`]。
因此，[`PreTrainedTokenizer`] 和 [`PreTrainedTokenizerFast`] 实现了使用所有tokenizers的主要方法：
- 分词（将字符串拆分为子词标记字符串），将tokens字符串转换为id并转换回来，以及编码/解码（即标记化并转换为整数）。
- 以独立于底层结构（BPE、SentencePiece……）的方式向词汇表中添加新tokens。
- 管理特殊tokens（如mask、句首等）：添加它们，将它们分配给tokenizer中的属性以便于访问，并确保它们在标记过程中不会被分割。
[`BatchEncoding`] 包含 [`~tokenization_utils_base.PreTrainedTokenizerBase`] 的编码方法（`__call__`、`encode_plus` 和 `batch_encode_plus`）的输出，并且是从 Python 字典派生的。当tokenizer是纯 Python tokenizer时，此类的行为就像标准的 Python 字典一样，并保存这些方法计算的各种模型输入（`input_ids`、`attention_mask` 等）。当分词器是“Fast”分词器时（即由 HuggingFace 的 [tokenizers 库](https://github.com/huggingface/tokenizers) 支持），此类还提供了几种高级对齐方法，可用于在原始字符串（字符和单词）与token空间之间进行映射（例如，获取包含给定字符的token的索引或与给定token对应的字符范围）。
## PreTrainedTokenizer
[[autodoc]] PreTrainedTokenizer
- __call__
- add_tokens
- add_special_tokens
- apply_chat_template
- batch_decode
- decode
- encode
- push_to_hub
- all
## PreTrainedTokenizerFast
[`PreTrainedTokenizerFast`] 依赖于 [tokenizers](https://huggingface.co/docs/tokenizers) 库。可以非常简单地将从 🤗 tokenizers 库获取的tokenizers加载到 🤗 transformers 中。查看 [使用 🤗 tokenizers 的分词器](../fast_tokenizers) 页面以了解如何执行此操作。
[[autodoc]] PreTrainedTokenizerFast
- __call__
- add_tokens
- add_special_tokens
- apply_chat_template
- batch_decode
- decode
- encode
- push_to_hub
- all
## BatchEncoding
[[autodoc]] BatchEncoding
| transformers/docs/source/zh/main_classes/tokenizer.md/0 | {
"file_path": "transformers/docs/source/zh/main_classes/tokenizer.md",
"repo_id": "transformers",
"token_count": 1932
} |
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# 导出为 ONNX
在生产环境中部署 🤗 Transformers 模型通常需要或者能够受益于，将模型导出为可在专门的运行时和硬件上加载和执行的序列化格式。
🤗 Optimum 是 Transformers 的扩展，可以通过其 `exporters` 模块将模型从 PyTorch 或 TensorFlow 导出为 ONNX 及 TFLite 等序列化格式。🤗 Optimum 还提供了一套性能优化工具，可以在目标硬件上以最高效率训练和运行模型。
本指南演示了如何使用 🤗 Optimum 将 🤗 Transformers 模型导出为 ONNX。有关将模型导出为 TFLite 的指南，请参考 [导出为 TFLite 页面](tflite)。
## 导出为 ONNX
[ONNX (Open Neural Network eXchange 开放神经网络交换)](http://onnx.ai) 是一个开放的标准，它定义了一组通用的运算符和一种通用的文件格式，用于表示包括 PyTorch 和 TensorFlow 在内的各种框架中的深度学习模型。当一个模型被导出为 ONNX时，这些运算符被用于构建计算图（通常被称为*中间表示*），该图表示数据在神经网络中的流动。
通过公开具有标准化运算符和数据类型的图，ONNX使得模型能够轻松在不同深度学习框架间切换。例如，在 PyTorch 中训练的模型可以被导出为 ONNX，然后再导入到 TensorFlow（反之亦然）。
导出为 ONNX 后，模型可以：
- 通过 [图优化（graph optimization）](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/optimization) 和 [量化（quantization）](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization) 等技术进行推理优化。
- 通过 [`ORTModelForXXX` 类](https://huggingface.co/docs/optimum/onnxruntime/package_reference/modeling_ort) 使用 ONNX Runtime 运行，它同样遵循你熟悉的 Transformers 中的 `AutoModel` API。
- 使用 [优化推理流水线（pipeline）](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/pipelines) 运行，其 API 与 🤗 Transformers 中的 [`pipeline`] 函数相同。
🤗 Optimum 通过利用配置对象提供对 ONNX 导出的支持。多种模型架构已经有现成的配置对象，并且配置对象也被设计得易于扩展以适用于其他架构。
现有的配置列表请参考 [🤗 Optimum 文档](https://huggingface.co/docs/optimum/exporters/onnx/overview)。
有两种方式可以将 🤗 Transformers 模型导出为 ONNX，这里我们展示这两种方法：
- 使用 🤗 Optimum 的 CLI（命令行）导出。
- 使用 🤗 Optimum 的 `optimum.onnxruntime` 模块导出。
### 使用 CLI 将 🤗 Transformers 模型导出为 ONNX
要将 🤗 Transformers 模型导出为 ONNX，首先需要安装额外的依赖项：
```bash
pip install optimum[exporters]
```
请参阅 [🤗 Optimum 文档](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) 以查看所有可用参数，或者在命令行中查看帮助：
```bash
optimum-cli export onnx --help
```
运行以下命令，以从 🤗 Hub 导出模型的检查点（checkpoint），以 `distilbert/distilbert-base-uncased-distilled-squad` 为例：
```bash
optimum-cli export onnx --model distilbert/distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
```
你应该能在日志中看到导出进度以及生成的 `model.onnx` 文件的保存位置，如下所示：
```bash
Validating ONNX model distilbert_base_uncased_squad_onnx/model.onnx...
-[✓] ONNX model output names match reference model (start_logits, end_logits)
- Validating ONNX Model output "start_logits":
-[✓] (2, 16) matches (2, 16)
-[✓] all values close (atol: 0.0001)
- Validating ONNX Model output "end_logits":
-[✓] (2, 16) matches (2, 16)
-[✓] all values close (atol: 0.0001)
The ONNX export succeeded and the exported model was saved at: distilbert_base_uncased_squad_onnx
```
上面的示例说明了从 🤗 Hub 导出检查点的过程。导出本地模型时，首先需要确保将模型的权重和分词器文件保存在同一目录（`local_path`）中。在使用 CLI 时，将 `local_path` 传递给 `model` 参数，而不是 🤗 Hub 上的检查点名称，并提供 `--task` 参数。你可以在 [🤗 Optimum 文档](https://huggingface.co/docs/optimum/exporters/task_manager)中查看支持的任务列表。如果未提供 `task` 参数，将默认导出不带特定任务头的模型架构。
```bash
optimum-cli export onnx --model local_path --task question-answering distilbert_base_uncased_squad_onnx/
```
生成的 `model.onnx` 文件可以在支持 ONNX 标准的 [许多加速引擎（accelerators）](https://onnx.ai/supported-tools.html#deployModel) 之一上运行。例如，我们可以使用 [ONNX Runtime](https://onnxruntime.ai/) 加载和运行模型，如下所示：
```python
>>> from transformers import AutoTokenizer
>>> from optimum.onnxruntime import ORTModelForQuestionAnswering
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert_base_uncased_squad_onnx")
>>> model = ORTModelForQuestionAnswering.from_pretrained("distilbert_base_uncased_squad_onnx")
>>> inputs = tokenizer("What am I using?", "Using DistilBERT with ONNX Runtime!", return_tensors="pt")
>>> outputs = model(**inputs)
```
从 Hub 导出 TensorFlow 检查点的过程也一样。例如，以下是从 [Keras 组织](https://huggingface.co/keras-io) 导出纯 TensorFlow 检查点的命令：
```bash
optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_squad_onnx/
```
### 使用 `optimum.onnxruntime` 将 🤗 Transformers 模型导出为 ONNX
除了 CLI 之外，你还可以使用代码将 🤗 Transformers 模型导出为 ONNX，如下所示：
```python
>>> from optimum.onnxruntime import ORTModelForSequenceClassification
>>> from transformers import AutoTokenizer
>>> model_checkpoint = "distilbert_base_uncased_squad"
>>> save_directory = "onnx/"
>>> # 从 transformers 加载模型并将其导出为 ONNX
>>> ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True)
>>> tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
>>> # 保存 onnx 模型以及分词器
>>> ort_model.save_pretrained(save_directory)
>>> tokenizer.save_pretrained(save_directory)
```
### 导出尚未支持的架构的模型
如果你想要为当前无法导出的模型添加支持，请先检查 [`optimum.exporters.onnx`](https://huggingface.co/docs/optimum/exporters/onnx/overview) 是否支持该模型，如果不支持，你可以 [直接为 🤗 Optimum 贡献代码](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/contribute)。
### 使用 `transformers.onnx` 导出模型
<Tip warning={true}>
`tranformers.onnx` 不再进行维护，请如上所述，使用 🤗 Optimum 导出模型。这部分内容将在未来版本中删除。
</Tip>
要使用 `tranformers.onnx` 将 🤗 Transformers 模型导出为 ONNX，请安装额外的依赖项：
```bash
pip install transformers[onnx]
```
将 `transformers.onnx` 包作为 Python 模块使用，以使用现成的配置导出检查点：
```bash
python -m transformers.onnx --model=distilbert/distilbert-base-uncased onnx/
```
以上代码将导出由 `--model` 参数定义的检查点的 ONNX 图。传入任何 🤗 Hub 上或者存储与本地的检查点。生成的 `model.onnx` 文件可以在支持 ONNX 标准的众多加速引擎上运行。例如，使用 ONNX Runtime 加载并运行模型，如下所示：
```python
>>> from transformers import AutoTokenizer
>>> from onnxruntime import InferenceSession
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
>>> session = InferenceSession("onnx/model.onnx")
>>> # ONNX Runtime expects NumPy arrays as input
>>> inputs = tokenizer("Using DistilBERT with ONNX Runtime!", return_tensors="np")
>>> outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs))
```
可以通过查看每个模型的 ONNX 配置来获取所需的输出名（例如 `["last_hidden_state"]`）。例如，对于 DistilBERT，可以用以下代码获取输出名称：
```python
>>> from transformers.models.distilbert import DistilBertConfig, DistilBertOnnxConfig
>>> config = DistilBertConfig()
>>> onnx_config = DistilBertOnnxConfig(config)
>>> print(list(onnx_config.outputs.keys()))
["last_hidden_state"]
```
从 Hub 导出 TensorFlow 检查点的过程也一样。导出纯 TensorFlow 检查点的示例代码如下：
```bash
python -m transformers.onnx --model=keras-io/transformers-qa onnx/
```
要导出本地存储的模型，请将模型的权重和分词器文件保存在同一目录中（例如 `local-pt-checkpoint`），然后通过将 `transformers.onnx` 包的 `--model` 参数指向该目录，将其导出为 ONNX：
```bash
python -m transformers.onnx --model=local-pt-checkpoint onnx/
``` | transformers/docs/source/zh/serialization.md/0 | {
"file_path": "transformers/docs/source/zh/serialization.md",
"repo_id": "transformers",
"token_count": 4933
} |
<!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Language model training and inference examples
The following example showcases how to train a language model from scratch
using the JAX/Flax backend.
JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
Models written in JAX/Flax are **immutable** and updated in a purely functional
way which enables simple and efficient model parallelism.
## Masked language modeling
In the following, we demonstrate how to train a bi-directional transformer model
using masked language modeling objective as introduced in [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).
More specifically, we demonstrate how JAX/Flax can be leveraged
to pre-train [**`FacebookAI/roberta-base`**](https://huggingface.co/FacebookAI/roberta-base)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
To setup all relevant files for training, let's create a directory.
```bash
mkdir ./norwegian-roberta-base
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 10 minutes depending on your hardware ☕.
```python
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./norwegian-roberta-base/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**FacebookAI/roberta-base**`](https://huggingface.co/FacebookAI/roberta-base)
in the local model folder:
```python
from transformers import RobertaConfig
config = RobertaConfig.from_pretrained("FacebookAI/roberta-base", vocab_size=50265)
config.save_pretrained("./norwegian-roberta-base")
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
### Train model
Next we can run the example script to pretrain the model:
```bash
python run_mlm_flax.py \
--output_dir="./norwegian-roberta-base" \
--model_type="roberta" \
--config_name="./norwegian-roberta-base" \
--tokenizer_name="./norwegian-roberta-base" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="128" \
--per_device_eval_batch_size="128" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="18" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--logging_steps="500" \
--save_steps="2500" \
--eval_steps="2500" \
--push_to_hub
```
Training should converge at a loss and accuracy
of 1.78 and 0.64 respectively after 18 epochs on a single TPUv3-8.
This should take less than 18 hours.
Training statistics can be accessed on [tfhub.dev](https://tensorboard.dev/experiment/GdYmdak2TWeVz0DDRYOrrg).
For a step-by-step walkthrough of how to do masked language modeling in Flax, please have a
look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb) google colab.
## Causal language modeling
In the following, we demonstrate how to train an auto-regressive causal transformer model
in JAX/Flax.
More specifically, we pretrain a randomly initialized [**`openai-community/gpt2`**](https://huggingface.co/openai-community/gpt2) model in Norwegian on a single TPUv3-8.
to pre-train 124M [**`openai-community/gpt2`**](https://huggingface.co/openai-community/gpt2)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
To setup all relevant files for training, let's create a directory.
```bash
mkdir ./norwegian-gpt2
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 10 minutes depending on your hardware ☕.
```python
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50257, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./norwegian-gpt2/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**openai-community/gpt2**`](https://huggingface.co/openai-community/gpt2)
in the local model folder:
```python
from transformers import GPT2Config
config = GPT2Config.from_pretrained("openai-community/gpt2", resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0, vocab_size=50257)
config.save_pretrained("./norwegian-gpt2")
```
Great, we have set up our model repository. During training, we will now automatically
push the training logs and model weights to the repo.
### Train model
Finally, we can run the example script to pretrain the model:
```bash
python run_clm_flax.py \
--output_dir="./norwegian-gpt2" \
--model_type="gpt2" \
--config_name="./norwegian-gpt2" \
--tokenizer_name="./norwegian-gpt2" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--do_train --do_eval \
--block_size="512" \
--per_device_train_batch_size="64" \
--per_device_eval_batch_size="64" \
--learning_rate="5e-3" --warmup_steps="1000" \
--adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
--overwrite_output_dir \
--num_train_epochs="20" \
--logging_steps="500" \
--save_steps="2500" \
--eval_steps="2500" \
--push_to_hub
```
Training should converge at a loss and perplexity
of 3.24 and 25.72 respectively after 20 epochs on a single TPUv3-8.
This should take less than ~21 hours.
Training statistics can be accessed on [tfhub.dev](https://tensorboard.dev/experiment/2zEhLwJ0Qp2FAkI3WVH9qA).
For a step-by-step walkthrough of how to do causal language modeling in Flax, please have a
look at [this](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/causal_language_modeling_flax.ipynb) google colab.
## T5-like span-masked language modeling
In the following, we demonstrate how to train a T5 model using the span-masked language model
objective as proposed in the [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683).
More specifically, we demonstrate how JAX/Flax can be leveraged
to pre-train [**`google/t5-v1_1-base`**](https://huggingface.co/google/t5-v1_1-base)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
Let's start by creating a model repository to save the trained model and logs.
Here we call the model `"norwegian-t5-base"`, but you can change the model name as you like.
To setup all relevant files for training, let's create a directory.
```bash
cd ./norwegian-t5-base
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model.
We make use of the [tokenizers](https://github.com/huggingface/tokenizers) library to train
a sentencepiece unigram tokenizer as shown in [t5_tokenizer_model.py](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling/t5_tokenizer_model.py)
which is heavily inspired from [yandex-research/DeDLOC's tokenizer model](https://github.com/yandex-research/DeDLOC/blob/5c994bc64e573702a9a79add3ecd68b38f14b548/sahajbert/tokenizer/tokenizer_model.py) .
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 120 minutes depending on your hardware ☕☕☕ .
```python
import datasets
from t5_tokenizer_model import SentencePieceUnigramTokenizer
vocab_size = 32_000
input_sentence_size = None
# Initialize a dataset
dataset = datasets.load_dataset("oscar", name="unshuffled_deduplicated_no", split="train")
tokenizer = SentencePieceUnigramTokenizer(unk_token="<unk>", eos_token="</s>", pad_token="<pad>")
# Build an iterator over this dataset
def batch_iterator(input_sentence_size=None):
if input_sentence_size is None:
input_sentence_size = len(dataset)
batch_length = 100
for i in range(0, input_sentence_size, batch_length):
yield dataset[i: i + batch_length]["text"]
# Train tokenizer
tokenizer.train_from_iterator(
iterator=batch_iterator(input_sentence_size=input_sentence_size),
vocab_size=vocab_size,
show_progress=True,
)
# Save files to disk
tokenizer.save("./norwegian-t5-base/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**google/t5-v1_1-base**`](https://huggingface.co/google/t5-v1_1-base)
in the local model folder:
```python
from transformers import T5Config
config = T5Config.from_pretrained("google/t5-v1_1-base", vocab_size=tokenizer.get_vocab_size())
config.save_pretrained("./norwegian-t5-base")
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
### Train model
Next we can run the example script to pretrain the model:
```bash
python run_t5_mlm_flax.py \
--output_dir="./norwegian-t5-base" \
--model_type="t5" \
--config_name="./norwegian-t5-base" \
--tokenizer_name="./norwegian-t5-base" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="512" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--adafactor \
--learning_rate="0.005" \
--weight_decay="0.001" \
--warmup_steps="2000" \
--overwrite_output_dir \
--logging_steps="500" \
--save_steps="10000" \
--eval_steps="2500" \
--push_to_hub
```
Training should converge at a loss and accuracy
of 2.36 and 57.0 respectively after 3 epochs on a single TPUv3-8.
This should take around 4.5 hours.
Training statistics can be accessed on directly on the 🤗 [hub](https://huggingface.co/patrickvonplaten/t5-base-norwegian/tensorboard)
## BART: Denoising language modeling
In the following, we demonstrate how to train a BART model
using denoising language modeling objective as introduced in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461).
More specifically, we demonstrate how JAX/Flax can be leveraged
to pre-train [**`bart-base`**](https://huggingface.co/facebook/bart-base)
in Norwegian on a single TPUv3-8 pod.
The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
To setup all relevant files for training, let's create a directory.
```bash
mkdir ./norwegian-bart-base
```
### Train tokenizer
In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
The tokenizer is trained on the complete Norwegian dataset of OSCAR
and consequently saved in the cloned model directory.
This can take up to 10 minutes depending on your hardware ☕.
```python
from datasets import load_dataset
from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
# load dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
# Instantiate tokenizer
tokenizer = ByteLevelBPETokenizer()
def batch_iterator(batch_size=1000):
for i in range(0, len(dataset), batch_size):
yield dataset[i: i + batch_size]["text"]
# Customized training
tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>",
])
# Save files to disk
tokenizer.save("./norwegian-bart-base/tokenizer.json")
```
### Create configuration
Next, we create the model's configuration file. This is as simple
as loading and storing [`**facebook/bart-base**`](https://huggingface.co/facebook/bart-base)
in the local model folder:
```python
from transformers import BartConfig
config = BartConfig.from_pretrained("facebook/bart-base", vocab_size=50265)
config.save_pretrained("./norwegian-bart-base")
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
### Train model
Next we can run the example script to pretrain the model:
```bash
python run_bart_dlm_flax.py \
--output_dir="./norwegian-bart-base" \
--config_name="./norwegian-bart-base" \
--tokenizer_name="./norwegian-bart-base" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="1024" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--learning_rate="1e-4" \
--warmup_steps="2000" \
--overwrite_output_dir \
--logging_steps="500" \
--save_steps="2000" \
--eval_steps="2000" \
--push_to_hub
```
Training should converge at a loss and accuracy
of 1.36 and 0.77 respectively after 3 epochs on a single TPUv3-8.
This should take less than 6 hours.
Training statistics can be accessed on [tfhub.dev](https://tensorboard.dev/experiment/Maw62QlaSXWS0MOf2V2lbg/).
## Runtime evaluation
We also ran masked language modeling using PyTorch/XLA on a TPUv3-8, and PyTorch on 8 V100 GPUs. We report the
overall training time below.
For reproducibility, we state the training commands used for PyTorch/XLA and PyTorch further below.
| Task | [TPU v3-8 (Flax)](https://tensorboard.dev/experiment/GdYmdak2TWeVz0DDRYOrrg/) | [TPU v3-8 (Pytorch/XLA)](https://tensorboard.dev/experiment/7Jq1kcQQRAmy12KOdXek7A/)| [8 GPU (PyTorch)](https://tensorboard.dev/experiment/PJneV8FQRxa2unPw1QnVHA) |
|-------|-----------|------------|------------|
| MLM | 15h32m | 23h46m | 44h14m |
*All experiments are ran on Google Cloud Platform.
GPU experiments are ran without further optimizations besides JAX
transformations. GPU experiments are ran with full precision (fp32). "TPU v3-8"
are 8 TPU cores on 4 chips (each chips has 2 cores), while "8 GPU" are 8 GPU chips.
### Script to run MLM with PyTorch/XLA on TPUv3-8
For comparison one can run the same pre-training with PyTorch/XLA on TPU. To set up PyTorch/XLA on Cloud TPU VMs, please
refer to [this](https://cloud.google.com/tpu/docs/pytorch-xla-ug-tpu-vm) guide.
Having created the tokenizer and configuration in `norwegian-roberta-base`, we create the following symbolic links:
```bash
ln -s ~/transformers/examples/pytorch/language-modeling/run_mlm.py ./
ln -s ~/transformers/examples/pytorch/xla_spawn.py ./
```
, set the following environment variables:
```bash
export XRT_TPU_CONFIG="localservice;0;localhost:51011"
unset LD_PRELOAD
export NUM_TPUS=8
export TOKENIZERS_PARALLELISM=0
export MODEL_DIR="./norwegian-roberta-base"
mkdir -p ${MODEL_DIR}
```
, and start training as follows:
```bash
python3 xla_spawn.py --num_cores ${NUM_TPUS} run_mlm.py --output_dir="./runs" \
--model_type="roberta" \
--config_name="${MODEL_DIR}" \
--tokenizer_name="${MODEL_DIR}" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="128" \
--per_device_eval_batch_size="128" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="18" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--do_train \
--do_eval \
--logging_steps="500" \
--eval_strategy="epoch" \
--report_to="tensorboard" \
--save_strategy="no"
```
### Script to compare pre-training with PyTorch on 8 GPU V100's
For comparison you can run the same pre-training with PyTorch on GPU. Note that we have to make use of `gradient_accumulation`
because the maximum batch size that fits on a single V100 GPU is 32 instead of 128.
Having created the tokenizer and configuration in `norwegian-roberta-base`, we create the following symbolic links:
```bash
ln -s ~/transformers/examples/pytorch/language-modeling/run_mlm.py ./
```
, set some environment variables:
```bash
export NUM_GPUS=8
export TOKENIZERS_PARALLELISM=0
export MODEL_DIR="./norwegian-roberta-base"
mkdir -p ${MODEL_DIR}
```
, and can start training as follows:
```bash
python3 -m torch.distributed.launch --nproc_per_node ${NUM_GPUS} run_mlm.py \
--output_dir="${MODEL_DIR}" \
--model_type="roberta" \
--config_name="${MODEL_DIR}" \
--tokenizer_name="${MODEL_DIR}" \
--dataset_name="oscar" \
--dataset_config_name="unshuffled_deduplicated_no" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size="32" \
--gradient_accumulation="4" \
--learning_rate="3e-4" \
--warmup_steps="1000" \
--overwrite_output_dir \
--num_train_epochs="18" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--do_train \
--do_eval \
--logging_steps="500" \
--eval_strategy="steps" \
--report_to="tensorboard" \
--save_strategy="no"
```
## Language model inference with bfloat16
The following example demonstrates performing inference with a language model using the JAX/Flax backend.
The example script run_bert_flax.py uses bert-base-uncased, and the model is loaded into `FlaxBertModel`.
The input data are randomly generated tokens, and the model is also jitted with JAX.
By default, it uses float32 precision for inference. To enable bfloat16, add the flag shown in the command below.
```bash
python3 run_bert_flax.py --precision bfloat16
> NOTE: For JAX Versions after v0.4.33 or later, users will need to set the below environment variables as a \
> temporary workaround to use Bfloat16 datatype. \
> This restriction is expected to be removed in future version
```bash
export XLA_FLAGS=--xla_cpu_use_thunk_runtime=false
```
bfloat16 gives better performance on GPUs and also Intel CPUs (Sapphire Rapids or later) with Advanced Matrix Extension (Intel AMX).
By changing the dtype for `FlaxBertModel `to `jax.numpy.bfloat16`, you get the performance benefits of the underlying hardware.
```python
import jax
model = FlaxBertModel.from_pretrained("bert-base-uncased", config=config, dtype=jax.numpy.bfloat16)
```
Switching from float32 to bfloat16 can increase the speed of an AWS c7i.4xlarge with Intel Sapphire Rapids by more than 2x.
| transformers/examples/flax/language-modeling/README.md/0 | {
"file_path": "transformers/examples/flax/language-modeling/README.md",
"repo_id": "transformers",
"token_count": 7218
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Benchmarking the library on inference and training"""
from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments
def main():
parser = HfArgumentParser(PyTorchBenchmarkArguments)
try:
benchmark_args = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
arg_error_msg = "Arg --no_{0} is no longer used, please use --no-{0} instead."
begin_error_msg = " ".join(str(e).split(" ")[:-1])
full_error_msg = ""
depreciated_args = eval(str(e).split(" ")[-1])
wrong_args = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(arg)
if len(wrong_args) > 0:
full_error_msg = full_error_msg + begin_error_msg + str(wrong_args)
raise ValueError(full_error_msg)
benchmark = PyTorchBenchmark(args=benchmark_args)
benchmark.run()
if __name__ == "__main__":
main()
| transformers/examples/legacy/benchmarking/run_benchmark.py/0 | {
"file_path": "transformers/examples/legacy/benchmarking/run_benchmark.py",
"repo_id": "transformers",
"token_count": 699
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" OpenAI GPT model fine-tuning script.
Adapted from https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/train.py
It self adapted from https://github.com/openai/finetune-transformer-lm/blob/master/train.py
This script with default values fine-tunes and evaluate a pretrained OpenAI GPT on the RocStories dataset:
python run_openai_gpt.py \
--model_name openai-community/openai-gpt \
--do_train \
--do_eval \
--train_dataset "$ROC_STORIES_DIR/cloze_test_val__spring2016 - cloze_test_ALL_val.csv" \
--eval_dataset "$ROC_STORIES_DIR/cloze_test_test__spring2016 - cloze_test_ALL_test.csv" \
--output_dir ../log \
--train_batch_size 16 \
"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
logger = logging.getLogger(__name__)
def accuracy(out, labels):
outputs = np.argmax(out, axis=1)
return np.sum(outputs == labels)
def load_rocstories_dataset(dataset_path):
"""Output a list of tuples(story, 1st continuation, 2nd continuation, label)"""
with open(dataset_path, encoding="utf_8") as f:
f = csv.reader(f)
output = []
next(f) # skip the first line
for line in tqdm(f):
output.append((" ".join(line[1:5]), line[5], line[6], int(line[-1]) - 1))
return output
def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, delimiter_token, clf_token):
"""Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)
To Transformer inputs of shape (n_batch, n_alternative, length) comprising for each batch, continuation:
input_ids[batch, alternative, :] = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
"""
tensor_datasets = []
for dataset in encoded_datasets:
n_batch = len(dataset)
input_ids = np.zeros((n_batch, 2, input_len), dtype=np.int64)
mc_token_ids = np.zeros((n_batch, 2), dtype=np.int64)
lm_labels = np.full((n_batch, 2, input_len), fill_value=-100, dtype=np.int64)
mc_labels = np.zeros((n_batch,), dtype=np.int64)
for (
i,
(story, cont1, cont2, mc_label),
) in enumerate(dataset):
with_cont1 = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
with_cont2 = [start_token] + story[:cap_length] + [delimiter_token] + cont2[:cap_length] + [clf_token]
input_ids[i, 0, : len(with_cont1)] = with_cont1
input_ids[i, 1, : len(with_cont2)] = with_cont2
mc_token_ids[i, 0] = len(with_cont1) - 1
mc_token_ids[i, 1] = len(with_cont2) - 1
lm_labels[i, 0, : len(with_cont1)] = with_cont1
lm_labels[i, 1, : len(with_cont2)] = with_cont2
mc_labels[i] = mc_label
all_inputs = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(t) for t in all_inputs))
return tensor_datasets
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, default="openai-community/openai-gpt", help="pretrained model name")
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--train_dataset", type=str, default="")
parser.add_argument("--eval_dataset", type=str, default="")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--num_train_epochs", type=int, default=3)
parser.add_argument("--train_batch_size", type=int, default=8)
parser.add_argument("--eval_batch_size", type=int, default=16)
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", type=int, default=1)
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--learning_rate", type=float, default=6.25e-5)
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--lr_schedule", type=str, default="warmup_linear")
parser.add_argument("--weight_decay", type=float, default=0.01)
parser.add_argument("--lm_coef", type=float, default=0.9)
parser.add_argument("--n_valid", type=int, default=374)
parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
args = parser.parse_args()
print(args)
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(device, n_gpu))
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
special_tokens = ["_start_", "_delimiter_", "_classify_"]
tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_name)
tokenizer.add_tokens(special_tokens)
special_tokens_ids = tokenizer.convert_tokens_to_ids(special_tokens)
model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name)
model.resize_token_embeddings(len(tokenizer))
model.to(device)
# Load and encode the datasets
def tokenize_and_encode(obj):
"""Tokenize and encode a nested object"""
if isinstance(obj, str):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj))
elif isinstance(obj, int):
return obj
return [tokenize_and_encode(o) for o in obj]
logger.info("Encoding dataset...")
train_dataset = load_rocstories_dataset(args.train_dataset)
eval_dataset = load_rocstories_dataset(args.eval_dataset)
datasets = (train_dataset, eval_dataset)
encoded_datasets = tokenize_and_encode(datasets)
# Compute the max input length for the Transformer
max_length = model.config.n_positions // 2 - 2
input_length = max(
len(story[:max_length]) + max(len(cont1[:max_length]), len(cont2[:max_length])) + 3
for dataset in encoded_datasets
for story, cont1, cont2, _ in dataset
)
input_length = min(input_length, model.config.n_positions) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
tensor_datasets = pre_process_datasets(encoded_datasets, input_length, max_length, *special_tokens_ids)
train_tensor_dataset, eval_tensor_dataset = tensor_datasets[0], tensor_datasets[1]
train_data = TensorDataset(*train_tensor_dataset)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
eval_data = TensorDataset(*eval_tensor_dataset)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
param_optimizer = list(model.named_parameters())
no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
if args.do_train:
nb_tr_steps, tr_loss, exp_average_loss = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_steps = 0
tqdm_bar = tqdm(train_dataloader, desc="Training")
for step, batch in enumerate(tqdm_bar):
batch = tuple(t.to(device) for t in batch)
input_ids, mc_token_ids, lm_labels, mc_labels = batch
losses = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels)
loss = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
exp_average_loss = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
tqdm_bar.desc = "Training loss: {:.2e} lr: {:.2e}".format(exp_average_loss, scheduler.get_lr()[0])
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(model, "module") else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned
model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
tokenizer = OpenAIGPTTokenizer.from_pretrained(args.output_dir)
model.to(device)
if args.do_eval:
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for batch in tqdm(eval_dataloader, desc="Evaluating"):
batch = tuple(t.to(device) for t in batch)
input_ids, mc_token_ids, lm_labels, mc_labels = batch
with torch.no_grad():
_, mc_loss, _, mc_logits = model(
input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels
)
mc_logits = mc_logits.detach().cpu().numpy()
mc_labels = mc_labels.to("cpu").numpy()
tmp_eval_accuracy = accuracy(mc_logits, mc_labels)
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
train_loss = tr_loss / nb_tr_steps if args.do_train else None
result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if __name__ == "__main__":
main()
| transformers/examples/legacy/run_openai_gpt.py/0 | {
"file_path": "transformers/examples/legacy/run_openai_gpt.py",
"repo_id": "transformers",
"token_count": 6176
} |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(DEFAULT_REPO), "Tatoeba directory does not exist.")
class TatoebaConversionTester(unittest.TestCase):
@cached_property
def resolver(self):
tmp_dir = tempfile.mkdtemp()
return TatoebaConverter(save_dir=tmp_dir)
@slow
def test_resolver(self):
self.resolver.convert_models(["heb-eng"])
@slow
def test_model_card(self):
content, mmeta = self.resolver.write_model_card("opus-mt-he-en", dry_run=True)
assert mmeta["long_pair"] == "heb-eng"
| transformers/examples/legacy/seq2seq/old_test_tatoeba_conversion.py/0 | {
"file_path": "transformers/examples/legacy/seq2seq/old_test_tatoeba_conversion.py",
"repo_id": "transformers",
"token_count": 467
} |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fine-tuning the library models for named entity recognition on CoNLL-2003."""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
logger = logging.getLogger(__name__)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
task_type: Optional[str] = field(
default="NER", metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
data_dir: str = field(
metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}
)
labels: Optional[str] = field(
default=None,
metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."},
)
max_seq_length: int = field(
default=128,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
" --overwrite_output_dir to overcome."
)
module = import_module("tasks")
try:
token_classification_task_clazz = getattr(module, model_args.task_type)
token_classification_task: TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
set_seed(training_args.seed)
# Prepare CONLL-2003 task
labels = token_classification_task.get_labels(data_args.labels)
label_map: Dict[int, str] = dict(enumerate(labels))
num_labels = len(labels)
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
id2label=label_map,
label2id={label: i for i, label in enumerate(labels)},
cache_dir=model_args.cache_dir,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast,
)
model = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# Get datasets
train_dataset = (
TokenClassificationDataset(
token_classification_task=token_classification_task,
data_dir=data_args.data_dir,
tokenizer=tokenizer,
labels=labels,
model_type=config.model_type,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.train,
)
if training_args.do_train
else None
)
eval_dataset = (
TokenClassificationDataset(
token_classification_task=token_classification_task,
data_dir=data_args.data_dir,
tokenizer=tokenizer,
labels=labels,
model_type=config.model_type,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.dev,
)
if training_args.do_eval
else None
)
def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
preds = np.argmax(predictions, axis=2)
batch_size, seq_len = preds.shape
out_label_list = [[] for _ in range(batch_size)]
preds_list = [[] for _ in range(batch_size)]
for i in range(batch_size):
for j in range(seq_len):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]])
preds_list[i].append(label_map[preds[i][j]])
return preds_list, out_label_list
def compute_metrics(p: EvalPrediction) -> Dict:
preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)
return {
"accuracy_score": accuracy_score(out_label_list, preds_list),
"precision": precision_score(out_label_list, preds_list),
"recall": recall_score(out_label_list, preds_list),
"f1": f1_score(out_label_list, preds_list),
}
# Data collator
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
data_collator=data_collator,
)
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
result = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
if trainer.is_world_process_zero():
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in result.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
results.update(result)
# Predict
if training_args.do_predict:
test_dataset = TokenClassificationDataset(
token_classification_task=token_classification_task,
data_dir=data_args.data_dir,
tokenizer=tokenizer,
labels=labels,
model_type=config.model_type,
max_seq_length=data_args.max_seq_length,
overwrite_cache=data_args.overwrite_cache,
mode=Split.test,
)
predictions, label_ids, metrics = trainer.predict(test_dataset)
preds_list, _ = align_predictions(predictions, label_ids)
output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
if trainer.is_world_process_zero():
with open(output_test_results_file, "w") as writer:
for key, value in metrics.items():
logger.info(" %s = %s", key, value)
writer.write("%s = %s\n" % (key, value))
# Save predictions
output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")
if trainer.is_world_process_zero():
with open(output_test_predictions_file, "w") as writer:
with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
token_classification_task.write_predictions_to_file(writer, f, preds_list)
return results
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| transformers/examples/legacy/token-classification/run_ner.py/0 | {
"file_path": "transformers/examples/legacy/token-classification/run_ner.py",
"repo_id": "transformers",
"token_count": 5024
} |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from examples/modular-transformers/modular_from_uppercase_model.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_from_uppercase_model.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
from typing import Optional, Tuple
import torch
from torch import nn
from ...activations import ACT2FN
from ...pytorch_utils import is_torch_greater_or_equal_than_2_2
from ...utils import is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging
from .configuration_from_uppercase_model import FromUppercaseModelConfig
if is_flash_attn_2_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
class FromUppercaseModelAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
class FromUppercaseModelFlashAttention2(FromUppercaseModelAttention):
"""
FromUppercaseModelAttention flash attention module. This module inherits from `FromUppercaseModelAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
# Adapted from transformers.models.llama.modeling_llama.LlamaFlashAttention2.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
output_attentions = False
batch_size, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim)
key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim)
value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim)
dropout_rate = self.dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32.
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=dropout_rate,
is_causal=causal_attention_mask is not None,
use_top_left_mask=self._flash_attn_uses_top_left_mask,
)
attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim).contiguous()
attn_output = self.out_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights
class FromUppercaseModelSdpaAttention(FromUppercaseModelAttention):
"""
SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`FromUppercaseModelAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from FromUppercaseModelAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"FromUppercaseModelModel is using FromUppercaseModelSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not "
"support `output_attentions=True`. Falling back to the manual attention implementation, but specifying "
"the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can "
'be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
# FROM_UPPERCASE_MODEL text model uses both `causal_attention_mask` and `attention_mask`
if attention_mask is not None and causal_attention_mask is not None:
attn_mask = attention_mask + causal_attention_mask
elif causal_attention_mask is not None:
attn_mask = causal_attention_mask
else:
attn_mask = attention_mask
bsz, tgt_len, embed_dim = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if not is_torch_greater_or_equal_than_2_2 and query_states.device.type == "cuda" and attn_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# FROM_UPPERCASE_MODEL text model uses both `causal_attention_mask` and `attention_mask` sequentially.
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attn_mask,
dropout_p=self.dropout if self.training else 0.0,
scale=self.scale,
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, None
class FromUppercaseModelMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
FROM_UPPERCASE_MODEL_ATTENTION_CLASSES = {
"eager": FromUppercaseModelAttention,
"sdpa": FromUppercaseModelSdpaAttention,
"flash_attention_2": FromUppercaseModelFlashAttention2,
}
class FromUppercaseModelEncoderLayer(nn.Module):
def __init__(self, config: FromUppercaseModelConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = FROM_UPPERCASE_MODEL_ATTENTION_CLASSES[config._attn_implementation](config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = FromUppercaseModelMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
| transformers/examples/modular-transformers/modeling_from_uppercase_model.py/0 | {
"file_path": "transformers/examples/modular-transformers/modeling_from_uppercase_model.py",
"repo_id": "transformers",
"token_count": 7332
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for causal language modeling using
Fill-in-the middle (FIM) objective on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=text-generation
"""
# You should adapt this script on your own causal language modeling task. Pointers for this are left as comments.
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional
import datasets
import evaluate
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
is_torch_tpu_available,
set_seed,
)
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.testing_utils import CaptureLogger
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.49.0.dev0")
require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
)
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
)
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether to trust the execution of code from datasets/models defined on the Hub."
" This option should only be set to `True` for repositories you trust and in which you have read the"
" code, as it will execute code present on the Hub on your local machine."
)
},
)
torch_dtype: Optional[str] = field(
default=None,
metadata={
"help": (
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
"dtype will be automatically derived from the model's weights."
),
"choices": ["auto", "bfloat16", "float16", "float32"],
},
)
low_cpu_mem_usage: bool = field(
default=False,
metadata={
"help": (
"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. "
"set True will benefit LLM loading time and RAM consumption."
)
},
)
pad_to_multiple_of: bool = field(
default=False,
metadata={
"help": (
"Whether to pad the embedding layer to a multiple depending on the device. ",
"For NVIDIA GPUs, this will be a multiple of 8, for TPUs a multiple of 128.",
)
},
)
attn_implementation: Optional[str] = field(
default="sdpa", metadata={"help": ("The attention implementation to use. ")}
)
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
block_size: Optional[int] = field(
default=None,
metadata={
"help": (
"Optional input sequence length after tokenization. "
"The training dataset will be truncated in block of this size for training. "
"Default to the model max input length for single sentence inputs (take into account special tokens)."
)
},
)
fim_rate: Optional[float] = field(
default=0.5,
metadata={
"help": (
"Optional probability with which the FIM transformation is applied to the example. "
"Default is 0.5. A rate of 1.0 means every example will undergo FIM transformation, "
"while a rate of 0.0 means no example will."
)
},
)
fim_spm_rate: Optional[float] = field(
default=0.5,
metadata={
"help": (
"Within the examples undergoing FIM transformation, this rate determines the probability "
"of applying the Sentence Permutation Mode (SPM). "
"Default is 0.5. A rate of 1.0 means all FIM transformations will use SPM, "
"while a rate of 0.0 means none will."
)
},
)
truncate_or_pad: Optional[bool] = field(
default=True,
metadata={
"help": (
"Indicates whether the transformed example should be truncated or padded to maintain "
"the same length as the original example. "
"Default is True. If False, the function will not truncate or pad the examples."
)
},
)
fim_prefix_token: Optional[str] = field(
default="<fim_prefix>",
metadata={"help": ("Fill-in-Middle Prefix token. Defaults to '<fim_prefix>'.")},
)
fim_middle_token: Optional[str] = field(
default="<fim_middle>",
metadata={"help": ("Fill-in-Middle Middle token. Defaults to '<fim_middle>'.")},
)
fim_suffix_token: Optional[str] = field(
default="<fim_suffix>",
metadata={"help": ("Fill-in-Middle Suffix token. Defaults to '<fim_suffix>'.")},
)
pad_token: Optional[str] = field(
default="<fim_pad>",
metadata={
"help": (
"Fill-in-Middle Pad token. Used only when 'truncate_or_pad' is set to True. "
"Defaults to '<fim_pad>'."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[int] = field(
default=5,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
keep_linebreaks: bool = field(
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
)
def __post_init__(self):
if self.streaming:
require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`")
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_fim", model_args, data_args)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# Set a numpy random state for FIM transformations
np_rng = np.random.RandomState(seed=training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
streaming=data_args.streaming,
trust_remote_code=model_args.trust_remote_code,
)
if "validation" not in raw_datasets.keys():
raw_datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
token=model_args.token,
streaming=data_args.streaming,
trust_remote_code=model_args.trust_remote_code,
)
raw_datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
token=model_args.token,
streaming=data_args.streaming,
trust_remote_code=model_args.trust_remote_code,
)
else:
data_files = {}
dataset_args = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = (
data_args.train_file.split(".")[-1]
if data_args.train_file is not None
else data_args.validation_file.split(".")[-1]
)
if extension == "txt":
extension = "text"
dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
raw_datasets = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
**dataset_args,
)
# If no validation data is there, validation_split_percentage will be used to divide the dataset.
if "validation" not in raw_datasets.keys():
raw_datasets["validation"] = load_dataset(
extension,
data_files=data_files,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
token=model_args.token,
**dataset_args,
)
raw_datasets["train"] = load_dataset(
extension,
data_files=data_files,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
token=model_args.token,
**dataset_args,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"token": model_args.token,
"trust_remote_code": model_args.trust_remote_code,
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.info(f"New config: {config}")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"token": model_args.token,
"trust_remote_code": model_args.trust_remote_code,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.model_name_or_path:
torch_dtype = (
model_args.torch_dtype
if model_args.torch_dtype in ["auto", None]
else getattr(torch, model_args.torch_dtype)
)
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
torch_dtype=torch_dtype,
low_cpu_mem_usage=model_args.low_cpu_mem_usage,
attn_implementation=model_args.attn_implementation,
)
else:
model = AutoModelForCausalLM.from_config(
config,
trust_remote_code=model_args.trust_remote_code,
attn_implementation=model_args.attn_implementation,
)
n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
# Add the new FIM tokens to the tokenizer and resize model's vocab embeddings
special_tokens = [data_args.fim_prefix_token, data_args.fim_middle_token, data_args.fim_suffix_token]
if data_args.truncate_or_pad:
special_tokens.append(data_args.pad_token)
# Get the factor by which the embedding layer should be padded based on the device
pad_factor = 1
if torch.cuda.is_availble():
pad_factor = 8
elif is_torch_tpu_available():
pad_factor = 128
# Add the new tokens to the tokenizer
tokenizer.add_tokens(special_tokens)
original_embeddings = model.get_input_embeddings()
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(original_embeddings.weight, modifier_rank=0):
# Get the pre-expansion embeddings of the model and resize the embedding layer
model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=pad_factor)
embeddings = model.get_input_embeddings()
# Sample the embeddings for the new tokens from a multivariate normal distribution
# We do this so that the new embeddings are close to the original embeddings and not necessarily zero
# More on this: https://nlp.stanford.edu/~johnhew/vocab-expansion.html
mean = original_embeddings.mean(dim=0)
n = original_embeddings.size()[0]
sigma = ((original_embeddings - mean).T @ (original_embeddings - mean)) / n
dist = torch.distributions.multivariate_normal.MultivariateNormal(
mean,
covariance_matrix=1e-5 * sigma,
)
new_token_embeddings = torch.stack(
tuple((dist.sample() for _ in range(len(special_tokens)))),
dim=0,
)
else:
original_embeddings = model.get_input_embeddings()
# Get the pre-expansion embeddings of the model and resize the embedding layer
model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=pad_factor)
embeddings = model.get_input_embeddings()
# Sample the embeddings for the new tokens from a multivariate normal distribution
# We do this so that the new embeddings are close to the original embeddings and not necessarily zero
# More on this: https://nlp.stanford.edu/~johnhew/vocab-expansion.html
mean = original_embeddings.mean(dim=0)
n = original_embeddings.size()[0]
sigma = ((original_embeddings - mean).T @ (original_embeddings - mean)) / n
dist = torch.distributions.multivariate_normal.MultivariateNormal(
mean,
covariance_matrix=1e-5 * sigma,
)
new_token_embeddings = torch.stack(
tuple((dist.sample() for _ in range(len(special_tokens)))),
dim=0,
)
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(embeddings.weight, modifier_rank=0):
# Set the new tokens' embeddings to the newly sampled embeddings
embeddings.weight.data[-len(special_tokens) :] = new_token_embeddings
else:
# Set the new tokens' embeddings to the newly sampled embeddings
embeddings.weight.data[-len(special_tokens) :] = new_token_embeddings
# Update the model's embeddings with the new embeddings
model.set_input_embeddings(embeddings)
logger.info("Added special tokens to the tokenizer and resized model's embedding layer")
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = list(raw_datasets["train"].features)
else:
column_names = list(raw_datasets["validation"].features)
text_column_name = "text" if "text" in column_names else column_names[0]
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
def tokenize_function(examples):
with CaptureLogger(tok_logger) as cl:
output = tokenizer(examples[text_column_name])
# clm-fim input could be much much longer than block_size
if "Token indices sequence length is longer than the" in cl.out:
tok_logger.warning(
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
" before being passed to the model."
)
return output
with training_args.main_process_first(desc="dataset map tokenization"):
if not data_args.streaming:
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset",
)
else:
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
remove_columns=column_names,
)
if data_args.block_size is None:
block_size = tokenizer.model_max_length
if block_size > config.max_position_embeddings:
logger.warning(
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
f"Using block_size={min(1024, config.max_position_embeddings)} instead. You can change that default value by passing --block_size xxx."
)
block_size = min(1024, config.max_position_embeddings)
else:
if data_args.block_size > tokenizer.model_max_length:
logger.warning(
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model "
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
)
block_size = min(data_args.block_size, tokenizer.model_max_length)
# Data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, and if the total_length < block_size we exclude this batch and return an empty dict.
# We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
total_length = (total_length // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
# Get the FIM-specific token ids
prefix_tok_id = tokenizer.convert_tokens_to_ids(data_args.fim_prefix_token)
middle_tok_id = tokenizer.convert_tokens_to_ids(data_args.fim_middle_token)
suffix_tok_id = tokenizer.convert_tokens_to_ids(data_args.fim_suffix_token)
pad_tok_id = None
# If truncate_or_pad is on, also get pad token id
if data_args.truncate_or_pad:
pad_tok_id = tokenizer.convert_tokens_to_ids(data_args.pad_token)
# The two functions below perform the FIM transformation on the data (either PSM or SPM or PSM+SPM)
# Don't call fim_transform directly in .map()
# Adapted from https://github.com/loubnabnl/santacoder-finetuning/blob/main/fim.py#L22C13-L83
def fim_transform(example):
"""
This function performs FIM transformation on a single example (list of tokens)
"""
if np_rng.binomial(1, data_args.fim_rate):
boundaries = sorted(np_rng.randint(low=0, high=len(example) + 1, size=2))
prefix = example[: boundaries[0]]
middle = example[boundaries[0] : boundaries[1]]
suffix = example[boundaries[1] :]
if data_args.truncate_or_pad:
total_length = len(prefix) + len(middle) + len(suffix) + 3
diff = total_length - len(example)
if diff > 0:
suffix = suffix[: max(0, len(suffix) - diff)]
elif diff < 0:
suffix.extend([pad_tok_id] * (-diff))
if np_rng.binomial(1, data_args.fim_spm_rate):
# Apply Suffix-Prefix-Middle (SPM) transformation
transformed_example = [prefix_tok_id, suffix_tok_id] + suffix + [middle_tok_id] + prefix + middle
else:
# Apply Prefix-Suffix-Middle (PSM) transformation
transformed_example = [prefix_tok_id] + prefix + [suffix_tok_id] + suffix + [middle_tok_id] + middle
else:
transformed_example = example
return transformed_example
# Below function is the one you are supposed to call in the .map() function
def apply_fim(examples):
"""
Apply FIM transformation to a batch of examples
"""
fim_transform_ids = [fim_transform(ids) for ids in examples["input_ids"]]
examples["input_ids"] = fim_transform_ids
examples["labels"] = fim_transform_ids
# If your application requires custom attention mask, please adjust this function's below line.
# Since FIM transformation increases the number of tokens in input_ids and labels
# but leaves the number of tokens unchanged in attention_masks which would cause problems
examples["attention_mask"] = [[1] * len(mask) for mask in examples["input_ids"]]
return examples
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
# to preprocess.
#
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
# https://huggingface.co/docs/datasets/process#map
# FIM transformations are only supposed to be applied before group_texts processing otherwise some sentences will
# have 3-4 more tokens than others due to probabilistic addition of FIM-specific tokens which will raise errors
with training_args.main_process_first(desc="processing texts together"):
if not data_args.streaming:
fim_datasets = tokenized_datasets.map(
apply_fim,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc="Performing FIM transformation",
)
lm_datasets = fim_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc=f"Grouping texts in chunks of {block_size}",
)
else:
fim_datasets = tokenized_datasets.map(
apply_fim,
batched=True,
)
lm_datasets = fim_datasets.map(
group_texts,
batched=True,
)
if training_args.do_train:
if "train" not in tokenized_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = lm_datasets["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
if training_args.do_eval:
if "validation" not in tokenized_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = lm_datasets["validation"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
def preprocess_logits_for_metrics(logits, labels):
if isinstance(logits, tuple):
# Depending on the model and config, logits may contain extra tensors,
# like past_key_values, but logits always come first
logits = logits[0]
return logits.argmax(dim=-1)
metric = evaluate.load("accuracy")
def compute_metrics(eval_preds):
preds, labels = eval_preds
# preds have the same shape as the labels, after the argmax(-1) has been calculated
# by preprocess_logits_for_metrics but we need to shift the labels
labels = labels[:, 1:].reshape(-1)
preds = preds[:, :-1].reshape(-1)
return metric.compute(predictions=preds, references=labels)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
processing_class=tokenizer,
# Data collator will default to DataCollatorWithPadding, so we change it.
data_collator=default_data_collator,
compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
preprocess_logits_for_metrics=(
preprocess_logits_for_metrics if training_args.do_eval and not is_torch_tpu_available() else None
),
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:
kwargs["dataset_args"] = data_args.dataset_config_name
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
else:
kwargs["dataset"] = data_args.dataset_name
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| transformers/examples/pytorch/language-modeling/run_fim.py/0 | {
"file_path": "transformers/examples/pytorch/language-modeling/run_fim.py",
"repo_id": "transformers",
"token_count": 15847
} |
<!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Automatic Speech Recognition Examples
## Table of Contents
- [Automatic Speech Recognition with CTC](#connectionist-temporal-classification)
- [Single GPU example](#single-gpu-ctc)
- [Multi GPU example](#multi-gpu-ctc)
- [Examples](#examples-ctc)
- [TIMIT](#timit-ctc)
- [Librispeech](#librispeech-ctc)
- [Common Voice](#common-voice-ctc)
- [Multilingual Librispeech](#multilingual-librispeech-ctc)
- [Automatic Speech Recognition with CTC and Adapter Layers](#connectionist-temporal-classification-with-adapters)
- [Massive Multilingual Speech (MMS)](#mms-model)
- [Examples](#examples-ctc-adapter)
- [Common Voice](#common-voice-ctc-adapter)
- [Automatic Speech Recognition with Sequence-to-Sequence](#sequence-to-sequence)
- [Whisper Model](#whisper-model)
- [Speech-Encoder-Decoder Model](#warm-started-speech-encoder-decoder-model)
- [Examples](#examples-seq2seq)
- [Librispeech](#librispeech-seq2seq)
## Connectionist Temporal Classification
The script [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) can be used to fine-tune any pretrained [Connectionist Temporal Classification Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForCTC) for automatic speech
recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset.
Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, *e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), have shown to require only
very little annotated data to yield good performance on automatic speech recognition datasets.
In the script [`run_speech_recognition_ctc`], we first create a vocabulary from all unique characters of both the training data and evaluation data. Then, we preprocesses the speech recognition dataset, which includes correct resampling, normalization and padding. Finally, the pretrained speech recognition model is fine-tuned on the annotated speech recognition datasets using CTC loss.
---
**NOTE**
If you encounter problems with data preprocessing by setting `--preprocessing_num_workers` > 1,
you might want to set the environment variable `OMP_NUM_THREADS` to 1 as follows:
```bash
OMP_NUM_THREADS=1 python run_speech_recognition_ctc ...
```
If the environment variable is not set, the training script might freeze, *i.e.* see: https://github.com/pytorch/audio/issues/1021#issuecomment-726915239
---
### Single GPU CTC
The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
```bash
python run_speech_recognition_ctc.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
--dataset_config_name="tr" \
--output_dir="./wav2vec2-common_voice-tr-demo" \
--overwrite_output_dir \
--num_train_epochs="15" \
--per_device_train_batch_size="16" \
--gradient_accumulation_steps="2" \
--learning_rate="3e-4" \
--warmup_steps="500" \
--eval_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="400" \
--eval_steps="100" \
--layerdrop="0.0" \
--save_total_limit="3" \
--freeze_feature_encoder \
--gradient_checkpointing \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--fp16 \
--group_by_length \
--push_to_hub \
--do_train --do_eval
```
On a single V100 GPU, this script should run in *ca.* 1 hour 20 minutes and yield a CTC loss of **0.39** and word error rate
of **0.35**.
### Multi GPU CTC
The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
```bash
torchrun \
--nproc_per_node 8 run_speech_recognition_ctc.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
--dataset_config_name="tr" \
--output_dir="./wav2vec2-common_voice-tr-demo-dist" \
--overwrite_output_dir \
--num_train_epochs="15" \
--per_device_train_batch_size="4" \
--learning_rate="3e-4" \
--warmup_steps="500" \
--eval_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="400" \
--eval_steps="100" \
--logging_steps="1" \
--layerdrop="0.0" \
--save_total_limit="3" \
--freeze_feature_encoder \
--gradient_checkpointing \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--fp16 \
--group_by_length \
--push_to_hub \
--do_train --do_eval
```
On 8 V100 GPUs, this script should run in *ca.* 18 minutes and yield a CTC loss of **0.39** and word error rate
of **0.36**.
### Multi GPU CTC with Dataset Streaming
The following command shows how to use [Dataset Streaming mode](https://huggingface.co/docs/datasets/dataset_streaming)
to fine-tune [XLS-R](https://huggingface.co/transformers/main/model_doc/xls_r.html)
on [Common Voice](https://huggingface.co/datasets/common_voice) using 4 GPUs in half-precision.
Streaming mode imposes several constraints on training:
1. We need to construct a tokenizer beforehand and define it via `--tokenizer_name_or_path`.
2. `--num_train_epochs` has to be replaced by `--max_steps`. Similarly, all other epoch-based arguments have to be
replaced by step-based ones.
3. Full dataset shuffling on each epoch is not possible, since we don't have the whole dataset available at once.
However, the `--shuffle_buffer_size` argument controls how many examples we can pre-download before shuffling them.
```bash
**torchrun \
--nproc_per_node 4 run_speech_recognition_ctc_streaming.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
--tokenizer_name_or_path="anton-l/wav2vec2-tokenizer-turkish" \
--dataset_config_name="tr" \
--train_split_name="train+validation" \
--eval_split_name="test" \
--output_dir="wav2vec2-xls-r-common_voice-tr-ft" \
--overwrite_output_dir \
--max_steps="5000" \
--per_device_train_batch_size="8" \
--gradient_accumulation_steps="2" \
--learning_rate="5e-4" \
--warmup_steps="500" \
--eval_strategy="steps" \
--text_column_name="sentence" \
--save_steps="500" \
--eval_steps="500" \
--logging_steps="1" \
--layerdrop="0.0" \
--eval_metrics wer cer \
--save_total_limit="1" \
--mask_time_prob="0.3" \
--mask_time_length="10" \
--mask_feature_prob="0.1" \
--mask_feature_length="64" \
--freeze_feature_encoder \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--max_duration_in_seconds="20" \
--shuffle_buffer_size="500" \
--fp16 \
--push_to_hub \
--do_train --do_eval \
--gradient_checkpointing**
```
On 4 V100 GPUs, this script should run in *ca.* 3h 31min and yield a CTC loss of **0.35** and word error rate
of **0.29**.
### Examples CTC
The following tables present a couple of example runs on the most popular speech-recognition datasets.
The presented performances are by no means optimal as no hyper-parameter tuning was done. Nevertheless,
they can serve as a baseline to improve upon.
#### TIMIT CTC
- [TIMIT](https://huggingface.co/datasets/timit_asr)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 0.21 | - | 1 GPU TITAN RTX | 32min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned/blob/main/run.sh) |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 0.21 | - | 1 GPU TITAN RTX | 32min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-base-timit-fine-tuned/blob/main/run.sh) |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) | 0.22 | - | 1 GPU TITAN RTX | 35min | [here](https://huggingface.co/patrickvonplaten/unispeech-large-1500h-cv-timit) | [run.sh](https://huggingface.co/patrickvonplaten/unispeech-large-1500h-cv-timit/blob/main/run.sh) |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 0.30 | - | 1 GPU TITAN RTX | 28min | [here](https://huggingface.co/patrickvonplaten/sew-small-100k-timit) | [run.sh](https://huggingface.co/patrickvonplaten/sew-small-100k-timit/blob/main/run.sh) |
| [TIMIT](https://huggingface.co/datasets/timit_asr)| - | [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) | 0.68 | - | 1 GPU TITAN RTX | 26min | [here](https://huggingface.co/patrickvonplaten/distilhubert-timit) | [run.sh](https://huggingface.co/patrickvonplaten/distilhubert-timit/blob/main/run.sh) |
#### Librispeech CTC
- [Librispeech](https://huggingface.co/datasets/librispeech_asr)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) | 0.049 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-large) | [run.sh](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-large/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) | 0.068 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-base-plus) | [run.sh](https://huggingface.co/patrickvonplaten/wavlm-libri-clean-100h-base-plus/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) | 0.042 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) | 0.042 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) | 0.088 | - | 8 GPU V100 | 1h30min | [here](https://huggingface.co/patrickvonplaten/hubert-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/hubert-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` | [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 0.167 | | 8 GPU V100 | 54min | [here](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft) | [run.sh](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft/blob/main/run.sh) |
#### Common Voice CTC
- [Common Voice](https://huggingface.co/datasets/common_voice)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
| [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0)| `"tr"` | [facebook/wav2vec2-large-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | - | 0.099 | 8 GPU V100 | 23min | [here](https://huggingface.co/patrickvonplaten/xls-r-300m-tr-phoneme) | [run.sh](https://huggingface.co/patrickvonplaten/xls-r-300m-tr-phoneme/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0)| `"it"` | [facebook/wav2vec2-large-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | - | 0.077 | 8 GPU V100 | 23min | [here](https://huggingface.co/patrickvonplaten/xls-r-300m-it-phoneme) | [run.sh](https://huggingface.co/patrickvonplaten/xls-r-300m-it-phoneme/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0)| `"sv-SE"` | [facebook/wav2vec2-large-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | - | 0.099 | 8 GPU V100 | 23min | [here](https://huggingface.co/patrickvonplaten/xls-r-300m-sv-phoneme) | [run.sh](https://huggingface.co/patrickvonplaten/xls-r-300m-sv-phoneme/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.36 | - | 8 GPU V100 | 18min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo-dist/blob/main/run_dist.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.31 | - | 8 GPU V100 | 1h05 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-common_voice-tr-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-common_voice-tr-ft/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.35 | - | 1 GPU V100 | 1h20min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-demo/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | 0.31 | - | 8 GPU V100 | 1h05 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` | [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) | 0.21 | - | 2 GPU Titan 24 GB RAM | 15h10 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-xls-r-1b-common_voice-tr-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-1b-common_voice-tr-ft/blob/main/run.sh) |
| [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"` in streaming mode | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | 0.29 | - | 4 GPU V100 | 3h31 | [here](https://huggingface.co/anton-l/wav2vec2-xls-r-common_voice-tr-ft-stream) | [run.sh](https://huggingface.co/anton-l/wav2vec2-xls-r-common_voice-tr-ft-stream/blob/main/run.sh) |
#### Multilingual Librispeech CTC
- [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
| [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)| `"german"` | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | 0.13 | - | 1 GPU Titan 24 GB RAM | 15h04 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-xlsr-53-300m-mls-german-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-xlsr-53-300m-mls-german-ft/blob/main/run.sh) |
| [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)| `"german"` | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) | 0.15 | - | 1 GPU Titan 24 GB RAM | 15h04 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-300m-mls-german-ft) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-300m-mls-german-ft/blob/main/run.sh) |
## Connectionist Temporal Classification With Adapters
The script [`run_speech_recognition_ctc_adapter.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py) can be used to fine-tune adapter layers for [Wav2Vec2-like models like MMS](https://huggingface.co/docs/transformers/main/en/model_doc/mms) for automatic speech recognition.
### MMS Model
The [Massive Multilingual Speech (MMS) model](https://huggingface.co/facebook/mms-1b-all) has been pre-trained and fine-tuned
on 1000+ languages. The model makes use of adapter attention layers to fine-tune only a small part
of the model on a specific language. The model already comes with fine-tuned adapter layers for 1000+ languages and
can be used for inference for 1000+ languages out of the box.
However, for improved performance or more specific use cases one can re-initialize the adapter weights, freeze all
other weights and fine-tune them on a specific dataset as shown in the [example below](#examples-ctc-adapter).
Note that the adapter weights include low dimensional linear layers for every attention block as well as the final language
model head layers.
### Examples CTC Adapter
In the following we will look at how one can fine-tune adapter weights for any of the
[MMS CTC checkpoints](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&other=mms&sort=downloads) in less than 1 hour.
#### Common Voice CTC Adapter
As in the examples [above](#examples-ctc), we fine-tune on Common Voice's 6 dataset in Turkish as an example.
Contrary to [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) before there is a `--target_language` which has to be defined to state for which
language or concept the adapter layers shall be trained. The adapter weights will then
accordingly be called `adapter.{<target_language}.safetensors`.
Let's run an example script. Make sure to be logged in so that your model can be directly uploaded to the Hub.
```bash
huggingface-cli login
```
Now, let's run an example and upload it to the Hub under `wav2vec2-common_voice-tr-mms-demo`.
```sh
python run_speech_recognition_ctc.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/mms-1b-all" \
--dataset_config_name="tr" \
--output_dir="./wav2vec2-common_voice-tr-mms-demo" \
--num_train_epochs="4" \
--per_device_train_batch_size="32" \
--learning_rate="1e-3" \
--warmup_steps="100" \
--eval_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="200" \
--eval_steps="100" \
--save_total_limit="3" \
--target_language="tur" \
--gradient_checkpointing \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--fp16 \
--group_by_length \
--do_train --do_eval \
--push_to_hub
```
This should take less than 10 minutes on most GPUs and you should very quickly get word error rates
below 27%.
For an example run, you can have a look at [`patrickvonplaten/wav2vec2-common_voice-tr-mms-demo`](https://huggingface.co/patrickvonplaten/wav2vec2-common_voice-tr-mms-demo).
If you'd like to train another adapter model with the same base model, you can simply re-use the same `--output_dir`,
but make sure to pass the `--output_dir` folder also to `--tokenizer_name_or_path` so that the vocabulary is not
overwritten but **extended**. Assuming you would like to train adapter weights on Swedish in addition to Turkish and save
the adapter weights in the same model repo, you can run:
```sh
python run_speech_recognition_ctc.py \
--dataset_name="common_voice" \
--model_name_or_path="facebook/mms-1b-all" \
--dataset_config_name="sw" \
--output_dir="./wav2vec2-common_voice-tr-mms-demo" \
--tokenizer_name_or_path="./wav2vec2-common_voice-tr-mms-demo" \
--num_train_epochs="4" \
--per_device_train_batch_size="32" \
--learning_rate="1e-3" \
--warmup_steps="100" \
--eval_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="200" \
--eval_steps="100" \
--save_total_limit="3" \
--target_language="swe" \
--gradient_checkpointing \
--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
--fp16 \
--group_by_length \
--do_train --do_eval \
--push_to_hub
```
Now you should have both `adapter.tur.safetensors` and `adapter.swe.safetensors` in the model repo
and you can load the respective language with:
```py
model.load_adapter("tur") # or "swe"
```
respectively.
## Sequence to Sequence
The script [`run_speech_recognition_seq2seq.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) can be used to fine-tune any [Speech Sequence-to-Sequence Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForSpeechSeq2Seq) for automatic speech
recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset. This includes the Whisper model from OpenAI or a warm-started Speech-Encoder-Decoder Model, examples for which are included below.
### Whisper Model
We can load all components of the Whisper model directly from the pretrained checkpoint, including the pretrained model weights, feature extractor and tokenizer. We simply have to specify our fine-tuning dataset and training hyperparameters.
#### Single GPU Whisper Training
The following example shows how to fine-tune the [Whisper small](https://huggingface.co/openai/whisper-small) checkpoint on the Hindi subset of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) using a single GPU device in half-precision:
```bash
python run_speech_recognition_seq2seq.py \
--model_name_or_path="openai/whisper-small" \
--dataset_name="mozilla-foundation/common_voice_11_0" \
--dataset_config_name="hi" \
--language="hindi" \
--task="transcribe" \
--train_split_name="train+validation" \
--eval_split_name="test" \
--max_steps="5000" \
--output_dir="./whisper-small-hi" \
--per_device_train_batch_size="16" \
--gradient_accumulation_steps="2" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-5" \
--warmup_steps="500" \
--eval_strategy="steps" \
--eval_steps="1000" \
--save_strategy="steps" \
--save_steps="1000" \
--generation_max_length="225" \
--preprocessing_num_workers="16" \
--max_duration_in_seconds="30" \
--text_column_name="sentence" \
--freeze_feature_encoder="False" \
--gradient_checkpointing \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--predict_with_generate \
--use_auth_token
```
On a single V100, training should take approximately 8 hours, with a final cross-entropy loss of **1e-4** and word error rate of **32.6%**.
If training on a different language, you should be sure to change the `language` argument. The `language` and `task`
arguments should be omitted for English speech recognition.
#### Multi GPU Whisper Training
The following example shows how to fine-tune the [Whisper small](https://huggingface.co/openai/whisper-small) checkpoint on the Hindi subset of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) using 2 GPU devices in half-precision:
```bash
torchrun \
--nproc_per_node 2 run_speech_recognition_seq2seq.py \
--model_name_or_path="openai/whisper-small" \
--dataset_name="mozilla-foundation/common_voice_11_0" \
--dataset_config_name="hi" \
--language="hindi" \
--task="transcribe" \
--train_split_name="train+validation" \
--eval_split_name="test" \
--max_steps="5000" \
--output_dir="./whisper-small-hi" \
--per_device_train_batch_size="16" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-5" \
--warmup_steps="500" \
--eval_strategy="steps" \
--eval_steps="1000" \
--save_strategy="steps" \
--save_steps="1000" \
--generation_max_length="225" \
--preprocessing_num_workers="16" \
--max_duration_in_seconds="30" \
--text_column_name="sentence" \
--freeze_feature_encoder="False" \
--gradient_checkpointing \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--predict_with_generate \
--use_auth_token
```
On two V100s, training should take approximately 4 hours, with a final cross-entropy loss of **1e-4** and word error rate of **32.6%**.
### Warm-Started Speech-Encoder-Decoder Model
A very common use case is to leverage a pretrained speech encoder model,
*e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html) or [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), with a pretrained text decoder model, *e.g.* [BART](https://huggingface.co/docs/transformers/main/en/model_doc/bart#transformers.BartForCausalLM) or [GPT-2](https://huggingface.co/docs/transformers/main/en/model_doc/gpt2#transformers.GPT2ForCausalLM), to create a [Speech-Encoder-Decoder Model](https://huggingface.co/docs/transformers/main/en/model_doc/speech-encoder-decoder#speech-encoder-decoder-models).
By pairing a pretrained speech model with a pretrained text model, the warm-started model has prior knowledge of both the source audio and target text domains. However, the cross-attention weights between the encoder and decoder are randomly initialised. Thus, the model requires fine-tuning to learn the cross-attention weights and align the encoder mapping with that of the decoder. We can perform this very fine-tuning procedure using the example script.
As an example, let's instantiate a *Wav2Vec2-2-Bart* model with the `SpeechEncoderDecoderModel` framework. First create an empty repo on `hf.co`:
```bash
huggingface-cli repo create wav2vec2-2-bart-base
git clone https://huggingface.co/<your-user-name>/wav2vec2-2-bart-base
cd wav2vec2-2-bart-base
```
Next, run the following script **inside** the just cloned repo:
```python
from transformers import SpeechEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer, Wav2Vec2Processor
# checkpoints to leverage
encoder_id = "facebook/wav2vec2-base"
decoder_id = "facebook/bart-base"
# load and save speech-encoder-decoder model
# set some hyper-parameters for training and evaluation
model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id, encoder_add_adapter=True, encoder_feat_proj_dropout=0.0, encoder_layerdrop=0.0, max_length=200, num_beams=5)
model.config.decoder_start_token_id = model.decoder.config.bos_token_id
model.config.pad_token_id = model.decoder.config.pad_token_id
model.config.eos_token_id = model.decoder.config.eos_token_id
model.save_pretrained("./")
# load and save processor
feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)
tokenizer = AutoTokenizer.from_pretrained(decoder_id)
processor = Wav2Vec2Processor(feature_extractor, tokenizer)
processor.save_pretrained("./")
```
Finally, we can upload all files:
```bash
git lfs install
git add . && git commit -m "upload model files" && git push
```
and link the official `run_speech_recognition_seq2seq.py` script to the folder:
```bash
ln -s $(realpath <path/to/transformers>/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) ./
```
Note that we have added a randomly initialized _adapter layer_ to `wav2vec2-base` with the argument
`encoder_add_adapter=True`. This adapter sub-samples the output sequence of
`wav2vec2-base` along the time dimension. By default, a single
output vector of `wav2vec2-base` has a receptive field of *ca.* 25ms (*cf.*
Section *4.2* of the [official Wav2Vec2 paper](https://arxiv.org/pdf/2006.11477.pdf)), which represents a little less a single character. On the other hand, BART
makes use of a sentence-piece tokenizer as an input processor, so that a single
hidden vector of `bart-base` represents *ca.* 4 characters. To better align the
receptive field of the *Wav2Vec2* output vectors with *BART*'s hidden-states in the cross-attention
mechanism, we further subsample *Wav2Vec2*'s output by a factor of 8 by
adding a convolution-based adapter.
Having warm-started the speech-encoder-decoder model under `<your-user-name>/wav2vec2-2-bart`, we can now fine-tune it on the task of speech recognition.
In the script [`run_speech_recognition_seq2seq`], we load the warm-started model,
feature extractor, and tokenizer, process a speech recognition dataset,
and subsequently make use of the [`Seq2SeqTrainer`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Seq2SeqTrainer) to train our system.
Note that it is important to align the target transcriptions with the decoder's vocabulary. For example, the [`Librispeech`](https://huggingface.co/datasets/librispeech_asr) dataset only contains capitalized letters in the transcriptions,
whereas BART was pretrained mostly on normalized text. Thus, it is recommended to add the argument
`--do_lower_case` to the fine-tuning script when using a warm-started `SpeechEncoderDecoderModel`.
The model is fine-tuned on the standard cross-entropy language modeling
loss for sequence-to-sequence (just like *T5* or *BART* in natural language processing).
---
**NOTE**
If you encounter problems with data preprocessing by setting `--preprocessing_num_workers` > 1,
you might want to set the environment variable `OMP_NUM_THREADS` to 1 as follows:
```bash
OMP_NUM_THREADS=1 python run_speech_recognition_ctc ...
```
If the environment variable is not set, the training script might freeze, *i.e.* see: https://github.com/pytorch/audio/issues/1021#issuecomment-726915239.
---
#### Single GPU Seq2Seq
The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
```bash
python run_speech_recognition_seq2seq.py \
--dataset_name="librispeech_asr" \
--model_name_or_path="./" \
--dataset_config_name="clean" \
--train_split_name="train.100" \
--eval_split_name="validation" \
--output_dir="./" \
--preprocessing_num_workers="16" \
--length_column_name="input_length" \
--overwrite_output_dir \
--num_train_epochs="5" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="8" \
--gradient_accumulation_steps="8" \
--learning_rate="3e-4" \
--warmup_steps="400" \
--eval_strategy="steps" \
--text_column_name="text" \
--save_steps="400" \
--eval_steps="400" \
--logging_steps="10" \
--save_total_limit="1" \
--freeze_feature_encoder \
--gradient_checkpointing \
--fp16 \
--group_by_length \
--predict_with_generate \
--generation_max_length="40" \
--generation_num_beams="1" \
--do_train --do_eval \
--do_lower_case
```
On a single V100 GPU, this script should run in *ca.* 5 hours and yield a
cross-entropy loss of **0.405** and word error rate of **0.0728**.
#### Multi GPU Seq2Seq
The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
```bash
torchrun \
--nproc_per_node 8 run_speech_recognition_seq2seq.py \
--dataset_name="librispeech_asr" \
--model_name_or_path="./" \
--dataset_config_name="clean" \
--train_split_name="train.100" \
--eval_split_name="validation" \
--output_dir="./" \
--preprocessing_num_workers="16" \
--length_column_name="input_length" \
--overwrite_output_dir \
--num_train_epochs="5" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="8" \
--gradient_accumulation_steps="1" \
--learning_rate="3e-4" \
--warmup_steps="400" \
--eval_strategy="steps" \
--text_column_name="text" \
--save_steps="400" \
--eval_steps="400" \
--logging_steps="10" \
--save_total_limit="1" \
--freeze_feature_encoder \
--gradient_checkpointing \
--fp16 \
--group_by_length \
--predict_with_generate \
--do_train --do_eval \
--do_lower_case
```
On 8 V100 GPUs, this script should run in *ca.* 45 minutes and yield a cross-entropy loss of **0.405** and word error rate of **0.0728**
### Examples Seq2Seq
#### Librispeech Seq2Seq
- [Librispeech](https://huggingface.co/datasets/librispeech_asr)
| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
|----------------------------------------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------|----------------------------|------------|---------------|-----------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Librispeech](https://huggingface.co/datasets/librispeech_asr) | `"clean"` - `"train.100"` | [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) and [facebook/bart-base](https://huggingface.co/facebook/bart-base) | 0.0728 | - | 8 GPU V100 | 45min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base) | [create_model.py](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base/blob/main/create_model.py) & [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base/blob/main/run_librispeech.sh) |
| [Librispeech](https://huggingface.co/datasets/librispeech_asr) | `"clean"` - `"train.100"` | [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) and [facebook/bart-large](https://huggingface.co/facebook/bart-large) | 0.0486 | - | 8 GPU V100 | 1h20min | [here](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large) | [create_model.py](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large/blob/main/create_model.py) & [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large/blob/main/run_librispeech.sh) |
| transformers/examples/pytorch/speech-recognition/README.md/0 | {
"file_path": "transformers/examples/pytorch/speech-recognition/README.md",
"repo_id": "transformers",
"token_count": 14232
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning multi-lingual models on XNLI (e.g. Bert, DistilBERT, XLM).
Adapted from `examples/text-classification/run_glue.py`"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.49.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
logger = logging.getLogger(__name__)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
max_seq_length: Optional[int] = field(
default=128,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
},
)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
default=None, metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
language: str = field(
default=None, metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}
)
train_language: Optional[str] = field(
default=None, metadata={"help": "Train language if it is different from the evaluation language."}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
do_lower_case: Optional[bool] = field(
default=False,
metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
)
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
"execute code present on the Hub on your local machine."
)
},
)
ignore_mismatched_sizes: bool = field(
default=False,
metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_xnli", model_args)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
train_dataset = load_dataset(
"xnli",
model_args.language,
split="train",
cache_dir=model_args.cache_dir,
token=model_args.token,
)
else:
train_dataset = load_dataset(
"xnli",
model_args.train_language,
split="train",
cache_dir=model_args.cache_dir,
token=model_args.token,
)
label_list = train_dataset.features["label"].names
if training_args.do_eval:
eval_dataset = load_dataset(
"xnli",
model_args.language,
split="validation",
cache_dir=model_args.cache_dir,
token=model_args.token,
)
label_list = eval_dataset.features["label"].names
if training_args.do_predict:
predict_dataset = load_dataset(
"xnli",
model_args.language,
split="test",
cache_dir=model_args.cache_dir,
token=model_args.token,
)
label_list = predict_dataset.features["label"].names
# Labels
num_labels = len(label_list)
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
id2label={str(i): label for i, label in enumerate(label_list)},
label2id={label: i for i, label in enumerate(label_list)},
finetuning_task="xnli",
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
do_lower_case=model_args.do_lower_case,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
def preprocess_function(examples):
# Tokenize the texts
return tokenizer(
examples["premise"],
examples["hypothesis"],
padding=padding,
max_length=data_args.max_seq_length,
truncation=True,
)
if training_args.do_train:
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
if training_args.do_eval:
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
with training_args.main_process_first(desc="validation dataset map pre-processing"):
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
if training_args.do_predict:
if data_args.max_predict_samples is not None:
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
predict_dataset = predict_dataset.select(range(max_predict_samples))
with training_args.main_process_first(desc="prediction dataset map pre-processing"):
predict_dataset = predict_dataset.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# Get the metric function
metric = evaluate.load("xnli", cache_dir=model_args.cache_dir)
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(p: EvalPrediction):
preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
preds = np.argmax(preds, axis=1)
return metric.compute(predictions=preds, references=p.label_ids)
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
data_collator = default_data_collator
elif training_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
processing_class=tokenizer,
data_collator=data_collator,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate(eval_dataset=eval_dataset)
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict")
max_predict_samples = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
)
metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
predictions = np.argmax(predictions, axis=1)
output_predict_file = os.path.join(training_args.output_dir, "predictions.txt")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as writer:
writer.write("index\tprediction\n")
for index, item in enumerate(predictions):
item = label_list[item]
writer.write(f"{index}\t{item}\n")
if __name__ == "__main__":
main()
| transformers/examples/pytorch/text-classification/run_xnli.py/0 | {
"file_path": "transformers/examples/pytorch/text-classification/run_xnli.py",
"repo_id": "transformers",
"token_count": 7388
} |
<!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Research projects
This folder contains various research projects using 🤗 Transformers. They are not maintained and require a specific
version of 🤗 Transformers that is indicated in the requirements file of each folder. Updating them to the most recent version of the library will require some work.
To use any of them, just run the command
```bash
pip install -r requirements.txt
```
inside the folder of your choice.
If you need help with any of those, contact the author(s), indicated at the top of the `README` of each folder.
| transformers/examples/research_projects/README.md/0 | {
"file_path": "transformers/examples/research_projects/README.md",
"repo_id": "transformers",
"token_count": 280
} |
# MIT License
# Copyright (c) 2019 Yang Liu and the HuggingFace team
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import copy
import math
import numpy as np
import torch
from configuration_bertabs import BertAbsConfig
from torch import nn
from torch.nn.init import xavier_uniform_
from transformers import BertConfig, BertModel, PreTrainedModel
MAX_SIZE = 5000
class BertAbsPreTrainedModel(PreTrainedModel):
config_class = BertAbsConfig
load_tf_weights = False
base_model_prefix = "bert"
class BertAbs(BertAbsPreTrainedModel):
def __init__(self, args, checkpoint=None, bert_extractive_checkpoint=None):
super().__init__(args)
self.args = args
self.bert = Bert()
# If pre-trained weights are passed for Bert, load these.
load_bert_pretrained_extractive = True if bert_extractive_checkpoint else False
if load_bert_pretrained_extractive:
self.bert.model.load_state_dict(
{n[11:]: p for n, p in bert_extractive_checkpoint.items() if n.startswith("bert.model")},
strict=True,
)
self.vocab_size = self.bert.model.config.vocab_size
if args.max_pos > 512:
my_pos_embeddings = nn.Embedding(args.max_pos, self.bert.model.config.hidden_size)
my_pos_embeddings.weight.data[:512] = self.bert.model.embeddings.position_embeddings.weight.data
my_pos_embeddings.weight.data[512:] = self.bert.model.embeddings.position_embeddings.weight.data[-1][
None, :
].repeat(args.max_pos - 512, 1)
self.bert.model.embeddings.position_embeddings = my_pos_embeddings
tgt_embeddings = nn.Embedding(self.vocab_size, self.bert.model.config.hidden_size, padding_idx=0)
tgt_embeddings.weight = copy.deepcopy(self.bert.model.embeddings.word_embeddings.weight)
self.decoder = TransformerDecoder(
self.args.dec_layers,
self.args.dec_hidden_size,
heads=self.args.dec_heads,
d_ff=self.args.dec_ff_size,
dropout=self.args.dec_dropout,
embeddings=tgt_embeddings,
vocab_size=self.vocab_size,
)
gen_func = nn.LogSoftmax(dim=-1)
self.generator = nn.Sequential(nn.Linear(args.dec_hidden_size, args.vocab_size), gen_func)
self.generator[0].weight = self.decoder.embeddings.weight
load_from_checkpoints = False if checkpoint is None else True
if load_from_checkpoints:
self.load_state_dict(checkpoint)
def init_weights(self):
for module in self.decoder.modules():
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=0.02)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
for p in self.generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
else:
p.data.zero_()
def forward(
self,
encoder_input_ids,
decoder_input_ids,
token_type_ids,
encoder_attention_mask,
decoder_attention_mask,
):
encoder_output = self.bert(
input_ids=encoder_input_ids,
token_type_ids=token_type_ids,
attention_mask=encoder_attention_mask,
)
encoder_hidden_states = encoder_output[0]
dec_state = self.decoder.init_decoder_state(encoder_input_ids, encoder_hidden_states)
decoder_outputs, _ = self.decoder(decoder_input_ids[:, :-1], encoder_hidden_states, dec_state)
return decoder_outputs
class Bert(nn.Module):
"""This class is not really necessary and should probably disappear."""
def __init__(self):
super().__init__()
config = BertConfig.from_pretrained("google-bert/bert-base-uncased")
self.model = BertModel(config)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, **kwargs):
self.eval()
with torch.no_grad():
encoder_outputs, _ = self.model(
input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, **kwargs
)
return encoder_outputs
class TransformerDecoder(nn.Module):
"""
The Transformer decoder from "Attention is All You Need".
Args:
num_layers (int): number of encoder layers.
d_model (int): size of the model
heads (int): number of heads
d_ff (int): size of the inner FF layer
dropout (float): dropout parameters
embeddings (:obj:`onmt.modules.Embeddings`):
embeddings to use, should have positional encodings
attn_type (str): if using a separate copy attention
"""
def __init__(self, num_layers, d_model, heads, d_ff, dropout, embeddings, vocab_size):
super().__init__()
# Basic attributes.
self.decoder_type = "transformer"
self.num_layers = num_layers
self.embeddings = embeddings
self.pos_emb = PositionalEncoding(dropout, self.embeddings.embedding_dim)
# Build TransformerDecoder.
self.transformer_layers = nn.ModuleList(
[TransformerDecoderLayer(d_model, heads, d_ff, dropout) for _ in range(num_layers)]
)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
# forward(input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask)
# def forward(self, input_ids, state, attention_mask=None, memory_lengths=None,
# step=None, cache=None, encoder_attention_mask=None, encoder_hidden_states=None, memory_masks=None):
def forward(
self,
input_ids,
encoder_hidden_states=None,
state=None,
attention_mask=None,
memory_lengths=None,
step=None,
cache=None,
encoder_attention_mask=None,
):
"""
See :obj:`onmt.modules.RNNDecoderBase.forward()`
memory_bank = encoder_hidden_states
"""
# Name conversion
tgt = input_ids
memory_bank = encoder_hidden_states
memory_mask = encoder_attention_mask
# src_words = state.src
src_words = state.src
src_batch, src_len = src_words.size()
padding_idx = self.embeddings.padding_idx
# Decoder padding mask
tgt_words = tgt
tgt_batch, tgt_len = tgt_words.size()
tgt_pad_mask = tgt_words.data.eq(padding_idx).unsqueeze(1).expand(tgt_batch, tgt_len, tgt_len)
# Encoder padding mask
if memory_mask is not None:
src_len = memory_mask.size(-1)
src_pad_mask = memory_mask.expand(src_batch, tgt_len, src_len)
else:
src_pad_mask = src_words.data.eq(padding_idx).unsqueeze(1).expand(src_batch, tgt_len, src_len)
# Pass through the embeddings
emb = self.embeddings(input_ids)
output = self.pos_emb(emb, step)
assert emb.dim() == 3 # len x batch x embedding_dim
if state.cache is None:
saved_inputs = []
for i in range(self.num_layers):
prev_layer_input = None
if state.cache is None:
if state.previous_input is not None:
prev_layer_input = state.previous_layer_inputs[i]
output, all_input = self.transformer_layers[i](
output,
memory_bank,
src_pad_mask,
tgt_pad_mask,
previous_input=prev_layer_input,
layer_cache=state.cache["layer_{}".format(i)] if state.cache is not None else None,
step=step,
)
if state.cache is None:
saved_inputs.append(all_input)
if state.cache is None:
saved_inputs = torch.stack(saved_inputs)
output = self.layer_norm(output)
if state.cache is None:
state = state.update_state(tgt, saved_inputs)
# Decoders in transformers return a tuple. Beam search will fail
# if we don't follow this convention.
return output, state # , state
def init_decoder_state(self, src, memory_bank, with_cache=False):
"""Init decoder state"""
state = TransformerDecoderState(src)
if with_cache:
state._init_cache(memory_bank, self.num_layers)
return state
class PositionalEncoding(nn.Module):
def __init__(self, dropout, dim, max_len=5000):
pe = torch.zeros(max_len, dim)
position = torch.arange(0, max_len).unsqueeze(1)
div_term = torch.exp((torch.arange(0, dim, 2, dtype=torch.float) * -(math.log(10000.0) / dim)))
pe[:, 0::2] = torch.sin(position.float() * div_term)
pe[:, 1::2] = torch.cos(position.float() * div_term)
pe = pe.unsqueeze(0)
super().__init__()
self.register_buffer("pe", pe)
self.dropout = nn.Dropout(p=dropout)
self.dim = dim
def forward(self, emb, step=None):
emb = emb * math.sqrt(self.dim)
if step:
emb = emb + self.pe[:, step][:, None, :]
else:
emb = emb + self.pe[:, : emb.size(1)]
emb = self.dropout(emb)
return emb
def get_emb(self, emb):
return self.pe[:, : emb.size(1)]
class TransformerDecoderLayer(nn.Module):
"""
Args:
d_model (int): the dimension of keys/values/queries in
MultiHeadedAttention, also the input size of
the first-layer of the PositionwiseFeedForward.
heads (int): the number of heads for MultiHeadedAttention.
d_ff (int): the second-layer of the PositionwiseFeedForward.
dropout (float): dropout probability(0-1.0).
self_attn_type (string): type of self-attention scaled-dot, average
"""
def __init__(self, d_model, heads, d_ff, dropout):
super().__init__()
self.self_attn = MultiHeadedAttention(heads, d_model, dropout=dropout)
self.context_attn = MultiHeadedAttention(heads, d_model, dropout=dropout)
self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)
self.layer_norm_1 = nn.LayerNorm(d_model, eps=1e-6)
self.layer_norm_2 = nn.LayerNorm(d_model, eps=1e-6)
self.drop = nn.Dropout(dropout)
mask = self._get_attn_subsequent_mask(MAX_SIZE)
# Register self.mask as a saved_state in TransformerDecoderLayer, so
# it gets TransformerDecoderLayer's cuda behavior automatically.
self.register_buffer("mask", mask)
def forward(
self,
inputs,
memory_bank,
src_pad_mask,
tgt_pad_mask,
previous_input=None,
layer_cache=None,
step=None,
):
"""
Args:
inputs (`FloatTensor`): `[batch_size x 1 x model_dim]`
memory_bank (`FloatTensor`): `[batch_size x src_len x model_dim]`
src_pad_mask (`LongTensor`): `[batch_size x 1 x src_len]`
tgt_pad_mask (`LongTensor`): `[batch_size x 1 x 1]`
Returns:
(`FloatTensor`, `FloatTensor`, `FloatTensor`):
* output `[batch_size x 1 x model_dim]`
* attn `[batch_size x 1 x src_len]`
* all_input `[batch_size x current_step x model_dim]`
"""
dec_mask = torch.gt(tgt_pad_mask + self.mask[:, : tgt_pad_mask.size(1), : tgt_pad_mask.size(1)], 0)
input_norm = self.layer_norm_1(inputs)
all_input = input_norm
if previous_input is not None:
all_input = torch.cat((previous_input, input_norm), dim=1)
dec_mask = None
query = self.self_attn(
all_input,
all_input,
input_norm,
mask=dec_mask,
layer_cache=layer_cache,
type="self",
)
query = self.drop(query) + inputs
query_norm = self.layer_norm_2(query)
mid = self.context_attn(
memory_bank,
memory_bank,
query_norm,
mask=src_pad_mask,
layer_cache=layer_cache,
type="context",
)
output = self.feed_forward(self.drop(mid) + query)
return output, all_input
# return output
def _get_attn_subsequent_mask(self, size):
"""
Get an attention mask to avoid using the subsequent info.
Args:
size: int
Returns:
(`LongTensor`):
* subsequent_mask `[1 x size x size]`
"""
attn_shape = (1, size, size)
subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype("uint8")
subsequent_mask = torch.from_numpy(subsequent_mask)
return subsequent_mask
class MultiHeadedAttention(nn.Module):
"""
Multi-Head Attention module from
"Attention is All You Need"
:cite:`DBLP:journals/corr/VaswaniSPUJGKP17`.
Similar to standard `dot` attention but uses
multiple attention distributions simulataneously
to select relevant items.
.. mermaid::
graph BT
A[key]
B[value]
C[query]
O[output]
subgraph Attn
D[Attn 1]
E[Attn 2]
F[Attn N]
end
A --> D
C --> D
A --> E
C --> E
A --> F
C --> F
D --> O
E --> O
F --> O
B --> O
Also includes several additional tricks.
Args:
head_count (int): number of parallel heads
model_dim (int): the dimension of keys/values/queries,
must be divisible by head_count
dropout (float): dropout parameter
"""
def __init__(self, head_count, model_dim, dropout=0.1, use_final_linear=True):
assert model_dim % head_count == 0
self.dim_per_head = model_dim // head_count
self.model_dim = model_dim
super().__init__()
self.head_count = head_count
self.linear_keys = nn.Linear(model_dim, head_count * self.dim_per_head)
self.linear_values = nn.Linear(model_dim, head_count * self.dim_per_head)
self.linear_query = nn.Linear(model_dim, head_count * self.dim_per_head)
self.softmax = nn.Softmax(dim=-1)
self.dropout = nn.Dropout(dropout)
self.use_final_linear = use_final_linear
if self.use_final_linear:
self.final_linear = nn.Linear(model_dim, model_dim)
def forward(
self,
key,
value,
query,
mask=None,
layer_cache=None,
type=None,
predefined_graph_1=None,
):
"""
Compute the context vector and the attention vectors.
Args:
key (`FloatTensor`): set of `key_len`
key vectors `[batch, key_len, dim]`
value (`FloatTensor`): set of `key_len`
value vectors `[batch, key_len, dim]`
query (`FloatTensor`): set of `query_len`
query vectors `[batch, query_len, dim]`
mask: binary mask indicating which keys have
non-zero attention `[batch, query_len, key_len]`
Returns:
(`FloatTensor`, `FloatTensor`) :
* output context vectors `[batch, query_len, dim]`
* one of the attention vectors `[batch, query_len, key_len]`
"""
batch_size = key.size(0)
dim_per_head = self.dim_per_head
head_count = self.head_count
def shape(x):
"""projection"""
return x.view(batch_size, -1, head_count, dim_per_head).transpose(1, 2)
def unshape(x):
"""compute context"""
return x.transpose(1, 2).contiguous().view(batch_size, -1, head_count * dim_per_head)
# 1) Project key, value, and query.
if layer_cache is not None:
if type == "self":
query, key, value = (
self.linear_query(query),
self.linear_keys(query),
self.linear_values(query),
)
key = shape(key)
value = shape(value)
if layer_cache is not None:
device = key.device
if layer_cache["self_keys"] is not None:
key = torch.cat((layer_cache["self_keys"].to(device), key), dim=2)
if layer_cache["self_values"] is not None:
value = torch.cat((layer_cache["self_values"].to(device), value), dim=2)
layer_cache["self_keys"] = key
layer_cache["self_values"] = value
elif type == "context":
query = self.linear_query(query)
if layer_cache is not None:
if layer_cache["memory_keys"] is None:
key, value = self.linear_keys(key), self.linear_values(value)
key = shape(key)
value = shape(value)
else:
key, value = (
layer_cache["memory_keys"],
layer_cache["memory_values"],
)
layer_cache["memory_keys"] = key
layer_cache["memory_values"] = value
else:
key, value = self.linear_keys(key), self.linear_values(value)
key = shape(key)
value = shape(value)
else:
key = self.linear_keys(key)
value = self.linear_values(value)
query = self.linear_query(query)
key = shape(key)
value = shape(value)
query = shape(query)
# 2) Calculate and scale scores.
query = query / math.sqrt(dim_per_head)
scores = torch.matmul(query, key.transpose(2, 3))
if mask is not None:
mask = mask.unsqueeze(1).expand_as(scores)
scores = scores.masked_fill(mask, -1e18)
# 3) Apply attention dropout and compute context vectors.
attn = self.softmax(scores)
if predefined_graph_1 is not None:
attn_masked = attn[:, -1] * predefined_graph_1
attn_masked = attn_masked / (torch.sum(attn_masked, 2).unsqueeze(2) + 1e-9)
attn = torch.cat([attn[:, :-1], attn_masked.unsqueeze(1)], 1)
drop_attn = self.dropout(attn)
if self.use_final_linear:
context = unshape(torch.matmul(drop_attn, value))
output = self.final_linear(context)
return output
else:
context = torch.matmul(drop_attn, value)
return context
class DecoderState:
"""Interface for grouping together the current state of a recurrent
decoder. In the simplest case just represents the hidden state of
the model. But can also be used for implementing various forms of
input_feeding and non-recurrent models.
Modules need to implement this to utilize beam search decoding.
"""
def detach(self):
"""Need to document this"""
self.hidden = tuple([_.detach() for _ in self.hidden])
self.input_feed = self.input_feed.detach()
def beam_update(self, idx, positions, beam_size):
"""Need to document this"""
for e in self._all:
sizes = e.size()
br = sizes[1]
if len(sizes) == 3:
sent_states = e.view(sizes[0], beam_size, br // beam_size, sizes[2])[:, :, idx]
else:
sent_states = e.view(sizes[0], beam_size, br // beam_size, sizes[2], sizes[3])[:, :, idx]
sent_states.data.copy_(sent_states.data.index_select(1, positions))
def map_batch_fn(self, fn):
raise NotImplementedError()
class TransformerDecoderState(DecoderState):
"""Transformer Decoder state base class"""
def __init__(self, src):
"""
Args:
src (FloatTensor): a sequence of source words tensors
with optional feature tensors, of size (len x batch).
"""
self.src = src
self.previous_input = None
self.previous_layer_inputs = None
self.cache = None
@property
def _all(self):
"""
Contains attributes that need to be updated in self.beam_update().
"""
if self.previous_input is not None and self.previous_layer_inputs is not None:
return (self.previous_input, self.previous_layer_inputs, self.src)
else:
return (self.src,)
def detach(self):
if self.previous_input is not None:
self.previous_input = self.previous_input.detach()
if self.previous_layer_inputs is not None:
self.previous_layer_inputs = self.previous_layer_inputs.detach()
self.src = self.src.detach()
def update_state(self, new_input, previous_layer_inputs):
state = TransformerDecoderState(self.src)
state.previous_input = new_input
state.previous_layer_inputs = previous_layer_inputs
return state
def _init_cache(self, memory_bank, num_layers):
self.cache = {}
for l in range(num_layers):
layer_cache = {"memory_keys": None, "memory_values": None}
layer_cache["self_keys"] = None
layer_cache["self_values"] = None
self.cache["layer_{}".format(l)] = layer_cache
def repeat_beam_size_times(self, beam_size):
"""Repeat beam_size times along batch dimension."""
self.src = self.src.data.repeat(1, beam_size, 1)
def map_batch_fn(self, fn):
def _recursive_map(struct, batch_dim=0):
for k, v in struct.items():
if v is not None:
if isinstance(v, dict):
_recursive_map(v)
else:
struct[k] = fn(v, batch_dim)
self.src = fn(self.src, 0)
if self.cache is not None:
_recursive_map(self.cache)
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
class PositionwiseFeedForward(nn.Module):
"""A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of the second-layer
of the FNN.
dropout (float): dropout probability in :math:`[0, 1)`.
"""
def __init__(self, d_model, d_ff, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
self.actv = gelu
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
def forward(self, x):
inter = self.dropout_1(self.actv(self.w_1(self.layer_norm(x))))
output = self.dropout_2(self.w_2(inter))
return output + x
#
# TRANSLATOR
# The following code is used to generate summaries using the
# pre-trained weights and beam search.
#
def build_predictor(args, tokenizer, symbols, model, logger=None):
# we should be able to refactor the global scorer a lot
scorer = GNMTGlobalScorer(args.alpha, length_penalty="wu")
translator = Translator(args, model, tokenizer, symbols, global_scorer=scorer, logger=logger)
return translator
class GNMTGlobalScorer:
"""
NMT re-ranking score from
"Google's Neural Machine Translation System" :cite:`wu2016google`
Args:
alpha (float): length parameter
beta (float): coverage parameter
"""
def __init__(self, alpha, length_penalty):
self.alpha = alpha
penalty_builder = PenaltyBuilder(length_penalty)
self.length_penalty = penalty_builder.length_penalty()
def score(self, beam, logprobs):
"""
Rescores a prediction based on penalty functions
"""
normalized_probs = self.length_penalty(beam, logprobs, self.alpha)
return normalized_probs
class PenaltyBuilder:
"""
Returns the Length and Coverage Penalty function for Beam Search.
Args:
length_pen (str): option name of length pen
cov_pen (str): option name of cov pen
"""
def __init__(self, length_pen):
self.length_pen = length_pen
def length_penalty(self):
if self.length_pen == "wu":
return self.length_wu
elif self.length_pen == "avg":
return self.length_average
else:
return self.length_none
"""
Below are all the different penalty terms implemented so far
"""
def length_wu(self, beam, logprobs, alpha=0.0):
"""
NMT length re-ranking score from
"Google's Neural Machine Translation System" :cite:`wu2016google`.
"""
modifier = ((5 + len(beam.next_ys)) ** alpha) / ((5 + 1) ** alpha)
return logprobs / modifier
def length_average(self, beam, logprobs, alpha=0.0):
"""
Returns the average probability of tokens in a sequence.
"""
return logprobs / len(beam.next_ys)
def length_none(self, beam, logprobs, alpha=0.0, beta=0.0):
"""
Returns unmodified scores.
"""
return logprobs
class Translator:
"""
Uses a model to translate a batch of sentences.
Args:
model (:obj:`onmt.modules.NMTModel`):
NMT model to use for translation
fields (dict of Fields): data fields
beam_size (int): size of beam to use
n_best (int): number of translations produced
max_length (int): maximum length output to produce
global_scores (:obj:`GlobalScorer`):
object to rescore final translations
copy_attn (bool): use copy attention during translation
beam_trace (bool): trace beam search for debugging
logger(logging.Logger): logger.
"""
def __init__(self, args, model, vocab, symbols, global_scorer=None, logger=None):
self.logger = logger
self.args = args
self.model = model
self.generator = self.model.generator
self.vocab = vocab
self.symbols = symbols
self.start_token = symbols["BOS"]
self.end_token = symbols["EOS"]
self.global_scorer = global_scorer
self.beam_size = args.beam_size
self.min_length = args.min_length
self.max_length = args.max_length
def translate(self, batch, step, attn_debug=False):
"""Generates summaries from one batch of data."""
self.model.eval()
with torch.no_grad():
batch_data = self.translate_batch(batch)
translations = self.from_batch(batch_data)
return translations
def translate_batch(self, batch, fast=False):
"""
Translate a batch of sentences.
Mostly a wrapper around :obj:`Beam`.
Args:
batch (:obj:`Batch`): a batch from a dataset object
fast (bool): enables fast beam search (may not support all features)
"""
with torch.no_grad():
return self._fast_translate_batch(batch, self.max_length, min_length=self.min_length)
# Where the beam search lives
# I have no idea why it is being called from the method above
def _fast_translate_batch(self, batch, max_length, min_length=0):
"""Beam Search using the encoder inputs contained in `batch`."""
# The batch object is funny
# Instead of just looking at the size of the arguments we encapsulate
# a size argument.
# Where is it defined?
beam_size = self.beam_size
batch_size = batch.batch_size
src = batch.src
segs = batch.segs
mask_src = batch.mask_src
src_features = self.model.bert(src, segs, mask_src)
dec_states = self.model.decoder.init_decoder_state(src, src_features, with_cache=True)
device = src_features.device
# Tile states and memory beam_size times.
dec_states.map_batch_fn(lambda state, dim: tile(state, beam_size, dim=dim))
src_features = tile(src_features, beam_size, dim=0)
batch_offset = torch.arange(batch_size, dtype=torch.long, device=device)
beam_offset = torch.arange(0, batch_size * beam_size, step=beam_size, dtype=torch.long, device=device)
alive_seq = torch.full([batch_size * beam_size, 1], self.start_token, dtype=torch.long, device=device)
# Give full probability to the first beam on the first step.
topk_log_probs = torch.tensor([0.0] + [float("-inf")] * (beam_size - 1), device=device).repeat(batch_size)
# Structure that holds finished hypotheses.
hypotheses = [[] for _ in range(batch_size)] # noqa: F812
results = {}
results["predictions"] = [[] for _ in range(batch_size)] # noqa: F812
results["scores"] = [[] for _ in range(batch_size)] # noqa: F812
results["gold_score"] = [0] * batch_size
results["batch"] = batch
for step in range(max_length):
decoder_input = alive_seq[:, -1].view(1, -1)
# Decoder forward.
decoder_input = decoder_input.transpose(0, 1)
dec_out, dec_states = self.model.decoder(decoder_input, src_features, dec_states, step=step)
# Generator forward.
log_probs = self.generator(dec_out.transpose(0, 1).squeeze(0))
vocab_size = log_probs.size(-1)
if step < min_length:
log_probs[:, self.end_token] = -1e20
# Multiply probs by the beam probability.
log_probs += topk_log_probs.view(-1).unsqueeze(1)
alpha = self.global_scorer.alpha
length_penalty = ((5.0 + (step + 1)) / 6.0) ** alpha
# Flatten probs into a list of possibilities.
curr_scores = log_probs / length_penalty
if self.args.block_trigram:
cur_len = alive_seq.size(1)
if cur_len > 3:
for i in range(alive_seq.size(0)):
fail = False
words = [int(w) for w in alive_seq[i]]
words = [self.vocab.ids_to_tokens[w] for w in words]
words = " ".join(words).replace(" ##", "").split()
if len(words) <= 3:
continue
trigrams = [(words[i - 1], words[i], words[i + 1]) for i in range(1, len(words) - 1)]
trigram = tuple(trigrams[-1])
if trigram in trigrams[:-1]:
fail = True
if fail:
curr_scores[i] = -10e20
curr_scores = curr_scores.reshape(-1, beam_size * vocab_size)
topk_scores, topk_ids = curr_scores.topk(beam_size, dim=-1)
# Recover log probs.
topk_log_probs = topk_scores * length_penalty
# Resolve beam origin and true word ids.
topk_beam_index = topk_ids.div(vocab_size)
topk_ids = topk_ids.fmod(vocab_size)
# Map beam_index to batch_index in the flat representation.
batch_index = topk_beam_index + beam_offset[: topk_beam_index.size(0)].unsqueeze(1)
select_indices = batch_index.view(-1)
# Append last prediction.
alive_seq = torch.cat([alive_seq.index_select(0, select_indices), topk_ids.view(-1, 1)], -1)
is_finished = topk_ids.eq(self.end_token)
if step + 1 == max_length:
is_finished.fill_(1)
# End condition is top beam is finished.
end_condition = is_finished[:, 0].eq(1)
# Save finished hypotheses.
if is_finished.any():
predictions = alive_seq.view(-1, beam_size, alive_seq.size(-1))
for i in range(is_finished.size(0)):
b = batch_offset[i]
if end_condition[i]:
is_finished[i].fill_(1)
finished_hyp = is_finished[i].nonzero().view(-1)
# Store finished hypotheses for this batch.
for j in finished_hyp:
hypotheses[b].append((topk_scores[i, j], predictions[i, j, 1:]))
# If the batch reached the end, save the n_best hypotheses.
if end_condition[i]:
best_hyp = sorted(hypotheses[b], key=lambda x: x[0], reverse=True)
score, pred = best_hyp[0]
results["scores"][b].append(score)
results["predictions"][b].append(pred)
non_finished = end_condition.eq(0).nonzero().view(-1)
# If all sentences are translated, no need to go further.
if len(non_finished) == 0:
break
# Remove finished batches for the next step.
topk_log_probs = topk_log_probs.index_select(0, non_finished)
batch_index = batch_index.index_select(0, non_finished)
batch_offset = batch_offset.index_select(0, non_finished)
alive_seq = predictions.index_select(0, non_finished).view(-1, alive_seq.size(-1))
# Reorder states.
select_indices = batch_index.view(-1)
src_features = src_features.index_select(0, select_indices)
dec_states.map_batch_fn(lambda state, dim: state.index_select(dim, select_indices))
return results
def from_batch(self, translation_batch):
batch = translation_batch["batch"]
assert len(translation_batch["gold_score"]) == len(translation_batch["predictions"])
batch_size = batch.batch_size
preds, _, _, tgt_str, src = (
translation_batch["predictions"],
translation_batch["scores"],
translation_batch["gold_score"],
batch.tgt_str,
batch.src,
)
translations = []
for b in range(batch_size):
pred_sents = self.vocab.convert_ids_to_tokens([int(n) for n in preds[b][0]])
pred_sents = " ".join(pred_sents).replace(" ##", "")
gold_sent = " ".join(tgt_str[b].split())
raw_src = [self.vocab.ids_to_tokens[int(t)] for t in src[b]][:500]
raw_src = " ".join(raw_src)
translation = (pred_sents, gold_sent, raw_src)
translations.append(translation)
return translations
def tile(x, count, dim=0):
"""
Tiles x on dimension dim count times.
"""
perm = list(range(len(x.size())))
if dim != 0:
perm[0], perm[dim] = perm[dim], perm[0]
x = x.permute(perm).contiguous()
out_size = list(x.size())
out_size[0] *= count
batch = x.size(0)
x = x.view(batch, -1).transpose(0, 1).repeat(count, 1).transpose(0, 1).contiguous().view(*out_size)
if dim != 0:
x = x.permute(perm).contiguous()
return x
#
# Optimizer for training. We keep this here in case we want to add
# a finetuning script.
#
class BertSumOptimizer:
"""Specific optimizer for BertSum.
As described in [1], the authors fine-tune BertSum for abstractive
summarization using two Adam Optimizers with different warm-up steps and
learning rate. They also use a custom learning rate scheduler.
[1] Liu, Yang, and Mirella Lapata. "Text summarization with pretrained encoders."
arXiv preprint arXiv:1908.08345 (2019).
"""
def __init__(self, model, lr, warmup_steps, beta_1=0.99, beta_2=0.999, eps=1e-8):
self.encoder = model.encoder
self.decoder = model.decoder
self.lr = lr
self.warmup_steps = warmup_steps
self.optimizers = {
"encoder": torch.optim.Adam(
model.encoder.parameters(),
lr=lr["encoder"],
betas=(beta_1, beta_2),
eps=eps,
),
"decoder": torch.optim.Adam(
model.decoder.parameters(),
lr=lr["decoder"],
betas=(beta_1, beta_2),
eps=eps,
),
}
self._step = 0
self.current_learning_rates = {}
def _update_rate(self, stack):
return self.lr[stack] * min(self._step ** (-0.5), self._step * self.warmup_steps[stack] ** (-1.5))
def zero_grad(self):
self.optimizer_decoder.zero_grad()
self.optimizer_encoder.zero_grad()
def step(self):
self._step += 1
for stack, optimizer in self.optimizers.items():
new_rate = self._update_rate(stack)
for param_group in optimizer.param_groups:
param_group["lr"] = new_rate
optimizer.step()
self.current_learning_rates[stack] = new_rate
| transformers/examples/research_projects/bertabs/modeling_bertabs.py/0 | {
"file_path": "transformers/examples/research_projects/bertabs/modeling_bertabs.py",
"repo_id": "transformers",
"token_count": 17828
} |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
EOF_STRINGS = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class TokenizedDataset(IterableDataset):
"""Tokenize and preprocess the dataset
Multiple copies of the same prompt are sent sequentially.
See compute_code for more details.
"""
def __init__(self, tokenizer, dataset, n_tasks=None, n_copies=1):
self.tokenizer = tokenizer
self.dataset = dataset
self.n_tasks = len(dataset) if n_tasks is None else n_tasks
self.n_copies = n_copies
def __iter__(self):
prompts = []
for task in range(self.n_tasks):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]["prompt"].strip())
outputs = self.tokenizer(prompts, padding=True, return_tensors="pt")
for task in range(self.n_tasks):
for _ in range(self.n_copies):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class EndOfFunctionCriteria(StoppingCriteria):
"""Custom `StoppingCriteria` which checks if all generated functions in the batch are completed."""
def __init__(self, start_length, eof_strings, tokenizer):
self.start_length = start_length
self.eof_strings = eof_strings
self.tokenizer = tokenizer
def __call__(self, input_ids, scores, **kwargs):
"""Returns true if all generated sequences contain any of the end-of-function strings."""
decoded_generations = self.tokenizer.batch_decode(input_ids[:, self.start_length :])
done = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings))
return all(done)
def remove_last_block(string):
"""Remove the last block of the code containing EOF_STRINGS"""
string_list = re.split("(%s)" % "|".join(EOF_STRINGS), string)
# last string should be ""
return "".join(string_list[:-2])
def complete_code(accelerator, model, tokenizer, dataloader, n_tasks, batch_size=20, **gen_kwargs):
"""Generate multiple codes for each task in the dataset. This function leverage accelerator to distribute
the processing to multiple GPUs.
dataloader, a wrapper around a TokenizeDataset objectm is supposed to send all the prompts from
the evalution dataset to the modelm as the following:
[p_0_0, p_0_1, ..., p_0_nc-1, p_1_0, ..., p_nt-1_nc-1]
where nc is the number of copies of the prompt, and nt is the number of tasks.
nc is such that num_sample = nc * batch_size
Parameters
----------
accelerator: Accelerator
model: transformers.PreTrainedModel
Code generation model. AutoTokenizer.from_pretrained(model_ckpt), ex model_ckpt = "lvwerra/codeparrot"
tokenizer: transformers.AutoTokenizer
The tokenizer used to train model
dataloader: DataLoader
The dataloader is a wrapper around a TokenizeDataset object. It is designed to be used with multiple GPUs.
n_tasks: int
The number of tasks in the dataset. It is used to determine the length of the output.
Should be aligned with the number of tasks in the TokenizeDataset.
batch_size: int
num_return_sequences per copy of the prompt such that num_sample = batch_size * n_copies
gen_kwargs: dict
Keyword arguments for the generation function of the model.
Returns
-------
code_gens: list of list of str, of length n_tasks
List of generated codes for each task.
Each element is a list of generated codes for each task, with length num_samples
"""
gen_token_dict = defaultdict(list) # dict of list of generated tokens
for step, batch in tqdm(enumerate(dataloader)):
with torch.no_grad():
gen_kwargs["stopping_criteria"][0].start_length = batch["ids"].shape[-1]
generated_tokens = accelerator.unwrap_model(model).generate(
input_ids=batch["ids"][:, : batch["input_len"]], num_return_sequences=batch_size, **gen_kwargs
)
# each task is generated batch_size times
generated_tasks = batch["task_id"].repeat(batch_size)
generated_tokens = accelerator.pad_across_processes(
generated_tokens, dim=1, pad_index=tokenizer.pad_token_id
)
generated_tokens, generated_tasks = accelerator.gather((generated_tokens, generated_tasks))
generated_tokens = generated_tokens.cpu().numpy()
generated_tasks = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(generated_tasks, generated_tokens):
gen_token_dict[task].append(generated_tokens)
code_gens = [[] for _ in range(n_tasks)]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
gen_code = tokenizer.decode(s, skip_special_tokens=True, clean_up_tokenization_spaces=True)
code_gens[task].append(remove_last_block(gen_code))
return code_gens
def main():
# Setup configuration
parser = HfArgumentParser(HumanEvalArguments)
args = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
os.environ["HF_ALLOW_CODE_EVAL"] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
os.environ["TOKENIZERS_PARALLELISM"] = "false"
if args.num_workers is None:
args.num_workers = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
accelerator = Accelerator()
set_seed(args.seed, device_specific=True)
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(args.model_ckpt)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
# Generation settings
gen_kwargs = {
"do_sample": args.do_sample,
"temperature": args.temperature,
"max_new_tokens": args.max_new_tokens,
"top_p": args.top_p,
"top_k": args.top_k,
"stopping_criteria": StoppingCriteriaList([EndOfFunctionCriteria(0, EOF_STRINGS, tokenizer)]),
}
# Load evaluation dataset and metric
human_eval = load_dataset("openai_humaneval")
code_eval_metric = load_metric("code_eval")
n_tasks = args.num_tasks if args.num_tasks is not None else len(human_eval["test"])
n_copies = args.n_samples // args.batch_size
human_eval_tokenized = TokenizedDataset(tokenizer, human_eval["test"], n_copies=n_copies, n_tasks=n_tasks)
# do not confuse args.batch_size, which is actually the num_return_sequences
human_eval_loader = DataLoader(human_eval_tokenized, batch_size=1)
# Run a quick test to see if code evaluation is enabled
try:
_ = code_eval_metric.compute(references=[""], predictions=[[""]])
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
" flag to enable code evaluation."
)
raise exception
model, human_eval_loader = accelerator.prepare(model, human_eval_loader)
generations = complete_code(
accelerator,
model,
tokenizer,
human_eval_loader,
n_tasks=n_tasks,
batch_size=args.batch_size,
**gen_kwargs,
)
if accelerator.is_main_process:
references = []
for task in tqdm(range(n_tasks)):
test_func = human_eval["test"][task]["test"]
entry_point = f"check({human_eval['test'][task]['entry_point']})"
references.append("\n" + test_func + "\n" + entry_point)
# Evaluate completions with "code_eval" metric
pass_at_k, _ = code_eval_metric.compute(
references=references, predictions=generations, num_workers=args.num_workers
)
print(f"Results: {pass_at_k}")
# Save results to json file
with open(args.output_file, "w") as fp:
json.dump(pass_at_k, fp)
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| transformers/examples/research_projects/codeparrot/scripts/human_eval.py/0 | {
"file_path": "transformers/examples/research_projects/codeparrot/scripts/human_eval.py",
"repo_id": "transformers",
"token_count": 3551
} |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def entropy(x):
"""Calculate entropy of a pre-softmax logit Tensor"""
exp_x = torch.exp(x)
A = torch.sum(exp_x, dim=1) # sum of exp(x_i)
B = torch.sum(x * exp_x, dim=1) # sum of x_i * exp(x_i)
return torch.log(A) - B / A
class DeeBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
self.highway = nn.ModuleList([BertHighway(config) for _ in range(config.num_hidden_layers)])
self.early_exit_entropy = [-1 for _ in range(config.num_hidden_layers)]
def set_early_exit_entropy(self, x):
if isinstance(x, (float, int)):
for i in range(len(self.early_exit_entropy)):
self.early_exit_entropy[i] = x
else:
self.early_exit_entropy = x
def init_highway_pooler(self, pooler):
loaded_model = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name])
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
):
all_hidden_states = ()
all_attentions = ()
all_highway_exits = ()
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask
)
hidden_states = layer_outputs[0]
if self.output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
current_outputs = (hidden_states,)
if self.output_hidden_states:
current_outputs = current_outputs + (all_hidden_states,)
if self.output_attentions:
current_outputs = current_outputs + (all_attentions,)
highway_exit = self.highway[i](current_outputs)
# logits, pooled_output
if not self.training:
highway_logits = highway_exit[0]
highway_entropy = entropy(highway_logits)
highway_exit = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
all_highway_exits = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
new_output = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(new_output, i + 1)
else:
all_highway_exits = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
outputs = outputs + (all_attentions,)
outputs = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
"The Bert Model transformer with early exiting (DeeBERT). ",
BERT_START_DOCSTRING,
)
class DeeBertModel(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = BertEmbeddings(config)
self.encoder = DeeBertEncoder(config)
self.pooler = BertPooler(config)
self.init_weights()
def init_highway_pooler(self):
self.encoder.init_highway_pooler(self.pooler)
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
):
r"""
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during pre-training.
This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
Tuple of each early exit's results (total length: number of layers)
Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
encoder_extended_attention_mask = encoder_extended_attention_mask.to(
dtype=next(self.parameters()).dtype
) # fp16 compatibility
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
outputs = (
sequence_output,
pooled_output,
) + encoder_outputs[1:] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class HighwayException(Exception):
def __init__(self, message, exit_layer):
self.message = message
self.exit_layer = exit_layer # start from 1!
class BertHighway(nn.Module):
"""A module to provide a shortcut
from (the output of one non-final BertLayer in BertEncoder) to (cross-entropy computation in BertForSequenceClassification)
"""
def __init__(self, config):
super().__init__()
self.pooler = BertPooler(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, encoder_outputs):
# Pooler
pooler_input = encoder_outputs[0]
pooler_output = self.pooler(pooler_input)
# "return" pooler_output
# BertModel
bmodel_output = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
pooled_output = bmodel_output[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
return logits, pooled_output
@add_start_docstrings(
"""Bert Model (with early exiting - DeeBERT) with a classifier on top,
also takes care of multi-layer training. """,
BERT_START_DOCSTRING,
)
class DeeBertForSequenceClassification(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.num_layers = config.num_hidden_layers
self.bert = DeeBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
self.init_weights()
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_layer=-1,
train_highway=False,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss.
Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
highway_exits (:obj:`tuple(tuple(torch.Tensor))`:
Tuple of each early exit's results (total length: number of layers)
Each tuple is again, a tuple of length 2 - the first entry is logits and the second entry is hidden states.
"""
exit_layer = self.num_layers
try:
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
)
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
outputs = e.message
exit_layer = e.exit_layer
logits = outputs[0]
if not self.training:
original_entropy = entropy(logits)
highway_entropy = []
highway_logits_all = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
# work with highway exits
highway_losses = []
for highway_exit in outputs[-1]:
highway_logits = highway_exit[0]
if not self.training:
highway_logits_all.append(highway_logits)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
highway_loss = loss_fct(highway_logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
highway_loss = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
highway_losses.append(highway_loss)
if train_highway:
outputs = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
outputs = (loss,) + outputs
if not self.training:
outputs = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
outputs = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| transformers/examples/research_projects/deebert/src/modeling_highway_bert.py/0 | {
"file_path": "transformers/examples/research_projects/deebert/src/modeling_highway_bert.py",
"repo_id": "transformers",
"token_count": 7800
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning (m)LUKE model on token classification tasks (NER, POS, CHUNKS) relying on the accelerate library 🤗
without using a Trainer.
"""
import argparse
import logging
import math
import os
import random
from pathlib import Path
import datasets
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs
from datasets import ClassLabel, load_dataset, load_metric
from huggingface_hub import Repository, create_repo
from luke_utils import DataCollatorForLukeTokenClassification, is_punctuation, padding_tensor
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import transformers
from transformers import (
AdamW,
LukeConfig,
LukeForEntitySpanClassification,
LukeTokenizer,
SchedulerType,
default_data_collator,
get_scheduler,
set_seed,
)
from transformers.utils.versions import require_version
logger = logging.getLogger(__name__)
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
def parse_args():
parser = argparse.ArgumentParser(
description="Finetune (m)LUKE on a token classification task (such as NER) with the accelerate library"
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
"--train_file", type=str, default=None, help="A csv or a json file containing the training data."
)
parser.add_argument(
"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
)
parser.add_argument(
"--text_column_name",
type=str,
default=None,
help="The column name of text to input in the file (a csv or JSON file).",
)
parser.add_argument(
"--label_column_name",
type=str,
default=None,
help="The column name of label to input in the file (a csv or JSON file).",
)
parser.add_argument(
"--max_length",
type=int,
default=128,
help=(
"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
" sequences shorter will be padded if `--pad_to_max_length` is passed."
),
)
parser.add_argument(
"--max_entity_length",
type=int,
default=32,
help=(
"The maximum total input entity length after tokenization (Used only for (M)Luke models). Sequences longer"
" than this will be truncated, sequences shorter will be padded if `--pad_to_max_length` is passed."
),
)
parser.add_argument(
"--max_mention_length",
type=int,
default=30,
help=(
"The maximum total input mention length after tokenization (Used only for (M)Luke models). Sequences"
" longer than this will be truncated, sequences shorter will be padded if `--pad_to_max_length` is passed."
),
)
parser.add_argument(
"--pad_to_max_length",
action="store_true",
help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
)
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
required=True,
)
parser.add_argument(
"--config_name",
type=str,
default=None,
help="Pretrained config name or path if not the same as model_name",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=8,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=8,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="linear",
help="The scheduler type to use.",
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
)
parser.add_argument(
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--label_all_tokens",
action="store_true",
help="Setting labels of all special tokens to -100 and thus PyTorch will ignore them.",
)
parser.add_argument(
"--return_entity_level_metrics",
action="store_true",
help="Indication whether entity level metrics are to be returner.",
)
parser.add_argument(
"--task_name",
type=str,
default="ner",
choices=["ner", "pos", "chunk"],
help="The name of the task.",
)
parser.add_argument(
"--debug",
action="store_true",
help="Activate debug mode and run training only with a subset of data.",
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
)
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
args = parser.parse_args()
# Sanity checks
if args.task_name is None and args.train_file is None and args.validation_file is None:
raise ValueError("Need either a task name or a training/validation file.")
else:
if args.train_file is not None:
extension = args.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if args.validation_file is not None:
extension = args.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
if args.push_to_hub:
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
return args
def main():
args = parse_args()
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
handler = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(kwargs_handlers=[handler])
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
# Retrieve of infer repo_name
repo_name = args.hub_model_id
if repo_name is None:
repo_name = Path(args.output_dir).absolute().name
# Create repo and retrieve repo_id
repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
# Clone repo locally
repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets for token classification task available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'tokens' or the first column if no column called
# 'tokens' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
else:
data_files = {}
if args.train_file is not None:
data_files["train"] = args.train_file
extension = args.train_file.split(".")[-1]
if args.validation_file is not None:
data_files["validation"] = args.validation_file
extension = args.validation_file.split(".")[-1]
raw_datasets = load_dataset(extension, data_files=data_files)
# Trim a number of training examples
if args.debug:
for split in raw_datasets.keys():
raw_datasets[split] = raw_datasets[split].select(range(100))
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.
if raw_datasets["train"] is not None:
column_names = raw_datasets["train"].column_names
features = raw_datasets["train"].features
else:
column_names = raw_datasets["validation"].column_names
features = raw_datasets["validation"].features
if args.text_column_name is not None:
text_column_name = args.text_column_name
elif "tokens" in column_names:
text_column_name = "tokens"
else:
text_column_name = column_names[0]
if args.label_column_name is not None:
label_column_name = args.label_column_name
elif f"{args.task_name}_tags" in column_names:
label_column_name = f"{args.task_name}_tags"
else:
label_column_name = column_names[1]
# In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
# unique labels.
def get_label_list(labels):
unique_labels = set()
for label in labels:
unique_labels = unique_labels | set(label)
label_list = list(unique_labels)
label_list.sort()
return label_list
if isinstance(features[label_column_name].feature, ClassLabel):
label_list = features[label_column_name].feature.names
# No need to convert the labels since they are already ints.
else:
label_list = get_label_list(raw_datasets["train"][label_column_name])
num_labels = len(label_list)
# Map that sends B-Xxx label to its I-Xxx counterpart
b_to_i_label = []
for idx, label in enumerate(label_list):
if label.startswith("B-") and label.replace("B-", "I-") in label_list:
b_to_i_label.append(label_list.index(label.replace("B-", "I-")))
else:
b_to_i_label.append(idx)
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if args.config_name:
config = LukeConfig.from_pretrained(args.config_name, num_labels=num_labels)
elif args.model_name_or_path:
config = LukeConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels)
else:
logger.warning("You are instantiating a new config instance from scratch.")
tokenizer_name_or_path = args.tokenizer_name if args.tokenizer_name else args.model_name_or_path
if not tokenizer_name_or_path:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
tokenizer = LukeTokenizer.from_pretrained(
tokenizer_name_or_path,
use_fast=False,
task="entity_span_classification",
max_entity_length=args.max_entity_length,
max_mention_length=args.max_mention_length,
)
if args.model_name_or_path:
model = LukeForEntitySpanClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
)
else:
logger.info("Training new model from scratch")
model = LukeForEntitySpanClassification.from_config(config)
model.resize_token_embeddings(len(tokenizer))
# Preprocessing the datasets.
# First we tokenize all the texts.
padding = "max_length" if args.pad_to_max_length else False
def compute_sentence_boundaries_for_luke(examples):
sentence_boundaries = []
for tokens in examples[text_column_name]:
sentence_boundaries.append([0, len(tokens)])
examples["sentence_boundaries"] = sentence_boundaries
return examples
def compute_entity_spans_for_luke(examples):
all_entity_spans = []
texts = []
all_labels_entity_spans = []
all_original_entity_spans = []
for labels, tokens, sentence_boundaries in zip(
examples[label_column_name], examples[text_column_name], examples["sentence_boundaries"]
):
subword_lengths = [len(tokenizer.tokenize(token)) for token in tokens]
total_subword_length = sum(subword_lengths)
_, context_end = sentence_boundaries
if total_subword_length > args.max_length - 2:
cur_length = sum(subword_lengths[:context_end])
idx = context_end - 1
while cur_length > args.max_length - 2:
cur_length -= subword_lengths[idx]
context_end -= 1
idx -= 1
text = ""
sentence_words = tokens[:context_end]
sentence_subword_lengths = subword_lengths[:context_end]
word_start_char_positions = []
word_end_char_positions = []
labels_positions = {}
for word, label in zip(sentence_words, labels):
if word[0] == "'" or (len(word) == 1 and is_punctuation(word)):
text = text.rstrip()
word_start_char_positions.append(len(text))
text += word
word_end_char_positions.append(len(text))
text += " "
labels_positions[(word_start_char_positions[-1], word_end_char_positions[-1])] = label
text = text.rstrip()
texts.append(text)
entity_spans = []
labels_entity_spans = []
original_entity_spans = []
for word_start in range(len(sentence_words)):
for word_end in range(word_start, len(sentence_words)):
if (
sum(sentence_subword_lengths[word_start:word_end]) <= tokenizer.max_mention_length
and len(entity_spans) < tokenizer.max_entity_length
):
entity_spans.append((word_start_char_positions[word_start], word_end_char_positions[word_end]))
original_entity_spans.append((word_start, word_end + 1))
if (
word_start_char_positions[word_start],
word_end_char_positions[word_end],
) in labels_positions:
labels_entity_spans.append(
labels_positions[
(word_start_char_positions[word_start], word_end_char_positions[word_end])
]
)
else:
labels_entity_spans.append(0)
all_entity_spans.append(entity_spans)
all_labels_entity_spans.append(labels_entity_spans)
all_original_entity_spans.append(original_entity_spans)
examples["entity_spans"] = all_entity_spans
examples["text"] = texts
examples["labels_entity_spans"] = all_labels_entity_spans
examples["original_entity_spans"] = all_original_entity_spans
return examples
def tokenize_and_align_labels(examples):
entity_spans = []
for v in examples["entity_spans"]:
entity_spans.append(list(map(tuple, v)))
tokenized_inputs = tokenizer(
examples["text"],
entity_spans=entity_spans,
max_length=args.max_length,
padding=padding,
truncation=True,
)
if padding == "max_length":
tokenized_inputs["labels"] = padding_tensor(
examples["labels_entity_spans"], -100, tokenizer.padding_side, tokenizer.max_entity_length
)
tokenized_inputs["original_entity_spans"] = padding_tensor(
examples["original_entity_spans"], (-1, -1), tokenizer.padding_side, tokenizer.max_entity_length
)
tokenized_inputs[label_column_name] = padding_tensor(
examples[label_column_name], -1, tokenizer.padding_side, tokenizer.max_entity_length
)
else:
tokenized_inputs["labels"] = [ex[: tokenizer.max_entity_length] for ex in examples["labels_entity_spans"]]
tokenized_inputs["original_entity_spans"] = [
ex[: tokenizer.max_entity_length] for ex in examples["original_entity_spans"]
]
tokenized_inputs[label_column_name] = [
ex[: tokenizer.max_entity_length] for ex in examples[label_column_name]
]
return tokenized_inputs
with accelerator.main_process_first():
raw_datasets = raw_datasets.map(
compute_sentence_boundaries_for_luke,
batched=True,
desc="Adding sentence boundaries",
)
raw_datasets = raw_datasets.map(
compute_entity_spans_for_luke,
batched=True,
desc="Adding sentence spans",
)
processed_raw_datasets = raw_datasets.map(
tokenize_and_align_labels,
batched=True,
remove_columns=raw_datasets["train"].column_names,
desc="Running tokenizer on dataset",
)
train_dataset = processed_raw_datasets["train"]
eval_dataset = processed_raw_datasets["validation"]
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
# DataLoaders creation:
if args.pad_to_max_length:
# If padding was already done ot max length, we use the default data collator that will just convert everything
# to tensors.
data_collator = default_data_collator
else:
# Otherwise, `DataCollatorForTokenClassification` will apply dynamic padding for us (by padding to the maximum length of
# the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
# of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
# For fp8, we pad to multiple of 16.
if accelerator.mixed_precision == "fp8":
pad_to_multiple_of = 16
elif accelerator.mixed_precision != "no":
pad_to_multiple_of = 8
else:
pad_to_multiple_of = None
data_collator = DataCollatorForLukeTokenClassification(tokenizer, pad_to_multiple_of=pad_to_multiple_of)
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
)
eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
# Optimizer
# Split weights in two groups, one with weight decay and the other not.
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
# Use the device given by the `accelerator` object.
device = accelerator.device
model.to(device)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader
)
# Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
# shorter in multiprocess)
# Scheduler and math around the number of training steps.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
else:
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=args.max_train_steps,
)
# Metrics
metric = load_metric("seqeval")
def get_luke_labels(outputs, ner_tags, original_entity_spans):
true_predictions = []
true_labels = []
for output, original_spans, tags in zip(outputs.logits, original_entity_spans, ner_tags):
true_tags = [val for val in tags if val != -1]
true_original_spans = [val for val in original_spans if val != (-1, -1)]
max_indices = torch.argmax(output, axis=1)
max_logits = torch.max(output, axis=1).values
predictions = []
for logit, index, span in zip(max_logits, max_indices, true_original_spans):
if index != 0:
predictions.append((logit, span, label_list[index]))
predicted_sequence = [label_list[0]] * len(true_tags)
for _, span, label in sorted(predictions, key=lambda o: o[0], reverse=True):
if all(o == label_list[0] for o in predicted_sequence[span[0] : span[1]]):
predicted_sequence[span[0]] = label
if span[1] - span[0] > 1:
predicted_sequence[span[0] + 1 : span[1]] = [label] * (span[1] - span[0] - 1)
true_predictions.append(predicted_sequence)
true_labels.append([label_list[tag_id] for tag_id in true_tags])
return true_predictions, true_labels
def compute_metrics():
results = metric.compute()
if args.return_entity_level_metrics:
# Unpack nested dictionaries
final_results = {}
for key, value in results.items():
if isinstance(value, dict):
for n, v in value.items():
final_results[f"{key}_{n}"] = v
else:
final_results[key] = value
return final_results
else:
return {
"precision": results["overall_precision"],
"recall": results["overall_recall"],
"f1": results["overall_f1"],
"accuracy": results["overall_accuracy"],
}
# Train!
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
completed_steps = 0
for epoch in range(args.num_train_epochs):
model.train()
for step, batch in enumerate(train_dataloader):
_ = batch.pop("original_entity_spans")
outputs = model(**batch)
loss = outputs.loss
loss = loss / args.gradient_accumulation_steps
accelerator.backward(loss)
if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
completed_steps += 1
if completed_steps >= args.max_train_steps:
break
model.eval()
for step, batch in enumerate(eval_dataloader):
original_entity_spans = batch.pop("original_entity_spans")
with torch.no_grad():
outputs = model(**batch)
preds, refs = get_luke_labels(outputs, batch[label_column_name], original_entity_spans)
metric.add_batch(
predictions=preds,
references=refs,
) # predictions and preferences are expected to be a nested list of labels, not label_ids
eval_metric = compute_metrics()
accelerator.print(f"epoch {epoch}:", eval_metric)
if args.push_to_hub and epoch < args.num_train_epochs - 1:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
repo.push_to_hub(
commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
)
if args.output_dir is not None:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
if __name__ == "__main__":
main()
| transformers/examples/research_projects/luke/run_luke_ner_no_trainer.py/0 | {
"file_path": "transformers/examples/research_projects/luke/run_luke_ner_no_trainer.py",
"repo_id": "transformers",
"token_count": 12598
} |
# Movement Pruning: Adaptive Sparsity by Fine-Tuning
Author: @VictorSanh
*Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning; however, it is less effective in the transfer learning regime that has become standard for state-of-the-art natural language processing applications. We propose the use of *movement pruning*, a simple, deterministic first-order weight pruning method that is more adaptive to pretrained model fine-tuning. Experiments show that when pruning large pretrained language models, movement pruning shows significant improvements in high-sparsity regimes. When combined with distillation, the approach achieves minimal accuracy loss with down to only 3% of the model parameters:*
| Fine-pruning+Distillation<br>(Teacher=BERT-base fine-tuned) | BERT base<br>fine-tuned | Remaining<br>Weights (%) | Magnitude Pruning | L0 Regularization | Movement Pruning | Soft Movement Pruning |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| SQuAD - Dev<br>EM/F1 | 80.4/88.1 | 10%<br>3% | 70.2/80.1<br>45.5/59.6 | 72.4/81.9<br>64.3/75.8 | 75.6/84.3<br>67.5/78.0 | **76.6/84.9**<br>**72.7/82.3** |
| MNLI - Dev<br>acc/MM acc | 84.5/84.9 | 10%<br>3% | 78.3/79.3<br>69.4/70.6 | 78.7/79.7<br>76.0/76.2 | 80.1/80.4<br>76.5/77.4 | **81.2/81.8**<br>**79.5/80.1** |
| QQP - Dev<br>acc/F1 | 91.4/88.4 | 10%<br>3% | 79.8/65.0<br>72.4/57.8 | 88.1/82.8<br>87.0/81.9 | 89.7/86.2<br>86.1/81.5 | **90.2/86.8**<br>**89.1/85.5** |
This page contains information on how to fine-prune pre-trained models such as `BERT` to obtain extremely sparse models with movement pruning. In contrast to magnitude pruning which selects weights that are far from 0, movement pruning retains weights that are moving away from 0.
For more information, we invite you to check out [our paper](https://arxiv.org/abs/2005.07683).
You can also have a look at this fun *Explain Like I'm Five* introductory [slide deck](https://www.slideshare.net/VictorSanh/movement-pruning-explain-like-im-five-234205241).
<div align="center">
<img src="https://www.seekpng.com/png/detail/166-1669328_how-to-make-emmental-cheese-at-home-icooker.png" width="400">
</div>
## Extreme sparsity and efficient storage
One promise of extreme pruning is to obtain extremely small models that can be easily sent (and stored) on edge devices. By setting weights to 0., we reduce the amount of information we need to store, and thus decreasing the memory size. We are able to obtain extremely sparse fine-pruned models with movement pruning: ~95% of the dense performance with ~5% of total remaining weights in the BERT encoder.
In [this notebook](https://github.com/huggingface/transformers/blob/main/examples/research_projects/movement-pruning/Saving_PruneBERT.ipynb), we showcase how we can leverage standard tools that exist out-of-the-box to efficiently store an extremely sparse question answering model (only 6% of total remaining weights in the encoder). We are able to reduce the memory size of the encoder **from the 340MB (the original dense BERT) to 11MB**, without any additional training of the model (every operation is performed *post fine-pruning*). It is sufficiently small to store it on a [91' floppy disk](https://en.wikipedia.org/wiki/Floptical) 📎!
While movement pruning does not directly optimize for memory footprint (but rather the number of non-null weights), we hypothetize that further memory compression ratios can be achieved with specific quantization aware trainings (see for instance [Q8BERT](https://arxiv.org/abs/1910.06188), [And the Bit Goes Down](https://arxiv.org/abs/1907.05686) or [Quant-Noise](https://arxiv.org/abs/2004.07320)).
## Fine-pruned models
As examples, we release two English PruneBERT checkpoints (models fine-pruned from a pre-trained `BERT` checkpoint), one on SQuAD and the other on MNLI.
- **`prunebert-base-uncased-6-finepruned-w-distil-squad`**<br/>
Pre-trained `BERT-base-uncased` fine-pruned with soft movement pruning on SQuAD v1.1. We use an additional distillation signal from `BERT-base-uncased` finetuned on SQuAD. The encoder counts 6% of total non-null weights and reaches 83.8 F1 score. The model can be accessed with: `pruned_bert = BertForQuestionAnswering.from_pretrained("huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad")`
- **`prunebert-base-uncased-6-finepruned-w-distil-mnli`**<br/>
Pre-trained `BERT-base-uncased` fine-pruned with soft movement pruning on MNLI. We use an additional distillation signal from `BERT-base-uncased` finetuned on MNLI. The encoder counts 6% of total non-null weights and reaches 80.7 (matched) accuracy. The model can be accessed with: `pruned_bert = BertForSequenceClassification.from_pretrained("huggingface/prunebert-base-uncased-6-finepruned-w-distil-mnli")`
## How to fine-prune?
### Setup
The code relies on the 🤗 Transformers library. In addition to the dependencies listed in the [`examples`](https://github.com/huggingface/transformers/tree/main/examples) folder, you should install a few additional dependencies listed in the `requirements.txt` file: `pip install -r requirements.txt`.
Note that we built our experiments on top of a stabilized version of the library (commit https://github.com/huggingface/transformers/commit/352d5472b0c1dec0f420d606d16747d851b4bda8): we do not guarantee that everything is still compatible with the latest version of the main branch.
### Fine-pruning with movement pruning
Below, we detail how to reproduce the results reported in the paper. We use SQuAD as a running example. Commands (and scripts) can be easily adapted for other tasks.
The following command fine-prunes a pre-trained `BERT-base` on SQuAD using movement pruning towards 15% of remaining weights (85% sparsity). Note that we freeze all the embeddings modules (from their pre-trained value) and only prune the Fully Connected layers in the encoder (12 layers of Transformer Block).
```bash
SERIALIZATION_DIR=<OUTPUT_DIR>
SQUAD_DATA=<SQUAD_DATA>
python examples/movement-pruning/masked_run_squad.py \
--output_dir $SERIALIZATION_DIR \
--data_dir $SQUAD_DATA \
--train_file train-v1.1.json \
--predict_file dev-v1.1.json \
--do_train --do_eval --do_lower_case \
--model_type masked_bert \
--model_name_or_path google-bert/bert-base-uncased \
--per_gpu_train_batch_size 16 \
--warmup_steps 5400 \
--num_train_epochs 10 \
--learning_rate 3e-5 --mask_scores_learning_rate 1e-2 \
--initial_threshold 1 --final_threshold 0.15 \
--initial_warmup 1 --final_warmup 2 \
--pruning_method topK --mask_init constant --mask_scale 0.
```
### Fine-pruning with other methods
We can also explore other fine-pruning methods by changing the `pruning_method` parameter:
Soft movement pruning
```bash
python examples/movement-pruning/masked_run_squad.py \
--output_dir $SERIALIZATION_DIR \
--data_dir $SQUAD_DATA \
--train_file train-v1.1.json \
--predict_file dev-v1.1.json \
--do_train --do_eval --do_lower_case \
--model_type masked_bert \
--model_name_or_path google-bert/bert-base-uncased \
--per_gpu_train_batch_size 16 \
--warmup_steps 5400 \
--num_train_epochs 10 \
--learning_rate 3e-5 --mask_scores_learning_rate 1e-2 \
--initial_threshold 0 --final_threshold 0.1 \
--initial_warmup 1 --final_warmup 2 \
--pruning_method sigmoied_threshold --mask_init constant --mask_scale 0. \
--regularization l1 --final_lambda 400.
```
L0 regularization
```bash
python examples/movement-pruning/masked_run_squad.py \
--output_dir $SERIALIZATION_DIR \
--data_dir $SQUAD_DATA \
--train_file train-v1.1.json \
--predict_file dev-v1.1.json \
--do_train --do_eval --do_lower_case \
--model_type masked_bert \
--model_name_or_path google-bert/bert-base-uncased \
--per_gpu_train_batch_size 16 \
--warmup_steps 5400 \
--num_train_epochs 10 \
--learning_rate 3e-5 --mask_scores_learning_rate 1e-1 \
--initial_threshold 1. --final_threshold 1. \
--initial_warmup 1 --final_warmup 1 \
--pruning_method l0 --mask_init constant --mask_scale 2.197 \
--regularization l0 --final_lambda 125.
```
Iterative Magnitude Pruning
```bash
python examples/movement-pruning/masked_run_squad.py \
--output_dir ./dbg \
--data_dir examples/distillation/data/squad_data \
--train_file train-v1.1.json \
--predict_file dev-v1.1.json \
--do_train --do_eval --do_lower_case \
--model_type masked_bert \
--model_name_or_path google-bert/bert-base-uncased \
--per_gpu_train_batch_size 16 \
--warmup_steps 5400 \
--num_train_epochs 10 \
--learning_rate 3e-5 \
--initial_threshold 1 --final_threshold 0.15 \
--initial_warmup 1 --final_warmup 2 \
--pruning_method magnitude
```
### After fine-pruning
**Counting parameters**
Regularization based pruning methods (soft movement pruning and L0 regularization) rely on the penalty to induce sparsity. The multiplicative coefficient controls the sparsity level.
To obtain the effective sparsity level in the encoder, we simply count the number of activated (non-null) weights:
```bash
python examples/movement-pruning/counts_parameters.py \
--pruning_method sigmoied_threshold \
--threshold 0.1 \
--serialization_dir $SERIALIZATION_DIR
```
**Pruning once for all**
Once the model has been fine-pruned, the pruned weights can be set to 0. once for all (reducing the amount of information to store). In our running experiments, we can convert a `MaskedBertForQuestionAnswering` (a BERT model augmented to enable on-the-fly pruning capabilities) to a standard `BertForQuestionAnswering`:
```bash
python examples/movement-pruning/bertarize.py \
--pruning_method sigmoied_threshold \
--threshold 0.1 \
--model_name_or_path $SERIALIZATION_DIR
```
## Hyper-parameters
For reproducibility purposes, we share the detailed results presented in the paper. These [tables](https://docs.google.com/spreadsheets/d/17JgRq_OFFTniUrz6BZWW_87DjFkKXpI1kYDSsseT_7g/edit?usp=sharing) exhaustively describe the individual hyper-parameters used for each data point.
## Inference speed
Early experiments show that even though models fine-pruned with (soft) movement pruning are extremely sparse, they do not benefit from significant improvement in terms of inference speed when using the standard PyTorch inference.
We are currently benchmarking and exploring inference setups specifically for sparse architectures.
In particular, hardware manufacturers are announcing devices that will speedup inference for sparse networks considerably.
## Citation
If you find this resource useful, please consider citing the following paper:
```bibtex
@article{sanh2020movement,
title={Movement Pruning: Adaptive Sparsity by Fine-Tuning},
author={Victor Sanh and Thomas Wolf and Alexander M. Rush},
year={2020},
eprint={2005.07683},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| transformers/examples/research_projects/movement-pruning/README.md/0 | {
"file_path": "transformers/examples/research_projects/movement-pruning/README.md",
"repo_id": "transformers",
"token_count": 4045
} |
<!---
Copyright 2021 NVIDIA Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Huggingface QDQBERT Quantization Example
The QDQBERT model adds fake quantization (pair of QuantizeLinear/DequantizeLinear ops) to:
* linear layer inputs and weights
* matmul inputs
* residual add inputs
In this example, we use QDQBERT model to do quantization on SQuAD task, including Quantization Aware Training (QAT), Post Training Quantization (PTQ) and inferencing using TensorRT.
Required:
- [pytorch-quantization toolkit](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization)
- [TensorRT >= 8.2](https://developer.nvidia.com/tensorrt)
- PyTorch >= 1.10.0
## Setup the environment with Dockerfile
Under the directory of `transformers/`, build the docker image:
```bash
docker build . -f examples/research_projects/quantization-qdqbert/Dockerfile -t bert_quantization:latest
```
Run the docker:
```bash
docker run --gpus all --privileged --rm -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 bert_quantization:latest
```
In the container:
```bash
cd transformers/examples/research_projects/quantization-qdqbert/
```
## Quantization Aware Training (QAT)
Calibrate the pretrained model and finetune with quantization awared:
```bash
python3 run_quant_qa.py \
--model_name_or_path google-bert/bert-base-uncased \
--dataset_name squad \
--max_seq_length 128 \
--doc_stride 32 \
--output_dir calib/google-bert/bert-base-uncased \
--do_calib \
--calibrator percentile \
--percentile 99.99
```
```bash
python3 run_quant_qa.py \
--model_name_or_path calib/google-bert/bert-base-uncased \
--dataset_name squad \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--learning_rate 4e-5 \
--num_train_epochs 2 \
--max_seq_length 128 \
--doc_stride 32 \
--output_dir finetuned_int8/google-bert/bert-base-uncased \
--tokenizer_name google-bert/bert-base-uncased \
--save_steps 0
```
### Export QAT model to ONNX
To export the QAT model finetuned above:
```bash
python3 run_quant_qa.py \
--model_name_or_path finetuned_int8/google-bert/bert-base-uncased \
--output_dir ./ \
--save_onnx \
--per_device_eval_batch_size 1 \
--max_seq_length 128 \
--doc_stride 32 \
--dataset_name squad \
--tokenizer_name google-bert/bert-base-uncased
```
Use `--recalibrate-weights` to calibrate the weight ranges according to the quantizer axis. Use `--quant-per-tensor` for per tensor quantization (default is per channel).
Recalibrating will affect the accuracy of the model, but the change should be minimal (< 0.5 F1).
### Benchmark the INT8 QAT ONNX model inference with TensorRT using dummy input
```bash
trtexec --onnx=model.onnx --explicitBatch --workspace=16384 --int8 --shapes=input_ids:64x128,attention_mask:64x128,token_type_ids:64x128 --verbose
```
### Benchmark the INT8 QAT ONNX model inference with [ONNX Runtime-TRT](https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html) using dummy input
```bash
python3 ort-infer-benchmark.py
```
### Evaluate the INT8 QAT ONNX model inference with TensorRT
```bash
python3 evaluate-hf-trt-qa.py \
--onnx_model_path=./model.onnx \
--output_dir ./ \
--per_device_eval_batch_size 64 \
--max_seq_length 128 \
--doc_stride 32 \
--dataset_name squad \
--tokenizer_name google-bert/bert-base-uncased \
--int8 \
--seed 42
```
## Fine-tuning of FP32 model for comparison
Finetune a fp32 precision model with [transformers/examples/pytorch/question-answering/](../../pytorch/question-answering/):
```bash
python3 ../../pytorch/question-answering/run_qa.py \
--model_name_or_path google-bert/bert-base-uncased \
--dataset_name squad \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 128 \
--doc_stride 32 \
--output_dir ./finetuned_fp32/google-bert/bert-base-uncased \
--save_steps 0 \
--do_train \
--do_eval
```
## Post Training Quantization (PTQ)
### PTQ by calibrating and evaluating the finetuned FP32 model above:
```bash
python3 run_quant_qa.py \
--model_name_or_path ./finetuned_fp32/google-bert/bert-base-uncased \
--dataset_name squad \
--calibrator percentile \
--percentile 99.99 \
--max_seq_length 128 \
--doc_stride 32 \
--output_dir ./calib/google-bert/bert-base-uncased \
--save_steps 0 \
--do_calib \
--do_eval
```
### Export the INT8 PTQ model to ONNX
```bash
python3 run_quant_qa.py \
--model_name_or_path ./calib/google-bert/bert-base-uncased \
--output_dir ./ \
--save_onnx \
--per_device_eval_batch_size 1 \
--max_seq_length 128 \
--doc_stride 32 \
--dataset_name squad \
--tokenizer_name google-bert/bert-base-uncased
```
### Evaluate the INT8 PTQ ONNX model inference with TensorRT
```bash
python3 evaluate-hf-trt-qa.py \
--onnx_model_path=./model.onnx \
--output_dir ./ \
--per_device_eval_batch_size 64 \
--max_seq_length 128 \
--doc_stride 32 \
--dataset_name squad \
--tokenizer_name google-bert/bert-base-uncased \
--int8 \
--seed 42
```
### Quantization options
Some useful options to support different implementations and optimizations. These should be specified for both calibration and finetuning.
|argument|description|
|--------|-----------|
|`--quant-per-tensor`| quantize weights with one quantization range per tensor |
|`--fuse-qkv` | use a single range (the max) for quantizing QKV weights and output activations |
|`--clip-gelu N` | clip the output of GELU to a maximum of N when quantizing (e.g. 10) |
|`--disable-dropout` | disable dropout for consistent activation ranges |
| transformers/examples/research_projects/quantization-qdqbert/README.md/0 | {
"file_path": "transformers/examples/research_projects/quantization-qdqbert/README.md",
"repo_id": "transformers",
"token_count": 2147
} |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
logger = logging.getLogger(__name__)
class RagPyTorchDistributedRetriever(RagRetriever):
"""
A distributed retriever built on top of the ``torch.distributed`` communication package. During training all workers
initialize their own instance of the retriever, however, only the main worker loads the index into memory. The index is stored
in cpu memory. The index will also work well in a non-distributed setup.
Args:
config (:class:`~transformers.RagConfig`):
The configuration of the RAG model this Retriever is used with. Contains parameters indicating which ``Index`` to build.
question_encoder_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
The tokenizer that was used to tokenize the question.
It is used to decode the question and then use the generator_tokenizer.
generator_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
The tokenizer used for the generator part of the RagModel.
index (:class:`~transformers.models.rag.retrieval_rag.Index`, optional, defaults to the one defined by the configuration):
If specified, use this index instead of the one built using the configuration
"""
def __init__(self, config, question_encoder_tokenizer, generator_tokenizer, index=None):
super().__init__(
config,
question_encoder_tokenizer=question_encoder_tokenizer,
generator_tokenizer=generator_tokenizer,
index=index,
init_retrieval=False,
)
self.process_group = None
def init_retrieval(self, distributed_port: int):
"""
Retriever initialization function, needs to be called from the training process. The function sets some common parameters
and environment variables. On top of that, (only) the main process in the process group loads the index into memory.
Args:
distributed_port (:obj:`int`):
The port on which the main communication of the training run is carried out. We set the port for retrieval-related
communication as ``distributed_port + 1``.
"""
logger.info("initializing retrieval")
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("dist initialized")
# needs to be set manually
os.environ["GLOO_SOCKET_IFNAME"] = self._infer_socket_ifname()
# avoid clash with the NCCL port
os.environ["MASTER_PORT"] = str(distributed_port + 1)
self.process_group = dist.new_group(ranks=None, backend="gloo")
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("dist not initialized / main")
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group)
def _is_main(self):
return dist.get_rank(group=self.process_group) == 0
def _scattered(self, scatter_list, target_shape, target_type=torch.float32):
target_tensor = torch.empty(target_shape, dtype=target_type)
dist.scatter(target_tensor, src=0, scatter_list=scatter_list, group=self.process_group)
return target_tensor
def _infer_socket_ifname(self):
addrs = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
ifname = next((addr for addr in addrs if addr.startswith("e")), None)
return ifname
def retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, List[dict]]:
"""
Retrieves documents for specified ``question_hidden_states``. The main process, which has the access to the index stored in memory, gathers queries
from all the processes in the main training process group, performs the retrieval and scatters back the results.
Args:
question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`):
A batch of query vectors to retrieve with.
n_docs (:obj:`int`):
The number of docs retrieved per query.
Output:
retrieved_doc_embeds (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs, dim)`
The retrieval embeddings of the retrieved docs per query.
doc_ids (:obj:`np.ndarray` of shape :obj:`batch_size, n_docs`)
The ids of the documents in the index
doc_dicts (:obj:`List[dict]`):
The retrieved_doc_embeds examples per query.
"""
# single GPU training
if not dist.is_initialized():
doc_ids, retrieved_doc_embeds = self._main_retrieve(question_hidden_states, n_docs)
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(doc_ids)
# distributed training
world_size = dist.get_world_size(group=self.process_group)
# gather logic
gather_list = None
if self._is_main():
gather_list = [torch.empty(question_hidden_states.shape, dtype=torch.float32) for _ in range(world_size)]
dist.gather(torch.tensor(question_hidden_states), dst=0, gather_list=gather_list, group=self.process_group)
# scatter logic
n_queries = question_hidden_states.shape[0]
scatter_ids = []
scatter_vectors = []
if self._is_main():
assert len(gather_list) == world_size
ids, vectors = self._main_retrieve(torch.cat(gather_list).numpy(), n_docs)
ids, vectors = torch.tensor(ids), torch.tensor(vectors)
scatter_ids = self._chunk_tensor(ids, n_queries)
scatter_vectors = self._chunk_tensor(vectors, n_queries)
doc_ids = self._scattered(scatter_ids, [n_queries, n_docs], target_type=torch.int64)
retrieved_doc_embeds = self._scattered(scatter_vectors, [n_queries, n_docs, question_hidden_states.shape[1]])
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(doc_ids)
| transformers/examples/research_projects/rag/distributed_pytorch_retriever.py/0 | {
"file_path": "transformers/examples/research_projects/rag/distributed_pytorch_retriever.py",
"repo_id": "transformers",
"token_count": 2561
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
"""Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition in streaming mode"""
import logging
import os
import re
import sys
import warnings
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Union
import datasets
import numpy as np
import torch
from datasets import IterableDatasetDict, interleave_datasets, load_dataset, load_metric
from torch.utils.data import IterableDataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForCTC,
AutoProcessor,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainerCallback,
TrainingArguments,
Wav2Vec2Processor,
set_seed,
)
from transformers.trainer_pt_utils import IterableDatasetShard
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risk.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.18.2", "To fix: pip install 'datasets>=1.18.2'")
logger = logging.getLogger(__name__)
def list_field(default=None, metadata=None):
return field(default_factory=lambda: default, metadata=metadata)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
tokenizer_name_or_path: Optional[str] = field(
default=None,
metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"},
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
freeze_feature_encoder: bool = field(
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
)
attention_dropout: float = field(
default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
)
activation_dropout: float = field(
default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
)
feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
hidden_dropout: float = field(
default=0.0,
metadata={
"help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
},
)
final_dropout: float = field(
default=0.0,
metadata={"help": "The dropout probability for the final projection layer."},
)
mask_time_prob: float = field(
default=0.05,
metadata={
"help": (
"Probability of each feature vector along the time axis to be chosen as the start of the vector "
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature "
"vectors will be masked along the time axis."
)
},
)
mask_time_length: int = field(
default=10,
metadata={"help": "Length of vector span to mask along the time axis."},
)
mask_feature_prob: float = field(
default=0.0,
metadata={
"help": (
"Probability of each feature vector along the feature axis to be chosen as the start of the vectorspan"
" to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature"
" bins will be masked along the time axis."
)
},
)
mask_feature_length: int = field(
default=10,
metadata={"help": "Length of vector span to mask along the feature axis."},
)
layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
ctc_loss_reduction: Optional[str] = field(
default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
dataset_name: str = field(
metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
dataset_config_name: str = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_split_name: str = field(
default="train+validation",
metadata={
"help": (
"The name of the training data set split to use (via the datasets library). Defaults to "
"'train+validation'"
)
},
)
eval_split_name: str = field(
default="test",
metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'test'"
},
)
audio_column_name: str = field(
default="audio",
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
)
text_column_name: str = field(
default="text",
metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
},
)
shuffle_buffer_size: Optional[int] = field(
default=500,
metadata={
"help": (
"The number of streamed examples to download before shuffling them. The large the buffer, "
"the closer it is to real offline shuffling."
)
},
)
chars_to_ignore: Optional[List[str]] = list_field(
default=None,
metadata={"help": "A list of characters to remove from the transcripts."},
)
eval_metrics: List[str] = list_field(
default=["wer"],
metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
)
max_duration_in_seconds: float = field(
default=20.0,
metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds."},
)
preprocessing_only: bool = field(
default=False,
metadata={
"help": (
"Whether to only do data preprocessing and skip training. This is especially useful when data"
" preprocessing errors out in distributed training due to timeout. In this case, one should run the"
" preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets"
" can consequently be loaded in distributed training"
)
},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"If :obj:`True`, will use the token generated when running"
":obj:`huggingface-cli login` as HTTP bearer authorization for remote files."
)
},
)
phoneme_language: Optional[str] = field(
default=None,
metadata={
"help": (
"The target language that should be used be"
" passed to the tokenizer for tokenization. Note that"
" this is only relevant if the model classifies the"
" input audio to a sequence of phoneme sequences."
)
},
)
@dataclass
class DataCollatorCTCWithPadding:
"""
Data collator that will dynamically pad the inputs received.
Args:
processor (:class:`~transformers.AutoProcessor`)
The processor used for proccessing the data.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
max_length_labels (:obj:`int`, `optional`):
Maximum length of the ``labels`` returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).
"""
processor: AutoProcessor
padding: Union[bool, str] = "longest"
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
pad_to_multiple_of_labels: Optional[int] = None
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lengths and need
# different padding methods
input_features = []
label_features = []
for feature in features:
if self.max_length and feature["input_values"].shape[-1] > self.max_length:
continue
input_features.append({"input_values": feature["input_values"]})
label_features.append({"input_ids": feature["labels"]})
batch = self.processor.pad(
input_features,
padding=self.padding,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors="pt",
)
labels_batch = self.processor.pad(
labels=label_features,
padding=self.padding,
pad_to_multiple_of=self.pad_to_multiple_of_labels,
return_tensors="pt",
)
# replace padding with -100 to ignore loss correctly
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
batch["labels"] = labels
return batch
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s", training_args)
# Set seed before initializing model.
set_seed(training_args.seed)
# 1. First, let's load the dataset
raw_datasets = IterableDatasetDict()
raw_column_names = {}
def load_streaming_dataset(split, sampling_rate, **kwargs):
if "+" in split:
dataset_splits = [load_dataset(split=split_name, **kwargs) for split_name in split.split("+")]
# `features` and `cast_column` won't be available after interleaving, so we'll use them here
features = dataset_splits[0].features
# make sure that the dataset decodes audio with a correct sampling rate
dataset_splits = [
dataset.cast_column(data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate))
for dataset in dataset_splits
]
interleaved_dataset = interleave_datasets(dataset_splits)
return interleaved_dataset, features
else:
dataset = load_dataset(split=split, **kwargs)
features = dataset.features
# make sure that the dataset decodes audio with a correct sampling rate
dataset = dataset.cast_column(
data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate)
)
return dataset, features
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate and normalize the input
# via the `feature_extractor`
feature_extractor = AutoFeatureExtractor.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, token=data_args.use_auth_token
)
if training_args.do_train:
raw_datasets["train"], train_features = load_streaming_dataset(
path=data_args.dataset_name,
name=data_args.dataset_config_name,
split=data_args.train_split_name,
token=data_args.use_auth_token,
streaming=True,
sampling_rate=feature_extractor.sampling_rate,
)
raw_column_names["train"] = list(train_features.keys())
if data_args.audio_column_name not in raw_column_names["train"]:
raise ValueError(
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'."
" Make sure to set `--audio_column_name` to the correct audio column - one of"
f" {', '.join(raw_column_names['train'])}."
)
if data_args.text_column_name not in raw_column_names["train"]:
raise ValueError(
f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--text_column_name` to the correct text column - one of "
f"{', '.join(raw_column_names['train'])}."
)
if data_args.max_train_samples is not None:
raw_datasets["train"] = raw_datasets["train"].take(range(data_args.max_train_samples))
if training_args.do_eval:
raw_datasets["eval"], eval_features = load_streaming_dataset(
path=data_args.dataset_name,
name=data_args.dataset_config_name,
split=data_args.eval_split_name,
token=data_args.use_auth_token,
streaming=True,
sampling_rate=feature_extractor.sampling_rate,
)
raw_column_names["eval"] = list(eval_features.keys())
if data_args.max_eval_samples is not None:
raw_datasets["eval"] = raw_datasets["eval"].take(range(data_args.max_eval_samples))
# 2. We remove some special characters from the datasets
# that make training complicated and do not help in transcribing the speech
# E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
# that could be easily picked up by the model
chars_to_ignore_regex = (
f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
)
text_column_name = data_args.text_column_name
def remove_special_characters(batch):
if chars_to_ignore_regex is not None:
batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
else:
batch["target_text"] = batch[text_column_name].lower() + " "
return batch
with training_args.main_process_first(desc="dataset map special characters removal"):
for split, dataset in raw_datasets.items():
raw_datasets[split] = dataset.map(
remove_special_characters,
).remove_columns([text_column_name])
# 3. Next, let's load the config as we might need it to create
# the tokenizer
config = AutoConfig.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, token=data_args.use_auth_token
)
# 4. Now we can instantiate the tokenizer and model
# Note for distributed training, the .from_pretrained methods guarantee that only
# one local process can concurrently download model & vocab.
tokenizer_name_or_path = model_args.tokenizer_name_or_path
if tokenizer_name_or_path is None:
raise ValueError(
"Tokenizer has to be created before training in streaming mode. Please specify --tokenizer_name_or_path"
)
# load feature_extractor and tokenizer
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_name_or_path,
config=config,
token=data_args.use_auth_token,
)
# adapt config
config.update(
{
"feat_proj_dropout": model_args.feat_proj_dropout,
"attention_dropout": model_args.attention_dropout,
"hidden_dropout": model_args.hidden_dropout,
"final_dropout": model_args.final_dropout,
"mask_time_prob": model_args.mask_time_prob,
"mask_time_length": model_args.mask_time_length,
"mask_feature_prob": model_args.mask_feature_prob,
"mask_feature_length": model_args.mask_feature_length,
"gradient_checkpointing": training_args.gradient_checkpointing,
"layerdrop": model_args.layerdrop,
"ctc_loss_reduction": model_args.ctc_loss_reduction,
"pad_token_id": tokenizer.pad_token_id,
"vocab_size": len(tokenizer),
"activation_dropout": model_args.activation_dropout,
}
)
# create model
model = AutoModelForCTC.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
config=config,
token=data_args.use_auth_token,
)
# freeze encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
# 5. Now we preprocess the datasets including loading the audio, resampling and normalization
audio_column_name = data_args.audio_column_name
# `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
phoneme_language = data_args.phoneme_language
# Preprocessing the datasets.
# We need to read the audio files as arrays and tokenize the targets.
def prepare_dataset(batch):
# load audio
sample = batch[audio_column_name]
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
batch["input_values"] = inputs.input_values[0]
batch["input_length"] = len(batch["input_values"])
# encode targets
additional_kwargs = {}
if phoneme_language is not None:
additional_kwargs["phonemizer_lang"] = phoneme_language
batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
return batch
vectorized_datasets = IterableDatasetDict()
with training_args.main_process_first(desc="dataset map preprocessing"):
for split, dataset in raw_datasets.items():
vectorized_datasets[split] = (
dataset.map(prepare_dataset)
.remove_columns(raw_column_names[split] + ["target_text"])
.with_format("torch")
)
if split == "train":
vectorized_datasets[split] = vectorized_datasets[split].shuffle(
buffer_size=data_args.shuffle_buffer_size,
seed=training_args.seed,
)
# 6. Next, we can prepare the training.
# Let's use word error rate (WER) as our evaluation metric,
# instantiate a data collator and the trainer
# Define evaluation metrics during training, *i.e.* word error rate, character error rate
eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
def compute_metrics(pred):
pred_logits = pred.predictions
pred_ids = np.argmax(pred_logits, axis=-1)
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
pred_str = tokenizer.batch_decode(pred_ids)
# we do not want to group tokens when computing the metrics
label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
return metrics
# Now save everything to be able to create a single processor later
if is_main_process(training_args.local_rank):
# save feature extractor, tokenizer and config
feature_extractor.save_pretrained(training_args.output_dir)
tokenizer.save_pretrained(training_args.output_dir)
config.save_pretrained(training_args.output_dir)
try:
processor = AutoProcessor.from_pretrained(training_args.output_dir)
except (OSError, KeyError):
warnings.warn(
"Loading a processor from a feature extractor config that does not"
" include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
" attribute to your `preprocessor_config.json` file to suppress this warning: "
" `'processor_class': 'Wav2Vec2Processor'`",
FutureWarning,
)
processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
# Instantiate custom data collator
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
data_collator = DataCollatorCTCWithPadding(processor=processor, max_length=max_input_length)
# trainer callback to reinitialize and reshuffle the streamable datasets at the beginning of each epoch
class ShuffleCallback(TrainerCallback):
def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):
if isinstance(train_dataloader.dataset, IterableDatasetShard):
pass # set_epoch() is handled by the Trainer
elif isinstance(train_dataloader.dataset, IterableDataset):
train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
# Initialize Trainer
trainer = Trainer(
model=model,
data_collator=data_collator,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
tokenizer=processor,
callbacks=[ShuffleCallback()],
)
# 7. Finally, we can start training
# Training
if training_args.do_train:
# use last checkpoint if exist
if last_checkpoint is not None:
checkpoint = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path):
checkpoint = model_args.model_name_or_path
else:
checkpoint = None
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model()
metrics = train_result.metrics
if data_args.max_train_samples:
metrics["train_samples"] = data_args.max_train_samples
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
if data_args.max_eval_samples:
metrics["eval_samples"] = data_args.max_eval_samples
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Write model card and (optionally) push to hub
config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
kwargs = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "automatic-speech-recognition",
"tags": ["automatic-speech-recognition", data_args.dataset_name],
"dataset_args": (
f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:"
f" {data_args.eval_split_name}"
),
"dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
}
if "common_voice" in data_args.dataset_name:
kwargs["language"] = config_name
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
return results
if __name__ == "__main__":
main()
| transformers/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_streaming.py/0 | {
"file_path": "transformers/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_streaming.py",
"repo_id": "transformers",
"token_count": 11352
} |
#!/usr/bin/env bash
export PYTHONPATH="../":"${PYTHONPATH}"
export WANDB_PROJECT=dmar
export MAX_LEN=128
export m=sshleifer/student_marian_en_ro_6_1
python finetune.py \
--learning_rate=3e-4 \
--do_train \
--fp16 \
--data_dir wmt_en_ro \
--max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
--freeze_encoder --freeze_embeds \
--train_batch_size=48 --eval_batch_size=64 \
--tokenizer_name $m --model_name_or_path $m --num_train_epochs=1 \
--warmup_steps 500 --logger_name wandb --gpus 1 \
--fp16_opt_level=O1 --task translation \
"$@"
| transformers/examples/research_projects/seq2seq-distillation/dynamic_bs_example.sh/0 | {
"file_path": "transformers/examples/research_projects/seq2seq-distillation/dynamic_bs_example.sh",
"repo_id": "transformers",
"token_count": 267
} |
# SynthID Text
This project showcases the use of SynthIDText for watermarking LLMs. The code shown in this repo also
demostrates the training of the detector for detecting such watermarked text. This detector can be uploaded onto
a private HF hub repo (private for security reasons) and can be initialized again through pretrained model loading also shown in this script.
See our blog post: https://huggingface.co/blog/synthid-text
## Python version
User would need python 3.9 to run this example.
## Installation and running
Once you install transformers you would need to install requirements for this project through requirements.txt provided in this folder.
```
pip install -r requirements.txt
```
## To run the detector training
```
python detector_training.py --model_name=google/gemma-7b-it
```
Check the script for more parameters are are tunable and check out paper at link
https://www.nature.com/articles/s41586-024-08025-4 for more information on these parameters.
## Caveat
Make sure to run the training of the detector and the detection on the same hardware
CPU, GPU or TPU to get consistent results (we use detecterministic randomness which is hardware dependent).
| transformers/examples/research_projects/synthid_text/README.md/0 | {
"file_path": "transformers/examples/research_projects/synthid_text/README.md",
"repo_id": "transformers",
"token_count": 300
} |
"""
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.import copy
"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class ResizeShortestEdge:
def __init__(self, short_edge_length, max_size=sys.maxsize):
"""
Args:
short_edge_length (list[min, max])
max_size (int): maximum allowed longest edge length.
"""
self.interp_method = "bilinear"
self.max_size = max_size
self.short_edge_length = short_edge_length
def __call__(self, imgs):
img_augs = []
for img in imgs:
h, w = img.shape[:2]
# later: provide list and randomly choose index for resize
size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1)
if size == 0:
return img
scale = size * 1.0 / min(h, w)
if h < w:
newh, neww = size, scale * w
else:
newh, neww = scale * h, size
if max(newh, neww) > self.max_size:
scale = self.max_size * 1.0 / max(newh, neww)
newh = newh * scale
neww = neww * scale
neww = int(neww + 0.5)
newh = int(newh + 0.5)
if img.dtype == np.uint8:
pil_image = Image.fromarray(img)
pil_image = pil_image.resize((neww, newh), PILImageResampling.BILINEAR)
img = np.asarray(pil_image)
else:
img = img.permute(2, 0, 1).unsqueeze(0) # 3, 0, 1) # hw(c) -> nchw
img = nn.functional.interpolate(
img, (newh, neww), mode=self.interp_method, align_corners=False
).squeeze(0)
img_augs.append(img)
return img_augs
class Preprocess:
def __init__(self, cfg):
self.aug = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST)
self.input_format = cfg.INPUT.FORMAT
self.size_divisibility = cfg.SIZE_DIVISIBILITY
self.pad_value = cfg.PAD_VALUE
self.max_image_size = cfg.INPUT.MAX_SIZE_TEST
self.device = cfg.MODEL.DEVICE
self.pixel_std = torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD), 1, 1)
self.pixel_mean = torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD), 1, 1)
self.normalizer = lambda x: (x - self.pixel_mean) / self.pixel_std
def pad(self, images):
max_size = tuple(max(s) for s in zip(*[img.shape for img in images]))
image_sizes = [im.shape[-2:] for im in images]
images = [
nn.functional.pad(
im,
[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],
value=self.pad_value,
)
for size, im in zip(image_sizes, images)
]
return torch.stack(images), torch.tensor(image_sizes)
def __call__(self, images, single_image=False):
with torch.no_grad():
if not isinstance(images, list):
images = [images]
if single_image:
assert len(images) == 1
for i in range(len(images)):
if isinstance(images[i], torch.Tensor):
images.insert(i, images.pop(i).to(self.device).float())
elif not isinstance(images[i], torch.Tensor):
images.insert(
i,
torch.as_tensor(img_tensorize(images.pop(i), input_format=self.input_format))
.to(self.device)
.float(),
)
# resize smallest edge
raw_sizes = torch.tensor([im.shape[:2] for im in images])
images = self.aug(images)
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
images = [self.normalizer(x) for x in images]
# now pad them to do the following operations
images, sizes = self.pad(images)
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
scales_yx = torch.true_divide(raw_sizes, sizes)
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def _scale_box(boxes, scale_yx):
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def _clip_box(tensor, box_size: Tuple[int, int]):
assert torch.isfinite(tensor).all(), "Box tensor contains infinite or NaN!"
h, w = box_size
tensor[:, 0].clamp_(min=0, max=w)
tensor[:, 1].clamp_(min=0, max=h)
tensor[:, 2].clamp_(min=0, max=w)
tensor[:, 3].clamp_(min=0, max=h)
| transformers/examples/research_projects/visual_bert/processing_image.py/0 | {
"file_path": "transformers/examples/research_projects/visual_bert/processing_image.py",
"repo_id": "transformers",
"token_count": 2801
} |
#!/usr/bin/env bash
python run_asr.py \
--output_dir="./wav2vec2-base-timit-asr" \
--num_train_epochs="30" \
--per_device_train_batch_size="20" \
--per_device_eval_batch_size="20" \
--eval_strategy="steps" \
--save_steps="500" \
--eval_steps="100" \
--logging_steps="50" \
--learning_rate="5e-4" \
--warmup_steps="3000" \
--model_name_or_path="facebook/wav2vec2-base" \
--fp16 \
--dataset_name="timit_asr" \
--train_split_name="train" \
--validation_split_name="test" \
--orthography="timit" \
--preprocessing_num_workers="$(nproc)" \
--group_by_length \
--freeze_feature_extractor \
--verbose_logging \
| transformers/examples/research_projects/wav2vec2/finetune_base_timit_asr.sh/0 | {
"file_path": "transformers/examples/research_projects/wav2vec2/finetune_base_timit_asr.sh",
"repo_id": "transformers",
"token_count": 256
} |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional
import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
DESCRIPTION = """
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
"""
logger = logging.getLogger(__name__)
@dataclass
class TeacherModelArguments:
teacher_name_or_path: Optional[str] = field(
default="roberta-large-mnli", metadata={"help": "The NLI/zero-shot teacher model to be distilled."}
)
hypothesis_template: Optional[str] = field(
default="This example is {}.",
metadata={
"help": (
"Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}} "
"where class name is inserted."
)
},
)
teacher_batch_size: Optional[int] = field(
default=32, metadata={"help": "Batch size for generating teacher predictions."}
)
multi_label: Optional[bool] = field(
default=False,
metadata={
"help": (
"Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called "
"multi-class multi-label classification)."
)
},
)
temperature: Optional[float] = field(
default=1.0, metadata={"help": "Temperature applied to teacher softmax for distillation."}
)
@dataclass
class StudentModelArguments:
student_name_or_path: Optional[str] = field(
default="distilbert-base-uncased", metadata={"help": "The NLI/zero-shot teacher model to be distilled."}
)
@dataclass
class DataTrainingArguments:
data_file: str = field(metadata={"help": "Text file with one unlabeled instance per line."})
class_names_file: str = field(metadata={"help": "Text file with one class name per line."})
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."},
)
@dataclass
class DistillTrainingArguments(TrainingArguments):
output_dir: Optional[str] = field(
default=None,
metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
)
per_device_train_batch_size: int = field(
default=32, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=128, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
)
num_train_epochs: float = field(default=1.0, metadata={"help": "Total number of training epochs to perform."})
do_train: bool = field(default=True, metadata={"help": "Whether to run training of student model."})
do_eval: bool = field(
default=True,
metadata={
"help": (
"Whether to evaluate the agreement of the final student predictions and the teacher predictions "
"after training."
)
},
)
save_total_limit: Optional[int] = field(
default=0,
metadata={
"help": (
"Limit the total amount of checkpoints. "
"Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."
)
},
)
class DistillationTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
target_p = inputs["labels"]
outputs = model(inputs["input_ids"], attention_mask=inputs["attention_mask"])
logits = outputs[0]
loss = -torch.sum(target_p * logits.log_softmax(dim=-1), axis=-1).mean()
if return_outputs:
return loss, outputs
return loss
def read_lines(path):
lines = []
with open(path, "r") as f:
for line in f:
line = line.strip()
if len(line) > 0:
lines.append(line)
return lines
def get_premise_hypothesis_pairs(examples, class_names, hypothesis_template):
premises = []
hypotheses = []
for example in examples:
for name in class_names:
premises.append(example)
hypotheses.append(hypothesis_template.format(name))
return premises, hypotheses
def get_entailment_id(config):
for label, ind in config.label2id.items():
if label.lower().startswith("entail"):
return ind
logger.warning("Could not identify entailment dimension from teacher config label2id. Setting to -1.")
return -1
def get_teacher_predictions(
model_path: str,
examples: List[str],
class_names: List[str],
hypothesis_template: str,
batch_size: int,
temperature: float,
multi_label: bool,
use_fast_tokenizer: bool,
no_cuda: bool,
fp16: bool,
):
"""
Gets predictions by the same method as the zero-shot pipeline but with DataParallel & more efficient batching
"""
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model_config = model.config
if not no_cuda and torch.cuda.is_available():
model = nn.DataParallel(model.cuda())
batch_size *= len(model.device_ids)
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=use_fast_tokenizer)
premises, hypotheses = get_premise_hypothesis_pairs(examples, class_names, hypothesis_template)
logits = []
for i in tqdm(range(0, len(premises), batch_size)):
batch_premises = premises[i : i + batch_size]
batch_hypotheses = hypotheses[i : i + batch_size]
encodings = tokenizer(
batch_premises,
batch_hypotheses,
padding=True,
truncation="only_first",
return_tensors="pt",
)
with torch.cuda.amp.autocast(enabled=fp16):
with torch.no_grad():
outputs = model(**encodings)
logits.append(outputs.logits.detach().cpu().float())
entail_id = get_entailment_id(model_config)
contr_id = -1 if entail_id == 0 else 0
logits = torch.cat(logits, dim=0) # N*K x 3
nli_logits = logits.reshape(len(examples), len(class_names), -1)[..., [contr_id, entail_id]] # N x K x 2
if multi_label:
# softmax over (contr, entail) logits for each class independently
nli_prob = (nli_logits / temperature).softmax(-1)
else:
# softmax over entail logits across classes s.t. class probabilities sum to 1.
nli_prob = (nli_logits / temperature).softmax(1)
return nli_prob[..., 1] # N x K
def main():
parser = HfArgumentParser(
(DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments),
description=DESCRIPTION,
)
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
data_args, teacher_args, student_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1])
)
else:
data_args, teacher_args, student_args, training_args = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
utils.logging.set_verbosity_info()
utils.logging.enable_default_handler()
utils.logging.enable_explicit_format()
if training_args.local_rank != -1:
raise ValueError("Distributed training is not currently supported.")
if training_args.tpu_num_cores is not None:
raise ValueError("TPU acceleration is not currently supported.")
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# 1. read in data
examples = read_lines(data_args.data_file)
class_names = read_lines(data_args.class_names_file)
# 2. get teacher predictions and load into dataset
logger.info("Generating predictions from zero-shot teacher model")
teacher_soft_preds = get_teacher_predictions(
teacher_args.teacher_name_or_path,
examples,
class_names,
teacher_args.hypothesis_template,
teacher_args.teacher_batch_size,
teacher_args.temperature,
teacher_args.multi_label,
data_args.use_fast_tokenizer,
training_args.no_cuda,
training_args.fp16,
)
dataset = Dataset.from_dict(
{
"text": examples,
"labels": teacher_soft_preds,
}
)
# 3. create student
logger.info("Initializing student model")
model = AutoModelForSequenceClassification.from_pretrained(
student_args.student_name_or_path, num_labels=len(class_names)
)
tokenizer = AutoTokenizer.from_pretrained(student_args.student_name_or_path, use_fast=data_args.use_fast_tokenizer)
model.config.id2label = dict(enumerate(class_names))
model.config.label2id = {label: i for i, label in enumerate(class_names)}
# 4. train student on teacher predictions
dataset = dataset.map(tokenizer, input_columns="text")
dataset.set_format("torch")
def compute_metrics(p, return_outputs=False):
preds = p.predictions.argmax(-1)
proxy_labels = p.label_ids.argmax(-1) # "label_ids" are actually distributions
return {"agreement": (preds == proxy_labels).mean().item()}
trainer = DistillationTrainer(
model=model,
tokenizer=tokenizer,
args=training_args,
train_dataset=dataset,
compute_metrics=compute_metrics,
)
if training_args.do_train:
logger.info("Training student model on teacher predictions")
trainer.train()
if training_args.do_eval:
agreement = trainer.evaluate(eval_dataset=dataset)["eval_agreement"]
logger.info(f"Agreement of student and teacher predictions: {agreement * 100:0.2f}%")
trainer.save_model()
if __name__ == "__main__":
main()
| transformers/examples/research_projects/zero-shot-distillation/distill_classifier.py/0 | {
"file_path": "transformers/examples/research_projects/zero-shot-distillation/distill_classifier.py",
"repo_id": "transformers",
"token_count": 4815
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning the library models for sequence classification on GLUE."""
# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
import json
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import tensorflow as tf
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorWithPadding,
DefaultDataCollator,
HfArgumentParser,
PretrainedConfig,
PushToHubCallback,
TFAutoModelForSequenceClassification,
TFTrainingArguments,
create_optimizer,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.49.0.dev0")
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
logger = logging.getLogger(__name__)
# region Command-line arguments
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
task_name: str = field(
metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
)
predict_file: str = field(
metadata={"help": "A file containing user-supplied examples to make predictions for"},
default=None,
)
max_seq_length: int = field(
default=128,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=False,
metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
},
)
def __post_init__(self):
self.task_name = self.task_name.lower()
if self.task_name not in task_to_keys.keys():
raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
)
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
"execute code present on the Hub on your local machine."
)
},
)
# endregion
def main():
# region Argument parsing
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_glue", model_args, data_args, framework="tensorflow")
if not (training_args.do_train or training_args.do_eval or training_args.do_predict):
exit("Must specify at least one of --do_train, --do_eval or --do_predict!")
# endregion
# region Checkpoints
checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
checkpoint = get_last_checkpoint(training_args.output_dir)
if checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# endregion
# region Logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info(f"Training/evaluation parameters {training_args}")
# endregion
# region Dataset and labels
# Set seed before initializing model.
set_seed(training_args.seed)
# Downloading and loading a dataset from the hub. In distributed training, the load_dataset function guarantee
# that only one local process can concurrently download the dataset.
datasets = load_dataset(
"nyu-mll/glue",
data_args.task_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.
is_regression = data_args.task_name == "stsb"
if not is_regression:
label_list = datasets["train"].features["label"].names
num_labels = len(label_list)
else:
num_labels = 1
if data_args.predict_file is not None:
logger.info("Preparing user-supplied file for predictions...")
data_files = {"data": data_args.predict_file}
for key in data_files.keys():
logger.info(f"Loading a local file for {key}: {data_files[key]}")
if data_args.predict_file.endswith(".csv"):
# Loading a dataset from local csv files
user_dataset = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir)
else:
# Loading a dataset from local json files
user_dataset = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir)
needed_keys = task_to_keys[data_args.task_name]
for key in needed_keys:
assert key in user_dataset["data"].features, f"Your supplied predict_file is missing the {key} key!"
datasets["user_data"] = user_dataset["data"]
# endregion
# region Load model config and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
# endregion
# region Dataset preprocessing
sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
label_to_id = None
if config.label2id != PretrainedConfig(num_labels=num_labels).label2id and not is_regression:
# Some have all caps in their config, some don't.
label_name_to_id = {k.lower(): v for k, v in config.label2id.items()}
if sorted(label_name_to_id.keys()) == sorted(label_list):
label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}
else:
logger.warning(
"Your model seems to have been trained with labels, but they don't match the dataset: "
f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}."
"\nIgnoring the model labels as a result.",
)
label_to_id = {label: i for i, label in enumerate(label_list)}
if label_to_id is not None:
config.label2id = label_to_id
config.id2label = {id: label for label, id in config.label2id.items()}
elif data_args.task_name is not None and not is_regression:
config.label2id = {l: i for i, l in enumerate(label_list)}
config.id2label = {id: label for label, id in config.label2id.items()}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
)
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
def preprocess_function(examples):
# Tokenize the texts
args = (
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
)
result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)
return result
datasets = datasets.map(preprocess_function, batched=True, load_from_cache_file=not data_args.overwrite_cache)
if data_args.pad_to_max_length:
data_collator = DefaultDataCollator(return_tensors="np")
else:
data_collator = DataCollatorWithPadding(tokenizer, return_tensors="np")
# endregion
# region Metric function
metric = evaluate.load("glue", data_args.task_name, cache_dir=model_args.cache_dir)
def compute_metrics(preds, label_ids):
preds = preds["logits"]
preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
result = metric.compute(predictions=preds, references=label_ids)
if len(result) > 1:
result["combined_score"] = np.mean(list(result.values())).item()
return result
# endregion
with training_args.strategy.scope():
# region Load pretrained model
if checkpoint is None:
model_path = model_args.model_name_or_path
else:
model_path = checkpoint
model = TFAutoModelForSequenceClassification.from_pretrained(
model_path,
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
# endregion
# region Convert data to a tf.data.Dataset
dataset_options = tf.data.Options()
dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
num_replicas = training_args.strategy.num_replicas_in_sync
tf_data = {}
max_samples = {
"train": data_args.max_train_samples,
"validation": data_args.max_eval_samples,
"validation_matched": data_args.max_eval_samples,
"validation_mismatched": data_args.max_eval_samples,
"test": data_args.max_predict_samples,
"test_matched": data_args.max_predict_samples,
"test_mismatched": data_args.max_predict_samples,
"user_data": None,
}
for key in datasets.keys():
if key == "train" or key.startswith("validation"):
assert "label" in datasets[key].features, f"Missing labels from {key} data!"
if key == "train":
shuffle = True
batch_size = training_args.per_device_train_batch_size * num_replicas
else:
shuffle = False
batch_size = training_args.per_device_eval_batch_size * num_replicas
samples_limit = max_samples[key]
dataset = datasets[key]
if samples_limit is not None:
dataset = dataset.select(range(samples_limit))
# model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in
# training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also
# use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names
# yourself if you use this method, whereas they are automatically inferred from the model input names when
# using model.prepare_tf_dataset()
# For more info see the docs:
# https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset
# https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset
data = model.prepare_tf_dataset(
dataset,
shuffle=shuffle,
batch_size=batch_size,
collate_fn=data_collator,
tokenizer=tokenizer,
)
data = data.with_options(dataset_options)
tf_data[key] = data
# endregion
# region Optimizer, loss and compilation
if training_args.do_train:
num_train_steps = len(tf_data["train"]) * training_args.num_train_epochs
if training_args.warmup_steps > 0:
num_warmup_steps = training_args.warmup_steps
elif training_args.warmup_ratio > 0:
num_warmup_steps = int(num_train_steps * training_args.warmup_ratio)
else:
num_warmup_steps = 0
optimizer, schedule = create_optimizer(
init_lr=training_args.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
adam_beta1=training_args.adam_beta1,
adam_beta2=training_args.adam_beta2,
adam_epsilon=training_args.adam_epsilon,
weight_decay_rate=training_args.weight_decay,
adam_global_clipnorm=training_args.max_grad_norm,
)
else:
optimizer = "sgd" # Just write anything because we won't be using it
if is_regression:
metrics = []
else:
metrics = ["accuracy"]
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=optimizer, metrics=metrics, jit_compile=training_args.xla)
# endregion
# region Preparing push_to_hub and model card
push_to_hub_model_id = training_args.push_to_hub_model_id
model_name = model_args.model_name_or_path.split("/")[-1]
if not push_to_hub_model_id:
push_to_hub_model_id = f"{model_name}-finetuned-glue"
model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
model_card_kwargs["task_name"] = data_args.task_name
if training_args.push_to_hub:
callbacks = [
PushToHubCallback(
output_dir=training_args.output_dir,
hub_model_id=push_to_hub_model_id,
hub_token=training_args.push_to_hub_token,
tokenizer=tokenizer,
**model_card_kwargs,
)
]
else:
callbacks = []
# endregion
# region Training and validation
if training_args.do_train:
if training_args.do_eval and not data_args.task_name == "mnli":
# Do both evaluation and training in the Keras fit loop, unless the task is MNLI
# because MNLI has two validation sets
validation_data = tf_data["validation"]
else:
validation_data = None
model.fit(
tf_data["train"],
validation_data=validation_data,
epochs=int(training_args.num_train_epochs),
callbacks=callbacks,
)
# endregion
# region Evaluation
if training_args.do_eval:
# We normally do validation as part of the Keras fit loop, but we run it independently
# if there was no fit() step (because we didn't train the model) or if the task is MNLI,
# because MNLI has a separate validation-mismatched validation set
# In this example, we compute advanced metrics only at the end of training, and only compute
# loss and accuracy on the validation set each epoch, but
# if you'd like to compute metrics every epoch that are too complex to be written as
# standard Keras metrics, you can use our KerasMetricCallback. See
# https://huggingface.co/docs/transformers/main/en/main_classes/keras_callbacks
logger.info("*** Evaluate ***")
# Loop to handle MNLI double evaluation (matched, mis-matched)
if data_args.task_name == "mnli":
tasks = ["mnli", "mnli-mm"]
tf_datasets = [tf_data["validation_matched"], tf_data["validation_mismatched"]]
raw_datasets = [datasets["validation_matched"], datasets["validation_mismatched"]]
else:
tasks = [data_args.task_name]
tf_datasets = [tf_data["validation"]]
raw_datasets = [datasets["validation"]]
for raw_dataset, tf_dataset, task in zip(raw_datasets, tf_datasets, tasks):
eval_predictions = model.predict(tf_dataset)
eval_metrics = compute_metrics(eval_predictions, raw_dataset["label"])
print(f"Evaluation metrics ({task}):")
print(eval_metrics)
if training_args.output_dir is not None:
output_eval_file = os.path.join(training_args.output_dir, "all_results.json")
with open(output_eval_file, "w") as writer:
writer.write(json.dumps(eval_metrics))
# endregion
# region Prediction
if training_args.do_predict or data_args.predict_file:
logger.info("*** Predict ***")
# Loop to handle MNLI double evaluation (matched, mis-matched)
tasks = []
tf_datasets = []
raw_datasets = []
if training_args.do_predict:
if data_args.task_name == "mnli":
tasks.extend(["mnli", "mnli-mm"])
tf_datasets.extend([tf_data["test_matched"], tf_data["test_mismatched"]])
raw_datasets.extend([datasets["test_matched"], datasets["test_mismatched"]])
else:
tasks.append(data_args.task_name)
tf_datasets.append(tf_data["test"])
raw_datasets.append(datasets["test"])
if data_args.predict_file:
tasks.append("user_data")
tf_datasets.append(tf_data["user_data"])
raw_datasets.append(datasets["user_data"])
for raw_dataset, tf_dataset, task in zip(raw_datasets, tf_datasets, tasks):
test_predictions = model.predict(tf_dataset)
if "label" in raw_dataset:
test_metrics = compute_metrics(test_predictions, raw_dataset["label"])
print(f"Test metrics ({task}):")
print(test_metrics)
if is_regression:
predictions_to_write = np.squeeze(test_predictions["logits"])
else:
predictions_to_write = np.argmax(test_predictions["logits"], axis=1)
output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
with open(output_predict_file, "w") as writer:
logger.info(f"***** Writing prediction results for {task} *****")
writer.write("index\tprediction\n")
for index, item in enumerate(predictions_to_write):
if is_regression:
writer.write(f"{index}\t{item:3.3f}\n")
else:
item = model.config.id2label[item]
writer.write(f"{index}\t{item}\n")
# endregion
if training_args.output_dir is not None and not training_args.push_to_hub:
# If we're not pushing to hub, at least save a local copy when we're done
model.save_pretrained(training_args.output_dir)
if __name__ == "__main__":
main()
| transformers/examples/tensorflow/text-classification/run_glue.py/0 | {
"file_path": "transformers/examples/tensorflow/text-classification/run_glue.py",
"repo_id": "transformers",
"token_count": 11104
} |
#!/usr/bin/env bash
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script acquires data and converts it to fsmt model
# it covers:
# - facebook/wmt19-ru-en
# - facebook/wmt19-en-ru
# - facebook/wmt19-de-en
# - facebook/wmt19-en-de
# this script needs to be run from the top level of the transformers repo
if [ ! -d "src/transformers" ]; then
echo "Error: This script needs to be run from the top of the transformers repo"
exit 1
fi
mkdir data
# get data (run once)
cd data
wget https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz
wget https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz
wget https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz
wget https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz
tar -xvzf wmt19.en-de.joined-dict.ensemble.tar.gz
tar -xvzf wmt19.de-en.joined-dict.ensemble.tar.gz
tar -xvzf wmt19.en-ru.ensemble.tar.gz
tar -xvzf wmt19.ru-en.ensemble.tar.gz
cd -
# run conversions and uploads
export PAIR=ru-en
PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/wmt19.$PAIR.ensemble/model4.pt --pytorch_dump_folder_path data/wmt19-$PAIR
export PAIR=en-ru
PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/wmt19.$PAIR.ensemble/model4.pt --pytorch_dump_folder_path data/wmt19-$PAIR
export PAIR=de-en
PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/wmt19.$PAIR.joined-dict.ensemble/model4.pt --pytorch_dump_folder_path data/wmt19-$PAIR
export PAIR=en-de
PYTHONPATH="src" python src/transformers/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py --fsmt_checkpoint_path data/wmt19.$PAIR.joined-dict.ensemble/model4.pt --pytorch_dump_folder_path data/wmt19-$PAIR
# upload
cd data
transformers-cli upload -y wmt19-ru-en
transformers-cli upload -y wmt19-en-ru
transformers-cli upload -y wmt19-de-en
transformers-cli upload -y wmt19-en-de
cd -
# if updating just small files and not the large models, here is a script to generate the right commands:
perl -le 'for $f (@ARGV) { print qq[transformers-cli upload -y $_/$f --filename $_/$f] for map { "wmt19-$_" } ("en-ru", "ru-en", "de-en", "en-de")}' vocab-src.json vocab-tgt.json tokenizer_config.json config.json
# add/remove files as needed
| transformers/scripts/fsmt/convert-facebook-wmt19.sh/0 | {
"file_path": "transformers/scripts/fsmt/convert-facebook-wmt19.sh",
"repo_id": "transformers",
"token_count": 1121
} |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# When adding a new object to this init, remember to add it twice: once inside the `_import_structure` dictionary and
# once inside the `if TYPE_CHECKING` branch. The `TYPE_CHECKING` should have import statements as usual, but they are
# only there for type checking. The `_import_structure` is a dictionary submodule to list of object names, and is used
# to defer the actual importing for when the objects are requested. This way `import transformers` provides the names
# in the namespace without actually importing anything (and especially none of the backends).
__version__ = "4.49.0.dev0"
from typing import TYPE_CHECKING
# Check the dependencies satisfy the minimal versions required.
from . import dependency_versions_check
from .utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_bitsandbytes_available,
is_essentia_available,
is_flax_available,
is_g2p_en_available,
is_keras_nlp_available,
is_librosa_available,
is_pretty_midi_available,
is_scipy_available,
is_sentencepiece_available,
is_speech_available,
is_tensorflow_text_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torchaudio_available,
is_torchvision_available,
is_vision_available,
logging,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Base objects, independent of any specific backend
_import_structure = {
"agents": [
"Agent",
"CodeAgent",
"HfApiEngine",
"ManagedAgent",
"PipelineTool",
"ReactAgent",
"ReactCodeAgent",
"ReactJsonAgent",
"Tool",
"Toolbox",
"ToolCollection",
"TransformersEngine",
"launch_gradio_demo",
"load_tool",
"stream_to_gradio",
"tool",
],
"audio_utils": [],
"commands": [],
"configuration_utils": ["PretrainedConfig"],
"convert_graph_to_onnx": [],
"convert_slow_tokenizers_checkpoints_to_fast": [],
"convert_tf_hub_seq_to_seq_bert_to_pytorch": [],
"data": [
"DataProcessor",
"InputExample",
"InputFeatures",
"SingleSentenceClassificationProcessor",
"SquadExample",
"SquadFeatures",
"SquadV1Processor",
"SquadV2Processor",
"glue_compute_metrics",
"glue_convert_examples_to_features",
"glue_output_modes",
"glue_processors",
"glue_tasks_num_labels",
"squad_convert_examples_to_features",
"xnli_compute_metrics",
"xnli_output_modes",
"xnli_processors",
"xnli_tasks_num_labels",
],
"data.data_collator": [
"DataCollator",
"DataCollatorForLanguageModeling",
"DataCollatorForPermutationLanguageModeling",
"DataCollatorForSeq2Seq",
"DataCollatorForSOP",
"DataCollatorForTokenClassification",
"DataCollatorForWholeWordMask",
"DataCollatorWithFlattening",
"DataCollatorWithPadding",
"DefaultDataCollator",
"default_data_collator",
],
"data.metrics": [],
"data.processors": [],
"debug_utils": [],
"dependency_versions_check": [],
"dependency_versions_table": [],
"dynamic_module_utils": [],
"feature_extraction_sequence_utils": ["SequenceFeatureExtractor"],
"feature_extraction_utils": ["BatchFeature", "FeatureExtractionMixin"],
"file_utils": [],
"generation": [
"AsyncTextIteratorStreamer",
"CompileConfig",
"GenerationConfig",
"TextIteratorStreamer",
"TextStreamer",
"WatermarkingConfig",
],
"hf_argparser": ["HfArgumentParser"],
"hyperparameter_search": [],
"image_transforms": [],
"integrations": [
"is_clearml_available",
"is_comet_available",
"is_dvclive_available",
"is_neptune_available",
"is_optuna_available",
"is_ray_available",
"is_ray_tune_available",
"is_sigopt_available",
"is_tensorboard_available",
"is_wandb_available",
],
"loss": [],
"modelcard": ["ModelCard"],
# Losses
"modeling_tf_pytorch_utils": [
"convert_tf_weight_name_to_pt_weight_name",
"load_pytorch_checkpoint_in_tf2_model",
"load_pytorch_model_in_tf2_model",
"load_pytorch_weights_in_tf2_model",
"load_tf2_checkpoint_in_pytorch_model",
"load_tf2_model_in_pytorch_model",
"load_tf2_weights_in_pytorch_model",
],
# Models
"models": [],
"models.albert": ["AlbertConfig"],
"models.align": [
"AlignConfig",
"AlignProcessor",
"AlignTextConfig",
"AlignVisionConfig",
],
"models.altclip": [
"AltCLIPConfig",
"AltCLIPProcessor",
"AltCLIPTextConfig",
"AltCLIPVisionConfig",
],
"models.aria": [
"AriaConfig",
"AriaProcessor",
"AriaTextConfig",
],
"models.audio_spectrogram_transformer": [
"ASTConfig",
"ASTFeatureExtractor",
],
"models.auto": [
"CONFIG_MAPPING",
"FEATURE_EXTRACTOR_MAPPING",
"IMAGE_PROCESSOR_MAPPING",
"MODEL_NAMES_MAPPING",
"PROCESSOR_MAPPING",
"TOKENIZER_MAPPING",
"AutoConfig",
"AutoFeatureExtractor",
"AutoImageProcessor",
"AutoProcessor",
"AutoTokenizer",
],
"models.autoformer": ["AutoformerConfig"],
"models.bamba": ["BambaConfig"],
"models.bark": [
"BarkCoarseConfig",
"BarkConfig",
"BarkFineConfig",
"BarkProcessor",
"BarkSemanticConfig",
],
"models.bart": ["BartConfig", "BartTokenizer"],
"models.barthez": [],
"models.bartpho": [],
"models.beit": ["BeitConfig"],
"models.bert": [
"BasicTokenizer",
"BertConfig",
"BertTokenizer",
"WordpieceTokenizer",
],
"models.bert_generation": ["BertGenerationConfig"],
"models.bert_japanese": [
"BertJapaneseTokenizer",
"CharacterTokenizer",
"MecabTokenizer",
],
"models.bertweet": ["BertweetTokenizer"],
"models.big_bird": ["BigBirdConfig"],
"models.bigbird_pegasus": ["BigBirdPegasusConfig"],
"models.biogpt": [
"BioGptConfig",
"BioGptTokenizer",
],
"models.bit": ["BitConfig"],
"models.blenderbot": [
"BlenderbotConfig",
"BlenderbotTokenizer",
],
"models.blenderbot_small": [
"BlenderbotSmallConfig",
"BlenderbotSmallTokenizer",
],
"models.blip": [
"BlipConfig",
"BlipProcessor",
"BlipTextConfig",
"BlipVisionConfig",
],
"models.blip_2": [
"Blip2Config",
"Blip2Processor",
"Blip2QFormerConfig",
"Blip2VisionConfig",
],
"models.bloom": ["BloomConfig"],
"models.bridgetower": [
"BridgeTowerConfig",
"BridgeTowerProcessor",
"BridgeTowerTextConfig",
"BridgeTowerVisionConfig",
],
"models.bros": [
"BrosConfig",
"BrosProcessor",
],
"models.byt5": ["ByT5Tokenizer"],
"models.camembert": ["CamembertConfig"],
"models.canine": [
"CanineConfig",
"CanineTokenizer",
],
"models.chameleon": [
"ChameleonConfig",
"ChameleonProcessor",
"ChameleonVQVAEConfig",
],
"models.chinese_clip": [
"ChineseCLIPConfig",
"ChineseCLIPProcessor",
"ChineseCLIPTextConfig",
"ChineseCLIPVisionConfig",
],
"models.clap": [
"ClapAudioConfig",
"ClapConfig",
"ClapProcessor",
"ClapTextConfig",
],
"models.clip": [
"CLIPConfig",
"CLIPProcessor",
"CLIPTextConfig",
"CLIPTokenizer",
"CLIPVisionConfig",
],
"models.clipseg": [
"CLIPSegConfig",
"CLIPSegProcessor",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
"models.clvp": [
"ClvpConfig",
"ClvpDecoderConfig",
"ClvpEncoderConfig",
"ClvpFeatureExtractor",
"ClvpProcessor",
"ClvpTokenizer",
],
"models.code_llama": [],
"models.codegen": [
"CodeGenConfig",
"CodeGenTokenizer",
],
"models.cohere": ["CohereConfig"],
"models.cohere2": ["Cohere2Config"],
"models.colpali": [
"ColPaliConfig",
"ColPaliProcessor",
],
"models.conditional_detr": ["ConditionalDetrConfig"],
"models.convbert": [
"ConvBertConfig",
"ConvBertTokenizer",
],
"models.convnext": ["ConvNextConfig"],
"models.convnextv2": ["ConvNextV2Config"],
"models.cpm": [],
"models.cpmant": [
"CpmAntConfig",
"CpmAntTokenizer",
],
"models.ctrl": [
"CTRLConfig",
"CTRLTokenizer",
],
"models.cvt": ["CvtConfig"],
"models.dab_detr": ["DabDetrConfig"],
"models.dac": ["DacConfig", "DacFeatureExtractor"],
"models.data2vec": [
"Data2VecAudioConfig",
"Data2VecTextConfig",
"Data2VecVisionConfig",
],
"models.dbrx": ["DbrxConfig"],
"models.deberta": [
"DebertaConfig",
"DebertaTokenizer",
],
"models.deberta_v2": ["DebertaV2Config"],
"models.decision_transformer": ["DecisionTransformerConfig"],
"models.deformable_detr": ["DeformableDetrConfig"],
"models.deit": ["DeiTConfig"],
"models.deprecated": [],
"models.deprecated.bort": [],
"models.deprecated.deta": ["DetaConfig"],
"models.deprecated.efficientformer": ["EfficientFormerConfig"],
"models.deprecated.ernie_m": ["ErnieMConfig"],
"models.deprecated.gptsan_japanese": [
"GPTSanJapaneseConfig",
"GPTSanJapaneseTokenizer",
],
"models.deprecated.graphormer": ["GraphormerConfig"],
"models.deprecated.jukebox": [
"JukeboxConfig",
"JukeboxPriorConfig",
"JukeboxTokenizer",
"JukeboxVQVAEConfig",
],
"models.deprecated.mctct": [
"MCTCTConfig",
"MCTCTFeatureExtractor",
"MCTCTProcessor",
],
"models.deprecated.mega": ["MegaConfig"],
"models.deprecated.mmbt": ["MMBTConfig"],
"models.deprecated.nat": ["NatConfig"],
"models.deprecated.nezha": ["NezhaConfig"],
"models.deprecated.open_llama": ["OpenLlamaConfig"],
"models.deprecated.qdqbert": ["QDQBertConfig"],
"models.deprecated.realm": [
"RealmConfig",
"RealmTokenizer",
],
"models.deprecated.retribert": [
"RetriBertConfig",
"RetriBertTokenizer",
],
"models.deprecated.speech_to_text_2": [
"Speech2Text2Config",
"Speech2Text2Processor",
"Speech2Text2Tokenizer",
],
"models.deprecated.tapex": ["TapexTokenizer"],
"models.deprecated.trajectory_transformer": ["TrajectoryTransformerConfig"],
"models.deprecated.transfo_xl": [
"TransfoXLConfig",
"TransfoXLCorpus",
"TransfoXLTokenizer",
],
"models.deprecated.tvlt": [
"TvltConfig",
"TvltFeatureExtractor",
"TvltProcessor",
],
"models.deprecated.van": ["VanConfig"],
"models.deprecated.vit_hybrid": ["ViTHybridConfig"],
"models.deprecated.xlm_prophetnet": ["XLMProphetNetConfig"],
"models.depth_anything": ["DepthAnythingConfig"],
"models.detr": ["DetrConfig"],
"models.dialogpt": [],
"models.diffllama": ["DiffLlamaConfig"],
"models.dinat": ["DinatConfig"],
"models.dinov2": ["Dinov2Config"],
"models.dinov2_with_registers": ["Dinov2WithRegistersConfig"],
"models.distilbert": [
"DistilBertConfig",
"DistilBertTokenizer",
],
"models.dit": [],
"models.donut": [
"DonutProcessor",
"DonutSwinConfig",
],
"models.dpr": [
"DPRConfig",
"DPRContextEncoderTokenizer",
"DPRQuestionEncoderTokenizer",
"DPRReaderOutput",
"DPRReaderTokenizer",
],
"models.dpt": ["DPTConfig"],
"models.efficientnet": ["EfficientNetConfig"],
"models.electra": [
"ElectraConfig",
"ElectraTokenizer",
],
"models.emu3": [
"Emu3Config",
"Emu3Processor",
"Emu3TextConfig",
"Emu3VQVAEConfig",
],
"models.encodec": [
"EncodecConfig",
"EncodecFeatureExtractor",
],
"models.encoder_decoder": ["EncoderDecoderConfig"],
"models.ernie": ["ErnieConfig"],
"models.esm": ["EsmConfig", "EsmTokenizer"],
"models.falcon": ["FalconConfig"],
"models.falcon_mamba": ["FalconMambaConfig"],
"models.fastspeech2_conformer": [
"FastSpeech2ConformerConfig",
"FastSpeech2ConformerHifiGanConfig",
"FastSpeech2ConformerTokenizer",
"FastSpeech2ConformerWithHifiGanConfig",
],
"models.flaubert": ["FlaubertConfig", "FlaubertTokenizer"],
"models.flava": [
"FlavaConfig",
"FlavaImageCodebookConfig",
"FlavaImageConfig",
"FlavaMultimodalConfig",
"FlavaTextConfig",
],
"models.fnet": ["FNetConfig"],
"models.focalnet": ["FocalNetConfig"],
"models.fsmt": [
"FSMTConfig",
"FSMTTokenizer",
],
"models.funnel": [
"FunnelConfig",
"FunnelTokenizer",
],
"models.fuyu": ["FuyuConfig"],
"models.gemma": ["GemmaConfig"],
"models.gemma2": ["Gemma2Config"],
"models.git": [
"GitConfig",
"GitProcessor",
"GitVisionConfig",
],
"models.glm": ["GlmConfig"],
"models.glpn": ["GLPNConfig"],
"models.got_ocr2": [
"GotOcr2Config",
"GotOcr2Processor",
"GotOcr2VisionConfig",
],
"models.gpt2": [
"GPT2Config",
"GPT2Tokenizer",
],
"models.gpt_bigcode": ["GPTBigCodeConfig"],
"models.gpt_neo": ["GPTNeoConfig"],
"models.gpt_neox": ["GPTNeoXConfig"],
"models.gpt_neox_japanese": ["GPTNeoXJapaneseConfig"],
"models.gpt_sw3": [],
"models.gptj": ["GPTJConfig"],
"models.granite": ["GraniteConfig"],
"models.granitemoe": ["GraniteMoeConfig"],
"models.grounding_dino": [
"GroundingDinoConfig",
"GroundingDinoProcessor",
],
"models.groupvit": [
"GroupViTConfig",
"GroupViTTextConfig",
"GroupViTVisionConfig",
],
"models.helium": ["HeliumConfig"],
"models.herbert": ["HerbertTokenizer"],
"models.hiera": ["HieraConfig"],
"models.hubert": ["HubertConfig"],
"models.ibert": ["IBertConfig"],
"models.idefics": ["IdeficsConfig"],
"models.idefics2": ["Idefics2Config"],
"models.idefics3": ["Idefics3Config"],
"models.ijepa": ["IJepaConfig"],
"models.imagegpt": ["ImageGPTConfig"],
"models.informer": ["InformerConfig"],
"models.instructblip": [
"InstructBlipConfig",
"InstructBlipProcessor",
"InstructBlipQFormerConfig",
"InstructBlipVisionConfig",
],
"models.instructblipvideo": [
"InstructBlipVideoConfig",
"InstructBlipVideoProcessor",
"InstructBlipVideoQFormerConfig",
"InstructBlipVideoVisionConfig",
],
"models.jamba": ["JambaConfig"],
"models.jetmoe": ["JetMoeConfig"],
"models.kosmos2": [
"Kosmos2Config",
"Kosmos2Processor",
],
"models.layoutlm": [
"LayoutLMConfig",
"LayoutLMTokenizer",
],
"models.layoutlmv2": [
"LayoutLMv2Config",
"LayoutLMv2FeatureExtractor",
"LayoutLMv2ImageProcessor",
"LayoutLMv2Processor",
"LayoutLMv2Tokenizer",
],
"models.layoutlmv3": [
"LayoutLMv3Config",
"LayoutLMv3FeatureExtractor",
"LayoutLMv3ImageProcessor",
"LayoutLMv3Processor",
"LayoutLMv3Tokenizer",
],
"models.layoutxlm": ["LayoutXLMProcessor"],
"models.led": ["LEDConfig", "LEDTokenizer"],
"models.levit": ["LevitConfig"],
"models.lilt": ["LiltConfig"],
"models.llama": ["LlamaConfig"],
"models.llava": [
"LlavaConfig",
"LlavaProcessor",
],
"models.llava_next": [
"LlavaNextConfig",
"LlavaNextProcessor",
],
"models.llava_next_video": [
"LlavaNextVideoConfig",
"LlavaNextVideoProcessor",
],
"models.llava_onevision": ["LlavaOnevisionConfig", "LlavaOnevisionProcessor"],
"models.longformer": [
"LongformerConfig",
"LongformerTokenizer",
],
"models.longt5": ["LongT5Config"],
"models.luke": [
"LukeConfig",
"LukeTokenizer",
],
"models.lxmert": [
"LxmertConfig",
"LxmertTokenizer",
],
"models.m2m_100": ["M2M100Config"],
"models.mamba": ["MambaConfig"],
"models.mamba2": ["Mamba2Config"],
"models.marian": ["MarianConfig"],
"models.markuplm": [
"MarkupLMConfig",
"MarkupLMFeatureExtractor",
"MarkupLMProcessor",
"MarkupLMTokenizer",
],
"models.mask2former": ["Mask2FormerConfig"],
"models.maskformer": [
"MaskFormerConfig",
"MaskFormerSwinConfig",
],
"models.mbart": ["MBartConfig"],
"models.mbart50": [],
"models.megatron_bert": ["MegatronBertConfig"],
"models.megatron_gpt2": [],
"models.mgp_str": [
"MgpstrConfig",
"MgpstrProcessor",
"MgpstrTokenizer",
],
"models.mimi": ["MimiConfig"],
"models.mistral": ["MistralConfig"],
"models.mixtral": ["MixtralConfig"],
"models.mllama": [
"MllamaConfig",
"MllamaProcessor",
],
"models.mluke": [],
"models.mobilebert": [
"MobileBertConfig",
"MobileBertTokenizer",
],
"models.mobilenet_v1": ["MobileNetV1Config"],
"models.mobilenet_v2": ["MobileNetV2Config"],
"models.mobilevit": ["MobileViTConfig"],
"models.mobilevitv2": ["MobileViTV2Config"],
"models.modernbert": ["ModernBertConfig"],
"models.moonshine": ["MoonshineConfig"],
"models.moshi": [
"MoshiConfig",
"MoshiDepthConfig",
],
"models.mpnet": [
"MPNetConfig",
"MPNetTokenizer",
],
"models.mpt": ["MptConfig"],
"models.mra": ["MraConfig"],
"models.mt5": ["MT5Config"],
"models.musicgen": [
"MusicgenConfig",
"MusicgenDecoderConfig",
],
"models.musicgen_melody": [
"MusicgenMelodyConfig",
"MusicgenMelodyDecoderConfig",
],
"models.mvp": ["MvpConfig", "MvpTokenizer"],
"models.myt5": ["MyT5Tokenizer"],
"models.nemotron": ["NemotronConfig"],
"models.nllb": [],
"models.nllb_moe": ["NllbMoeConfig"],
"models.nougat": ["NougatProcessor"],
"models.nystromformer": ["NystromformerConfig"],
"models.olmo": ["OlmoConfig"],
"models.olmo2": ["Olmo2Config"],
"models.olmoe": ["OlmoeConfig"],
"models.omdet_turbo": [
"OmDetTurboConfig",
"OmDetTurboProcessor",
],
"models.oneformer": [
"OneFormerConfig",
"OneFormerProcessor",
],
"models.openai": [
"OpenAIGPTConfig",
"OpenAIGPTTokenizer",
],
"models.opt": ["OPTConfig"],
"models.owlv2": [
"Owlv2Config",
"Owlv2Processor",
"Owlv2TextConfig",
"Owlv2VisionConfig",
],
"models.owlvit": [
"OwlViTConfig",
"OwlViTProcessor",
"OwlViTTextConfig",
"OwlViTVisionConfig",
],
"models.paligemma": ["PaliGemmaConfig"],
"models.patchtsmixer": ["PatchTSMixerConfig"],
"models.patchtst": ["PatchTSTConfig"],
"models.pegasus": [
"PegasusConfig",
"PegasusTokenizer",
],
"models.pegasus_x": ["PegasusXConfig"],
"models.perceiver": [
"PerceiverConfig",
"PerceiverTokenizer",
],
"models.persimmon": ["PersimmonConfig"],
"models.phi": ["PhiConfig"],
"models.phi3": ["Phi3Config"],
"models.phimoe": ["PhimoeConfig"],
"models.phobert": ["PhobertTokenizer"],
"models.pix2struct": [
"Pix2StructConfig",
"Pix2StructProcessor",
"Pix2StructTextConfig",
"Pix2StructVisionConfig",
],
"models.pixtral": ["PixtralProcessor", "PixtralVisionConfig"],
"models.plbart": ["PLBartConfig"],
"models.poolformer": ["PoolFormerConfig"],
"models.pop2piano": ["Pop2PianoConfig"],
"models.prophetnet": [
"ProphetNetConfig",
"ProphetNetTokenizer",
],
"models.pvt": ["PvtConfig"],
"models.pvt_v2": ["PvtV2Config"],
"models.qwen2": [
"Qwen2Config",
"Qwen2Tokenizer",
],
"models.qwen2_5_vl": [
"Qwen2_5_VLConfig",
"Qwen2_5_VLProcessor",
],
"models.qwen2_audio": [
"Qwen2AudioConfig",
"Qwen2AudioEncoderConfig",
"Qwen2AudioProcessor",
],
"models.qwen2_moe": ["Qwen2MoeConfig"],
"models.qwen2_vl": [
"Qwen2VLConfig",
"Qwen2VLProcessor",
],
"models.rag": ["RagConfig", "RagRetriever", "RagTokenizer"],
"models.recurrent_gemma": ["RecurrentGemmaConfig"],
"models.reformer": ["ReformerConfig"],
"models.regnet": ["RegNetConfig"],
"models.rembert": ["RemBertConfig"],
"models.resnet": ["ResNetConfig"],
"models.roberta": [
"RobertaConfig",
"RobertaTokenizer",
],
"models.roberta_prelayernorm": ["RobertaPreLayerNormConfig"],
"models.roc_bert": [
"RoCBertConfig",
"RoCBertTokenizer",
],
"models.roformer": [
"RoFormerConfig",
"RoFormerTokenizer",
],
"models.rt_detr": ["RTDetrConfig", "RTDetrResNetConfig"],
"models.rt_detr_v2": ["RTDetrV2Config"],
"models.rwkv": ["RwkvConfig"],
"models.sam": [
"SamConfig",
"SamMaskDecoderConfig",
"SamProcessor",
"SamPromptEncoderConfig",
"SamVisionConfig",
],
"models.seamless_m4t": [
"SeamlessM4TConfig",
"SeamlessM4TFeatureExtractor",
"SeamlessM4TProcessor",
],
"models.seamless_m4t_v2": ["SeamlessM4Tv2Config"],
"models.segformer": ["SegformerConfig"],
"models.seggpt": ["SegGptConfig"],
"models.sew": ["SEWConfig"],
"models.sew_d": ["SEWDConfig"],
"models.siglip": [
"SiglipConfig",
"SiglipProcessor",
"SiglipTextConfig",
"SiglipVisionConfig",
],
"models.speech_encoder_decoder": ["SpeechEncoderDecoderConfig"],
"models.speech_to_text": [
"Speech2TextConfig",
"Speech2TextFeatureExtractor",
"Speech2TextProcessor",
],
"models.speecht5": [
"SpeechT5Config",
"SpeechT5FeatureExtractor",
"SpeechT5HifiGanConfig",
"SpeechT5Processor",
],
"models.splinter": [
"SplinterConfig",
"SplinterTokenizer",
],
"models.squeezebert": [
"SqueezeBertConfig",
"SqueezeBertTokenizer",
],
"models.stablelm": ["StableLmConfig"],
"models.starcoder2": ["Starcoder2Config"],
"models.superglue": ["SuperGlueConfig"],
"models.superpoint": ["SuperPointConfig"],
"models.swiftformer": ["SwiftFormerConfig"],
"models.swin": ["SwinConfig"],
"models.swin2sr": ["Swin2SRConfig"],
"models.swinv2": ["Swinv2Config"],
"models.switch_transformers": ["SwitchTransformersConfig"],
"models.t5": ["T5Config"],
"models.table_transformer": ["TableTransformerConfig"],
"models.tapas": [
"TapasConfig",
"TapasTokenizer",
],
"models.textnet": ["TextNetConfig"],
"models.time_series_transformer": ["TimeSeriesTransformerConfig"],
"models.timesformer": ["TimesformerConfig"],
"models.timm_backbone": ["TimmBackboneConfig"],
"models.timm_wrapper": ["TimmWrapperConfig"],
"models.trocr": [
"TrOCRConfig",
"TrOCRProcessor",
],
"models.tvp": [
"TvpConfig",
"TvpProcessor",
],
"models.udop": [
"UdopConfig",
"UdopProcessor",
],
"models.umt5": ["UMT5Config"],
"models.unispeech": ["UniSpeechConfig"],
"models.unispeech_sat": ["UniSpeechSatConfig"],
"models.univnet": [
"UnivNetConfig",
"UnivNetFeatureExtractor",
],
"models.upernet": ["UperNetConfig"],
"models.video_llava": ["VideoLlavaConfig"],
"models.videomae": ["VideoMAEConfig"],
"models.vilt": [
"ViltConfig",
"ViltFeatureExtractor",
"ViltImageProcessor",
"ViltProcessor",
],
"models.vipllava": ["VipLlavaConfig"],
"models.vision_encoder_decoder": ["VisionEncoderDecoderConfig"],
"models.vision_text_dual_encoder": [
"VisionTextDualEncoderConfig",
"VisionTextDualEncoderProcessor",
],
"models.visual_bert": ["VisualBertConfig"],
"models.vit": ["ViTConfig"],
"models.vit_mae": ["ViTMAEConfig"],
"models.vit_msn": ["ViTMSNConfig"],
"models.vitdet": ["VitDetConfig"],
"models.vitmatte": ["VitMatteConfig"],
"models.vitpose": ["VitPoseConfig"],
"models.vitpose_backbone": ["VitPoseBackboneConfig"],
"models.vits": [
"VitsConfig",
"VitsTokenizer",
],
"models.vivit": ["VivitConfig"],
"models.wav2vec2": [
"Wav2Vec2Config",
"Wav2Vec2CTCTokenizer",
"Wav2Vec2FeatureExtractor",
"Wav2Vec2Processor",
"Wav2Vec2Tokenizer",
],
"models.wav2vec2_bert": [
"Wav2Vec2BertConfig",
"Wav2Vec2BertProcessor",
],
"models.wav2vec2_conformer": ["Wav2Vec2ConformerConfig"],
"models.wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"],
"models.wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"],
"models.wavlm": ["WavLMConfig"],
"models.whisper": [
"WhisperConfig",
"WhisperFeatureExtractor",
"WhisperProcessor",
"WhisperTokenizer",
],
"models.x_clip": [
"XCLIPConfig",
"XCLIPProcessor",
"XCLIPTextConfig",
"XCLIPVisionConfig",
],
"models.xglm": ["XGLMConfig"],
"models.xlm": ["XLMConfig", "XLMTokenizer"],
"models.xlm_roberta": ["XLMRobertaConfig"],
"models.xlm_roberta_xl": ["XLMRobertaXLConfig"],
"models.xlnet": ["XLNetConfig"],
"models.xmod": ["XmodConfig"],
"models.yolos": ["YolosConfig"],
"models.yoso": ["YosoConfig"],
"models.zamba": ["ZambaConfig"],
"models.zamba2": ["Zamba2Config"],
"models.zoedepth": ["ZoeDepthConfig"],
"onnx": [],
"pipelines": [
"AudioClassificationPipeline",
"AutomaticSpeechRecognitionPipeline",
"CsvPipelineDataFormat",
"DepthEstimationPipeline",
"DocumentQuestionAnsweringPipeline",
"FeatureExtractionPipeline",
"FillMaskPipeline",
"ImageClassificationPipeline",
"ImageFeatureExtractionPipeline",
"ImageSegmentationPipeline",
"ImageTextToTextPipeline",
"ImageToImagePipeline",
"ImageToTextPipeline",
"JsonPipelineDataFormat",
"MaskGenerationPipeline",
"NerPipeline",
"ObjectDetectionPipeline",
"PipedPipelineDataFormat",
"Pipeline",
"PipelineDataFormat",
"QuestionAnsweringPipeline",
"SummarizationPipeline",
"TableQuestionAnsweringPipeline",
"Text2TextGenerationPipeline",
"TextClassificationPipeline",
"TextGenerationPipeline",
"TextToAudioPipeline",
"TokenClassificationPipeline",
"TranslationPipeline",
"VideoClassificationPipeline",
"VisualQuestionAnsweringPipeline",
"ZeroShotAudioClassificationPipeline",
"ZeroShotClassificationPipeline",
"ZeroShotImageClassificationPipeline",
"ZeroShotObjectDetectionPipeline",
"pipeline",
],
"processing_utils": ["ProcessorMixin"],
"quantizers": [],
"testing_utils": [],
"tokenization_utils": ["PreTrainedTokenizer"],
"tokenization_utils_base": [
"AddedToken",
"BatchEncoding",
"CharSpan",
"PreTrainedTokenizerBase",
"SpecialTokensMixin",
"TokenSpan",
],
"trainer_callback": [
"DefaultFlowCallback",
"EarlyStoppingCallback",
"PrinterCallback",
"ProgressCallback",
"TrainerCallback",
"TrainerControl",
"TrainerState",
],
"trainer_utils": [
"EvalPrediction",
"IntervalStrategy",
"SchedulerType",
"enable_full_determinism",
"set_seed",
],
"training_args": ["TrainingArguments"],
"training_args_seq2seq": ["Seq2SeqTrainingArguments"],
"training_args_tf": ["TFTrainingArguments"],
"utils": [
"CONFIG_NAME",
"MODEL_CARD_NAME",
"PYTORCH_PRETRAINED_BERT_CACHE",
"PYTORCH_TRANSFORMERS_CACHE",
"SPIECE_UNDERLINE",
"TF2_WEIGHTS_NAME",
"TF_WEIGHTS_NAME",
"TRANSFORMERS_CACHE",
"WEIGHTS_NAME",
"TensorType",
"add_end_docstrings",
"add_start_docstrings",
"is_apex_available",
"is_av_available",
"is_bitsandbytes_available",
"is_datasets_available",
"is_faiss_available",
"is_flax_available",
"is_keras_nlp_available",
"is_phonemizer_available",
"is_psutil_available",
"is_py3nvml_available",
"is_pyctcdecode_available",
"is_sacremoses_available",
"is_safetensors_available",
"is_scipy_available",
"is_sentencepiece_available",
"is_sklearn_available",
"is_speech_available",
"is_tensorflow_text_available",
"is_tf_available",
"is_timm_available",
"is_tokenizers_available",
"is_torch_available",
"is_torch_mlu_available",
"is_torch_musa_available",
"is_torch_neuroncore_available",
"is_torch_npu_available",
"is_torch_tpu_available",
"is_torchvision_available",
"is_torch_xla_available",
"is_torch_xpu_available",
"is_vision_available",
"logging",
],
"utils.quantization_config": [
"AqlmConfig",
"AwqConfig",
"BitNetConfig",
"BitsAndBytesConfig",
"CompressedTensorsConfig",
"EetqConfig",
"FbgemmFp8Config",
"GPTQConfig",
"HiggsConfig",
"HqqConfig",
"QuantoConfig",
"TorchAoConfig",
"VptqConfig",
],
}
# sentencepiece-backed objects
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_sentencepiece_objects
_import_structure["utils.dummy_sentencepiece_objects"] = [
name for name in dir(dummy_sentencepiece_objects) if not name.startswith("_")
]
else:
_import_structure["models.albert"].append("AlbertTokenizer")
_import_structure["models.barthez"].append("BarthezTokenizer")
_import_structure["models.bartpho"].append("BartphoTokenizer")
_import_structure["models.bert_generation"].append("BertGenerationTokenizer")
_import_structure["models.big_bird"].append("BigBirdTokenizer")
_import_structure["models.camembert"].append("CamembertTokenizer")
_import_structure["models.code_llama"].append("CodeLlamaTokenizer")
_import_structure["models.cpm"].append("CpmTokenizer")
_import_structure["models.deberta_v2"].append("DebertaV2Tokenizer")
_import_structure["models.deprecated.ernie_m"].append("ErnieMTokenizer")
_import_structure["models.deprecated.xlm_prophetnet"].append("XLMProphetNetTokenizer")
_import_structure["models.fnet"].append("FNetTokenizer")
_import_structure["models.gemma"].append("GemmaTokenizer")
_import_structure["models.gpt_sw3"].append("GPTSw3Tokenizer")
_import_structure["models.layoutxlm"].append("LayoutXLMTokenizer")
_import_structure["models.llama"].append("LlamaTokenizer")
_import_structure["models.m2m_100"].append("M2M100Tokenizer")
_import_structure["models.marian"].append("MarianTokenizer")
_import_structure["models.mbart"].append("MBartTokenizer")
_import_structure["models.mbart50"].append("MBart50Tokenizer")
_import_structure["models.mluke"].append("MLukeTokenizer")
_import_structure["models.mt5"].append("MT5Tokenizer")
_import_structure["models.nllb"].append("NllbTokenizer")
_import_structure["models.pegasus"].append("PegasusTokenizer")
_import_structure["models.plbart"].append("PLBartTokenizer")
_import_structure["models.reformer"].append("ReformerTokenizer")
_import_structure["models.rembert"].append("RemBertTokenizer")
_import_structure["models.seamless_m4t"].append("SeamlessM4TTokenizer")
_import_structure["models.siglip"].append("SiglipTokenizer")
_import_structure["models.speech_to_text"].append("Speech2TextTokenizer")
_import_structure["models.speecht5"].append("SpeechT5Tokenizer")
_import_structure["models.t5"].append("T5Tokenizer")
_import_structure["models.udop"].append("UdopTokenizer")
_import_structure["models.xglm"].append("XGLMTokenizer")
_import_structure["models.xlm_roberta"].append("XLMRobertaTokenizer")
_import_structure["models.xlnet"].append("XLNetTokenizer")
# tokenizers-backed objects
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_tokenizers_objects
_import_structure["utils.dummy_tokenizers_objects"] = [
name for name in dir(dummy_tokenizers_objects) if not name.startswith("_")
]
else:
# Fast tokenizers structure
_import_structure["models.albert"].append("AlbertTokenizerFast")
_import_structure["models.bart"].append("BartTokenizerFast")
_import_structure["models.barthez"].append("BarthezTokenizerFast")
_import_structure["models.bert"].append("BertTokenizerFast")
_import_structure["models.big_bird"].append("BigBirdTokenizerFast")
_import_structure["models.blenderbot"].append("BlenderbotTokenizerFast")
_import_structure["models.blenderbot_small"].append("BlenderbotSmallTokenizerFast")
_import_structure["models.bloom"].append("BloomTokenizerFast")
_import_structure["models.camembert"].append("CamembertTokenizerFast")
_import_structure["models.clip"].append("CLIPTokenizerFast")
_import_structure["models.code_llama"].append("CodeLlamaTokenizerFast")
_import_structure["models.codegen"].append("CodeGenTokenizerFast")
_import_structure["models.cohere"].append("CohereTokenizerFast")
_import_structure["models.convbert"].append("ConvBertTokenizerFast")
_import_structure["models.cpm"].append("CpmTokenizerFast")
_import_structure["models.deberta"].append("DebertaTokenizerFast")
_import_structure["models.deberta_v2"].append("DebertaV2TokenizerFast")
_import_structure["models.deprecated.realm"].append("RealmTokenizerFast")
_import_structure["models.deprecated.retribert"].append("RetriBertTokenizerFast")
_import_structure["models.distilbert"].append("DistilBertTokenizerFast")
_import_structure["models.dpr"].extend(
[
"DPRContextEncoderTokenizerFast",
"DPRQuestionEncoderTokenizerFast",
"DPRReaderTokenizerFast",
]
)
_import_structure["models.electra"].append("ElectraTokenizerFast")
_import_structure["models.fnet"].append("FNetTokenizerFast")
_import_structure["models.funnel"].append("FunnelTokenizerFast")
_import_structure["models.gemma"].append("GemmaTokenizerFast")
_import_structure["models.gpt2"].append("GPT2TokenizerFast")
_import_structure["models.gpt_neox"].append("GPTNeoXTokenizerFast")
_import_structure["models.gpt_neox_japanese"].append("GPTNeoXJapaneseTokenizer")
_import_structure["models.herbert"].append("HerbertTokenizerFast")
_import_structure["models.layoutlm"].append("LayoutLMTokenizerFast")
_import_structure["models.layoutlmv2"].append("LayoutLMv2TokenizerFast")
_import_structure["models.layoutlmv3"].append("LayoutLMv3TokenizerFast")
_import_structure["models.layoutxlm"].append("LayoutXLMTokenizerFast")
_import_structure["models.led"].append("LEDTokenizerFast")
_import_structure["models.llama"].append("LlamaTokenizerFast")
_import_structure["models.longformer"].append("LongformerTokenizerFast")
_import_structure["models.lxmert"].append("LxmertTokenizerFast")
_import_structure["models.markuplm"].append("MarkupLMTokenizerFast")
_import_structure["models.mbart"].append("MBartTokenizerFast")
_import_structure["models.mbart50"].append("MBart50TokenizerFast")
_import_structure["models.mobilebert"].append("MobileBertTokenizerFast")
_import_structure["models.mpnet"].append("MPNetTokenizerFast")
_import_structure["models.mt5"].append("MT5TokenizerFast")
_import_structure["models.mvp"].append("MvpTokenizerFast")
_import_structure["models.nllb"].append("NllbTokenizerFast")
_import_structure["models.nougat"].append("NougatTokenizerFast")
_import_structure["models.openai"].append("OpenAIGPTTokenizerFast")
_import_structure["models.pegasus"].append("PegasusTokenizerFast")
_import_structure["models.qwen2"].append("Qwen2TokenizerFast")
_import_structure["models.reformer"].append("ReformerTokenizerFast")
_import_structure["models.rembert"].append("RemBertTokenizerFast")
_import_structure["models.roberta"].append("RobertaTokenizerFast")
_import_structure["models.roformer"].append("RoFormerTokenizerFast")
_import_structure["models.seamless_m4t"].append("SeamlessM4TTokenizerFast")
_import_structure["models.splinter"].append("SplinterTokenizerFast")
_import_structure["models.squeezebert"].append("SqueezeBertTokenizerFast")
_import_structure["models.t5"].append("T5TokenizerFast")
_import_structure["models.udop"].append("UdopTokenizerFast")
_import_structure["models.whisper"].append("WhisperTokenizerFast")
_import_structure["models.xglm"].append("XGLMTokenizerFast")
_import_structure["models.xlm_roberta"].append("XLMRobertaTokenizerFast")
_import_structure["models.xlnet"].append("XLNetTokenizerFast")
_import_structure["tokenization_utils_fast"] = ["PreTrainedTokenizerFast"]
try:
if not (is_sentencepiece_available() and is_tokenizers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_sentencepiece_and_tokenizers_objects
_import_structure["utils.dummy_sentencepiece_and_tokenizers_objects"] = [
name for name in dir(dummy_sentencepiece_and_tokenizers_objects) if not name.startswith("_")
]
else:
_import_structure["convert_slow_tokenizer"] = [
"SLOW_TO_FAST_CONVERTERS",
"convert_slow_tokenizer",
]
# Tensorflow-text-specific objects
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_tensorflow_text_objects
_import_structure["utils.dummy_tensorflow_text_objects"] = [
name for name in dir(dummy_tensorflow_text_objects) if not name.startswith("_")
]
else:
_import_structure["models.bert"].append("TFBertTokenizer")
# keras-nlp-specific objects
try:
if not is_keras_nlp_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_keras_nlp_objects
_import_structure["utils.dummy_keras_nlp_objects"] = [
name for name in dir(dummy_keras_nlp_objects) if not name.startswith("_")
]
else:
_import_structure["models.gpt2"].append("TFGPT2Tokenizer")
# Vision-specific objects
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_vision_objects
_import_structure["utils.dummy_vision_objects"] = [
name for name in dir(dummy_vision_objects) if not name.startswith("_")
]
else:
_import_structure["image_processing_base"] = ["ImageProcessingMixin"]
_import_structure["image_processing_utils"] = ["BaseImageProcessor"]
_import_structure["image_utils"] = ["ImageFeatureExtractionMixin"]
_import_structure["models.aria"].extend(["AriaImageProcessor"])
_import_structure["models.beit"].extend(["BeitFeatureExtractor", "BeitImageProcessor"])
_import_structure["models.bit"].extend(["BitImageProcessor"])
_import_structure["models.blip"].extend(["BlipImageProcessor"])
_import_structure["models.bridgetower"].append("BridgeTowerImageProcessor")
_import_structure["models.chameleon"].append("ChameleonImageProcessor")
_import_structure["models.chinese_clip"].extend(["ChineseCLIPFeatureExtractor", "ChineseCLIPImageProcessor"])
_import_structure["models.clip"].extend(["CLIPFeatureExtractor", "CLIPImageProcessor"])
_import_structure["models.conditional_detr"].extend(
["ConditionalDetrFeatureExtractor", "ConditionalDetrImageProcessor"]
)
_import_structure["models.convnext"].extend(["ConvNextFeatureExtractor", "ConvNextImageProcessor"])
_import_structure["models.deformable_detr"].extend(
["DeformableDetrFeatureExtractor", "DeformableDetrImageProcessor"]
)
_import_structure["models.deit"].extend(["DeiTFeatureExtractor", "DeiTImageProcessor"])
_import_structure["models.deprecated.deta"].append("DetaImageProcessor")
_import_structure["models.deprecated.efficientformer"].append("EfficientFormerImageProcessor")
_import_structure["models.deprecated.tvlt"].append("TvltImageProcessor")
_import_structure["models.deprecated.vit_hybrid"].extend(["ViTHybridImageProcessor"])
_import_structure["models.detr"].extend(["DetrFeatureExtractor", "DetrImageProcessor"])
_import_structure["models.donut"].extend(["DonutFeatureExtractor", "DonutImageProcessor"])
_import_structure["models.dpt"].extend(["DPTFeatureExtractor", "DPTImageProcessor"])
_import_structure["models.efficientnet"].append("EfficientNetImageProcessor")
_import_structure["models.emu3"].append("Emu3ImageProcessor")
_import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaImageProcessor", "FlavaProcessor"])
_import_structure["models.fuyu"].extend(["FuyuImageProcessor", "FuyuProcessor"])
_import_structure["models.glpn"].extend(["GLPNFeatureExtractor", "GLPNImageProcessor"])
_import_structure["models.got_ocr2"].extend(["GotOcr2ImageProcessor"])
_import_structure["models.grounding_dino"].extend(["GroundingDinoImageProcessor"])
_import_structure["models.idefics"].extend(["IdeficsImageProcessor"])
_import_structure["models.idefics2"].extend(["Idefics2ImageProcessor"])
_import_structure["models.idefics3"].extend(["Idefics3ImageProcessor"])
_import_structure["models.imagegpt"].extend(["ImageGPTFeatureExtractor", "ImageGPTImageProcessor"])
_import_structure["models.instructblipvideo"].extend(["InstructBlipVideoImageProcessor"])
_import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"])
_import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"])
_import_structure["models.levit"].extend(["LevitFeatureExtractor", "LevitImageProcessor"])
_import_structure["models.llava"].append("LlavaImageProcessor")
_import_structure["models.llava_next"].append("LlavaNextImageProcessor")
_import_structure["models.llava_next_video"].append("LlavaNextVideoImageProcessor")
_import_structure["models.llava_onevision"].extend(
["LlavaOnevisionImageProcessor", "LlavaOnevisionVideoProcessor"]
)
_import_structure["models.mask2former"].append("Mask2FormerImageProcessor")
_import_structure["models.maskformer"].extend(["MaskFormerFeatureExtractor", "MaskFormerImageProcessor"])
_import_structure["models.mllama"].extend(["MllamaImageProcessor"])
_import_structure["models.mobilenet_v1"].extend(["MobileNetV1FeatureExtractor", "MobileNetV1ImageProcessor"])
_import_structure["models.mobilenet_v2"].extend(["MobileNetV2FeatureExtractor", "MobileNetV2ImageProcessor"])
_import_structure["models.mobilevit"].extend(["MobileViTFeatureExtractor", "MobileViTImageProcessor"])
_import_structure["models.nougat"].append("NougatImageProcessor")
_import_structure["models.oneformer"].extend(["OneFormerImageProcessor"])
_import_structure["models.owlv2"].append("Owlv2ImageProcessor")
_import_structure["models.owlvit"].extend(["OwlViTFeatureExtractor", "OwlViTImageProcessor"])
_import_structure["models.perceiver"].extend(["PerceiverFeatureExtractor", "PerceiverImageProcessor"])
_import_structure["models.pix2struct"].extend(["Pix2StructImageProcessor"])
_import_structure["models.pixtral"].append("PixtralImageProcessor")
_import_structure["models.poolformer"].extend(["PoolFormerFeatureExtractor", "PoolFormerImageProcessor"])
_import_structure["models.pvt"].extend(["PvtImageProcessor"])
_import_structure["models.qwen2_5_vl"].extend(["Qwen2_5_VLImageProcessor"])
_import_structure["models.qwen2_vl"].extend(["Qwen2VLImageProcessor"])
_import_structure["models.rt_detr"].extend(["RTDetrImageProcessor"])
_import_structure["models.sam"].extend(["SamImageProcessor"])
_import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"])
_import_structure["models.seggpt"].extend(["SegGptImageProcessor"])
_import_structure["models.siglip"].append("SiglipImageProcessor")
_import_structure["models.superglue"].extend(["SuperGlueImageProcessor"])
_import_structure["models.superpoint"].extend(["SuperPointImageProcessor"])
_import_structure["models.swin2sr"].append("Swin2SRImageProcessor")
_import_structure["models.textnet"].extend(["TextNetImageProcessor"])
_import_structure["models.tvp"].append("TvpImageProcessor")
_import_structure["models.video_llava"].append("VideoLlavaImageProcessor")
_import_structure["models.videomae"].extend(["VideoMAEFeatureExtractor", "VideoMAEImageProcessor"])
_import_structure["models.vilt"].extend(["ViltFeatureExtractor", "ViltImageProcessor", "ViltProcessor"])
_import_structure["models.vit"].extend(["ViTFeatureExtractor", "ViTImageProcessor"])
_import_structure["models.vitmatte"].append("VitMatteImageProcessor")
_import_structure["models.vitpose"].append("VitPoseImageProcessor")
_import_structure["models.vivit"].append("VivitImageProcessor")
_import_structure["models.yolos"].extend(["YolosFeatureExtractor", "YolosImageProcessor"])
_import_structure["models.zoedepth"].append("ZoeDepthImageProcessor")
try:
if not is_torchvision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_torchvision_objects
_import_structure["utils.dummy_torchvision_objects"] = [
name for name in dir(dummy_torchvision_objects) if not name.startswith("_")
]
else:
_import_structure["image_processing_utils_fast"] = ["BaseImageProcessorFast"]
_import_structure["models.blip"].append("BlipImageProcessorFast")
_import_structure["models.clip"].append("CLIPImageProcessorFast")
_import_structure["models.convnext"].append("ConvNextImageProcessorFast")
_import_structure["models.deformable_detr"].append("DeformableDetrImageProcessorFast")
_import_structure["models.deit"].append("DeiTImageProcessorFast")
_import_structure["models.detr"].append("DetrImageProcessorFast")
_import_structure["models.llava"].append("LlavaImageProcessorFast")
_import_structure["models.llava_next"].append("LlavaNextImageProcessorFast")
_import_structure["models.llava_onevision"].append("LlavaOnevisionImageProcessorFast")
_import_structure["models.pixtral"].append("PixtralImageProcessorFast")
_import_structure["models.qwen2_vl"].append("Qwen2VLImageProcessorFast")
_import_structure["models.rt_detr"].append("RTDetrImageProcessorFast")
_import_structure["models.siglip"].append("SiglipImageProcessorFast")
_import_structure["models.vit"].append("ViTImageProcessorFast")
try:
if not (is_torchvision_available() and is_timm_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_timm_and_torchvision_objects
_import_structure["utils.dummy_timm_and_torchvision_objects"] = [
name for name in dir(dummy_timm_and_torchvision_objects) if not name.startswith("_")
]
else:
_import_structure["models.timm_wrapper"].extend(["TimmWrapperImageProcessor"])
# PyTorch-backed objects
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_pt_objects
_import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")]
else:
_import_structure["activations"] = []
_import_structure["cache_utils"] = [
"Cache",
"CacheConfig",
"DynamicCache",
"EncoderDecoderCache",
"HQQQuantizedCache",
"HybridCache",
"MambaCache",
"OffloadedCache",
"OffloadedStaticCache",
"QuantizedCache",
"QuantizedCacheConfig",
"QuantoQuantizedCache",
"SinkCache",
"SlidingWindowCache",
"StaticCache",
]
_import_structure["data.datasets"] = [
"GlueDataset",
"GlueDataTrainingArguments",
"LineByLineTextDataset",
"LineByLineWithRefDataset",
"LineByLineWithSOPTextDataset",
"SquadDataset",
"SquadDataTrainingArguments",
"TextDataset",
"TextDatasetForNextSentencePrediction",
]
_import_structure["generation"].extend(
[
"AlternatingCodebooksLogitsProcessor",
"BayesianDetectorConfig",
"BayesianDetectorModel",
"BeamScorer",
"BeamSearchScorer",
"ClassifierFreeGuidanceLogitsProcessor",
"ConstrainedBeamSearchScorer",
"Constraint",
"ConstraintListState",
"DisjunctiveConstraint",
"EncoderNoRepeatNGramLogitsProcessor",
"EncoderRepetitionPenaltyLogitsProcessor",
"EosTokenCriteria",
"EpsilonLogitsWarper",
"EtaLogitsWarper",
"ExponentialDecayLengthPenalty",
"ForcedBOSTokenLogitsProcessor",
"ForcedEOSTokenLogitsProcessor",
"GenerationMixin",
"HammingDiversityLogitsProcessor",
"InfNanRemoveLogitsProcessor",
"LogitNormalization",
"LogitsProcessor",
"LogitsProcessorList",
"MaxLengthCriteria",
"MaxTimeCriteria",
"MinLengthLogitsProcessor",
"MinNewTokensLengthLogitsProcessor",
"MinPLogitsWarper",
"NoBadWordsLogitsProcessor",
"NoRepeatNGramLogitsProcessor",
"PhrasalConstraint",
"PrefixConstrainedLogitsProcessor",
"RepetitionPenaltyLogitsProcessor",
"SequenceBiasLogitsProcessor",
"StoppingCriteria",
"StoppingCriteriaList",
"StopStringCriteria",
"SuppressTokensAtBeginLogitsProcessor",
"SuppressTokensLogitsProcessor",
"SynthIDTextWatermarkDetector",
"SynthIDTextWatermarkingConfig",
"SynthIDTextWatermarkLogitsProcessor",
"TemperatureLogitsWarper",
"TopKLogitsWarper",
"TopPLogitsWarper",
"TypicalLogitsWarper",
"UnbatchedClassifierFreeGuidanceLogitsProcessor",
"WatermarkDetector",
"WatermarkLogitsProcessor",
"WhisperTimeStampLogitsProcessor",
]
)
# PyTorch domain libraries integration
_import_structure["integrations.executorch"] = [
"TorchExportableModuleWithStaticCache",
"convert_and_export_with_cache",
]
_import_structure["modeling_flash_attention_utils"] = []
_import_structure["modeling_outputs"] = []
_import_structure["modeling_rope_utils"] = ["ROPE_INIT_FUNCTIONS"]
_import_structure["modeling_utils"] = ["PreTrainedModel"]
# PyTorch models structure
_import_structure["models.albert"].extend(
[
"AlbertForMaskedLM",
"AlbertForMultipleChoice",
"AlbertForPreTraining",
"AlbertForQuestionAnswering",
"AlbertForSequenceClassification",
"AlbertForTokenClassification",
"AlbertModel",
"AlbertPreTrainedModel",
"load_tf_weights_in_albert",
]
)
_import_structure["models.align"].extend(
[
"AlignModel",
"AlignPreTrainedModel",
"AlignTextModel",
"AlignVisionModel",
]
)
_import_structure["models.altclip"].extend(
[
"AltCLIPModel",
"AltCLIPPreTrainedModel",
"AltCLIPTextModel",
"AltCLIPVisionModel",
]
)
_import_structure["models.aria"].extend(
[
"AriaForConditionalGeneration",
"AriaPreTrainedModel",
"AriaTextForCausalLM",
"AriaTextModel",
"AriaTextPreTrainedModel",
]
)
_import_structure["models.audio_spectrogram_transformer"].extend(
[
"ASTForAudioClassification",
"ASTModel",
"ASTPreTrainedModel",
]
)
_import_structure["models.auto"].extend(
[
"MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
"MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING",
"MODEL_FOR_AUDIO_XVECTOR_MAPPING",
"MODEL_FOR_BACKBONE_MAPPING",
"MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
"MODEL_FOR_CAUSAL_LM_MAPPING",
"MODEL_FOR_CTC_MAPPING",
"MODEL_FOR_DEPTH_ESTIMATION_MAPPING",
"MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
"MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
"MODEL_FOR_IMAGE_MAPPING",
"MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
"MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING",
"MODEL_FOR_IMAGE_TO_IMAGE_MAPPING",
"MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
"MODEL_FOR_KEYPOINT_DETECTION_MAPPING",
"MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
"MODEL_FOR_MASKED_LM_MAPPING",
"MODEL_FOR_MASK_GENERATION_MAPPING",
"MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
"MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
"MODEL_FOR_OBJECT_DETECTION_MAPPING",
"MODEL_FOR_PRETRAINING_MAPPING",
"MODEL_FOR_QUESTION_ANSWERING_MAPPING",
"MODEL_FOR_RETRIEVAL_MAPPING",
"MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
"MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
"MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
"MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
"MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
"MODEL_FOR_TEXT_ENCODING_MAPPING",
"MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING",
"MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING",
"MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING",
"MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING",
"MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
"MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING",
"MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING",
"MODEL_FOR_VISION_2_SEQ_MAPPING",
"MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING",
"MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
"MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING",
"MODEL_MAPPING",
"MODEL_WITH_LM_HEAD_MAPPING",
"AutoBackbone",
"AutoModel",
"AutoModelForAudioClassification",
"AutoModelForAudioFrameClassification",
"AutoModelForAudioXVector",
"AutoModelForCausalLM",
"AutoModelForCTC",
"AutoModelForDepthEstimation",
"AutoModelForDocumentQuestionAnswering",
"AutoModelForImageClassification",
"AutoModelForImageSegmentation",
"AutoModelForImageTextToText",
"AutoModelForImageToImage",
"AutoModelForInstanceSegmentation",
"AutoModelForKeypointDetection",
"AutoModelForMaskedImageModeling",
"AutoModelForMaskedLM",
"AutoModelForMaskGeneration",
"AutoModelForMultipleChoice",
"AutoModelForNextSentencePrediction",
"AutoModelForObjectDetection",
"AutoModelForPreTraining",
"AutoModelForQuestionAnswering",
"AutoModelForSemanticSegmentation",
"AutoModelForSeq2SeqLM",
"AutoModelForSequenceClassification",
"AutoModelForSpeechSeq2Seq",
"AutoModelForTableQuestionAnswering",
"AutoModelForTextEncoding",
"AutoModelForTextToSpectrogram",
"AutoModelForTextToWaveform",
"AutoModelForTokenClassification",
"AutoModelForUniversalSegmentation",
"AutoModelForVideoClassification",
"AutoModelForVision2Seq",
"AutoModelForVisualQuestionAnswering",
"AutoModelForZeroShotImageClassification",
"AutoModelForZeroShotObjectDetection",
"AutoModelWithLMHead",
]
)
_import_structure["models.autoformer"].extend(
[
"AutoformerForPrediction",
"AutoformerModel",
"AutoformerPreTrainedModel",
]
)
_import_structure["models.bamba"].extend(
[
"BambaForCausalLM",
"BambaModel",
"BambaPreTrainedModel",
]
)
_import_structure["models.bark"].extend(
[
"BarkCausalModel",
"BarkCoarseModel",
"BarkFineModel",
"BarkModel",
"BarkPreTrainedModel",
"BarkSemanticModel",
]
)
_import_structure["models.bart"].extend(
[
"BartForCausalLM",
"BartForConditionalGeneration",
"BartForQuestionAnswering",
"BartForSequenceClassification",
"BartModel",
"BartPretrainedModel",
"BartPreTrainedModel",
"PretrainedBartModel",
]
)
_import_structure["models.beit"].extend(
[
"BeitBackbone",
"BeitForImageClassification",
"BeitForMaskedImageModeling",
"BeitForSemanticSegmentation",
"BeitModel",
"BeitPreTrainedModel",
]
)
_import_structure["models.bert"].extend(
[
"BertForMaskedLM",
"BertForMultipleChoice",
"BertForNextSentencePrediction",
"BertForPreTraining",
"BertForQuestionAnswering",
"BertForSequenceClassification",
"BertForTokenClassification",
"BertLMHeadModel",
"BertModel",
"BertPreTrainedModel",
"load_tf_weights_in_bert",
]
)
_import_structure["models.bert_generation"].extend(
[
"BertGenerationDecoder",
"BertGenerationEncoder",
"BertGenerationPreTrainedModel",
"load_tf_weights_in_bert_generation",
]
)
_import_structure["models.big_bird"].extend(
[
"BigBirdForCausalLM",
"BigBirdForMaskedLM",
"BigBirdForMultipleChoice",
"BigBirdForPreTraining",
"BigBirdForQuestionAnswering",
"BigBirdForSequenceClassification",
"BigBirdForTokenClassification",
"BigBirdModel",
"BigBirdPreTrainedModel",
"load_tf_weights_in_big_bird",
]
)
_import_structure["models.bigbird_pegasus"].extend(
[
"BigBirdPegasusForCausalLM",
"BigBirdPegasusForConditionalGeneration",
"BigBirdPegasusForQuestionAnswering",
"BigBirdPegasusForSequenceClassification",
"BigBirdPegasusModel",
"BigBirdPegasusPreTrainedModel",
]
)
_import_structure["models.biogpt"].extend(
[
"BioGptForCausalLM",
"BioGptForSequenceClassification",
"BioGptForTokenClassification",
"BioGptModel",
"BioGptPreTrainedModel",
]
)
_import_structure["models.bit"].extend(
[
"BitBackbone",
"BitForImageClassification",
"BitModel",
"BitPreTrainedModel",
]
)
_import_structure["models.blenderbot"].extend(
[
"BlenderbotForCausalLM",
"BlenderbotForConditionalGeneration",
"BlenderbotModel",
"BlenderbotPreTrainedModel",
]
)
_import_structure["models.blenderbot_small"].extend(
[
"BlenderbotSmallForCausalLM",
"BlenderbotSmallForConditionalGeneration",
"BlenderbotSmallModel",
"BlenderbotSmallPreTrainedModel",
]
)
_import_structure["models.blip"].extend(
[
"BlipForConditionalGeneration",
"BlipForImageTextRetrieval",
"BlipForQuestionAnswering",
"BlipModel",
"BlipPreTrainedModel",
"BlipTextModel",
"BlipVisionModel",
]
)
_import_structure["models.blip_2"].extend(
[
"Blip2ForConditionalGeneration",
"Blip2ForImageTextRetrieval",
"Blip2Model",
"Blip2PreTrainedModel",
"Blip2QFormerModel",
"Blip2TextModelWithProjection",
"Blip2VisionModel",
"Blip2VisionModelWithProjection",
]
)
_import_structure["models.bloom"].extend(
[
"BloomForCausalLM",
"BloomForQuestionAnswering",
"BloomForSequenceClassification",
"BloomForTokenClassification",
"BloomModel",
"BloomPreTrainedModel",
]
)
_import_structure["models.bridgetower"].extend(
[
"BridgeTowerForContrastiveLearning",
"BridgeTowerForImageAndTextRetrieval",
"BridgeTowerForMaskedLM",
"BridgeTowerModel",
"BridgeTowerPreTrainedModel",
]
)
_import_structure["models.bros"].extend(
[
"BrosForTokenClassification",
"BrosModel",
"BrosPreTrainedModel",
"BrosProcessor",
"BrosSpadeEEForTokenClassification",
"BrosSpadeELForTokenClassification",
]
)
_import_structure["models.camembert"].extend(
[
"CamembertForCausalLM",
"CamembertForMaskedLM",
"CamembertForMultipleChoice",
"CamembertForQuestionAnswering",
"CamembertForSequenceClassification",
"CamembertForTokenClassification",
"CamembertModel",
"CamembertPreTrainedModel",
]
)
_import_structure["models.canine"].extend(
[
"CanineForMultipleChoice",
"CanineForQuestionAnswering",
"CanineForSequenceClassification",
"CanineForTokenClassification",
"CanineModel",
"CaninePreTrainedModel",
"load_tf_weights_in_canine",
]
)
_import_structure["models.chameleon"].extend(
[
"ChameleonForConditionalGeneration",
"ChameleonModel",
"ChameleonPreTrainedModel",
"ChameleonProcessor",
"ChameleonVQVAE",
]
)
_import_structure["models.chinese_clip"].extend(
[
"ChineseCLIPModel",
"ChineseCLIPPreTrainedModel",
"ChineseCLIPTextModel",
"ChineseCLIPVisionModel",
]
)
_import_structure["models.clap"].extend(
[
"ClapAudioModel",
"ClapAudioModelWithProjection",
"ClapFeatureExtractor",
"ClapModel",
"ClapPreTrainedModel",
"ClapTextModel",
"ClapTextModelWithProjection",
]
)
_import_structure["models.clip"].extend(
[
"CLIPForImageClassification",
"CLIPModel",
"CLIPPreTrainedModel",
"CLIPTextModel",
"CLIPTextModelWithProjection",
"CLIPVisionModel",
"CLIPVisionModelWithProjection",
]
)
_import_structure["models.clipseg"].extend(
[
"CLIPSegForImageSegmentation",
"CLIPSegModel",
"CLIPSegPreTrainedModel",
"CLIPSegTextModel",
"CLIPSegVisionModel",
]
)
_import_structure["models.clvp"].extend(
[
"ClvpDecoder",
"ClvpEncoder",
"ClvpForCausalLM",
"ClvpModel",
"ClvpModelForConditionalGeneration",
"ClvpPreTrainedModel",
]
)
_import_structure["models.codegen"].extend(
[
"CodeGenForCausalLM",
"CodeGenModel",
"CodeGenPreTrainedModel",
]
)
_import_structure["models.cohere"].extend(["CohereForCausalLM", "CohereModel", "CoherePreTrainedModel"])
_import_structure["models.cohere2"].extend(["Cohere2ForCausalLM", "Cohere2Model", "Cohere2PreTrainedModel"])
_import_structure["models.colpali"].extend(
[
"ColPaliForRetrieval",
"ColPaliPreTrainedModel",
]
)
_import_structure["models.conditional_detr"].extend(
[
"ConditionalDetrForObjectDetection",
"ConditionalDetrForSegmentation",
"ConditionalDetrModel",
"ConditionalDetrPreTrainedModel",
]
)
_import_structure["models.convbert"].extend(
[
"ConvBertForMaskedLM",
"ConvBertForMultipleChoice",
"ConvBertForQuestionAnswering",
"ConvBertForSequenceClassification",
"ConvBertForTokenClassification",
"ConvBertModel",
"ConvBertPreTrainedModel",
"load_tf_weights_in_convbert",
]
)
_import_structure["models.convnext"].extend(
[
"ConvNextBackbone",
"ConvNextForImageClassification",
"ConvNextModel",
"ConvNextPreTrainedModel",
]
)
_import_structure["models.convnextv2"].extend(
[
"ConvNextV2Backbone",
"ConvNextV2ForImageClassification",
"ConvNextV2Model",
"ConvNextV2PreTrainedModel",
]
)
_import_structure["models.cpmant"].extend(
[
"CpmAntForCausalLM",
"CpmAntModel",
"CpmAntPreTrainedModel",
]
)
_import_structure["models.ctrl"].extend(
[
"CTRLForSequenceClassification",
"CTRLLMHeadModel",
"CTRLModel",
"CTRLPreTrainedModel",
]
)
_import_structure["models.cvt"].extend(
[
"CvtForImageClassification",
"CvtModel",
"CvtPreTrainedModel",
]
)
_import_structure["models.dab_detr"].extend(
[
"DabDetrForObjectDetection",
"DabDetrModel",
"DabDetrPreTrainedModel",
]
)
_import_structure["models.dac"].extend(
[
"DacModel",
"DacPreTrainedModel",
]
)
_import_structure["models.data2vec"].extend(
[
"Data2VecAudioForAudioFrameClassification",
"Data2VecAudioForCTC",
"Data2VecAudioForSequenceClassification",
"Data2VecAudioForXVector",
"Data2VecAudioModel",
"Data2VecAudioPreTrainedModel",
"Data2VecTextForCausalLM",
"Data2VecTextForMaskedLM",
"Data2VecTextForMultipleChoice",
"Data2VecTextForQuestionAnswering",
"Data2VecTextForSequenceClassification",
"Data2VecTextForTokenClassification",
"Data2VecTextModel",
"Data2VecTextPreTrainedModel",
"Data2VecVisionForImageClassification",
"Data2VecVisionForSemanticSegmentation",
"Data2VecVisionModel",
"Data2VecVisionPreTrainedModel",
]
)
_import_structure["models.dbrx"].extend(
[
"DbrxForCausalLM",
"DbrxModel",
"DbrxPreTrainedModel",
]
)
_import_structure["models.deberta"].extend(
[
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
)
_import_structure["models.deberta_v2"].extend(
[
"DebertaV2ForMaskedLM",
"DebertaV2ForMultipleChoice",
"DebertaV2ForQuestionAnswering",
"DebertaV2ForSequenceClassification",
"DebertaV2ForTokenClassification",
"DebertaV2Model",
"DebertaV2PreTrainedModel",
]
)
_import_structure["models.decision_transformer"].extend(
[
"DecisionTransformerGPT2Model",
"DecisionTransformerGPT2PreTrainedModel",
"DecisionTransformerModel",
"DecisionTransformerPreTrainedModel",
]
)
_import_structure["models.deformable_detr"].extend(
[
"DeformableDetrForObjectDetection",
"DeformableDetrModel",
"DeformableDetrPreTrainedModel",
]
)
_import_structure["models.deit"].extend(
[
"DeiTForImageClassification",
"DeiTForImageClassificationWithTeacher",
"DeiTForMaskedImageModeling",
"DeiTModel",
"DeiTPreTrainedModel",
]
)
_import_structure["models.deprecated.deta"].extend(
[
"DetaForObjectDetection",
"DetaModel",
"DetaPreTrainedModel",
]
)
_import_structure["models.deprecated.efficientformer"].extend(
[
"EfficientFormerForImageClassification",
"EfficientFormerForImageClassificationWithTeacher",
"EfficientFormerModel",
"EfficientFormerPreTrainedModel",
]
)
_import_structure["models.deprecated.ernie_m"].extend(
[
"ErnieMForInformationExtraction",
"ErnieMForMultipleChoice",
"ErnieMForQuestionAnswering",
"ErnieMForSequenceClassification",
"ErnieMForTokenClassification",
"ErnieMModel",
"ErnieMPreTrainedModel",
]
)
_import_structure["models.deprecated.gptsan_japanese"].extend(
[
"GPTSanJapaneseForConditionalGeneration",
"GPTSanJapaneseModel",
"GPTSanJapanesePreTrainedModel",
]
)
_import_structure["models.deprecated.graphormer"].extend(
[
"GraphormerForGraphClassification",
"GraphormerModel",
"GraphormerPreTrainedModel",
]
)
_import_structure["models.deprecated.jukebox"].extend(
[
"JukeboxModel",
"JukeboxPreTrainedModel",
"JukeboxPrior",
"JukeboxVQVAE",
]
)
_import_structure["models.deprecated.mctct"].extend(
[
"MCTCTForCTC",
"MCTCTModel",
"MCTCTPreTrainedModel",
]
)
_import_structure["models.deprecated.mega"].extend(
[
"MegaForCausalLM",
"MegaForMaskedLM",
"MegaForMultipleChoice",
"MegaForQuestionAnswering",
"MegaForSequenceClassification",
"MegaForTokenClassification",
"MegaModel",
"MegaPreTrainedModel",
]
)
_import_structure["models.deprecated.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"])
_import_structure["models.deprecated.nat"].extend(
[
"NatBackbone",
"NatForImageClassification",
"NatModel",
"NatPreTrainedModel",
]
)
_import_structure["models.deprecated.nezha"].extend(
[
"NezhaForMaskedLM",
"NezhaForMultipleChoice",
"NezhaForNextSentencePrediction",
"NezhaForPreTraining",
"NezhaForQuestionAnswering",
"NezhaForSequenceClassification",
"NezhaForTokenClassification",
"NezhaModel",
"NezhaPreTrainedModel",
]
)
_import_structure["models.deprecated.open_llama"].extend(
[
"OpenLlamaForCausalLM",
"OpenLlamaForSequenceClassification",
"OpenLlamaModel",
"OpenLlamaPreTrainedModel",
]
)
_import_structure["models.deprecated.qdqbert"].extend(
[
"QDQBertForMaskedLM",
"QDQBertForMultipleChoice",
"QDQBertForNextSentencePrediction",
"QDQBertForQuestionAnswering",
"QDQBertForSequenceClassification",
"QDQBertForTokenClassification",
"QDQBertLMHeadModel",
"QDQBertModel",
"QDQBertPreTrainedModel",
"load_tf_weights_in_qdqbert",
]
)
_import_structure["models.deprecated.realm"].extend(
[
"RealmEmbedder",
"RealmForOpenQA",
"RealmKnowledgeAugEncoder",
"RealmPreTrainedModel",
"RealmReader",
"RealmRetriever",
"RealmScorer",
"load_tf_weights_in_realm",
]
)
_import_structure["models.deprecated.retribert"].extend(
[
"RetriBertModel",
"RetriBertPreTrainedModel",
]
)
_import_structure["models.deprecated.speech_to_text_2"].extend(
["Speech2Text2ForCausalLM", "Speech2Text2PreTrainedModel"]
)
_import_structure["models.deprecated.trajectory_transformer"].extend(
[
"TrajectoryTransformerModel",
"TrajectoryTransformerPreTrainedModel",
]
)
_import_structure["models.deprecated.transfo_xl"].extend(
[
"AdaptiveEmbedding",
"TransfoXLForSequenceClassification",
"TransfoXLLMHeadModel",
"TransfoXLModel",
"TransfoXLPreTrainedModel",
"load_tf_weights_in_transfo_xl",
]
)
_import_structure["models.deprecated.tvlt"].extend(
[
"TvltForAudioVisualClassification",
"TvltForPreTraining",
"TvltModel",
"TvltPreTrainedModel",
]
)
_import_structure["models.deprecated.van"].extend(
[
"VanForImageClassification",
"VanModel",
"VanPreTrainedModel",
]
)
_import_structure["models.deprecated.vit_hybrid"].extend(
[
"ViTHybridForImageClassification",
"ViTHybridModel",
"ViTHybridPreTrainedModel",
]
)
_import_structure["models.deprecated.xlm_prophetnet"].extend(
[
"XLMProphetNetDecoder",
"XLMProphetNetEncoder",
"XLMProphetNetForCausalLM",
"XLMProphetNetForConditionalGeneration",
"XLMProphetNetModel",
"XLMProphetNetPreTrainedModel",
]
)
_import_structure["models.depth_anything"].extend(
[
"DepthAnythingForDepthEstimation",
"DepthAnythingPreTrainedModel",
]
)
_import_structure["models.detr"].extend(
[
"DetrForObjectDetection",
"DetrForSegmentation",
"DetrModel",
"DetrPreTrainedModel",
]
)
_import_structure["models.diffllama"].extend(
[
"DiffLlamaForCausalLM",
"DiffLlamaForQuestionAnswering",
"DiffLlamaForSequenceClassification",
"DiffLlamaForTokenClassification",
"DiffLlamaModel",
"DiffLlamaPreTrainedModel",
]
)
_import_structure["models.dinat"].extend(
[
"DinatBackbone",
"DinatForImageClassification",
"DinatModel",
"DinatPreTrainedModel",
]
)
_import_structure["models.dinov2"].extend(
[
"Dinov2Backbone",
"Dinov2ForImageClassification",
"Dinov2Model",
"Dinov2PreTrainedModel",
]
)
_import_structure["models.dinov2_with_registers"].extend(
[
"Dinov2WithRegistersBackbone",
"Dinov2WithRegistersForImageClassification",
"Dinov2WithRegistersModel",
"Dinov2WithRegistersPreTrainedModel",
]
)
_import_structure["models.distilbert"].extend(
[
"DistilBertForMaskedLM",
"DistilBertForMultipleChoice",
"DistilBertForQuestionAnswering",
"DistilBertForSequenceClassification",
"DistilBertForTokenClassification",
"DistilBertModel",
"DistilBertPreTrainedModel",
]
)
_import_structure["models.donut"].extend(
[
"DonutSwinModel",
"DonutSwinPreTrainedModel",
]
)
_import_structure["models.dpr"].extend(
[
"DPRContextEncoder",
"DPRPretrainedContextEncoder",
"DPRPreTrainedModel",
"DPRPretrainedQuestionEncoder",
"DPRPretrainedReader",
"DPRQuestionEncoder",
"DPRReader",
]
)
_import_structure["models.dpt"].extend(
[
"DPTForDepthEstimation",
"DPTForSemanticSegmentation",
"DPTModel",
"DPTPreTrainedModel",
]
)
_import_structure["models.efficientnet"].extend(
[
"EfficientNetForImageClassification",
"EfficientNetModel",
"EfficientNetPreTrainedModel",
]
)
_import_structure["models.electra"].extend(
[
"ElectraForCausalLM",
"ElectraForMaskedLM",
"ElectraForMultipleChoice",
"ElectraForPreTraining",
"ElectraForQuestionAnswering",
"ElectraForSequenceClassification",
"ElectraForTokenClassification",
"ElectraModel",
"ElectraPreTrainedModel",
"load_tf_weights_in_electra",
]
)
_import_structure["models.emu3"].extend(
[
"Emu3ForCausalLM",
"Emu3ForConditionalGeneration",
"Emu3PreTrainedModel",
"Emu3TextModel",
"Emu3VQVAE",
]
)
_import_structure["models.encodec"].extend(
[
"EncodecModel",
"EncodecPreTrainedModel",
]
)
_import_structure["models.encoder_decoder"].append("EncoderDecoderModel")
_import_structure["models.ernie"].extend(
[
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
)
_import_structure["models.esm"].extend(
[
"EsmFoldPreTrainedModel",
"EsmForMaskedLM",
"EsmForProteinFolding",
"EsmForSequenceClassification",
"EsmForTokenClassification",
"EsmModel",
"EsmPreTrainedModel",
]
)
_import_structure["models.falcon"].extend(
[
"FalconForCausalLM",
"FalconForQuestionAnswering",
"FalconForSequenceClassification",
"FalconForTokenClassification",
"FalconModel",
"FalconPreTrainedModel",
]
)
_import_structure["models.falcon_mamba"].extend(
[
"FalconMambaForCausalLM",
"FalconMambaModel",
"FalconMambaPreTrainedModel",
]
)
_import_structure["models.fastspeech2_conformer"].extend(
[
"FastSpeech2ConformerHifiGan",
"FastSpeech2ConformerModel",
"FastSpeech2ConformerPreTrainedModel",
"FastSpeech2ConformerWithHifiGan",
]
)
_import_structure["models.flaubert"].extend(
[
"FlaubertForMultipleChoice",
"FlaubertForQuestionAnswering",
"FlaubertForQuestionAnsweringSimple",
"FlaubertForSequenceClassification",
"FlaubertForTokenClassification",
"FlaubertModel",
"FlaubertPreTrainedModel",
"FlaubertWithLMHeadModel",
]
)
_import_structure["models.flava"].extend(
[
"FlavaForPreTraining",
"FlavaImageCodebook",
"FlavaImageModel",
"FlavaModel",
"FlavaMultimodalModel",
"FlavaPreTrainedModel",
"FlavaTextModel",
]
)
_import_structure["models.fnet"].extend(
[
"FNetForMaskedLM",
"FNetForMultipleChoice",
"FNetForNextSentencePrediction",
"FNetForPreTraining",
"FNetForQuestionAnswering",
"FNetForSequenceClassification",
"FNetForTokenClassification",
"FNetModel",
"FNetPreTrainedModel",
]
)
_import_structure["models.focalnet"].extend(
[
"FocalNetBackbone",
"FocalNetForImageClassification",
"FocalNetForMaskedImageModeling",
"FocalNetModel",
"FocalNetPreTrainedModel",
]
)
_import_structure["models.fsmt"].extend(["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"])
_import_structure["models.funnel"].extend(
[
"FunnelBaseModel",
"FunnelForMaskedLM",
"FunnelForMultipleChoice",
"FunnelForPreTraining",
"FunnelForQuestionAnswering",
"FunnelForSequenceClassification",
"FunnelForTokenClassification",
"FunnelModel",
"FunnelPreTrainedModel",
"load_tf_weights_in_funnel",
]
)
_import_structure["models.fuyu"].extend(["FuyuForCausalLM", "FuyuPreTrainedModel"])
_import_structure["models.gemma"].extend(
[
"GemmaForCausalLM",
"GemmaForSequenceClassification",
"GemmaForTokenClassification",
"GemmaModel",
"GemmaPreTrainedModel",
]
)
_import_structure["models.gemma2"].extend(
[
"Gemma2ForCausalLM",
"Gemma2ForSequenceClassification",
"Gemma2ForTokenClassification",
"Gemma2Model",
"Gemma2PreTrainedModel",
]
)
_import_structure["models.git"].extend(
[
"GitForCausalLM",
"GitModel",
"GitPreTrainedModel",
"GitVisionModel",
]
)
_import_structure["models.glm"].extend(
[
"GlmForCausalLM",
"GlmForSequenceClassification",
"GlmForTokenClassification",
"GlmModel",
"GlmPreTrainedModel",
]
)
_import_structure["models.glpn"].extend(
[
"GLPNForDepthEstimation",
"GLPNModel",
"GLPNPreTrainedModel",
]
)
_import_structure["models.got_ocr2"].extend(
[
"GotOcr2ForConditionalGeneration",
"GotOcr2PreTrainedModel",
]
)
_import_structure["models.gpt2"].extend(
[
"GPT2DoubleHeadsModel",
"GPT2ForQuestionAnswering",
"GPT2ForSequenceClassification",
"GPT2ForTokenClassification",
"GPT2LMHeadModel",
"GPT2Model",
"GPT2PreTrainedModel",
"load_tf_weights_in_gpt2",
]
)
_import_structure["models.gpt_bigcode"].extend(
[
"GPTBigCodeForCausalLM",
"GPTBigCodeForSequenceClassification",
"GPTBigCodeForTokenClassification",
"GPTBigCodeModel",
"GPTBigCodePreTrainedModel",
]
)
_import_structure["models.gpt_neo"].extend(
[
"GPTNeoForCausalLM",
"GPTNeoForQuestionAnswering",
"GPTNeoForSequenceClassification",
"GPTNeoForTokenClassification",
"GPTNeoModel",
"GPTNeoPreTrainedModel",
"load_tf_weights_in_gpt_neo",
]
)
_import_structure["models.gpt_neox"].extend(
[
"GPTNeoXForCausalLM",
"GPTNeoXForQuestionAnswering",
"GPTNeoXForSequenceClassification",
"GPTNeoXForTokenClassification",
"GPTNeoXModel",
"GPTNeoXPreTrainedModel",
]
)
_import_structure["models.gpt_neox_japanese"].extend(
[
"GPTNeoXJapaneseForCausalLM",
"GPTNeoXJapaneseModel",
"GPTNeoXJapanesePreTrainedModel",
]
)
_import_structure["models.gptj"].extend(
[
"GPTJForCausalLM",
"GPTJForQuestionAnswering",
"GPTJForSequenceClassification",
"GPTJModel",
"GPTJPreTrainedModel",
]
)
_import_structure["models.granite"].extend(
[
"GraniteForCausalLM",
"GraniteModel",
"GranitePreTrainedModel",
]
)
_import_structure["models.granitemoe"].extend(
[
"GraniteMoeForCausalLM",
"GraniteMoeModel",
"GraniteMoePreTrainedModel",
]
)
_import_structure["models.grounding_dino"].extend(
[
"GroundingDinoForObjectDetection",
"GroundingDinoModel",
"GroundingDinoPreTrainedModel",
]
)
_import_structure["models.groupvit"].extend(
[
"GroupViTModel",
"GroupViTPreTrainedModel",
"GroupViTTextModel",
"GroupViTVisionModel",
]
)
_import_structure["models.helium"].extend(
[
"HeliumForCausalLM",
"HeliumForSequenceClassification",
"HeliumForTokenClassification",
"HeliumModel",
"HeliumPreTrainedModel",
]
)
_import_structure["models.hiera"].extend(
[
"HieraBackbone",
"HieraForImageClassification",
"HieraForPreTraining",
"HieraModel",
"HieraPreTrainedModel",
]
)
_import_structure["models.hubert"].extend(
[
"HubertForCTC",
"HubertForSequenceClassification",
"HubertModel",
"HubertPreTrainedModel",
]
)
_import_structure["models.ibert"].extend(
[
"IBertForMaskedLM",
"IBertForMultipleChoice",
"IBertForQuestionAnswering",
"IBertForSequenceClassification",
"IBertForTokenClassification",
"IBertModel",
"IBertPreTrainedModel",
]
)
_import_structure["models.idefics"].extend(
[
"IdeficsForVisionText2Text",
"IdeficsModel",
"IdeficsPreTrainedModel",
"IdeficsProcessor",
]
)
_import_structure["models.idefics2"].extend(
[
"Idefics2ForConditionalGeneration",
"Idefics2Model",
"Idefics2PreTrainedModel",
"Idefics2Processor",
]
)
_import_structure["models.idefics3"].extend(
[
"Idefics3ForConditionalGeneration",
"Idefics3Model",
"Idefics3PreTrainedModel",
"Idefics3Processor",
"Idefics3VisionConfig",
"Idefics3VisionTransformer",
]
)
_import_structure["models.ijepa"].extend(
[
"IJepaForImageClassification",
"IJepaModel",
"IJepaPreTrainedModel",
]
)
_import_structure["models.imagegpt"].extend(
[
"ImageGPTForCausalImageModeling",
"ImageGPTForImageClassification",
"ImageGPTModel",
"ImageGPTPreTrainedModel",
"load_tf_weights_in_imagegpt",
]
)
_import_structure["models.informer"].extend(
[
"InformerForPrediction",
"InformerModel",
"InformerPreTrainedModel",
]
)
_import_structure["models.instructblip"].extend(
[
"InstructBlipForConditionalGeneration",
"InstructBlipPreTrainedModel",
"InstructBlipQFormerModel",
"InstructBlipVisionModel",
]
)
_import_structure["models.instructblipvideo"].extend(
[
"InstructBlipVideoForConditionalGeneration",
"InstructBlipVideoPreTrainedModel",
"InstructBlipVideoQFormerModel",
"InstructBlipVideoVisionModel",
]
)
_import_structure["models.jamba"].extend(
[
"JambaForCausalLM",
"JambaForSequenceClassification",
"JambaModel",
"JambaPreTrainedModel",
]
)
_import_structure["models.jetmoe"].extend(
[
"JetMoeForCausalLM",
"JetMoeForSequenceClassification",
"JetMoeModel",
"JetMoePreTrainedModel",
]
)
_import_structure["models.kosmos2"].extend(
[
"Kosmos2ForConditionalGeneration",
"Kosmos2Model",
"Kosmos2PreTrainedModel",
]
)
_import_structure["models.layoutlm"].extend(
[
"LayoutLMForMaskedLM",
"LayoutLMForQuestionAnswering",
"LayoutLMForSequenceClassification",
"LayoutLMForTokenClassification",
"LayoutLMModel",
"LayoutLMPreTrainedModel",
]
)
_import_structure["models.layoutlmv2"].extend(
[
"LayoutLMv2ForQuestionAnswering",
"LayoutLMv2ForSequenceClassification",
"LayoutLMv2ForTokenClassification",
"LayoutLMv2Model",
"LayoutLMv2PreTrainedModel",
]
)
_import_structure["models.layoutlmv3"].extend(
[
"LayoutLMv3ForQuestionAnswering",
"LayoutLMv3ForSequenceClassification",
"LayoutLMv3ForTokenClassification",
"LayoutLMv3Model",
"LayoutLMv3PreTrainedModel",
]
)
_import_structure["models.led"].extend(
[
"LEDForConditionalGeneration",
"LEDForQuestionAnswering",
"LEDForSequenceClassification",
"LEDModel",
"LEDPreTrainedModel",
]
)
_import_structure["models.levit"].extend(
[
"LevitForImageClassification",
"LevitForImageClassificationWithTeacher",
"LevitModel",
"LevitPreTrainedModel",
]
)
_import_structure["models.lilt"].extend(
[
"LiltForQuestionAnswering",
"LiltForSequenceClassification",
"LiltForTokenClassification",
"LiltModel",
"LiltPreTrainedModel",
]
)
_import_structure["models.llama"].extend(
[
"LlamaForCausalLM",
"LlamaForQuestionAnswering",
"LlamaForSequenceClassification",
"LlamaForTokenClassification",
"LlamaModel",
"LlamaPreTrainedModel",
]
)
_import_structure["models.llava"].extend(
[
"LlavaForConditionalGeneration",
"LlavaPreTrainedModel",
]
)
_import_structure["models.llava_next"].extend(
[
"LlavaNextForConditionalGeneration",
"LlavaNextPreTrainedModel",
]
)
_import_structure["models.llava_next_video"].extend(
[
"LlavaNextVideoForConditionalGeneration",
"LlavaNextVideoPreTrainedModel",
]
)
_import_structure["models.llava_onevision"].extend(
[
"LlavaOnevisionForConditionalGeneration",
"LlavaOnevisionPreTrainedModel",
]
)
_import_structure["models.longformer"].extend(
[
"LongformerForMaskedLM",
"LongformerForMultipleChoice",
"LongformerForQuestionAnswering",
"LongformerForSequenceClassification",
"LongformerForTokenClassification",
"LongformerModel",
"LongformerPreTrainedModel",
]
)
_import_structure["models.longt5"].extend(
[
"LongT5EncoderModel",
"LongT5ForConditionalGeneration",
"LongT5Model",
"LongT5PreTrainedModel",
]
)
_import_structure["models.luke"].extend(
[
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"LukeForMaskedLM",
"LukeForMultipleChoice",
"LukeForQuestionAnswering",
"LukeForSequenceClassification",
"LukeForTokenClassification",
"LukeModel",
"LukePreTrainedModel",
]
)
_import_structure["models.lxmert"].extend(
[
"LxmertEncoder",
"LxmertForPreTraining",
"LxmertForQuestionAnswering",
"LxmertModel",
"LxmertPreTrainedModel",
"LxmertVisualFeatureEncoder",
]
)
_import_structure["models.m2m_100"].extend(
[
"M2M100ForConditionalGeneration",
"M2M100Model",
"M2M100PreTrainedModel",
]
)
_import_structure["models.mamba"].extend(
[
"MambaForCausalLM",
"MambaModel",
"MambaPreTrainedModel",
]
)
_import_structure["models.mamba2"].extend(
[
"Mamba2ForCausalLM",
"Mamba2Model",
"Mamba2PreTrainedModel",
]
)
_import_structure["models.marian"].extend(
["MarianForCausalLM", "MarianModel", "MarianMTModel", "MarianPreTrainedModel"]
)
_import_structure["models.markuplm"].extend(
[
"MarkupLMForQuestionAnswering",
"MarkupLMForSequenceClassification",
"MarkupLMForTokenClassification",
"MarkupLMModel",
"MarkupLMPreTrainedModel",
]
)
_import_structure["models.mask2former"].extend(
[
"Mask2FormerForUniversalSegmentation",
"Mask2FormerModel",
"Mask2FormerPreTrainedModel",
]
)
_import_structure["models.maskformer"].extend(
[
"MaskFormerForInstanceSegmentation",
"MaskFormerModel",
"MaskFormerPreTrainedModel",
"MaskFormerSwinBackbone",
]
)
_import_structure["models.mbart"].extend(
[
"MBartForCausalLM",
"MBartForConditionalGeneration",
"MBartForQuestionAnswering",
"MBartForSequenceClassification",
"MBartModel",
"MBartPreTrainedModel",
]
)
_import_structure["models.megatron_bert"].extend(
[
"MegatronBertForCausalLM",
"MegatronBertForMaskedLM",
"MegatronBertForMultipleChoice",
"MegatronBertForNextSentencePrediction",
"MegatronBertForPreTraining",
"MegatronBertForQuestionAnswering",
"MegatronBertForSequenceClassification",
"MegatronBertForTokenClassification",
"MegatronBertModel",
"MegatronBertPreTrainedModel",
]
)
_import_structure["models.mgp_str"].extend(
[
"MgpstrForSceneTextRecognition",
"MgpstrModel",
"MgpstrPreTrainedModel",
]
)
_import_structure["models.mimi"].extend(
[
"MimiModel",
"MimiPreTrainedModel",
]
)
_import_structure["models.mistral"].extend(
[
"MistralForCausalLM",
"MistralForQuestionAnswering",
"MistralForSequenceClassification",
"MistralForTokenClassification",
"MistralModel",
"MistralPreTrainedModel",
]
)
_import_structure["models.mixtral"].extend(
[
"MixtralForCausalLM",
"MixtralForQuestionAnswering",
"MixtralForSequenceClassification",
"MixtralForTokenClassification",
"MixtralModel",
"MixtralPreTrainedModel",
]
)
_import_structure["models.mllama"].extend(
[
"MllamaForCausalLM",
"MllamaForConditionalGeneration",
"MllamaPreTrainedModel",
"MllamaProcessor",
"MllamaTextModel",
"MllamaVisionModel",
]
)
_import_structure["models.mobilebert"].extend(
[
"MobileBertForMaskedLM",
"MobileBertForMultipleChoice",
"MobileBertForNextSentencePrediction",
"MobileBertForPreTraining",
"MobileBertForQuestionAnswering",
"MobileBertForSequenceClassification",
"MobileBertForTokenClassification",
"MobileBertModel",
"MobileBertPreTrainedModel",
"load_tf_weights_in_mobilebert",
]
)
_import_structure["models.mobilenet_v1"].extend(
[
"MobileNetV1ForImageClassification",
"MobileNetV1Model",
"MobileNetV1PreTrainedModel",
"load_tf_weights_in_mobilenet_v1",
]
)
_import_structure["models.mobilenet_v2"].extend(
[
"MobileNetV2ForImageClassification",
"MobileNetV2ForSemanticSegmentation",
"MobileNetV2Model",
"MobileNetV2PreTrainedModel",
"load_tf_weights_in_mobilenet_v2",
]
)
_import_structure["models.mobilevit"].extend(
[
"MobileViTForImageClassification",
"MobileViTForSemanticSegmentation",
"MobileViTModel",
"MobileViTPreTrainedModel",
]
)
_import_structure["models.mobilevitv2"].extend(
[
"MobileViTV2ForImageClassification",
"MobileViTV2ForSemanticSegmentation",
"MobileViTV2Model",
"MobileViTV2PreTrainedModel",
]
)
_import_structure["models.modernbert"].extend(
[
"ModernBertForMaskedLM",
"ModernBertForSequenceClassification",
"ModernBertForTokenClassification",
"ModernBertModel",
"ModernBertPreTrainedModel",
]
)
_import_structure["models.moonshine"].extend(
[
"MoonshineForConditionalGeneration",
"MoonshineModel",
"MoonshinePreTrainedModel",
]
)
_import_structure["models.moshi"].extend(
[
"MoshiForCausalLM",
"MoshiForConditionalGeneration",
"MoshiModel",
"MoshiPreTrainedModel",
]
)
_import_structure["models.mpnet"].extend(
[
"MPNetForMaskedLM",
"MPNetForMultipleChoice",
"MPNetForQuestionAnswering",
"MPNetForSequenceClassification",
"MPNetForTokenClassification",
"MPNetModel",
"MPNetPreTrainedModel",
]
)
_import_structure["models.mpt"].extend(
[
"MptForCausalLM",
"MptForQuestionAnswering",
"MptForSequenceClassification",
"MptForTokenClassification",
"MptModel",
"MptPreTrainedModel",
]
)
_import_structure["models.mra"].extend(
[
"MraForMaskedLM",
"MraForMultipleChoice",
"MraForQuestionAnswering",
"MraForSequenceClassification",
"MraForTokenClassification",
"MraModel",
"MraPreTrainedModel",
]
)
_import_structure["models.mt5"].extend(
[
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5ForSequenceClassification",
"MT5ForTokenClassification",
"MT5Model",
"MT5PreTrainedModel",
]
)
_import_structure["models.musicgen"].extend(
[
"MusicgenForCausalLM",
"MusicgenForConditionalGeneration",
"MusicgenModel",
"MusicgenPreTrainedModel",
"MusicgenProcessor",
]
)
_import_structure["models.musicgen_melody"].extend(
[
"MusicgenMelodyForCausalLM",
"MusicgenMelodyForConditionalGeneration",
"MusicgenMelodyModel",
"MusicgenMelodyPreTrainedModel",
]
)
_import_structure["models.mvp"].extend(
[
"MvpForCausalLM",
"MvpForConditionalGeneration",
"MvpForQuestionAnswering",
"MvpForSequenceClassification",
"MvpModel",
"MvpPreTrainedModel",
]
)
_import_structure["models.nemotron"].extend(
[
"NemotronForCausalLM",
"NemotronForQuestionAnswering",
"NemotronForSequenceClassification",
"NemotronForTokenClassification",
"NemotronModel",
"NemotronPreTrainedModel",
]
)
_import_structure["models.nllb_moe"].extend(
[
"NllbMoeForConditionalGeneration",
"NllbMoeModel",
"NllbMoePreTrainedModel",
"NllbMoeSparseMLP",
"NllbMoeTop2Router",
]
)
_import_structure["models.nystromformer"].extend(
[
"NystromformerForMaskedLM",
"NystromformerForMultipleChoice",
"NystromformerForQuestionAnswering",
"NystromformerForSequenceClassification",
"NystromformerForTokenClassification",
"NystromformerModel",
"NystromformerPreTrainedModel",
]
)
_import_structure["models.olmo"].extend(
[
"OlmoForCausalLM",
"OlmoModel",
"OlmoPreTrainedModel",
]
)
_import_structure["models.olmo2"].extend(
[
"Olmo2ForCausalLM",
"Olmo2Model",
"Olmo2PreTrainedModel",
]
)
_import_structure["models.olmoe"].extend(
[
"OlmoeForCausalLM",
"OlmoeModel",
"OlmoePreTrainedModel",
]
)
_import_structure["models.omdet_turbo"].extend(
[
"OmDetTurboForObjectDetection",
"OmDetTurboPreTrainedModel",
]
)
_import_structure["models.oneformer"].extend(
[
"OneFormerForUniversalSegmentation",
"OneFormerModel",
"OneFormerPreTrainedModel",
]
)
_import_structure["models.openai"].extend(
[
"OpenAIGPTDoubleHeadsModel",
"OpenAIGPTForSequenceClassification",
"OpenAIGPTLMHeadModel",
"OpenAIGPTModel",
"OpenAIGPTPreTrainedModel",
"load_tf_weights_in_openai_gpt",
]
)
_import_structure["models.opt"].extend(
[
"OPTForCausalLM",
"OPTForQuestionAnswering",
"OPTForSequenceClassification",
"OPTModel",
"OPTPreTrainedModel",
]
)
_import_structure["models.owlv2"].extend(
[
"Owlv2ForObjectDetection",
"Owlv2Model",
"Owlv2PreTrainedModel",
"Owlv2TextModel",
"Owlv2VisionModel",
]
)
_import_structure["models.owlvit"].extend(
[
"OwlViTForObjectDetection",
"OwlViTModel",
"OwlViTPreTrainedModel",
"OwlViTTextModel",
"OwlViTVisionModel",
]
)
_import_structure["models.paligemma"].extend(
[
"PaliGemmaForConditionalGeneration",
"PaliGemmaPreTrainedModel",
"PaliGemmaProcessor",
]
)
_import_structure["models.patchtsmixer"].extend(
[
"PatchTSMixerForPrediction",
"PatchTSMixerForPretraining",
"PatchTSMixerForRegression",
"PatchTSMixerForTimeSeriesClassification",
"PatchTSMixerModel",
"PatchTSMixerPreTrainedModel",
]
)
_import_structure["models.patchtst"].extend(
[
"PatchTSTForClassification",
"PatchTSTForPrediction",
"PatchTSTForPretraining",
"PatchTSTForRegression",
"PatchTSTModel",
"PatchTSTPreTrainedModel",
]
)
_import_structure["models.pegasus"].extend(
[
"PegasusForCausalLM",
"PegasusForConditionalGeneration",
"PegasusModel",
"PegasusPreTrainedModel",
]
)
_import_structure["models.pegasus_x"].extend(
[
"PegasusXForConditionalGeneration",
"PegasusXModel",
"PegasusXPreTrainedModel",
]
)
_import_structure["models.perceiver"].extend(
[
"PerceiverForImageClassificationConvProcessing",
"PerceiverForImageClassificationFourier",
"PerceiverForImageClassificationLearned",
"PerceiverForMaskedLM",
"PerceiverForMultimodalAutoencoding",
"PerceiverForOpticalFlow",
"PerceiverForSequenceClassification",
"PerceiverModel",
"PerceiverPreTrainedModel",
]
)
_import_structure["models.persimmon"].extend(
[
"PersimmonForCausalLM",
"PersimmonForSequenceClassification",
"PersimmonForTokenClassification",
"PersimmonModel",
"PersimmonPreTrainedModel",
]
)
_import_structure["models.phi"].extend(
[
"PhiForCausalLM",
"PhiForSequenceClassification",
"PhiForTokenClassification",
"PhiModel",
"PhiPreTrainedModel",
]
)
_import_structure["models.phi3"].extend(
[
"Phi3ForCausalLM",
"Phi3ForSequenceClassification",
"Phi3ForTokenClassification",
"Phi3Model",
"Phi3PreTrainedModel",
]
)
_import_structure["models.phimoe"].extend(
[
"PhimoeForCausalLM",
"PhimoeForSequenceClassification",
"PhimoeModel",
"PhimoePreTrainedModel",
]
)
_import_structure["models.pix2struct"].extend(
[
"Pix2StructForConditionalGeneration",
"Pix2StructPreTrainedModel",
"Pix2StructTextModel",
"Pix2StructVisionModel",
]
)
_import_structure["models.pixtral"].extend(["PixtralPreTrainedModel", "PixtralVisionModel"])
_import_structure["models.plbart"].extend(
[
"PLBartForCausalLM",
"PLBartForConditionalGeneration",
"PLBartForSequenceClassification",
"PLBartModel",
"PLBartPreTrainedModel",
]
)
_import_structure["models.poolformer"].extend(
[
"PoolFormerForImageClassification",
"PoolFormerModel",
"PoolFormerPreTrainedModel",
]
)
_import_structure["models.pop2piano"].extend(
[
"Pop2PianoForConditionalGeneration",
"Pop2PianoPreTrainedModel",
]
)
_import_structure["models.prophetnet"].extend(
[
"ProphetNetDecoder",
"ProphetNetEncoder",
"ProphetNetForCausalLM",
"ProphetNetForConditionalGeneration",
"ProphetNetModel",
"ProphetNetPreTrainedModel",
]
)
_import_structure["models.pvt"].extend(
[
"PvtForImageClassification",
"PvtModel",
"PvtPreTrainedModel",
]
)
_import_structure["models.pvt_v2"].extend(
[
"PvtV2Backbone",
"PvtV2ForImageClassification",
"PvtV2Model",
"PvtV2PreTrainedModel",
]
)
_import_structure["models.qwen2"].extend(
[
"Qwen2ForCausalLM",
"Qwen2ForQuestionAnswering",
"Qwen2ForSequenceClassification",
"Qwen2ForTokenClassification",
"Qwen2Model",
"Qwen2PreTrainedModel",
]
)
_import_structure["models.qwen2_5_vl"].extend(
[
"Qwen2_5_VLForConditionalGeneration",
"Qwen2_5_VLModel",
"Qwen2_5_VLPreTrainedModel",
]
)
_import_structure["models.qwen2_audio"].extend(
[
"Qwen2AudioEncoder",
"Qwen2AudioForConditionalGeneration",
"Qwen2AudioPreTrainedModel",
]
)
_import_structure["models.qwen2_moe"].extend(
[
"Qwen2MoeForCausalLM",
"Qwen2MoeForQuestionAnswering",
"Qwen2MoeForSequenceClassification",
"Qwen2MoeForTokenClassification",
"Qwen2MoeModel",
"Qwen2MoePreTrainedModel",
]
)
_import_structure["models.qwen2_vl"].extend(
[
"Qwen2VLForConditionalGeneration",
"Qwen2VLModel",
"Qwen2VLPreTrainedModel",
]
)
_import_structure["models.rag"].extend(
[
"RagModel",
"RagPreTrainedModel",
"RagSequenceForGeneration",
"RagTokenForGeneration",
]
)
_import_structure["models.recurrent_gemma"].extend(
[
"RecurrentGemmaForCausalLM",
"RecurrentGemmaModel",
"RecurrentGemmaPreTrainedModel",
]
)
_import_structure["models.reformer"].extend(
[
"ReformerForMaskedLM",
"ReformerForQuestionAnswering",
"ReformerForSequenceClassification",
"ReformerModel",
"ReformerModelWithLMHead",
"ReformerPreTrainedModel",
]
)
_import_structure["models.regnet"].extend(
[
"RegNetForImageClassification",
"RegNetModel",
"RegNetPreTrainedModel",
]
)
_import_structure["models.rembert"].extend(
[
"RemBertForCausalLM",
"RemBertForMaskedLM",
"RemBertForMultipleChoice",
"RemBertForQuestionAnswering",
"RemBertForSequenceClassification",
"RemBertForTokenClassification",
"RemBertModel",
"RemBertPreTrainedModel",
"load_tf_weights_in_rembert",
]
)
_import_structure["models.resnet"].extend(
[
"ResNetBackbone",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
]
)
_import_structure["models.roberta"].extend(
[
"RobertaForCausalLM",
"RobertaForMaskedLM",
"RobertaForMultipleChoice",
"RobertaForQuestionAnswering",
"RobertaForSequenceClassification",
"RobertaForTokenClassification",
"RobertaModel",
"RobertaPreTrainedModel",
]
)
_import_structure["models.roberta_prelayernorm"].extend(
[
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
)
_import_structure["models.roc_bert"].extend(
[
"RoCBertForCausalLM",
"RoCBertForMaskedLM",
"RoCBertForMultipleChoice",
"RoCBertForPreTraining",
"RoCBertForQuestionAnswering",
"RoCBertForSequenceClassification",
"RoCBertForTokenClassification",
"RoCBertModel",
"RoCBertPreTrainedModel",
"load_tf_weights_in_roc_bert",
]
)
_import_structure["models.roformer"].extend(
[
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
)
_import_structure["models.rt_detr"].extend(
[
"RTDetrForObjectDetection",
"RTDetrModel",
"RTDetrPreTrainedModel",
"RTDetrResNetBackbone",
"RTDetrResNetPreTrainedModel",
]
)
_import_structure["models.rt_detr_v2"].extend(
["RTDetrV2ForObjectDetection", "RTDetrV2Model", "RTDetrV2PreTrainedModel"]
)
_import_structure["models.rwkv"].extend(
[
"RwkvForCausalLM",
"RwkvModel",
"RwkvPreTrainedModel",
]
)
_import_structure["models.sam"].extend(
[
"SamModel",
"SamPreTrainedModel",
]
)
_import_structure["models.seamless_m4t"].extend(
[
"SeamlessM4TCodeHifiGan",
"SeamlessM4TForSpeechToSpeech",
"SeamlessM4TForSpeechToText",
"SeamlessM4TForTextToSpeech",
"SeamlessM4TForTextToText",
"SeamlessM4THifiGan",
"SeamlessM4TModel",
"SeamlessM4TPreTrainedModel",
"SeamlessM4TTextToUnitForConditionalGeneration",
"SeamlessM4TTextToUnitModel",
]
)
_import_structure["models.seamless_m4t_v2"].extend(
[
"SeamlessM4Tv2ForSpeechToSpeech",
"SeamlessM4Tv2ForSpeechToText",
"SeamlessM4Tv2ForTextToSpeech",
"SeamlessM4Tv2ForTextToText",
"SeamlessM4Tv2Model",
"SeamlessM4Tv2PreTrainedModel",
]
)
_import_structure["models.segformer"].extend(
[
"SegformerDecodeHead",
"SegformerForImageClassification",
"SegformerForSemanticSegmentation",
"SegformerModel",
"SegformerPreTrainedModel",
]
)
_import_structure["models.seggpt"].extend(
[
"SegGptForImageSegmentation",
"SegGptModel",
"SegGptPreTrainedModel",
]
)
_import_structure["models.sew"].extend(
[
"SEWForCTC",
"SEWForSequenceClassification",
"SEWModel",
"SEWPreTrainedModel",
]
)
_import_structure["models.sew_d"].extend(
[
"SEWDForCTC",
"SEWDForSequenceClassification",
"SEWDModel",
"SEWDPreTrainedModel",
]
)
_import_structure["models.siglip"].extend(
[
"SiglipForImageClassification",
"SiglipModel",
"SiglipPreTrainedModel",
"SiglipTextModel",
"SiglipVisionModel",
]
)
_import_structure["models.speech_encoder_decoder"].extend(["SpeechEncoderDecoderModel"])
_import_structure["models.speech_to_text"].extend(
[
"Speech2TextForConditionalGeneration",
"Speech2TextModel",
"Speech2TextPreTrainedModel",
]
)
_import_structure["models.speecht5"].extend(
[
"SpeechT5ForSpeechToSpeech",
"SpeechT5ForSpeechToText",
"SpeechT5ForTextToSpeech",
"SpeechT5HifiGan",
"SpeechT5Model",
"SpeechT5PreTrainedModel",
]
)
_import_structure["models.splinter"].extend(
[
"SplinterForPreTraining",
"SplinterForQuestionAnswering",
"SplinterModel",
"SplinterPreTrainedModel",
]
)
_import_structure["models.squeezebert"].extend(
[
"SqueezeBertForMaskedLM",
"SqueezeBertForMultipleChoice",
"SqueezeBertForQuestionAnswering",
"SqueezeBertForSequenceClassification",
"SqueezeBertForTokenClassification",
"SqueezeBertModel",
"SqueezeBertPreTrainedModel",
]
)
_import_structure["models.stablelm"].extend(
[
"StableLmForCausalLM",
"StableLmForSequenceClassification",
"StableLmForTokenClassification",
"StableLmModel",
"StableLmPreTrainedModel",
]
)
_import_structure["models.starcoder2"].extend(
[
"Starcoder2ForCausalLM",
"Starcoder2ForSequenceClassification",
"Starcoder2ForTokenClassification",
"Starcoder2Model",
"Starcoder2PreTrainedModel",
]
)
_import_structure["models.superglue"].extend(
[
"SuperGlueForKeypointMatching",
"SuperGluePreTrainedModel",
]
)
_import_structure["models.superpoint"].extend(
[
"SuperPointForKeypointDetection",
"SuperPointPreTrainedModel",
]
)
_import_structure["models.swiftformer"].extend(
[
"SwiftFormerForImageClassification",
"SwiftFormerModel",
"SwiftFormerPreTrainedModel",
]
)
_import_structure["models.swin"].extend(
[
"SwinBackbone",
"SwinForImageClassification",
"SwinForMaskedImageModeling",
"SwinModel",
"SwinPreTrainedModel",
]
)
_import_structure["models.swin2sr"].extend(
[
"Swin2SRForImageSuperResolution",
"Swin2SRModel",
"Swin2SRPreTrainedModel",
]
)
_import_structure["models.swinv2"].extend(
[
"Swinv2Backbone",
"Swinv2ForImageClassification",
"Swinv2ForMaskedImageModeling",
"Swinv2Model",
"Swinv2PreTrainedModel",
]
)
_import_structure["models.switch_transformers"].extend(
[
"SwitchTransformersEncoderModel",
"SwitchTransformersForConditionalGeneration",
"SwitchTransformersModel",
"SwitchTransformersPreTrainedModel",
"SwitchTransformersSparseMLP",
"SwitchTransformersTop1Router",
]
)
_import_structure["models.t5"].extend(
[
"T5EncoderModel",
"T5ForConditionalGeneration",
"T5ForQuestionAnswering",
"T5ForSequenceClassification",
"T5ForTokenClassification",
"T5Model",
"T5PreTrainedModel",
"load_tf_weights_in_t5",
]
)
_import_structure["models.table_transformer"].extend(
[
"TableTransformerForObjectDetection",
"TableTransformerModel",
"TableTransformerPreTrainedModel",
]
)
_import_structure["models.tapas"].extend(
[
"TapasForMaskedLM",
"TapasForQuestionAnswering",
"TapasForSequenceClassification",
"TapasModel",
"TapasPreTrainedModel",
"load_tf_weights_in_tapas",
]
)
_import_structure["models.textnet"].extend(
[
"TextNetBackbone",
"TextNetForImageClassification",
"TextNetModel",
"TextNetPreTrainedModel",
]
)
_import_structure["models.time_series_transformer"].extend(
[
"TimeSeriesTransformerForPrediction",
"TimeSeriesTransformerModel",
"TimeSeriesTransformerPreTrainedModel",
]
)
_import_structure["models.timesformer"].extend(
[
"TimesformerForVideoClassification",
"TimesformerModel",
"TimesformerPreTrainedModel",
]
)
_import_structure["models.timm_backbone"].extend(["TimmBackbone"])
_import_structure["models.timm_wrapper"].extend(
["TimmWrapperForImageClassification", "TimmWrapperModel", "TimmWrapperPreTrainedModel"]
)
_import_structure["models.trocr"].extend(
[
"TrOCRForCausalLM",
"TrOCRPreTrainedModel",
]
)
_import_structure["models.tvp"].extend(
[
"TvpForVideoGrounding",
"TvpModel",
"TvpPreTrainedModel",
]
)
_import_structure["models.udop"].extend(
[
"UdopEncoderModel",
"UdopForConditionalGeneration",
"UdopModel",
"UdopPreTrainedModel",
],
)
_import_structure["models.umt5"].extend(
[
"UMT5EncoderModel",
"UMT5ForConditionalGeneration",
"UMT5ForQuestionAnswering",
"UMT5ForSequenceClassification",
"UMT5ForTokenClassification",
"UMT5Model",
"UMT5PreTrainedModel",
]
)
_import_structure["models.unispeech"].extend(
[
"UniSpeechForCTC",
"UniSpeechForPreTraining",
"UniSpeechForSequenceClassification",
"UniSpeechModel",
"UniSpeechPreTrainedModel",
]
)
_import_structure["models.unispeech_sat"].extend(
[
"UniSpeechSatForAudioFrameClassification",
"UniSpeechSatForCTC",
"UniSpeechSatForPreTraining",
"UniSpeechSatForSequenceClassification",
"UniSpeechSatForXVector",
"UniSpeechSatModel",
"UniSpeechSatPreTrainedModel",
]
)
_import_structure["models.univnet"].extend(
[
"UnivNetModel",
]
)
_import_structure["models.upernet"].extend(
[
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
)
_import_structure["models.video_llava"].extend(
[
"VideoLlavaForConditionalGeneration",
"VideoLlavaPreTrainedModel",
"VideoLlavaProcessor",
]
)
_import_structure["models.videomae"].extend(
[
"VideoMAEForPreTraining",
"VideoMAEForVideoClassification",
"VideoMAEModel",
"VideoMAEPreTrainedModel",
]
)
_import_structure["models.vilt"].extend(
[
"ViltForImageAndTextRetrieval",
"ViltForImagesAndTextClassification",
"ViltForMaskedLM",
"ViltForQuestionAnswering",
"ViltForTokenClassification",
"ViltModel",
"ViltPreTrainedModel",
]
)
_import_structure["models.vipllava"].extend(
[
"VipLlavaForConditionalGeneration",
"VipLlavaPreTrainedModel",
]
)
_import_structure["models.vision_encoder_decoder"].extend(["VisionEncoderDecoderModel"])
_import_structure["models.vision_text_dual_encoder"].extend(["VisionTextDualEncoderModel"])
_import_structure["models.visual_bert"].extend(
[
"VisualBertForMultipleChoice",
"VisualBertForPreTraining",
"VisualBertForQuestionAnswering",
"VisualBertForRegionToPhraseAlignment",
"VisualBertForVisualReasoning",
"VisualBertModel",
"VisualBertPreTrainedModel",
]
)
_import_structure["models.vit"].extend(
[
"ViTForImageClassification",
"ViTForMaskedImageModeling",
"ViTModel",
"ViTPreTrainedModel",
]
)
_import_structure["models.vit_mae"].extend(
[
"ViTMAEForPreTraining",
"ViTMAEModel",
"ViTMAEPreTrainedModel",
]
)
_import_structure["models.vit_msn"].extend(
[
"ViTMSNForImageClassification",
"ViTMSNModel",
"ViTMSNPreTrainedModel",
]
)
_import_structure["models.vitdet"].extend(
[
"VitDetBackbone",
"VitDetModel",
"VitDetPreTrainedModel",
]
)
_import_structure["models.vitmatte"].extend(
[
"VitMatteForImageMatting",
"VitMattePreTrainedModel",
]
)
_import_structure["models.vitpose"].extend(
[
"VitPoseForPoseEstimation",
"VitPosePreTrainedModel",
]
)
_import_structure["models.vitpose_backbone"].extend(
[
"VitPoseBackbone",
"VitPoseBackbonePreTrainedModel",
]
)
_import_structure["models.vits"].extend(
[
"VitsModel",
"VitsPreTrainedModel",
]
)
_import_structure["models.vivit"].extend(
[
"VivitForVideoClassification",
"VivitModel",
"VivitPreTrainedModel",
]
)
_import_structure["models.wav2vec2"].extend(
[
"Wav2Vec2ForAudioFrameClassification",
"Wav2Vec2ForCTC",
"Wav2Vec2ForMaskedLM",
"Wav2Vec2ForPreTraining",
"Wav2Vec2ForSequenceClassification",
"Wav2Vec2ForXVector",
"Wav2Vec2Model",
"Wav2Vec2PreTrainedModel",
]
)
_import_structure["models.wav2vec2_bert"].extend(
[
"Wav2Vec2BertForAudioFrameClassification",
"Wav2Vec2BertForCTC",
"Wav2Vec2BertForSequenceClassification",
"Wav2Vec2BertForXVector",
"Wav2Vec2BertModel",
"Wav2Vec2BertPreTrainedModel",
]
)
_import_structure["models.wav2vec2_conformer"].extend(
[
"Wav2Vec2ConformerForAudioFrameClassification",
"Wav2Vec2ConformerForCTC",
"Wav2Vec2ConformerForPreTraining",
"Wav2Vec2ConformerForSequenceClassification",
"Wav2Vec2ConformerForXVector",
"Wav2Vec2ConformerModel",
"Wav2Vec2ConformerPreTrainedModel",
]
)
_import_structure["models.wavlm"].extend(
[
"WavLMForAudioFrameClassification",
"WavLMForCTC",
"WavLMForSequenceClassification",
"WavLMForXVector",
"WavLMModel",
"WavLMPreTrainedModel",
]
)
_import_structure["models.whisper"].extend(
[
"WhisperForAudioClassification",
"WhisperForCausalLM",
"WhisperForConditionalGeneration",
"WhisperModel",
"WhisperPreTrainedModel",
]
)
_import_structure["models.x_clip"].extend(
[
"XCLIPModel",
"XCLIPPreTrainedModel",
"XCLIPTextModel",
"XCLIPVisionModel",
]
)
_import_structure["models.xglm"].extend(
[
"XGLMForCausalLM",
"XGLMModel",
"XGLMPreTrainedModel",
]
)
_import_structure["models.xlm"].extend(
[
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
)
_import_structure["models.xlm_roberta"].extend(
[
"XLMRobertaForCausalLM",
"XLMRobertaForMaskedLM",
"XLMRobertaForMultipleChoice",
"XLMRobertaForQuestionAnswering",
"XLMRobertaForSequenceClassification",
"XLMRobertaForTokenClassification",
"XLMRobertaModel",
"XLMRobertaPreTrainedModel",
]
)
_import_structure["models.xlm_roberta_xl"].extend(
[
"XLMRobertaXLForCausalLM",
"XLMRobertaXLForMaskedLM",
"XLMRobertaXLForMultipleChoice",
"XLMRobertaXLForQuestionAnswering",
"XLMRobertaXLForSequenceClassification",
"XLMRobertaXLForTokenClassification",
"XLMRobertaXLModel",
"XLMRobertaXLPreTrainedModel",
]
)
_import_structure["models.xlnet"].extend(
[
"XLNetForMultipleChoice",
"XLNetForQuestionAnswering",
"XLNetForQuestionAnsweringSimple",
"XLNetForSequenceClassification",
"XLNetForTokenClassification",
"XLNetLMHeadModel",
"XLNetModel",
"XLNetPreTrainedModel",
"load_tf_weights_in_xlnet",
]
)
_import_structure["models.xmod"].extend(
[
"XmodForCausalLM",
"XmodForMaskedLM",
"XmodForMultipleChoice",
"XmodForQuestionAnswering",
"XmodForSequenceClassification",
"XmodForTokenClassification",
"XmodModel",
"XmodPreTrainedModel",
]
)
_import_structure["models.yolos"].extend(
[
"YolosForObjectDetection",
"YolosModel",
"YolosPreTrainedModel",
]
)
_import_structure["models.yoso"].extend(
[
"YosoForMaskedLM",
"YosoForMultipleChoice",
"YosoForQuestionAnswering",
"YosoForSequenceClassification",
"YosoForTokenClassification",
"YosoModel",
"YosoPreTrainedModel",
]
)
_import_structure["models.zamba"].extend(
[
"ZambaForCausalLM",
"ZambaForSequenceClassification",
"ZambaModel",
"ZambaPreTrainedModel",
]
)
_import_structure["models.zamba2"].extend(
[
"Zamba2ForCausalLM",
"Zamba2ForSequenceClassification",
"Zamba2Model",
"Zamba2PreTrainedModel",
]
)
_import_structure["models.zoedepth"].extend(
[
"ZoeDepthForDepthEstimation",
"ZoeDepthPreTrainedModel",
]
)
_import_structure["optimization"] = [
"Adafactor",
"AdamW",
"get_constant_schedule",
"get_constant_schedule_with_warmup",
"get_cosine_schedule_with_warmup",
"get_cosine_with_hard_restarts_schedule_with_warmup",
"get_inverse_sqrt_schedule",
"get_linear_schedule_with_warmup",
"get_polynomial_decay_schedule_with_warmup",
"get_scheduler",
"get_wsd_schedule",
]
_import_structure["pytorch_utils"] = [
"Conv1D",
"apply_chunking_to_forward",
"prune_layer",
]
_import_structure["sagemaker"] = []
_import_structure["time_series_utils"] = []
_import_structure["trainer"] = ["Trainer"]
_import_structure["trainer_pt_utils"] = ["torch_distributed_zero_first"]
_import_structure["trainer_seq2seq"] = ["Seq2SeqTrainer"]
# TensorFlow-backed objects
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_tf_objects
_import_structure["utils.dummy_tf_objects"] = [name for name in dir(dummy_tf_objects) if not name.startswith("_")]
else:
_import_structure["activations_tf"] = []
_import_structure["generation"].extend(
[
"TFForcedBOSTokenLogitsProcessor",
"TFForcedEOSTokenLogitsProcessor",
"TFForceTokensLogitsProcessor",
"TFGenerationMixin",
"TFLogitsProcessor",
"TFLogitsProcessorList",
"TFLogitsWarper",
"TFMinLengthLogitsProcessor",
"TFNoBadWordsLogitsProcessor",
"TFNoRepeatNGramLogitsProcessor",
"TFRepetitionPenaltyLogitsProcessor",
"TFSuppressTokensAtBeginLogitsProcessor",
"TFSuppressTokensLogitsProcessor",
"TFTemperatureLogitsWarper",
"TFTopKLogitsWarper",
"TFTopPLogitsWarper",
]
)
_import_structure["keras_callbacks"] = ["KerasMetricCallback", "PushToHubCallback"]
_import_structure["modeling_tf_outputs"] = []
_import_structure["modeling_tf_utils"] = [
"TFPreTrainedModel",
"TFSequenceSummary",
"TFSharedEmbeddings",
"shape_list",
]
# TensorFlow models structure
_import_structure["models.albert"].extend(
[
"TFAlbertForMaskedLM",
"TFAlbertForMultipleChoice",
"TFAlbertForPreTraining",
"TFAlbertForQuestionAnswering",
"TFAlbertForSequenceClassification",
"TFAlbertForTokenClassification",
"TFAlbertMainLayer",
"TFAlbertModel",
"TFAlbertPreTrainedModel",
]
)
_import_structure["models.auto"].extend(
[
"TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
"TF_MODEL_FOR_CAUSAL_LM_MAPPING",
"TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
"TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
"TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
"TF_MODEL_FOR_MASKED_LM_MAPPING",
"TF_MODEL_FOR_MASK_GENERATION_MAPPING",
"TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
"TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
"TF_MODEL_FOR_PRETRAINING_MAPPING",
"TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
"TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
"TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
"TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
"TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
"TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
"TF_MODEL_FOR_TEXT_ENCODING_MAPPING",
"TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
"TF_MODEL_FOR_VISION_2_SEQ_MAPPING",
"TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
"TF_MODEL_MAPPING",
"TF_MODEL_WITH_LM_HEAD_MAPPING",
"TFAutoModel",
"TFAutoModelForAudioClassification",
"TFAutoModelForCausalLM",
"TFAutoModelForDocumentQuestionAnswering",
"TFAutoModelForImageClassification",
"TFAutoModelForMaskedImageModeling",
"TFAutoModelForMaskedLM",
"TFAutoModelForMaskGeneration",
"TFAutoModelForMultipleChoice",
"TFAutoModelForNextSentencePrediction",
"TFAutoModelForPreTraining",
"TFAutoModelForQuestionAnswering",
"TFAutoModelForSemanticSegmentation",
"TFAutoModelForSeq2SeqLM",
"TFAutoModelForSequenceClassification",
"TFAutoModelForSpeechSeq2Seq",
"TFAutoModelForTableQuestionAnswering",
"TFAutoModelForTextEncoding",
"TFAutoModelForTokenClassification",
"TFAutoModelForVision2Seq",
"TFAutoModelForZeroShotImageClassification",
"TFAutoModelWithLMHead",
]
)
_import_structure["models.bart"].extend(
[
"TFBartForConditionalGeneration",
"TFBartForSequenceClassification",
"TFBartModel",
"TFBartPretrainedModel",
]
)
_import_structure["models.bert"].extend(
[
"TFBertForMaskedLM",
"TFBertForMultipleChoice",
"TFBertForNextSentencePrediction",
"TFBertForPreTraining",
"TFBertForQuestionAnswering",
"TFBertForSequenceClassification",
"TFBertForTokenClassification",
"TFBertLMHeadModel",
"TFBertMainLayer",
"TFBertModel",
"TFBertPreTrainedModel",
]
)
_import_structure["models.blenderbot"].extend(
[
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
"TFBlenderbotPreTrainedModel",
]
)
_import_structure["models.blenderbot_small"].extend(
[
"TFBlenderbotSmallForConditionalGeneration",
"TFBlenderbotSmallModel",
"TFBlenderbotSmallPreTrainedModel",
]
)
_import_structure["models.blip"].extend(
[
"TFBlipForConditionalGeneration",
"TFBlipForImageTextRetrieval",
"TFBlipForQuestionAnswering",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipTextModel",
"TFBlipVisionModel",
]
)
_import_structure["models.camembert"].extend(
[
"TFCamembertForCausalLM",
"TFCamembertForMaskedLM",
"TFCamembertForMultipleChoice",
"TFCamembertForQuestionAnswering",
"TFCamembertForSequenceClassification",
"TFCamembertForTokenClassification",
"TFCamembertModel",
"TFCamembertPreTrainedModel",
]
)
_import_structure["models.clip"].extend(
[
"TFCLIPModel",
"TFCLIPPreTrainedModel",
"TFCLIPTextModel",
"TFCLIPVisionModel",
]
)
_import_structure["models.convbert"].extend(
[
"TFConvBertForMaskedLM",
"TFConvBertForMultipleChoice",
"TFConvBertForQuestionAnswering",
"TFConvBertForSequenceClassification",
"TFConvBertForTokenClassification",
"TFConvBertModel",
"TFConvBertPreTrainedModel",
]
)
_import_structure["models.convnext"].extend(
[
"TFConvNextForImageClassification",
"TFConvNextModel",
"TFConvNextPreTrainedModel",
]
)
_import_structure["models.convnextv2"].extend(
[
"TFConvNextV2ForImageClassification",
"TFConvNextV2Model",
"TFConvNextV2PreTrainedModel",
]
)
_import_structure["models.ctrl"].extend(
[
"TFCTRLForSequenceClassification",
"TFCTRLLMHeadModel",
"TFCTRLModel",
"TFCTRLPreTrainedModel",
]
)
_import_structure["models.cvt"].extend(
[
"TFCvtForImageClassification",
"TFCvtModel",
"TFCvtPreTrainedModel",
]
)
_import_structure["models.data2vec"].extend(
[
"TFData2VecVisionForImageClassification",
"TFData2VecVisionForSemanticSegmentation",
"TFData2VecVisionModel",
"TFData2VecVisionPreTrainedModel",
]
)
_import_structure["models.deberta"].extend(
[
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
)
_import_structure["models.deberta_v2"].extend(
[
"TFDebertaV2ForMaskedLM",
"TFDebertaV2ForMultipleChoice",
"TFDebertaV2ForQuestionAnswering",
"TFDebertaV2ForSequenceClassification",
"TFDebertaV2ForTokenClassification",
"TFDebertaV2Model",
"TFDebertaV2PreTrainedModel",
]
)
_import_structure["models.deit"].extend(
[
"TFDeiTForImageClassification",
"TFDeiTForImageClassificationWithTeacher",
"TFDeiTForMaskedImageModeling",
"TFDeiTModel",
"TFDeiTPreTrainedModel",
]
)
_import_structure["models.deprecated.efficientformer"].extend(
[
"TFEfficientFormerForImageClassification",
"TFEfficientFormerForImageClassificationWithTeacher",
"TFEfficientFormerModel",
"TFEfficientFormerPreTrainedModel",
]
)
_import_structure["models.deprecated.transfo_xl"].extend(
[
"TFAdaptiveEmbedding",
"TFTransfoXLForSequenceClassification",
"TFTransfoXLLMHeadModel",
"TFTransfoXLMainLayer",
"TFTransfoXLModel",
"TFTransfoXLPreTrainedModel",
]
)
_import_structure["models.distilbert"].extend(
[
"TFDistilBertForMaskedLM",
"TFDistilBertForMultipleChoice",
"TFDistilBertForQuestionAnswering",
"TFDistilBertForSequenceClassification",
"TFDistilBertForTokenClassification",
"TFDistilBertMainLayer",
"TFDistilBertModel",
"TFDistilBertPreTrainedModel",
]
)
_import_structure["models.dpr"].extend(
[
"TFDPRContextEncoder",
"TFDPRPretrainedContextEncoder",
"TFDPRPretrainedQuestionEncoder",
"TFDPRPretrainedReader",
"TFDPRQuestionEncoder",
"TFDPRReader",
]
)
_import_structure["models.electra"].extend(
[
"TFElectraForMaskedLM",
"TFElectraForMultipleChoice",
"TFElectraForPreTraining",
"TFElectraForQuestionAnswering",
"TFElectraForSequenceClassification",
"TFElectraForTokenClassification",
"TFElectraModel",
"TFElectraPreTrainedModel",
]
)
_import_structure["models.encoder_decoder"].append("TFEncoderDecoderModel")
_import_structure["models.esm"].extend(
[
"TFEsmForMaskedLM",
"TFEsmForSequenceClassification",
"TFEsmForTokenClassification",
"TFEsmModel",
"TFEsmPreTrainedModel",
]
)
_import_structure["models.flaubert"].extend(
[
"TFFlaubertForMultipleChoice",
"TFFlaubertForQuestionAnsweringSimple",
"TFFlaubertForSequenceClassification",
"TFFlaubertForTokenClassification",
"TFFlaubertModel",
"TFFlaubertPreTrainedModel",
"TFFlaubertWithLMHeadModel",
]
)
_import_structure["models.funnel"].extend(
[
"TFFunnelBaseModel",
"TFFunnelForMaskedLM",
"TFFunnelForMultipleChoice",
"TFFunnelForPreTraining",
"TFFunnelForQuestionAnswering",
"TFFunnelForSequenceClassification",
"TFFunnelForTokenClassification",
"TFFunnelModel",
"TFFunnelPreTrainedModel",
]
)
_import_structure["models.gpt2"].extend(
[
"TFGPT2DoubleHeadsModel",
"TFGPT2ForSequenceClassification",
"TFGPT2LMHeadModel",
"TFGPT2MainLayer",
"TFGPT2Model",
"TFGPT2PreTrainedModel",
]
)
_import_structure["models.gptj"].extend(
[
"TFGPTJForCausalLM",
"TFGPTJForQuestionAnswering",
"TFGPTJForSequenceClassification",
"TFGPTJModel",
"TFGPTJPreTrainedModel",
]
)
_import_structure["models.groupvit"].extend(
[
"TFGroupViTModel",
"TFGroupViTPreTrainedModel",
"TFGroupViTTextModel",
"TFGroupViTVisionModel",
]
)
_import_structure["models.hubert"].extend(
[
"TFHubertForCTC",
"TFHubertModel",
"TFHubertPreTrainedModel",
]
)
_import_structure["models.idefics"].extend(
[
"TFIdeficsForVisionText2Text",
"TFIdeficsModel",
"TFIdeficsPreTrainedModel",
]
)
_import_structure["models.layoutlm"].extend(
[
"TFLayoutLMForMaskedLM",
"TFLayoutLMForQuestionAnswering",
"TFLayoutLMForSequenceClassification",
"TFLayoutLMForTokenClassification",
"TFLayoutLMMainLayer",
"TFLayoutLMModel",
"TFLayoutLMPreTrainedModel",
]
)
_import_structure["models.layoutlmv3"].extend(
[
"TFLayoutLMv3ForQuestionAnswering",
"TFLayoutLMv3ForSequenceClassification",
"TFLayoutLMv3ForTokenClassification",
"TFLayoutLMv3Model",
"TFLayoutLMv3PreTrainedModel",
]
)
_import_structure["models.led"].extend(["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"])
_import_structure["models.longformer"].extend(
[
"TFLongformerForMaskedLM",
"TFLongformerForMultipleChoice",
"TFLongformerForQuestionAnswering",
"TFLongformerForSequenceClassification",
"TFLongformerForTokenClassification",
"TFLongformerModel",
"TFLongformerPreTrainedModel",
]
)
_import_structure["models.lxmert"].extend(
[
"TFLxmertForPreTraining",
"TFLxmertMainLayer",
"TFLxmertModel",
"TFLxmertPreTrainedModel",
"TFLxmertVisualFeatureEncoder",
]
)
_import_structure["models.marian"].extend(["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"])
_import_structure["models.mbart"].extend(
["TFMBartForConditionalGeneration", "TFMBartModel", "TFMBartPreTrainedModel"]
)
_import_structure["models.mistral"].extend(
["TFMistralForCausalLM", "TFMistralForSequenceClassification", "TFMistralModel", "TFMistralPreTrainedModel"]
)
_import_structure["models.mobilebert"].extend(
[
"TFMobileBertForMaskedLM",
"TFMobileBertForMultipleChoice",
"TFMobileBertForNextSentencePrediction",
"TFMobileBertForPreTraining",
"TFMobileBertForQuestionAnswering",
"TFMobileBertForSequenceClassification",
"TFMobileBertForTokenClassification",
"TFMobileBertMainLayer",
"TFMobileBertModel",
"TFMobileBertPreTrainedModel",
]
)
_import_structure["models.mobilevit"].extend(
[
"TFMobileViTForImageClassification",
"TFMobileViTForSemanticSegmentation",
"TFMobileViTModel",
"TFMobileViTPreTrainedModel",
]
)
_import_structure["models.mpnet"].extend(
[
"TFMPNetForMaskedLM",
"TFMPNetForMultipleChoice",
"TFMPNetForQuestionAnswering",
"TFMPNetForSequenceClassification",
"TFMPNetForTokenClassification",
"TFMPNetMainLayer",
"TFMPNetModel",
"TFMPNetPreTrainedModel",
]
)
_import_structure["models.mt5"].extend(["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"])
_import_structure["models.openai"].extend(
[
"TFOpenAIGPTDoubleHeadsModel",
"TFOpenAIGPTForSequenceClassification",
"TFOpenAIGPTLMHeadModel",
"TFOpenAIGPTMainLayer",
"TFOpenAIGPTModel",
"TFOpenAIGPTPreTrainedModel",
]
)
_import_structure["models.opt"].extend(
[
"TFOPTForCausalLM",
"TFOPTModel",
"TFOPTPreTrainedModel",
]
)
_import_structure["models.pegasus"].extend(
[
"TFPegasusForConditionalGeneration",
"TFPegasusModel",
"TFPegasusPreTrainedModel",
]
)
_import_structure["models.rag"].extend(
[
"TFRagModel",
"TFRagPreTrainedModel",
"TFRagSequenceForGeneration",
"TFRagTokenForGeneration",
]
)
_import_structure["models.regnet"].extend(
[
"TFRegNetForImageClassification",
"TFRegNetModel",
"TFRegNetPreTrainedModel",
]
)
_import_structure["models.rembert"].extend(
[
"TFRemBertForCausalLM",
"TFRemBertForMaskedLM",
"TFRemBertForMultipleChoice",
"TFRemBertForQuestionAnswering",
"TFRemBertForSequenceClassification",
"TFRemBertForTokenClassification",
"TFRemBertModel",
"TFRemBertPreTrainedModel",
]
)
_import_structure["models.resnet"].extend(
[
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
)
_import_structure["models.roberta"].extend(
[
"TFRobertaForCausalLM",
"TFRobertaForMaskedLM",
"TFRobertaForMultipleChoice",
"TFRobertaForQuestionAnswering",
"TFRobertaForSequenceClassification",
"TFRobertaForTokenClassification",
"TFRobertaMainLayer",
"TFRobertaModel",
"TFRobertaPreTrainedModel",
]
)
_import_structure["models.roberta_prelayernorm"].extend(
[
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
)
_import_structure["models.roformer"].extend(
[
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
)
_import_structure["models.sam"].extend(
[
"TFSamModel",
"TFSamPreTrainedModel",
]
)
_import_structure["models.segformer"].extend(
[
"TFSegformerDecodeHead",
"TFSegformerForImageClassification",
"TFSegformerForSemanticSegmentation",
"TFSegformerModel",
"TFSegformerPreTrainedModel",
]
)
_import_structure["models.speech_to_text"].extend(
[
"TFSpeech2TextForConditionalGeneration",
"TFSpeech2TextModel",
"TFSpeech2TextPreTrainedModel",
]
)
_import_structure["models.swiftformer"].extend(
[
"TFSwiftFormerForImageClassification",
"TFSwiftFormerModel",
"TFSwiftFormerPreTrainedModel",
]
)
_import_structure["models.swin"].extend(
[
"TFSwinForImageClassification",
"TFSwinForMaskedImageModeling",
"TFSwinModel",
"TFSwinPreTrainedModel",
]
)
_import_structure["models.t5"].extend(
[
"TFT5EncoderModel",
"TFT5ForConditionalGeneration",
"TFT5Model",
"TFT5PreTrainedModel",
]
)
_import_structure["models.tapas"].extend(
[
"TFTapasForMaskedLM",
"TFTapasForQuestionAnswering",
"TFTapasForSequenceClassification",
"TFTapasModel",
"TFTapasPreTrainedModel",
]
)
_import_structure["models.vision_encoder_decoder"].extend(["TFVisionEncoderDecoderModel"])
_import_structure["models.vision_text_dual_encoder"].extend(["TFVisionTextDualEncoderModel"])
_import_structure["models.vit"].extend(
[
"TFViTForImageClassification",
"TFViTModel",
"TFViTPreTrainedModel",
]
)
_import_structure["models.vit_mae"].extend(
[
"TFViTMAEForPreTraining",
"TFViTMAEModel",
"TFViTMAEPreTrainedModel",
]
)
_import_structure["models.wav2vec2"].extend(
[
"TFWav2Vec2ForCTC",
"TFWav2Vec2ForSequenceClassification",
"TFWav2Vec2Model",
"TFWav2Vec2PreTrainedModel",
]
)
_import_structure["models.whisper"].extend(
[
"TFWhisperForConditionalGeneration",
"TFWhisperModel",
"TFWhisperPreTrainedModel",
]
)
_import_structure["models.xglm"].extend(
[
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXGLMPreTrainedModel",
]
)
_import_structure["models.xlm"].extend(
[
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
)
_import_structure["models.xlm_roberta"].extend(
[
"TFXLMRobertaForCausalLM",
"TFXLMRobertaForMaskedLM",
"TFXLMRobertaForMultipleChoice",
"TFXLMRobertaForQuestionAnswering",
"TFXLMRobertaForSequenceClassification",
"TFXLMRobertaForTokenClassification",
"TFXLMRobertaModel",
"TFXLMRobertaPreTrainedModel",
]
)
_import_structure["models.xlnet"].extend(
[
"TFXLNetForMultipleChoice",
"TFXLNetForQuestionAnsweringSimple",
"TFXLNetForSequenceClassification",
"TFXLNetForTokenClassification",
"TFXLNetLMHeadModel",
"TFXLNetMainLayer",
"TFXLNetModel",
"TFXLNetPreTrainedModel",
]
)
_import_structure["optimization_tf"] = [
"AdamWeightDecay",
"GradientAccumulator",
"WarmUp",
"create_optimizer",
]
_import_structure["tf_utils"] = []
try:
if not (
is_librosa_available()
and is_essentia_available()
and is_scipy_available()
and is_torch_available()
and is_pretty_midi_available()
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import (
dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects,
)
_import_structure["utils.dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects"] = [
name
for name in dir(dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects)
if not name.startswith("_")
]
else:
_import_structure["models.pop2piano"].append("Pop2PianoFeatureExtractor")
_import_structure["models.pop2piano"].append("Pop2PianoTokenizer")
_import_structure["models.pop2piano"].append("Pop2PianoProcessor")
try:
if not is_torchaudio_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import (
dummy_torchaudio_objects,
)
_import_structure["utils.dummy_torchaudio_objects"] = [
name for name in dir(dummy_torchaudio_objects) if not name.startswith("_")
]
else:
_import_structure["models.musicgen_melody"].append("MusicgenMelodyFeatureExtractor")
_import_structure["models.musicgen_melody"].append("MusicgenMelodyProcessor")
# FLAX-backed objects
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils import dummy_flax_objects
_import_structure["utils.dummy_flax_objects"] = [
name for name in dir(dummy_flax_objects) if not name.startswith("_")
]
else:
_import_structure["generation"].extend(
[
"FlaxForcedBOSTokenLogitsProcessor",
"FlaxForcedEOSTokenLogitsProcessor",
"FlaxForceTokensLogitsProcessor",
"FlaxGenerationMixin",
"FlaxLogitsProcessor",
"FlaxLogitsProcessorList",
"FlaxLogitsWarper",
"FlaxMinLengthLogitsProcessor",
"FlaxTemperatureLogitsWarper",
"FlaxSuppressTokensAtBeginLogitsProcessor",
"FlaxSuppressTokensLogitsProcessor",
"FlaxTopKLogitsWarper",
"FlaxTopPLogitsWarper",
"FlaxWhisperTimeStampLogitsProcessor",
]
)
_import_structure["modeling_flax_outputs"] = []
_import_structure["modeling_flax_utils"] = ["FlaxPreTrainedModel"]
_import_structure["models.albert"].extend(
[
"FlaxAlbertForMaskedLM",
"FlaxAlbertForMultipleChoice",
"FlaxAlbertForPreTraining",
"FlaxAlbertForQuestionAnswering",
"FlaxAlbertForSequenceClassification",
"FlaxAlbertForTokenClassification",
"FlaxAlbertModel",
"FlaxAlbertPreTrainedModel",
]
)
_import_structure["models.auto"].extend(
[
"FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
"FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
"FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
"FLAX_MODEL_FOR_MASKED_LM_MAPPING",
"FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
"FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
"FLAX_MODEL_FOR_PRETRAINING_MAPPING",
"FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
"FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
"FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
"FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
"FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
"FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING",
"FLAX_MODEL_MAPPING",
"FlaxAutoModel",
"FlaxAutoModelForCausalLM",
"FlaxAutoModelForImageClassification",
"FlaxAutoModelForMaskedLM",
"FlaxAutoModelForMultipleChoice",
"FlaxAutoModelForNextSentencePrediction",
"FlaxAutoModelForPreTraining",
"FlaxAutoModelForQuestionAnswering",
"FlaxAutoModelForSeq2SeqLM",
"FlaxAutoModelForSequenceClassification",
"FlaxAutoModelForSpeechSeq2Seq",
"FlaxAutoModelForTokenClassification",
"FlaxAutoModelForVision2Seq",
]
)
# Flax models structure
_import_structure["models.bart"].extend(
[
"FlaxBartDecoderPreTrainedModel",
"FlaxBartForCausalLM",
"FlaxBartForConditionalGeneration",
"FlaxBartForQuestionAnswering",
"FlaxBartForSequenceClassification",
"FlaxBartModel",
"FlaxBartPreTrainedModel",
]
)
_import_structure["models.beit"].extend(
[
"FlaxBeitForImageClassification",
"FlaxBeitForMaskedImageModeling",
"FlaxBeitModel",
"FlaxBeitPreTrainedModel",
]
)
_import_structure["models.bert"].extend(
[
"FlaxBertForCausalLM",
"FlaxBertForMaskedLM",
"FlaxBertForMultipleChoice",
"FlaxBertForNextSentencePrediction",
"FlaxBertForPreTraining",
"FlaxBertForQuestionAnswering",
"FlaxBertForSequenceClassification",
"FlaxBertForTokenClassification",
"FlaxBertModel",
"FlaxBertPreTrainedModel",
]
)
_import_structure["models.big_bird"].extend(
[
"FlaxBigBirdForCausalLM",
"FlaxBigBirdForMaskedLM",
"FlaxBigBirdForMultipleChoice",
"FlaxBigBirdForPreTraining",
"FlaxBigBirdForQuestionAnswering",
"FlaxBigBirdForSequenceClassification",
"FlaxBigBirdForTokenClassification",
"FlaxBigBirdModel",
"FlaxBigBirdPreTrainedModel",
]
)
_import_structure["models.blenderbot"].extend(
[
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
"FlaxBlenderbotPreTrainedModel",
]
)
_import_structure["models.blenderbot_small"].extend(
[
"FlaxBlenderbotSmallForConditionalGeneration",
"FlaxBlenderbotSmallModel",
"FlaxBlenderbotSmallPreTrainedModel",
]
)
_import_structure["models.bloom"].extend(
[
"FlaxBloomForCausalLM",
"FlaxBloomModel",
"FlaxBloomPreTrainedModel",
]
)
_import_structure["models.clip"].extend(
[
"FlaxCLIPModel",
"FlaxCLIPPreTrainedModel",
"FlaxCLIPTextModel",
"FlaxCLIPTextPreTrainedModel",
"FlaxCLIPTextModelWithProjection",
"FlaxCLIPVisionModel",
"FlaxCLIPVisionPreTrainedModel",
]
)
_import_structure["models.dinov2"].extend(
[
"FlaxDinov2Model",
"FlaxDinov2ForImageClassification",
"FlaxDinov2PreTrainedModel",
]
)
_import_structure["models.distilbert"].extend(
[
"FlaxDistilBertForMaskedLM",
"FlaxDistilBertForMultipleChoice",
"FlaxDistilBertForQuestionAnswering",
"FlaxDistilBertForSequenceClassification",
"FlaxDistilBertForTokenClassification",
"FlaxDistilBertModel",
"FlaxDistilBertPreTrainedModel",
]
)
_import_structure["models.electra"].extend(
[
"FlaxElectraForCausalLM",
"FlaxElectraForMaskedLM",
"FlaxElectraForMultipleChoice",
"FlaxElectraForPreTraining",
"FlaxElectraForQuestionAnswering",
"FlaxElectraForSequenceClassification",
"FlaxElectraForTokenClassification",
"FlaxElectraModel",
"FlaxElectraPreTrainedModel",
]
)
_import_structure["models.encoder_decoder"].append("FlaxEncoderDecoderModel")
_import_structure["models.gpt2"].extend(["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"])
_import_structure["models.gpt_neo"].extend(
["FlaxGPTNeoForCausalLM", "FlaxGPTNeoModel", "FlaxGPTNeoPreTrainedModel"]
)
_import_structure["models.gptj"].extend(["FlaxGPTJForCausalLM", "FlaxGPTJModel", "FlaxGPTJPreTrainedModel"])
_import_structure["models.llama"].extend(["FlaxLlamaForCausalLM", "FlaxLlamaModel", "FlaxLlamaPreTrainedModel"])
_import_structure["models.gemma"].extend(["FlaxGemmaForCausalLM", "FlaxGemmaModel", "FlaxGemmaPreTrainedModel"])
_import_structure["models.longt5"].extend(
[
"FlaxLongT5ForConditionalGeneration",
"FlaxLongT5Model",
"FlaxLongT5PreTrainedModel",
]
)
_import_structure["models.marian"].extend(
[
"FlaxMarianModel",
"FlaxMarianMTModel",
"FlaxMarianPreTrainedModel",
]
)
_import_structure["models.mbart"].extend(
[
"FlaxMBartForConditionalGeneration",
"FlaxMBartForQuestionAnswering",
"FlaxMBartForSequenceClassification",
"FlaxMBartModel",
"FlaxMBartPreTrainedModel",
]
)
_import_structure["models.mistral"].extend(
[
"FlaxMistralForCausalLM",
"FlaxMistralModel",
"FlaxMistralPreTrainedModel",
]
)
_import_structure["models.mt5"].extend(["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"])
_import_structure["models.opt"].extend(
[
"FlaxOPTForCausalLM",
"FlaxOPTModel",
"FlaxOPTPreTrainedModel",
]
)
_import_structure["models.pegasus"].extend(
[
"FlaxPegasusForConditionalGeneration",
"FlaxPegasusModel",
"FlaxPegasusPreTrainedModel",
]
)
_import_structure["models.regnet"].extend(
[
"FlaxRegNetForImageClassification",
"FlaxRegNetModel",
"FlaxRegNetPreTrainedModel",
]
)
_import_structure["models.resnet"].extend(
[
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
)
_import_structure["models.roberta"].extend(
[
"FlaxRobertaForCausalLM",
"FlaxRobertaForMaskedLM",
"FlaxRobertaForMultipleChoice",
"FlaxRobertaForQuestionAnswering",
"FlaxRobertaForSequenceClassification",
"FlaxRobertaForTokenClassification",
"FlaxRobertaModel",
"FlaxRobertaPreTrainedModel",
]
)
_import_structure["models.roberta_prelayernorm"].extend(
[
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
)
_import_structure["models.roformer"].extend(
[
"FlaxRoFormerForMaskedLM",
"FlaxRoFormerForMultipleChoice",
"FlaxRoFormerForQuestionAnswering",
"FlaxRoFormerForSequenceClassification",
"FlaxRoFormerForTokenClassification",
"FlaxRoFormerModel",
"FlaxRoFormerPreTrainedModel",
]
)
_import_structure["models.speech_encoder_decoder"].append("FlaxSpeechEncoderDecoderModel")
_import_structure["models.t5"].extend(
[
"FlaxT5EncoderModel",
"FlaxT5ForConditionalGeneration",
"FlaxT5Model",
"FlaxT5PreTrainedModel",
]
)
_import_structure["models.vision_encoder_decoder"].append("FlaxVisionEncoderDecoderModel")
_import_structure["models.vision_text_dual_encoder"].extend(["FlaxVisionTextDualEncoderModel"])
_import_structure["models.vit"].extend(["FlaxViTForImageClassification", "FlaxViTModel", "FlaxViTPreTrainedModel"])
_import_structure["models.wav2vec2"].extend(
[
"FlaxWav2Vec2ForCTC",
"FlaxWav2Vec2ForPreTraining",
"FlaxWav2Vec2Model",
"FlaxWav2Vec2PreTrainedModel",
]
)
_import_structure["models.whisper"].extend(
[
"FlaxWhisperForConditionalGeneration",
"FlaxWhisperModel",
"FlaxWhisperPreTrainedModel",
"FlaxWhisperForAudioClassification",
]
)
_import_structure["models.xglm"].extend(
[
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXGLMPreTrainedModel",
]
)
_import_structure["models.xlm_roberta"].extend(
[
"FlaxXLMRobertaForMaskedLM",
"FlaxXLMRobertaForMultipleChoice",
"FlaxXLMRobertaForQuestionAnswering",
"FlaxXLMRobertaForSequenceClassification",
"FlaxXLMRobertaForTokenClassification",
"FlaxXLMRobertaModel",
"FlaxXLMRobertaForCausalLM",
"FlaxXLMRobertaPreTrainedModel",
]
)
# Direct imports for type-checking
if TYPE_CHECKING:
# Configuration
# Agents
from .agents import (
Agent,
CodeAgent,
HfApiEngine,
ManagedAgent,
PipelineTool,
ReactAgent,
ReactCodeAgent,
ReactJsonAgent,
Tool,
Toolbox,
ToolCollection,
TransformersEngine,
launch_gradio_demo,
load_tool,
stream_to_gradio,
tool,
)
from .configuration_utils import PretrainedConfig
# Data
from .data import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadV1Processor,
SquadV2Processor,
glue_compute_metrics,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_compute_metrics,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
from .data.data_collator import (
DataCollator,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeq2Seq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithFlattening,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .feature_extraction_sequence_utils import SequenceFeatureExtractor
# Feature Extractor
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
# Generation
from .generation import (
AsyncTextIteratorStreamer,
CompileConfig,
GenerationConfig,
TextIteratorStreamer,
TextStreamer,
WatermarkingConfig,
)
from .hf_argparser import HfArgumentParser
# Integrations
from .integrations import (
is_clearml_available,
is_comet_available,
is_dvclive_available,
is_neptune_available,
is_optuna_available,
is_ray_available,
is_ray_tune_available,
is_sigopt_available,
is_tensorboard_available,
is_wandb_available,
)
# Model Cards
from .modelcard import ModelCard
# TF 2.0 <=> PyTorch conversion utilities
from .modeling_tf_pytorch_utils import (
convert_tf_weight_name_to_pt_weight_name,
load_pytorch_checkpoint_in_tf2_model,
load_pytorch_model_in_tf2_model,
load_pytorch_weights_in_tf2_model,
load_tf2_checkpoint_in_pytorch_model,
load_tf2_model_in_pytorch_model,
load_tf2_weights_in_pytorch_model,
)
from .models.albert import AlbertConfig
from .models.align import (
AlignConfig,
AlignProcessor,
AlignTextConfig,
AlignVisionConfig,
)
from .models.altclip import (
AltCLIPConfig,
AltCLIPProcessor,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .models.aria import (
AriaConfig,
AriaProcessor,
AriaTextConfig,
)
from .models.audio_spectrogram_transformer import (
ASTConfig,
ASTFeatureExtractor,
)
from .models.auto import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_NAMES_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoImageProcessor,
AutoProcessor,
AutoTokenizer,
)
from .models.autoformer import (
AutoformerConfig,
)
from .models.bamba import BambaConfig
from .models.bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkProcessor,
BarkSemanticConfig,
)
from .models.bart import BartConfig, BartTokenizer
from .models.beit import BeitConfig
from .models.bert import (
BasicTokenizer,
BertConfig,
BertTokenizer,
WordpieceTokenizer,
)
from .models.bert_generation import BertGenerationConfig
from .models.bert_japanese import (
BertJapaneseTokenizer,
CharacterTokenizer,
MecabTokenizer,
)
from .models.bertweet import BertweetTokenizer
from .models.big_bird import BigBirdConfig
from .models.bigbird_pegasus import (
BigBirdPegasusConfig,
)
from .models.biogpt import (
BioGptConfig,
BioGptTokenizer,
)
from .models.bit import BitConfig
from .models.blenderbot import (
BlenderbotConfig,
BlenderbotTokenizer,
)
from .models.blenderbot_small import (
BlenderbotSmallConfig,
BlenderbotSmallTokenizer,
)
from .models.blip import (
BlipConfig,
BlipProcessor,
BlipTextConfig,
BlipVisionConfig,
)
from .models.blip_2 import (
Blip2Config,
Blip2Processor,
Blip2QFormerConfig,
Blip2VisionConfig,
)
from .models.bloom import BloomConfig
from .models.bridgetower import (
BridgeTowerConfig,
BridgeTowerProcessor,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .models.bros import (
BrosConfig,
BrosProcessor,
)
from .models.byt5 import ByT5Tokenizer
from .models.camembert import (
CamembertConfig,
)
from .models.canine import (
CanineConfig,
CanineTokenizer,
)
from .models.chameleon import (
ChameleonConfig,
ChameleonProcessor,
ChameleonVQVAEConfig,
)
from .models.chinese_clip import (
ChineseCLIPConfig,
ChineseCLIPProcessor,
ChineseCLIPTextConfig,
ChineseCLIPVisionConfig,
)
from .models.clap import (
ClapAudioConfig,
ClapConfig,
ClapProcessor,
ClapTextConfig,
)
from .models.clip import (
CLIPConfig,
CLIPProcessor,
CLIPTextConfig,
CLIPTokenizer,
CLIPVisionConfig,
)
from .models.clipseg import (
CLIPSegConfig,
CLIPSegProcessor,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .models.clvp import (
ClvpConfig,
ClvpDecoderConfig,
ClvpEncoderConfig,
ClvpFeatureExtractor,
ClvpProcessor,
ClvpTokenizer,
)
from .models.codegen import (
CodeGenConfig,
CodeGenTokenizer,
)
from .models.cohere import CohereConfig
from .models.cohere2 import Cohere2Config
from .models.colpali import (
ColPaliConfig,
ColPaliProcessor,
)
from .models.conditional_detr import (
ConditionalDetrConfig,
)
from .models.convbert import (
ConvBertConfig,
ConvBertTokenizer,
)
from .models.convnext import ConvNextConfig
from .models.convnextv2 import (
ConvNextV2Config,
)
from .models.cpmant import (
CpmAntConfig,
CpmAntTokenizer,
)
from .models.ctrl import (
CTRLConfig,
CTRLTokenizer,
)
from .models.cvt import CvtConfig
from .models.dab_detr import (
DabDetrConfig,
)
from .models.dac import (
DacConfig,
DacFeatureExtractor,
)
from .models.data2vec import (
Data2VecAudioConfig,
Data2VecTextConfig,
Data2VecVisionConfig,
)
from .models.dbrx import DbrxConfig
from .models.deberta import (
DebertaConfig,
DebertaTokenizer,
)
from .models.deberta_v2 import (
DebertaV2Config,
)
from .models.decision_transformer import (
DecisionTransformerConfig,
)
from .models.deformable_detr import (
DeformableDetrConfig,
)
from .models.deit import DeiTConfig
from .models.deprecated.deta import DetaConfig
from .models.deprecated.efficientformer import (
EfficientFormerConfig,
)
from .models.deprecated.ernie_m import ErnieMConfig
from .models.deprecated.gptsan_japanese import (
GPTSanJapaneseConfig,
GPTSanJapaneseTokenizer,
)
from .models.deprecated.graphormer import GraphormerConfig
from .models.deprecated.jukebox import (
JukeboxConfig,
JukeboxPriorConfig,
JukeboxTokenizer,
JukeboxVQVAEConfig,
)
from .models.deprecated.mctct import (
MCTCTConfig,
MCTCTFeatureExtractor,
MCTCTProcessor,
)
from .models.deprecated.mega import MegaConfig
from .models.deprecated.mmbt import MMBTConfig
from .models.deprecated.nat import NatConfig
from .models.deprecated.nezha import NezhaConfig
from .models.deprecated.open_llama import (
OpenLlamaConfig,
)
from .models.deprecated.qdqbert import QDQBertConfig
from .models.deprecated.realm import (
RealmConfig,
RealmTokenizer,
)
from .models.deprecated.retribert import (
RetriBertConfig,
RetriBertTokenizer,
)
from .models.deprecated.speech_to_text_2 import (
Speech2Text2Config,
Speech2Text2Processor,
Speech2Text2Tokenizer,
)
from .models.deprecated.tapex import TapexTokenizer
from .models.deprecated.trajectory_transformer import (
TrajectoryTransformerConfig,
)
from .models.deprecated.transfo_xl import (
TransfoXLConfig,
TransfoXLCorpus,
TransfoXLTokenizer,
)
from .models.deprecated.tvlt import (
TvltConfig,
TvltFeatureExtractor,
TvltProcessor,
)
from .models.deprecated.van import VanConfig
from .models.deprecated.vit_hybrid import (
ViTHybridConfig,
)
from .models.deprecated.xlm_prophetnet import (
XLMProphetNetConfig,
)
from .models.depth_anything import DepthAnythingConfig
from .models.detr import DetrConfig
from .models.diffllama import DiffLlamaConfig
from .models.dinat import DinatConfig
from .models.dinov2 import Dinov2Config
from .models.dinov2_with_registers import Dinov2WithRegistersConfig
from .models.distilbert import (
DistilBertConfig,
DistilBertTokenizer,
)
from .models.donut import (
DonutProcessor,
DonutSwinConfig,
)
from .models.dpr import (
DPRConfig,
DPRContextEncoderTokenizer,
DPRQuestionEncoderTokenizer,
DPRReaderOutput,
DPRReaderTokenizer,
)
from .models.dpt import DPTConfig
from .models.efficientnet import (
EfficientNetConfig,
)
from .models.electra import (
ElectraConfig,
ElectraTokenizer,
)
from .models.emu3 import (
Emu3Config,
Emu3Processor,
Emu3TextConfig,
Emu3VQVAEConfig,
)
from .models.encodec import (
EncodecConfig,
EncodecFeatureExtractor,
)
from .models.encoder_decoder import EncoderDecoderConfig
from .models.ernie import ErnieConfig
from .models.esm import EsmConfig, EsmTokenizer
from .models.falcon import FalconConfig
from .models.falcon_mamba import FalconMambaConfig
from .models.fastspeech2_conformer import (
FastSpeech2ConformerConfig,
FastSpeech2ConformerHifiGanConfig,
FastSpeech2ConformerTokenizer,
FastSpeech2ConformerWithHifiGanConfig,
)
from .models.flaubert import FlaubertConfig, FlaubertTokenizer
from .models.flava import (
FlavaConfig,
FlavaImageCodebookConfig,
FlavaImageConfig,
FlavaMultimodalConfig,
FlavaTextConfig,
)
from .models.fnet import FNetConfig
from .models.focalnet import FocalNetConfig
from .models.fsmt import (
FSMTConfig,
FSMTTokenizer,
)
from .models.funnel import (
FunnelConfig,
FunnelTokenizer,
)
from .models.fuyu import FuyuConfig
from .models.gemma import GemmaConfig
from .models.gemma2 import Gemma2Config
from .models.git import (
GitConfig,
GitProcessor,
GitVisionConfig,
)
from .models.glm import GlmConfig
from .models.glpn import GLPNConfig
from .models.got_ocr2 import GotOcr2Config, GotOcr2Processor, GotOcr2VisionConfig
from .models.gpt2 import (
GPT2Config,
GPT2Tokenizer,
)
from .models.gpt_bigcode import (
GPTBigCodeConfig,
)
from .models.gpt_neo import GPTNeoConfig
from .models.gpt_neox import GPTNeoXConfig
from .models.gpt_neox_japanese import (
GPTNeoXJapaneseConfig,
)
from .models.gptj import GPTJConfig
from .models.granite import GraniteConfig
from .models.granitemoe import GraniteMoeConfig
from .models.grounding_dino import (
GroundingDinoConfig,
GroundingDinoProcessor,
)
from .models.groupvit import (
GroupViTConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
from .models.helium import HeliumConfig
from .models.herbert import HerbertTokenizer
from .models.hiera import HieraConfig
from .models.hubert import HubertConfig
from .models.ibert import IBertConfig
from .models.idefics import (
IdeficsConfig,
)
from .models.idefics2 import Idefics2Config
from .models.idefics3 import Idefics3Config
from .models.ijepa import IJepaConfig
from .models.imagegpt import ImageGPTConfig
from .models.informer import InformerConfig
from .models.instructblip import (
InstructBlipConfig,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .models.instructblipvideo import (
InstructBlipVideoConfig,
InstructBlipVideoProcessor,
InstructBlipVideoQFormerConfig,
InstructBlipVideoVisionConfig,
)
from .models.jamba import JambaConfig
from .models.jetmoe import JetMoeConfig
from .models.kosmos2 import (
Kosmos2Config,
Kosmos2Processor,
)
from .models.layoutlm import (
LayoutLMConfig,
LayoutLMTokenizer,
)
from .models.layoutlmv2 import (
LayoutLMv2Config,
LayoutLMv2FeatureExtractor,
LayoutLMv2ImageProcessor,
LayoutLMv2Processor,
LayoutLMv2Tokenizer,
)
from .models.layoutlmv3 import (
LayoutLMv3Config,
LayoutLMv3FeatureExtractor,
LayoutLMv3ImageProcessor,
LayoutLMv3Processor,
LayoutLMv3Tokenizer,
)
from .models.layoutxlm import LayoutXLMProcessor
from .models.led import LEDConfig, LEDTokenizer
from .models.levit import LevitConfig
from .models.lilt import LiltConfig
from .models.llama import LlamaConfig
from .models.llava import (
LlavaConfig,
LlavaProcessor,
)
from .models.llava_next import (
LlavaNextConfig,
LlavaNextProcessor,
)
from .models.llava_next_video import (
LlavaNextVideoConfig,
LlavaNextVideoProcessor,
)
from .models.llava_onevision import (
LlavaOnevisionConfig,
LlavaOnevisionProcessor,
)
from .models.longformer import (
LongformerConfig,
LongformerTokenizer,
)
from .models.longt5 import LongT5Config
from .models.luke import (
LukeConfig,
LukeTokenizer,
)
from .models.lxmert import (
LxmertConfig,
LxmertTokenizer,
)
from .models.m2m_100 import M2M100Config
from .models.mamba import MambaConfig
from .models.mamba2 import Mamba2Config
from .models.marian import MarianConfig
from .models.markuplm import (
MarkupLMConfig,
MarkupLMFeatureExtractor,
MarkupLMProcessor,
MarkupLMTokenizer,
)
from .models.mask2former import (
Mask2FormerConfig,
)
from .models.maskformer import (
MaskFormerConfig,
MaskFormerSwinConfig,
)
from .models.mbart import MBartConfig
from .models.megatron_bert import (
MegatronBertConfig,
)
from .models.mgp_str import (
MgpstrConfig,
MgpstrProcessor,
MgpstrTokenizer,
)
from .models.mimi import (
MimiConfig,
)
from .models.mistral import MistralConfig
from .models.mixtral import MixtralConfig
from .models.mllama import (
MllamaConfig,
MllamaProcessor,
)
from .models.mobilebert import (
MobileBertConfig,
MobileBertTokenizer,
)
from .models.mobilenet_v1 import (
MobileNetV1Config,
)
from .models.mobilenet_v2 import (
MobileNetV2Config,
)
from .models.mobilevit import (
MobileViTConfig,
)
from .models.mobilevitv2 import (
MobileViTV2Config,
)
from .models.modernbert import ModernBertConfig
from .models.moonshine import MoonshineConfig
from .models.moshi import (
MoshiConfig,
MoshiDepthConfig,
)
from .models.mpnet import (
MPNetConfig,
MPNetTokenizer,
)
from .models.mpt import MptConfig
from .models.mra import MraConfig
from .models.mt5 import MT5Config
from .models.musicgen import (
MusicgenConfig,
MusicgenDecoderConfig,
)
from .models.musicgen_melody import (
MusicgenMelodyConfig,
MusicgenMelodyDecoderConfig,
)
from .models.mvp import MvpConfig, MvpTokenizer
from .models.myt5 import MyT5Tokenizer
from .models.nemotron import NemotronConfig
from .models.nllb_moe import NllbMoeConfig
from .models.nougat import NougatProcessor
from .models.nystromformer import (
NystromformerConfig,
)
from .models.olmo import OlmoConfig
from .models.olmo2 import Olmo2Config
from .models.olmoe import OlmoeConfig
from .models.omdet_turbo import (
OmDetTurboConfig,
OmDetTurboProcessor,
)
from .models.oneformer import (
OneFormerConfig,
OneFormerProcessor,
)
from .models.openai import (
OpenAIGPTConfig,
OpenAIGPTTokenizer,
)
from .models.opt import OPTConfig
from .models.owlv2 import (
Owlv2Config,
Owlv2Processor,
Owlv2TextConfig,
Owlv2VisionConfig,
)
from .models.owlvit import (
OwlViTConfig,
OwlViTProcessor,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .models.paligemma import (
PaliGemmaConfig,
)
from .models.patchtsmixer import (
PatchTSMixerConfig,
)
from .models.patchtst import PatchTSTConfig
from .models.pegasus import (
PegasusConfig,
PegasusTokenizer,
)
from .models.pegasus_x import (
PegasusXConfig,
)
from .models.perceiver import (
PerceiverConfig,
PerceiverTokenizer,
)
from .models.persimmon import (
PersimmonConfig,
)
from .models.phi import PhiConfig
from .models.phi3 import Phi3Config
from .models.phimoe import PhimoeConfig
from .models.phobert import PhobertTokenizer
from .models.pix2struct import (
Pix2StructConfig,
Pix2StructProcessor,
Pix2StructTextConfig,
Pix2StructVisionConfig,
)
from .models.pixtral import (
PixtralProcessor,
PixtralVisionConfig,
)
from .models.plbart import PLBartConfig
from .models.poolformer import (
PoolFormerConfig,
)
from .models.pop2piano import (
Pop2PianoConfig,
)
from .models.prophetnet import (
ProphetNetConfig,
ProphetNetTokenizer,
)
from .models.pvt import PvtConfig
from .models.pvt_v2 import PvtV2Config
from .models.qwen2 import Qwen2Config, Qwen2Tokenizer
from .models.qwen2_5_vl import (
Qwen2_5_VLConfig,
Qwen2_5_VLProcessor,
)
from .models.qwen2_audio import (
Qwen2AudioConfig,
Qwen2AudioEncoderConfig,
Qwen2AudioProcessor,
)
from .models.qwen2_moe import Qwen2MoeConfig
from .models.qwen2_vl import (
Qwen2VLConfig,
Qwen2VLProcessor,
)
from .models.rag import RagConfig, RagRetriever, RagTokenizer
from .models.recurrent_gemma import RecurrentGemmaConfig
from .models.reformer import ReformerConfig
from .models.regnet import RegNetConfig
from .models.rembert import RemBertConfig
from .models.resnet import ResNetConfig
from .models.roberta import (
RobertaConfig,
RobertaTokenizer,
)
from .models.roberta_prelayernorm import (
RobertaPreLayerNormConfig,
)
from .models.roc_bert import (
RoCBertConfig,
RoCBertTokenizer,
)
from .models.roformer import (
RoFormerConfig,
RoFormerTokenizer,
)
from .models.rt_detr import (
RTDetrConfig,
RTDetrResNetConfig,
)
from .models.rt_detr_v2 import RTDetrV2Config
from .models.rwkv import RwkvConfig
from .models.sam import (
SamConfig,
SamMaskDecoderConfig,
SamProcessor,
SamPromptEncoderConfig,
SamVisionConfig,
)
from .models.seamless_m4t import (
SeamlessM4TConfig,
SeamlessM4TFeatureExtractor,
SeamlessM4TProcessor,
)
from .models.seamless_m4t_v2 import (
SeamlessM4Tv2Config,
)
from .models.segformer import SegformerConfig
from .models.seggpt import SegGptConfig
from .models.sew import SEWConfig
from .models.sew_d import SEWDConfig
from .models.siglip import (
SiglipConfig,
SiglipProcessor,
SiglipTextConfig,
SiglipVisionConfig,
)
from .models.speech_encoder_decoder import SpeechEncoderDecoderConfig
from .models.speech_to_text import (
Speech2TextConfig,
Speech2TextFeatureExtractor,
Speech2TextProcessor,
)
from .models.speecht5 import (
SpeechT5Config,
SpeechT5FeatureExtractor,
SpeechT5HifiGanConfig,
SpeechT5Processor,
)
from .models.splinter import (
SplinterConfig,
SplinterTokenizer,
)
from .models.squeezebert import (
SqueezeBertConfig,
SqueezeBertTokenizer,
)
from .models.stablelm import StableLmConfig
from .models.starcoder2 import Starcoder2Config
from .models.superglue import SuperGlueConfig
from .models.superpoint import SuperPointConfig
from .models.swiftformer import (
SwiftFormerConfig,
)
from .models.swin import SwinConfig
from .models.swin2sr import Swin2SRConfig
from .models.swinv2 import Swinv2Config
from .models.switch_transformers import (
SwitchTransformersConfig,
)
from .models.t5 import T5Config
from .models.table_transformer import (
TableTransformerConfig,
)
from .models.tapas import (
TapasConfig,
TapasTokenizer,
)
from .models.textnet import TextNetConfig
from .models.time_series_transformer import (
TimeSeriesTransformerConfig,
)
from .models.timesformer import (
TimesformerConfig,
)
from .models.timm_backbone import TimmBackboneConfig
from .models.timm_wrapper import TimmWrapperConfig
from .models.trocr import (
TrOCRConfig,
TrOCRProcessor,
)
from .models.tvp import (
TvpConfig,
TvpProcessor,
)
from .models.udop import UdopConfig, UdopProcessor
from .models.umt5 import UMT5Config
from .models.unispeech import (
UniSpeechConfig,
)
from .models.unispeech_sat import (
UniSpeechSatConfig,
)
from .models.univnet import (
UnivNetConfig,
UnivNetFeatureExtractor,
)
from .models.upernet import UperNetConfig
from .models.video_llava import VideoLlavaConfig
from .models.videomae import VideoMAEConfig
from .models.vilt import (
ViltConfig,
ViltFeatureExtractor,
ViltImageProcessor,
ViltProcessor,
)
from .models.vipllava import (
VipLlavaConfig,
)
from .models.vision_encoder_decoder import VisionEncoderDecoderConfig
from .models.vision_text_dual_encoder import (
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from .models.visual_bert import (
VisualBertConfig,
)
from .models.vit import ViTConfig
from .models.vit_mae import ViTMAEConfig
from .models.vit_msn import ViTMSNConfig
from .models.vitdet import VitDetConfig
from .models.vitmatte import VitMatteConfig
from .models.vitpose import VitPoseConfig
from .models.vitpose_backbone import VitPoseBackboneConfig
from .models.vits import (
VitsConfig,
VitsTokenizer,
)
from .models.vivit import VivitConfig
from .models.wav2vec2 import (
Wav2Vec2Config,
Wav2Vec2CTCTokenizer,
Wav2Vec2FeatureExtractor,
Wav2Vec2Processor,
Wav2Vec2Tokenizer,
)
from .models.wav2vec2_bert import (
Wav2Vec2BertConfig,
Wav2Vec2BertProcessor,
)
from .models.wav2vec2_conformer import (
Wav2Vec2ConformerConfig,
)
from .models.wav2vec2_phoneme import Wav2Vec2PhonemeCTCTokenizer
from .models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
from .models.wavlm import WavLMConfig
from .models.whisper import (
WhisperConfig,
WhisperFeatureExtractor,
WhisperProcessor,
WhisperTokenizer,
)
from .models.x_clip import (
XCLIPConfig,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .models.xglm import XGLMConfig
from .models.xlm import XLMConfig, XLMTokenizer
from .models.xlm_roberta import (
XLMRobertaConfig,
)
from .models.xlm_roberta_xl import (
XLMRobertaXLConfig,
)
from .models.xlnet import XLNetConfig
from .models.xmod import XmodConfig
from .models.yolos import YolosConfig
from .models.yoso import YosoConfig
from .models.zamba import ZambaConfig
from .models.zamba2 import Zamba2Config
from .models.zoedepth import ZoeDepthConfig
# Pipelines
from .pipelines import (
AudioClassificationPipeline,
AutomaticSpeechRecognitionPipeline,
CsvPipelineDataFormat,
DepthEstimationPipeline,
DocumentQuestionAnsweringPipeline,
FeatureExtractionPipeline,
FillMaskPipeline,
ImageClassificationPipeline,
ImageFeatureExtractionPipeline,
ImageSegmentationPipeline,
ImageTextToTextPipeline,
ImageToImagePipeline,
ImageToTextPipeline,
JsonPipelineDataFormat,
MaskGenerationPipeline,
NerPipeline,
ObjectDetectionPipeline,
PipedPipelineDataFormat,
Pipeline,
PipelineDataFormat,
QuestionAnsweringPipeline,
SummarizationPipeline,
TableQuestionAnsweringPipeline,
Text2TextGenerationPipeline,
TextClassificationPipeline,
TextGenerationPipeline,
TextToAudioPipeline,
TokenClassificationPipeline,
TranslationPipeline,
VideoClassificationPipeline,
VisualQuestionAnsweringPipeline,
ZeroShotAudioClassificationPipeline,
ZeroShotClassificationPipeline,
ZeroShotImageClassificationPipeline,
ZeroShotObjectDetectionPipeline,
pipeline,
)
from .processing_utils import ProcessorMixin
# Tokenization
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_base import (
AddedToken,
BatchEncoding,
CharSpan,
PreTrainedTokenizerBase,
SpecialTokensMixin,
TokenSpan,
)
# Trainer
from .trainer_callback import (
DefaultFlowCallback,
EarlyStoppingCallback,
PrinterCallback,
ProgressCallback,
TrainerCallback,
TrainerControl,
TrainerState,
)
from .trainer_utils import (
EvalPrediction,
IntervalStrategy,
SchedulerType,
enable_full_determinism,
set_seed,
)
from .training_args import TrainingArguments
from .training_args_seq2seq import Seq2SeqTrainingArguments
from .training_args_tf import TFTrainingArguments
# Files and general utilities
from .utils import (
CONFIG_NAME,
MODEL_CARD_NAME,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
TensorType,
add_end_docstrings,
add_start_docstrings,
is_apex_available,
is_av_available,
is_bitsandbytes_available,
is_datasets_available,
is_faiss_available,
is_flax_available,
is_keras_nlp_available,
is_phonemizer_available,
is_psutil_available,
is_py3nvml_available,
is_pyctcdecode_available,
is_sacremoses_available,
is_safetensors_available,
is_scipy_available,
is_sentencepiece_available,
is_sklearn_available,
is_speech_available,
is_tensorflow_text_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_mlu_available,
is_torch_musa_available,
is_torch_neuroncore_available,
is_torch_npu_available,
is_torch_tpu_available,
is_torch_xla_available,
is_torch_xpu_available,
is_torchvision_available,
is_vision_available,
logging,
)
# bitsandbytes config
from .utils.quantization_config import (
AqlmConfig,
AwqConfig,
BitNetConfig,
BitsAndBytesConfig,
CompressedTensorsConfig,
EetqConfig,
FbgemmFp8Config,
GPTQConfig,
HiggsConfig,
HqqConfig,
QuantoConfig,
TorchAoConfig,
VptqConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_sentencepiece_objects import *
else:
from .models.albert import AlbertTokenizer
from .models.barthez import BarthezTokenizer
from .models.bartpho import BartphoTokenizer
from .models.bert_generation import BertGenerationTokenizer
from .models.big_bird import BigBirdTokenizer
from .models.camembert import CamembertTokenizer
from .models.code_llama import CodeLlamaTokenizer
from .models.cpm import CpmTokenizer
from .models.deberta_v2 import DebertaV2Tokenizer
from .models.deprecated.ernie_m import ErnieMTokenizer
from .models.deprecated.xlm_prophetnet import XLMProphetNetTokenizer
from .models.fnet import FNetTokenizer
from .models.gemma import GemmaTokenizer
from .models.gpt_sw3 import GPTSw3Tokenizer
from .models.layoutxlm import LayoutXLMTokenizer
from .models.llama import LlamaTokenizer
from .models.m2m_100 import M2M100Tokenizer
from .models.marian import MarianTokenizer
from .models.mbart import MBartTokenizer
from .models.mbart50 import MBart50Tokenizer
from .models.mluke import MLukeTokenizer
from .models.mt5 import MT5Tokenizer
from .models.nllb import NllbTokenizer
from .models.pegasus import PegasusTokenizer
from .models.plbart import PLBartTokenizer
from .models.reformer import ReformerTokenizer
from .models.rembert import RemBertTokenizer
from .models.seamless_m4t import SeamlessM4TTokenizer
from .models.siglip import SiglipTokenizer
from .models.speech_to_text import Speech2TextTokenizer
from .models.speecht5 import SpeechT5Tokenizer
from .models.t5 import T5Tokenizer
from .models.udop import UdopTokenizer
from .models.xglm import XGLMTokenizer
from .models.xlm_roberta import XLMRobertaTokenizer
from .models.xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_tokenizers_objects import *
else:
# Fast tokenizers imports
from .models.albert import AlbertTokenizerFast
from .models.bart import BartTokenizerFast
from .models.barthez import BarthezTokenizerFast
from .models.bert import BertTokenizerFast
from .models.big_bird import BigBirdTokenizerFast
from .models.blenderbot import BlenderbotTokenizerFast
from .models.blenderbot_small import BlenderbotSmallTokenizerFast
from .models.bloom import BloomTokenizerFast
from .models.camembert import CamembertTokenizerFast
from .models.clip import CLIPTokenizerFast
from .models.code_llama import CodeLlamaTokenizerFast
from .models.codegen import CodeGenTokenizerFast
from .models.cohere import CohereTokenizerFast
from .models.convbert import ConvBertTokenizerFast
from .models.cpm import CpmTokenizerFast
from .models.deberta import DebertaTokenizerFast
from .models.deberta_v2 import DebertaV2TokenizerFast
from .models.deprecated.realm import RealmTokenizerFast
from .models.deprecated.retribert import RetriBertTokenizerFast
from .models.distilbert import DistilBertTokenizerFast
from .models.dpr import (
DPRContextEncoderTokenizerFast,
DPRQuestionEncoderTokenizerFast,
DPRReaderTokenizerFast,
)
from .models.electra import ElectraTokenizerFast
from .models.fnet import FNetTokenizerFast
from .models.funnel import FunnelTokenizerFast
from .models.gemma import GemmaTokenizerFast
from .models.gpt2 import GPT2TokenizerFast
from .models.gpt_neox import GPTNeoXTokenizerFast
from .models.gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from .models.herbert import HerbertTokenizerFast
from .models.layoutlm import LayoutLMTokenizerFast
from .models.layoutlmv2 import LayoutLMv2TokenizerFast
from .models.layoutlmv3 import LayoutLMv3TokenizerFast
from .models.layoutxlm import LayoutXLMTokenizerFast
from .models.led import LEDTokenizerFast
from .models.llama import LlamaTokenizerFast
from .models.longformer import LongformerTokenizerFast
from .models.lxmert import LxmertTokenizerFast
from .models.markuplm import MarkupLMTokenizerFast
from .models.mbart import MBartTokenizerFast
from .models.mbart50 import MBart50TokenizerFast
from .models.mobilebert import MobileBertTokenizerFast
from .models.mpnet import MPNetTokenizerFast
from .models.mt5 import MT5TokenizerFast
from .models.mvp import MvpTokenizerFast
from .models.nllb import NllbTokenizerFast
from .models.nougat import NougatTokenizerFast
from .models.openai import OpenAIGPTTokenizerFast
from .models.pegasus import PegasusTokenizerFast
from .models.qwen2 import Qwen2TokenizerFast
from .models.reformer import ReformerTokenizerFast
from .models.rembert import RemBertTokenizerFast
from .models.roberta import RobertaTokenizerFast
from .models.roformer import RoFormerTokenizerFast
from .models.seamless_m4t import SeamlessM4TTokenizerFast
from .models.splinter import SplinterTokenizerFast
from .models.squeezebert import SqueezeBertTokenizerFast
from .models.t5 import T5TokenizerFast
from .models.udop import UdopTokenizerFast
from .models.whisper import WhisperTokenizerFast
from .models.xglm import XGLMTokenizerFast
from .models.xlm_roberta import XLMRobertaTokenizerFast
from .models.xlnet import XLNetTokenizerFast
from .tokenization_utils_fast import PreTrainedTokenizerFast
try:
if not (is_sentencepiece_available() and is_tokenizers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummies_sentencepiece_and_tokenizers_objects import *
else:
from .convert_slow_tokenizer import (
SLOW_TO_FAST_CONVERTERS,
convert_slow_tokenizer,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_tensorflow_text_objects import *
else:
from .models.bert import TFBertTokenizer
try:
if not is_keras_nlp_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_keras_nlp_objects import *
else:
from .models.gpt2 import TFGPT2Tokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_vision_objects import *
else:
from .image_processing_base import ImageProcessingMixin
from .image_processing_utils import BaseImageProcessor
from .image_utils import ImageFeatureExtractionMixin
from .models.aria import AriaImageProcessor
from .models.beit import BeitFeatureExtractor, BeitImageProcessor
from .models.bit import BitImageProcessor
from .models.blip import BlipImageProcessor
from .models.bridgetower import BridgeTowerImageProcessor
from .models.chameleon import ChameleonImageProcessor
from .models.chinese_clip import (
ChineseCLIPFeatureExtractor,
ChineseCLIPImageProcessor,
)
from .models.clip import CLIPFeatureExtractor, CLIPImageProcessor
from .models.conditional_detr import (
ConditionalDetrFeatureExtractor,
ConditionalDetrImageProcessor,
)
from .models.convnext import ConvNextFeatureExtractor, ConvNextImageProcessor
from .models.deformable_detr import DeformableDetrFeatureExtractor, DeformableDetrImageProcessor
from .models.deit import DeiTFeatureExtractor, DeiTImageProcessor
from .models.deprecated.deta import DetaImageProcessor
from .models.deprecated.efficientformer import EfficientFormerImageProcessor
from .models.deprecated.tvlt import TvltImageProcessor
from .models.deprecated.vit_hybrid import ViTHybridImageProcessor
from .models.detr import DetrFeatureExtractor, DetrImageProcessor
from .models.donut import DonutFeatureExtractor, DonutImageProcessor
from .models.dpt import DPTFeatureExtractor, DPTImageProcessor
from .models.efficientnet import EfficientNetImageProcessor
from .models.emu3 import Emu3ImageProcessor
from .models.flava import (
FlavaFeatureExtractor,
FlavaImageProcessor,
FlavaProcessor,
)
from .models.fuyu import FuyuImageProcessor, FuyuProcessor
from .models.glpn import GLPNFeatureExtractor, GLPNImageProcessor
from .models.got_ocr2 import GotOcr2ImageProcessor
from .models.grounding_dino import GroundingDinoImageProcessor
from .models.idefics import IdeficsImageProcessor
from .models.idefics2 import Idefics2ImageProcessor
from .models.idefics3 import Idefics3ImageProcessor
from .models.imagegpt import ImageGPTFeatureExtractor, ImageGPTImageProcessor
from .models.instructblipvideo import InstructBlipVideoImageProcessor
from .models.layoutlmv2 import (
LayoutLMv2FeatureExtractor,
LayoutLMv2ImageProcessor,
)
from .models.layoutlmv3 import (
LayoutLMv3FeatureExtractor,
LayoutLMv3ImageProcessor,
)
from .models.levit import LevitFeatureExtractor, LevitImageProcessor
from .models.llava import LlavaImageProcessor
from .models.llava_next import LlavaNextImageProcessor
from .models.llava_next_video import LlavaNextVideoImageProcessor
from .models.llava_onevision import LlavaOnevisionImageProcessor, LlavaOnevisionVideoProcessor
from .models.mask2former import Mask2FormerImageProcessor
from .models.maskformer import (
MaskFormerFeatureExtractor,
MaskFormerImageProcessor,
)
from .models.mllama import MllamaImageProcessor
from .models.mobilenet_v1 import (
MobileNetV1FeatureExtractor,
MobileNetV1ImageProcessor,
)
from .models.mobilenet_v2 import (
MobileNetV2FeatureExtractor,
MobileNetV2ImageProcessor,
)
from .models.mobilevit import MobileViTFeatureExtractor, MobileViTImageProcessor
from .models.nougat import NougatImageProcessor
from .models.oneformer import OneFormerImageProcessor
from .models.owlv2 import Owlv2ImageProcessor
from .models.owlvit import OwlViTFeatureExtractor, OwlViTImageProcessor
from .models.perceiver import PerceiverFeatureExtractor, PerceiverImageProcessor
from .models.pix2struct import Pix2StructImageProcessor
from .models.pixtral import PixtralImageProcessor
from .models.poolformer import (
PoolFormerFeatureExtractor,
PoolFormerImageProcessor,
)
from .models.pvt import PvtImageProcessor
from .models.qwen2_5_vl import Qwen2_5_VLImageProcessor
from .models.qwen2_vl import Qwen2VLImageProcessor
from .models.rt_detr import RTDetrImageProcessor
from .models.sam import SamImageProcessor
from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor
from .models.seggpt import SegGptImageProcessor
from .models.siglip import SiglipImageProcessor
from .models.superglue import SuperGlueImageProcessor
from .models.superpoint import SuperPointImageProcessor
from .models.swin2sr import Swin2SRImageProcessor
from .models.textnet import TextNetImageProcessor
from .models.tvp import TvpImageProcessor
from .models.video_llava import VideoLlavaImageProcessor
from .models.videomae import VideoMAEFeatureExtractor, VideoMAEImageProcessor
from .models.vilt import ViltFeatureExtractor, ViltImageProcessor, ViltProcessor
from .models.vit import ViTFeatureExtractor, ViTImageProcessor
from .models.vitmatte import VitMatteImageProcessor
from .models.vitpose import VitPoseImageProcessor
from .models.vivit import VivitImageProcessor
from .models.yolos import YolosFeatureExtractor, YolosImageProcessor
from .models.zoedepth import ZoeDepthImageProcessor
try:
if not is_torchvision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torchvision_objects import *
else:
from .image_processing_utils_fast import BaseImageProcessorFast
from .models.blip import BlipImageProcessorFast
from .models.clip import CLIPImageProcessorFast
from .models.convnext import ConvNextImageProcessorFast
from .models.deformable_detr import DeformableDetrImageProcessorFast
from .models.deit import DeiTImageProcessorFast
from .models.detr import DetrImageProcessorFast
from .models.llava import LlavaImageProcessorFast
from .models.llava_next import LlavaNextImageProcessorFast
from .models.llava_onevision import LlavaOnevisionImageProcessorFast
from .models.pixtral import PixtralImageProcessorFast
from .models.qwen2_vl import Qwen2VLImageProcessorFast
from .models.rt_detr import RTDetrImageProcessorFast
from .models.siglip import SiglipImageProcessorFast
from .models.vit import ViTImageProcessorFast
try:
if not (is_torchvision_available() and is_timm_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_timm_and_torchvision_objects import *
else:
from .models.timm_wrapper import TimmWrapperImageProcessor
# Modeling
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import *
else:
from .cache_utils import (
Cache,
CacheConfig,
DynamicCache,
EncoderDecoderCache,
HQQQuantizedCache,
HybridCache,
MambaCache,
OffloadedCache,
OffloadedStaticCache,
QuantizedCache,
QuantizedCacheConfig,
QuantoQuantizedCache,
SinkCache,
SlidingWindowCache,
StaticCache,
)
from .data.datasets import (
GlueDataset,
GlueDataTrainingArguments,
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
SquadDataset,
SquadDataTrainingArguments,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .generation import (
AlternatingCodebooksLogitsProcessor,
BayesianDetectorConfig,
BayesianDetectorModel,
BeamScorer,
BeamSearchScorer,
ClassifierFreeGuidanceLogitsProcessor,
ConstrainedBeamSearchScorer,
Constraint,
ConstraintListState,
DisjunctiveConstraint,
EncoderNoRepeatNGramLogitsProcessor,
EncoderRepetitionPenaltyLogitsProcessor,
EosTokenCriteria,
EpsilonLogitsWarper,
EtaLogitsWarper,
ExponentialDecayLengthPenalty,
ForcedBOSTokenLogitsProcessor,
ForcedEOSTokenLogitsProcessor,
GenerationMixin,
HammingDiversityLogitsProcessor,
InfNanRemoveLogitsProcessor,
LogitNormalization,
LogitsProcessor,
LogitsProcessorList,
MaxLengthCriteria,
MaxTimeCriteria,
MinLengthLogitsProcessor,
MinNewTokensLengthLogitsProcessor,
MinPLogitsWarper,
NoBadWordsLogitsProcessor,
NoRepeatNGramLogitsProcessor,
PhrasalConstraint,
PrefixConstrainedLogitsProcessor,
RepetitionPenaltyLogitsProcessor,
SequenceBiasLogitsProcessor,
StoppingCriteria,
StoppingCriteriaList,
StopStringCriteria,
SuppressTokensAtBeginLogitsProcessor,
SuppressTokensLogitsProcessor,
SynthIDTextWatermarkDetector,
SynthIDTextWatermarkingConfig,
SynthIDTextWatermarkLogitsProcessor,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
TypicalLogitsWarper,
UnbatchedClassifierFreeGuidanceLogitsProcessor,
WatermarkDetector,
WatermarkLogitsProcessor,
WhisperTimeStampLogitsProcessor,
)
from .integrations.executorch import (
TorchExportableModuleWithStaticCache,
convert_and_export_with_cache,
)
from .modeling_rope_utils import ROPE_INIT_FUNCTIONS
from .modeling_utils import PreTrainedModel
from .models.albert import (
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
from .models.align import (
AlignModel,
AlignPreTrainedModel,
AlignTextModel,
AlignVisionModel,
)
from .models.altclip import (
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
from .models.aria import (
AriaForConditionalGeneration,
AriaPreTrainedModel,
AriaTextForCausalLM,
AriaTextModel,
AriaTextPreTrainedModel,
)
from .models.audio_spectrogram_transformer import (
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
from .models.auto import (
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
MODEL_FOR_AUDIO_XVECTOR_MAPPING,
MODEL_FOR_BACKBONE_MAPPING,
MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_CTC_MAPPING,
MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_IMAGE_MAPPING,
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING,
MODEL_FOR_IMAGE_TO_IMAGE_MAPPING,
MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
MODEL_FOR_KEYPOINT_DETECTION_MAPPING,
MODEL_FOR_MASK_GENERATION_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
MODEL_FOR_OBJECT_DETECTION_MAPPING,
MODEL_FOR_PRETRAINING_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_RETRIEVAL_MAPPING,
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_TEXT_ENCODING_MAPPING,
MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING,
MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING,
MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING,
MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
MODEL_FOR_VISION_2_SEQ_MAPPING,
MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
MODEL_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoBackbone,
AutoModel,
AutoModelForAudioClassification,
AutoModelForAudioFrameClassification,
AutoModelForAudioXVector,
AutoModelForCausalLM,
AutoModelForCTC,
AutoModelForDepthEstimation,
AutoModelForDocumentQuestionAnswering,
AutoModelForImageClassification,
AutoModelForImageSegmentation,
AutoModelForImageTextToText,
AutoModelForImageToImage,
AutoModelForInstanceSegmentation,
AutoModelForKeypointDetection,
AutoModelForMaskedImageModeling,
AutoModelForMaskedLM,
AutoModelForMaskGeneration,
AutoModelForMultipleChoice,
AutoModelForNextSentencePrediction,
AutoModelForObjectDetection,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSemanticSegmentation,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoModelForSpeechSeq2Seq,
AutoModelForTableQuestionAnswering,
AutoModelForTextEncoding,
AutoModelForTextToSpectrogram,
AutoModelForTextToWaveform,
AutoModelForTokenClassification,
AutoModelForUniversalSegmentation,
AutoModelForVideoClassification,
AutoModelForVision2Seq,
AutoModelForVisualQuestionAnswering,
AutoModelForZeroShotImageClassification,
AutoModelForZeroShotObjectDetection,
AutoModelWithLMHead,
)
from .models.autoformer import (
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
from .models.bamba import BambaForCausalLM, BambaModel, BambaPreTrainedModel
from .models.bark import (
BarkCausalModel,
BarkCoarseModel,
BarkFineModel,
BarkModel,
BarkPreTrainedModel,
BarkSemanticModel,
)
from .models.bart import (
BartForCausalLM,
BartForConditionalGeneration,
BartForQuestionAnswering,
BartForSequenceClassification,
BartModel,
BartPreTrainedModel,
BartPretrainedModel,
PretrainedBartModel,
)
from .models.beit import (
BeitBackbone,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
from .models.bert import (
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
from .models.bert_generation import (
BertGenerationDecoder,
BertGenerationEncoder,
BertGenerationPreTrainedModel,
load_tf_weights_in_bert_generation,
)
from .models.big_bird import (
BigBirdForCausalLM,
BigBirdForMaskedLM,
BigBirdForMultipleChoice,
BigBirdForPreTraining,
BigBirdForQuestionAnswering,
BigBirdForSequenceClassification,
BigBirdForTokenClassification,
BigBirdModel,
BigBirdPreTrainedModel,
load_tf_weights_in_big_bird,
)
from .models.bigbird_pegasus import (
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
from .models.biogpt import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
from .models.bit import (
BitBackbone,
BitForImageClassification,
BitModel,
BitPreTrainedModel,
)
from .models.blenderbot import (
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
from .models.blenderbot_small import (
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
from .models.blip import (
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
from .models.blip_2 import (
Blip2ForConditionalGeneration,
Blip2ForImageTextRetrieval,
Blip2Model,
Blip2PreTrainedModel,
Blip2QFormerModel,
Blip2TextModelWithProjection,
Blip2VisionModel,
Blip2VisionModelWithProjection,
)
from .models.bloom import (
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
from .models.bridgetower import (
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
from .models.bros import (
BrosForTokenClassification,
BrosModel,
BrosPreTrainedModel,
BrosProcessor,
BrosSpadeEEForTokenClassification,
BrosSpadeELForTokenClassification,
)
from .models.camembert import (
CamembertForCausalLM,
CamembertForMaskedLM,
CamembertForMultipleChoice,
CamembertForQuestionAnswering,
CamembertForSequenceClassification,
CamembertForTokenClassification,
CamembertModel,
CamembertPreTrainedModel,
)
from .models.canine import (
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
from .models.chameleon import (
ChameleonForConditionalGeneration,
ChameleonModel,
ChameleonPreTrainedModel,
ChameleonProcessor,
ChameleonVQVAE,
)
from .models.chinese_clip import (
ChineseCLIPModel,
ChineseCLIPPreTrainedModel,
ChineseCLIPTextModel,
ChineseCLIPVisionModel,
)
from .models.clap import (
ClapAudioModel,
ClapAudioModelWithProjection,
ClapFeatureExtractor,
ClapModel,
ClapPreTrainedModel,
ClapTextModel,
ClapTextModelWithProjection,
)
from .models.clip import (
CLIPForImageClassification,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
from .models.clipseg import (
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
from .models.clvp import (
ClvpDecoder,
ClvpEncoder,
ClvpForCausalLM,
ClvpModel,
ClvpModelForConditionalGeneration,
ClvpPreTrainedModel,
)
from .models.codegen import (
CodeGenForCausalLM,
CodeGenModel,
CodeGenPreTrainedModel,
)
from .models.cohere import (
CohereForCausalLM,
CohereModel,
CoherePreTrainedModel,
)
from .models.cohere2 import (
Cohere2ForCausalLM,
Cohere2Model,
Cohere2PreTrainedModel,
)
from .models.colpali import (
ColPaliForRetrieval,
ColPaliPreTrainedModel,
)
from .models.conditional_detr import (
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
from .models.convbert import (
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
from .models.convnext import (
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
from .models.convnextv2 import (
ConvNextV2Backbone,
ConvNextV2ForImageClassification,
ConvNextV2Model,
ConvNextV2PreTrainedModel,
)
from .models.cpmant import (
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
from .models.ctrl import (
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
from .models.cvt import (
CvtForImageClassification,
CvtModel,
CvtPreTrainedModel,
)
from .models.dab_detr import (
DabDetrForObjectDetection,
DabDetrModel,
DabDetrPreTrainedModel,
)
from .models.dac import (
DacModel,
DacPreTrainedModel,
)
from .models.data2vec import (
Data2VecAudioForAudioFrameClassification,
Data2VecAudioForCTC,
Data2VecAudioForSequenceClassification,
Data2VecAudioForXVector,
Data2VecAudioModel,
Data2VecAudioPreTrainedModel,
Data2VecTextForCausalLM,
Data2VecTextForMaskedLM,
Data2VecTextForMultipleChoice,
Data2VecTextForQuestionAnswering,
Data2VecTextForSequenceClassification,
Data2VecTextForTokenClassification,
Data2VecTextModel,
Data2VecTextPreTrainedModel,
Data2VecVisionForImageClassification,
Data2VecVisionForSemanticSegmentation,
Data2VecVisionModel,
Data2VecVisionPreTrainedModel,
)
# PyTorch model imports
from .models.dbrx import (
DbrxForCausalLM,
DbrxModel,
DbrxPreTrainedModel,
)
from .models.deberta import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
from .models.deberta_v2 import (
DebertaV2ForMaskedLM,
DebertaV2ForMultipleChoice,
DebertaV2ForQuestionAnswering,
DebertaV2ForSequenceClassification,
DebertaV2ForTokenClassification,
DebertaV2Model,
DebertaV2PreTrainedModel,
)
from .models.decision_transformer import (
DecisionTransformerGPT2Model,
DecisionTransformerGPT2PreTrainedModel,
DecisionTransformerModel,
DecisionTransformerPreTrainedModel,
)
from .models.deformable_detr import (
DeformableDetrForObjectDetection,
DeformableDetrModel,
DeformableDetrPreTrainedModel,
)
from .models.deit import (
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
from .models.deprecated.deta import (
DetaForObjectDetection,
DetaModel,
DetaPreTrainedModel,
)
from .models.deprecated.efficientformer import (
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
from .models.deprecated.ernie_m import (
ErnieMForInformationExtraction,
ErnieMForMultipleChoice,
ErnieMForQuestionAnswering,
ErnieMForSequenceClassification,
ErnieMForTokenClassification,
ErnieMModel,
ErnieMPreTrainedModel,
)
from .models.deprecated.gptsan_japanese import (
GPTSanJapaneseForConditionalGeneration,
GPTSanJapaneseModel,
GPTSanJapanesePreTrainedModel,
)
from .models.deprecated.graphormer import (
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
from .models.deprecated.jukebox import (
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
from .models.deprecated.mctct import (
MCTCTForCTC,
MCTCTModel,
MCTCTPreTrainedModel,
)
from .models.deprecated.mega import (
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
from .models.deprecated.mmbt import (
MMBTForClassification,
MMBTModel,
ModalEmbeddings,
)
from .models.deprecated.nat import (
NatBackbone,
NatForImageClassification,
NatModel,
NatPreTrainedModel,
)
from .models.deprecated.nezha import (
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
from .models.deprecated.open_llama import (
OpenLlamaForCausalLM,
OpenLlamaForSequenceClassification,
OpenLlamaModel,
OpenLlamaPreTrainedModel,
)
from .models.deprecated.qdqbert import (
QDQBertForMaskedLM,
QDQBertForMultipleChoice,
QDQBertForNextSentencePrediction,
QDQBertForQuestionAnswering,
QDQBertForSequenceClassification,
QDQBertForTokenClassification,
QDQBertLMHeadModel,
QDQBertModel,
QDQBertPreTrainedModel,
load_tf_weights_in_qdqbert,
)
from .models.deprecated.realm import (
RealmEmbedder,
RealmForOpenQA,
RealmKnowledgeAugEncoder,
RealmPreTrainedModel,
RealmReader,
RealmRetriever,
RealmScorer,
load_tf_weights_in_realm,
)
from .models.deprecated.retribert import (
RetriBertModel,
RetriBertPreTrainedModel,
)
from .models.deprecated.speech_to_text_2 import (
Speech2Text2ForCausalLM,
Speech2Text2PreTrainedModel,
)
from .models.deprecated.trajectory_transformer import (
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
)
from .models.deprecated.transfo_xl import (
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
from .models.deprecated.tvlt import (
TvltForAudioVisualClassification,
TvltForPreTraining,
TvltModel,
TvltPreTrainedModel,
)
from .models.deprecated.van import (
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
from .models.deprecated.vit_hybrid import (
ViTHybridForImageClassification,
ViTHybridModel,
ViTHybridPreTrainedModel,
)
from .models.deprecated.xlm_prophetnet import (
XLMProphetNetDecoder,
XLMProphetNetEncoder,
XLMProphetNetForCausalLM,
XLMProphetNetForConditionalGeneration,
XLMProphetNetModel,
XLMProphetNetPreTrainedModel,
)
from .models.depth_anything import (
DepthAnythingForDepthEstimation,
DepthAnythingPreTrainedModel,
)
from .models.detr import (
DetrForObjectDetection,
DetrForSegmentation,
DetrModel,
DetrPreTrainedModel,
)
from .models.diffllama import (
DiffLlamaForCausalLM,
DiffLlamaForQuestionAnswering,
DiffLlamaForSequenceClassification,
DiffLlamaForTokenClassification,
DiffLlamaModel,
DiffLlamaPreTrainedModel,
)
from .models.dinat import (
DinatBackbone,
DinatForImageClassification,
DinatModel,
DinatPreTrainedModel,
)
from .models.dinov2 import (
Dinov2Backbone,
Dinov2ForImageClassification,
Dinov2Model,
Dinov2PreTrainedModel,
)
from .models.dinov2_with_registers import (
Dinov2WithRegistersBackbone,
Dinov2WithRegistersForImageClassification,
Dinov2WithRegistersModel,
Dinov2WithRegistersPreTrainedModel,
)
from .models.distilbert import (
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
from .models.donut import (
DonutSwinModel,
DonutSwinPreTrainedModel,
)
from .models.dpr import (
DPRContextEncoder,
DPRPretrainedContextEncoder,
DPRPreTrainedModel,
DPRPretrainedQuestionEncoder,
DPRPretrainedReader,
DPRQuestionEncoder,
DPRReader,
)
from .models.dpt import (
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
from .models.efficientnet import (
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
from .models.electra import (
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
from .models.emu3 import (
Emu3ForCausalLM,
Emu3ForConditionalGeneration,
Emu3PreTrainedModel,
Emu3TextModel,
Emu3VQVAE,
)
from .models.encodec import (
EncodecModel,
EncodecPreTrainedModel,
)
from .models.encoder_decoder import EncoderDecoderModel
from .models.ernie import (
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
from .models.esm import (
EsmFoldPreTrainedModel,
EsmForMaskedLM,
EsmForProteinFolding,
EsmForSequenceClassification,
EsmForTokenClassification,
EsmModel,
EsmPreTrainedModel,
)
from .models.falcon import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
from .models.falcon_mamba import (
FalconMambaForCausalLM,
FalconMambaModel,
FalconMambaPreTrainedModel,
)
from .models.fastspeech2_conformer import (
FastSpeech2ConformerHifiGan,
FastSpeech2ConformerModel,
FastSpeech2ConformerPreTrainedModel,
FastSpeech2ConformerWithHifiGan,
)
from .models.flaubert import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertPreTrainedModel,
FlaubertWithLMHeadModel,
)
from .models.flava import (
FlavaForPreTraining,
FlavaImageCodebook,
FlavaImageModel,
FlavaModel,
FlavaMultimodalModel,
FlavaPreTrainedModel,
FlavaTextModel,
)
from .models.fnet import (
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetModel,
FNetPreTrainedModel,
)
from .models.focalnet import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
from .models.fsmt import (
FSMTForConditionalGeneration,
FSMTModel,
PretrainedFSMTModel,
)
from .models.funnel import (
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
from .models.fuyu import (
FuyuForCausalLM,
FuyuPreTrainedModel,
)
from .models.gemma import (
GemmaForCausalLM,
GemmaForSequenceClassification,
GemmaForTokenClassification,
GemmaModel,
GemmaPreTrainedModel,
)
from .models.gemma2 import (
Gemma2ForCausalLM,
Gemma2ForSequenceClassification,
Gemma2ForTokenClassification,
Gemma2Model,
Gemma2PreTrainedModel,
)
from .models.git import (
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
from .models.glm import (
GlmForCausalLM,
GlmForSequenceClassification,
GlmForTokenClassification,
GlmModel,
GlmPreTrainedModel,
)
from .models.glpn import (
GLPNForDepthEstimation,
GLPNModel,
GLPNPreTrainedModel,
)
from .models.got_ocr2 import (
GotOcr2ForConditionalGeneration,
GotOcr2PreTrainedModel,
)
from .models.gpt2 import (
GPT2DoubleHeadsModel,
GPT2ForQuestionAnswering,
GPT2ForSequenceClassification,
GPT2ForTokenClassification,
GPT2LMHeadModel,
GPT2Model,
GPT2PreTrainedModel,
load_tf_weights_in_gpt2,
)
from .models.gpt_bigcode import (
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
from .models.gpt_neo import (
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
from .models.gpt_neox import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
from .models.gpt_neox_japanese import (
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
from .models.gptj import (
GPTJForCausalLM,
GPTJForQuestionAnswering,
GPTJForSequenceClassification,
GPTJModel,
GPTJPreTrainedModel,
)
from .models.granite import (
GraniteForCausalLM,
GraniteModel,
GranitePreTrainedModel,
)
from .models.granitemoe import (
GraniteMoeForCausalLM,
GraniteMoeModel,
GraniteMoePreTrainedModel,
)
from .models.grounding_dino import (
GroundingDinoForObjectDetection,
GroundingDinoModel,
GroundingDinoPreTrainedModel,
)
from .models.groupvit import (
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
from .models.helium import (
HeliumForCausalLM,
HeliumForSequenceClassification,
HeliumForTokenClassification,
HeliumModel,
HeliumPreTrainedModel,
)
from .models.hiera import (
HieraBackbone,
HieraForImageClassification,
HieraForPreTraining,
HieraModel,
HieraPreTrainedModel,
)
from .models.hubert import (
HubertForCTC,
HubertForSequenceClassification,
HubertModel,
HubertPreTrainedModel,
)
from .models.ibert import (
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
from .models.idefics import (
IdeficsForVisionText2Text,
IdeficsModel,
IdeficsPreTrainedModel,
IdeficsProcessor,
)
from .models.idefics2 import (
Idefics2ForConditionalGeneration,
Idefics2Model,
Idefics2PreTrainedModel,
Idefics2Processor,
)
from .models.idefics3 import (
Idefics3ForConditionalGeneration,
Idefics3Model,
Idefics3PreTrainedModel,
Idefics3Processor,
Idefics3VisionConfig,
Idefics3VisionTransformer,
)
from .models.ijepa import (
IJepaForImageClassification,
IJepaModel,
IJepaPreTrainedModel,
)
from .models.imagegpt import (
ImageGPTForCausalImageModeling,
ImageGPTForImageClassification,
ImageGPTModel,
ImageGPTPreTrainedModel,
load_tf_weights_in_imagegpt,
)
from .models.informer import (
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
from .models.instructblip import (
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
from .models.instructblipvideo import (
InstructBlipVideoForConditionalGeneration,
InstructBlipVideoPreTrainedModel,
InstructBlipVideoQFormerModel,
InstructBlipVideoVisionModel,
)
from .models.jamba import (
JambaForCausalLM,
JambaForSequenceClassification,
JambaModel,
JambaPreTrainedModel,
)
from .models.jetmoe import (
JetMoeForCausalLM,
JetMoeForSequenceClassification,
JetMoeModel,
JetMoePreTrainedModel,
)
from .models.kosmos2 import (
Kosmos2ForConditionalGeneration,
Kosmos2Model,
Kosmos2PreTrainedModel,
)
from .models.layoutlm import (
LayoutLMForMaskedLM,
LayoutLMForQuestionAnswering,
LayoutLMForSequenceClassification,
LayoutLMForTokenClassification,
LayoutLMModel,
LayoutLMPreTrainedModel,
)
from .models.layoutlmv2 import (
LayoutLMv2ForQuestionAnswering,
LayoutLMv2ForSequenceClassification,
LayoutLMv2ForTokenClassification,
LayoutLMv2Model,
LayoutLMv2PreTrainedModel,
)
from .models.layoutlmv3 import (
LayoutLMv3ForQuestionAnswering,
LayoutLMv3ForSequenceClassification,
LayoutLMv3ForTokenClassification,
LayoutLMv3Model,
LayoutLMv3PreTrainedModel,
)
from .models.led import (
LEDForConditionalGeneration,
LEDForQuestionAnswering,
LEDForSequenceClassification,
LEDModel,
LEDPreTrainedModel,
)
from .models.levit import (
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
LevitPreTrainedModel,
)
from .models.lilt import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
from .models.llama import (
LlamaForCausalLM,
LlamaForQuestionAnswering,
LlamaForSequenceClassification,
LlamaForTokenClassification,
LlamaModel,
LlamaPreTrainedModel,
)
from .models.llava import (
LlavaForConditionalGeneration,
LlavaPreTrainedModel,
)
from .models.llava_next import (
LlavaNextForConditionalGeneration,
LlavaNextPreTrainedModel,
)
from .models.llava_next_video import (
LlavaNextVideoForConditionalGeneration,
LlavaNextVideoPreTrainedModel,
)
from .models.llava_onevision import (
LlavaOnevisionForConditionalGeneration,
LlavaOnevisionPreTrainedModel,
)
from .models.longformer import (
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
)
from .models.longt5 import (
LongT5EncoderModel,
LongT5ForConditionalGeneration,
LongT5Model,
LongT5PreTrainedModel,
)
from .models.luke import (
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
from .models.lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
)
from .models.m2m_100 import (
M2M100ForConditionalGeneration,
M2M100Model,
M2M100PreTrainedModel,
)
from .models.mamba import (
MambaForCausalLM,
MambaModel,
MambaPreTrainedModel,
)
from .models.mamba2 import (
Mamba2ForCausalLM,
Mamba2Model,
Mamba2PreTrainedModel,
)
from .models.marian import MarianForCausalLM, MarianModel, MarianMTModel, MarianPreTrainedModel
from .models.markuplm import (
MarkupLMForQuestionAnswering,
MarkupLMForSequenceClassification,
MarkupLMForTokenClassification,
MarkupLMModel,
MarkupLMPreTrainedModel,
)
from .models.mask2former import (
Mask2FormerForUniversalSegmentation,
Mask2FormerModel,
Mask2FormerPreTrainedModel,
)
from .models.maskformer import (
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
MaskFormerSwinBackbone,
)
from .models.mbart import (
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
from .models.megatron_bert import (
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
from .models.mgp_str import (
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
from .models.mimi import (
MimiModel,
MimiPreTrainedModel,
)
from .models.mistral import (
MistralForCausalLM,
MistralForQuestionAnswering,
MistralForSequenceClassification,
MistralForTokenClassification,
MistralModel,
MistralPreTrainedModel,
)
from .models.mixtral import (
MixtralForCausalLM,
MixtralForQuestionAnswering,
MixtralForSequenceClassification,
MixtralForTokenClassification,
MixtralModel,
MixtralPreTrainedModel,
)
from .models.mllama import (
MllamaForCausalLM,
MllamaForConditionalGeneration,
MllamaPreTrainedModel,
MllamaProcessor,
MllamaTextModel,
MllamaVisionModel,
)
from .models.mobilebert import (
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
from .models.mobilenet_v1 import (
MobileNetV1ForImageClassification,
MobileNetV1Model,
MobileNetV1PreTrainedModel,
load_tf_weights_in_mobilenet_v1,
)
from .models.mobilenet_v2 import (
MobileNetV2ForImageClassification,
MobileNetV2ForSemanticSegmentation,
MobileNetV2Model,
MobileNetV2PreTrainedModel,
load_tf_weights_in_mobilenet_v2,
)
from .models.mobilevit import (
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
from .models.mobilevitv2 import (
MobileViTV2ForImageClassification,
MobileViTV2ForSemanticSegmentation,
MobileViTV2Model,
MobileViTV2PreTrainedModel,
)
from .models.modernbert import (
ModernBertForMaskedLM,
ModernBertForSequenceClassification,
ModernBertForTokenClassification,
ModernBertModel,
ModernBertPreTrainedModel,
)
from .models.moonshine import (
MoonshineForConditionalGeneration,
MoonshineModel,
MoonshinePreTrainedModel,
)
from .models.moshi import (
MoshiForCausalLM,
MoshiForConditionalGeneration,
MoshiModel,
MoshiPreTrainedModel,
)
from .models.mpnet import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
MPNetPreTrainedModel,
)
from .models.mpt import (
MptForCausalLM,
MptForQuestionAnswering,
MptForSequenceClassification,
MptForTokenClassification,
MptModel,
MptPreTrainedModel,
)
from .models.mra import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
MraPreTrainedModel,
)
from .models.mt5 import (
MT5EncoderModel,
MT5ForConditionalGeneration,
MT5ForQuestionAnswering,
MT5ForSequenceClassification,
MT5ForTokenClassification,
MT5Model,
MT5PreTrainedModel,
)
from .models.musicgen import (
MusicgenForCausalLM,
MusicgenForConditionalGeneration,
MusicgenModel,
MusicgenPreTrainedModel,
MusicgenProcessor,
)
from .models.musicgen_melody import (
MusicgenMelodyForCausalLM,
MusicgenMelodyForConditionalGeneration,
MusicgenMelodyModel,
MusicgenMelodyPreTrainedModel,
)
from .models.mvp import (
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
from .models.nemotron import (
NemotronForCausalLM,
NemotronForQuestionAnswering,
NemotronForSequenceClassification,
NemotronForTokenClassification,
NemotronModel,
NemotronPreTrainedModel,
)
from .models.nllb_moe import (
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTop2Router,
)
from .models.nystromformer import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
NystromformerPreTrainedModel,
)
from .models.olmo import (
OlmoForCausalLM,
OlmoModel,
OlmoPreTrainedModel,
)
from .models.olmo2 import (
Olmo2ForCausalLM,
Olmo2Model,
Olmo2PreTrainedModel,
)
from .models.olmoe import (
OlmoeForCausalLM,
OlmoeModel,
OlmoePreTrainedModel,
)
from .models.omdet_turbo import (
OmDetTurboForObjectDetection,
OmDetTurboPreTrainedModel,
)
from .models.oneformer import (
OneFormerForUniversalSegmentation,
OneFormerModel,
OneFormerPreTrainedModel,
)
from .models.openai import (
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
OpenAIGPTPreTrainedModel,
load_tf_weights_in_openai_gpt,
)
from .models.opt import (
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
from .models.owlv2 import (
Owlv2ForObjectDetection,
Owlv2Model,
Owlv2PreTrainedModel,
Owlv2TextModel,
Owlv2VisionModel,
)
from .models.owlvit import (
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
from .models.paligemma import (
PaliGemmaForConditionalGeneration,
PaliGemmaPreTrainedModel,
PaliGemmaProcessor,
)
from .models.patchtsmixer import (
PatchTSMixerForPrediction,
PatchTSMixerForPretraining,
PatchTSMixerForRegression,
PatchTSMixerForTimeSeriesClassification,
PatchTSMixerModel,
PatchTSMixerPreTrainedModel,
)
from .models.patchtst import (
PatchTSTForClassification,
PatchTSTForPrediction,
PatchTSTForPretraining,
PatchTSTForRegression,
PatchTSTModel,
PatchTSTPreTrainedModel,
)
from .models.pegasus import (
PegasusForCausalLM,
PegasusForConditionalGeneration,
PegasusModel,
PegasusPreTrainedModel,
)
from .models.pegasus_x import (
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
from .models.perceiver import (
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverModel,
PerceiverPreTrainedModel,
)
from .models.persimmon import (
PersimmonForCausalLM,
PersimmonForSequenceClassification,
PersimmonForTokenClassification,
PersimmonModel,
PersimmonPreTrainedModel,
)
from .models.phi import (
PhiForCausalLM,
PhiForSequenceClassification,
PhiForTokenClassification,
PhiModel,
PhiPreTrainedModel,
)
from .models.phi3 import (
Phi3ForCausalLM,
Phi3ForSequenceClassification,
Phi3ForTokenClassification,
Phi3Model,
Phi3PreTrainedModel,
)
from .models.phimoe import (
PhimoeForCausalLM,
PhimoeForSequenceClassification,
PhimoeModel,
PhimoePreTrainedModel,
)
from .models.pix2struct import (
Pix2StructForConditionalGeneration,
Pix2StructPreTrainedModel,
Pix2StructTextModel,
Pix2StructVisionModel,
)
from .models.pixtral import (
PixtralPreTrainedModel,
PixtralVisionModel,
)
from .models.plbart import (
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
from .models.poolformer import (
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
from .models.pop2piano import (
Pop2PianoForConditionalGeneration,
Pop2PianoPreTrainedModel,
)
from .models.prophetnet import (
ProphetNetDecoder,
ProphetNetEncoder,
ProphetNetForCausalLM,
ProphetNetForConditionalGeneration,
ProphetNetModel,
ProphetNetPreTrainedModel,
)
from .models.pvt import (
PvtForImageClassification,
PvtModel,
PvtPreTrainedModel,
)
from .models.pvt_v2 import (
PvtV2Backbone,
PvtV2ForImageClassification,
PvtV2Model,
PvtV2PreTrainedModel,
)
from .models.qwen2 import (
Qwen2ForCausalLM,
Qwen2ForQuestionAnswering,
Qwen2ForSequenceClassification,
Qwen2ForTokenClassification,
Qwen2Model,
Qwen2PreTrainedModel,
)
from .models.qwen2_5_vl import (
Qwen2_5_VLForConditionalGeneration,
Qwen2_5_VLModel,
Qwen2_5_VLPreTrainedModel,
)
from .models.qwen2_audio import (
Qwen2AudioEncoder,
Qwen2AudioForConditionalGeneration,
Qwen2AudioPreTrainedModel,
)
from .models.qwen2_moe import (
Qwen2MoeForCausalLM,
Qwen2MoeForQuestionAnswering,
Qwen2MoeForSequenceClassification,
Qwen2MoeForTokenClassification,
Qwen2MoeModel,
Qwen2MoePreTrainedModel,
)
from .models.qwen2_vl import (
Qwen2VLForConditionalGeneration,
Qwen2VLModel,
Qwen2VLPreTrainedModel,
)
from .models.rag import (
RagModel,
RagPreTrainedModel,
RagSequenceForGeneration,
RagTokenForGeneration,
)
from .models.recurrent_gemma import (
RecurrentGemmaForCausalLM,
RecurrentGemmaModel,
RecurrentGemmaPreTrainedModel,
)
from .models.reformer import (
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
from .models.regnet import (
RegNetForImageClassification,
RegNetModel,
RegNetPreTrainedModel,
)
from .models.rembert import (
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
from .models.resnet import (
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
from .models.roberta import (
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
from .models.roberta_prelayernorm import (
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
from .models.roc_bert import (
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
from .models.roformer import (
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
from .models.rt_detr import (
RTDetrForObjectDetection,
RTDetrModel,
RTDetrPreTrainedModel,
RTDetrResNetBackbone,
RTDetrResNetPreTrainedModel,
)
from .models.rt_detr_v2 import RTDetrV2ForObjectDetection, RTDetrV2Model, RTDetrV2PreTrainedModel
from .models.rwkv import (
RwkvForCausalLM,
RwkvModel,
RwkvPreTrainedModel,
)
from .models.sam import (
SamModel,
SamPreTrainedModel,
)
from .models.seamless_m4t import (
SeamlessM4TCodeHifiGan,
SeamlessM4TForSpeechToSpeech,
SeamlessM4TForSpeechToText,
SeamlessM4TForTextToSpeech,
SeamlessM4TForTextToText,
SeamlessM4THifiGan,
SeamlessM4TModel,
SeamlessM4TPreTrainedModel,
SeamlessM4TTextToUnitForConditionalGeneration,
SeamlessM4TTextToUnitModel,
)
from .models.seamless_m4t_v2 import (
SeamlessM4Tv2ForSpeechToSpeech,
SeamlessM4Tv2ForSpeechToText,
SeamlessM4Tv2ForTextToSpeech,
SeamlessM4Tv2ForTextToText,
SeamlessM4Tv2Model,
SeamlessM4Tv2PreTrainedModel,
)
from .models.segformer import (
SegformerDecodeHead,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
SegformerPreTrainedModel,
)
from .models.seggpt import (
SegGptForImageSegmentation,
SegGptModel,
SegGptPreTrainedModel,
)
from .models.sew import (
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
from .models.sew_d import (
SEWDForCTC,
SEWDForSequenceClassification,
SEWDModel,
SEWDPreTrainedModel,
)
from .models.siglip import (
SiglipForImageClassification,
SiglipModel,
SiglipPreTrainedModel,
SiglipTextModel,
SiglipVisionModel,
)
from .models.speech_encoder_decoder import SpeechEncoderDecoderModel
from .models.speech_to_text import (
Speech2TextForConditionalGeneration,
Speech2TextModel,
Speech2TextPreTrainedModel,
)
from .models.speecht5 import (
SpeechT5ForSpeechToSpeech,
SpeechT5ForSpeechToText,
SpeechT5ForTextToSpeech,
SpeechT5HifiGan,
SpeechT5Model,
SpeechT5PreTrainedModel,
)
from .models.splinter import (
SplinterForPreTraining,
SplinterForQuestionAnswering,
SplinterModel,
SplinterPreTrainedModel,
)
from .models.squeezebert import (
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertPreTrainedModel,
)
from .models.stablelm import (
StableLmForCausalLM,
StableLmForSequenceClassification,
StableLmForTokenClassification,
StableLmModel,
StableLmPreTrainedModel,
)
from .models.starcoder2 import (
Starcoder2ForCausalLM,
Starcoder2ForSequenceClassification,
Starcoder2ForTokenClassification,
Starcoder2Model,
Starcoder2PreTrainedModel,
)
from .models.superglue import (
SuperGlueForKeypointMatching,
SuperGluePreTrainedModel,
)
from .models.superpoint import (
SuperPointForKeypointDetection,
SuperPointPreTrainedModel,
)
from .models.swiftformer import (
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
from .models.swin import (
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
from .models.swin2sr import (
Swin2SRForImageSuperResolution,
Swin2SRModel,
Swin2SRPreTrainedModel,
)
from .models.swinv2 import (
Swinv2Backbone,
Swinv2ForImageClassification,
Swinv2ForMaskedImageModeling,
Swinv2Model,
Swinv2PreTrainedModel,
)
from .models.switch_transformers import (
SwitchTransformersEncoderModel,
SwitchTransformersForConditionalGeneration,
SwitchTransformersModel,
SwitchTransformersPreTrainedModel,
SwitchTransformersSparseMLP,
SwitchTransformersTop1Router,
)
from .models.t5 import (
T5EncoderModel,
T5ForConditionalGeneration,
T5ForQuestionAnswering,
T5ForSequenceClassification,
T5ForTokenClassification,
T5Model,
T5PreTrainedModel,
load_tf_weights_in_t5,
)
from .models.table_transformer import (
TableTransformerForObjectDetection,
TableTransformerModel,
TableTransformerPreTrainedModel,
)
from .models.tapas import (
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
from .models.textnet import (
TextNetBackbone,
TextNetForImageClassification,
TextNetModel,
TextNetPreTrainedModel,
)
from .models.time_series_transformer import (
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
from .models.timesformer import (
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
from .models.timm_backbone import TimmBackbone
from .models.timm_wrapper import (
TimmWrapperForImageClassification,
TimmWrapperModel,
TimmWrapperPreTrainedModel,
)
from .models.trocr import (
TrOCRForCausalLM,
TrOCRPreTrainedModel,
)
from .models.tvp import (
TvpForVideoGrounding,
TvpModel,
TvpPreTrainedModel,
)
from .models.udop import (
UdopEncoderModel,
UdopForConditionalGeneration,
UdopModel,
UdopPreTrainedModel,
)
from .models.umt5 import (
UMT5EncoderModel,
UMT5ForConditionalGeneration,
UMT5ForQuestionAnswering,
UMT5ForSequenceClassification,
UMT5ForTokenClassification,
UMT5Model,
UMT5PreTrainedModel,
)
from .models.unispeech import (
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
from .models.unispeech_sat import (
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForCTC,
UniSpeechSatForPreTraining,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
UniSpeechSatModel,
UniSpeechSatPreTrainedModel,
)
from .models.univnet import UnivNetModel
from .models.upernet import (
UperNetForSemanticSegmentation,
UperNetPreTrainedModel,
)
from .models.video_llava import (
VideoLlavaForConditionalGeneration,
VideoLlavaPreTrainedModel,
VideoLlavaProcessor,
)
from .models.videomae import (
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
VideoMAEPreTrainedModel,
)
from .models.vilt import (
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltForTokenClassification,
ViltModel,
ViltPreTrainedModel,
)
from .models.vipllava import (
VipLlavaForConditionalGeneration,
VipLlavaPreTrainedModel,
)
from .models.vision_encoder_decoder import VisionEncoderDecoderModel
from .models.vision_text_dual_encoder import VisionTextDualEncoderModel
from .models.visual_bert import (
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForRegionToPhraseAlignment,
VisualBertForVisualReasoning,
VisualBertModel,
VisualBertPreTrainedModel,
)
from .models.vit import (
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
from .models.vit_mae import (
ViTMAEForPreTraining,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
from .models.vit_msn import (
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
from .models.vitdet import (
VitDetBackbone,
VitDetModel,
VitDetPreTrainedModel,
)
from .models.vitmatte import (
VitMatteForImageMatting,
VitMattePreTrainedModel,
)
from .models.vitpose import (
VitPoseForPoseEstimation,
VitPosePreTrainedModel,
)
from .models.vitpose_backbone import VitPoseBackbone, VitPoseBackbonePreTrainedModel
from .models.vits import (
VitsModel,
VitsPreTrainedModel,
)
from .models.vivit import (
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
from .models.wav2vec2 import (
Wav2Vec2ForAudioFrameClassification,
Wav2Vec2ForCTC,
Wav2Vec2ForMaskedLM,
Wav2Vec2ForPreTraining,
Wav2Vec2ForSequenceClassification,
Wav2Vec2ForXVector,
Wav2Vec2Model,
Wav2Vec2PreTrainedModel,
)
from .models.wav2vec2_bert import (
Wav2Vec2BertForAudioFrameClassification,
Wav2Vec2BertForCTC,
Wav2Vec2BertForSequenceClassification,
Wav2Vec2BertForXVector,
Wav2Vec2BertModel,
Wav2Vec2BertPreTrainedModel,
)
from .models.wav2vec2_conformer import (
Wav2Vec2ConformerForAudioFrameClassification,
Wav2Vec2ConformerForCTC,
Wav2Vec2ConformerForPreTraining,
Wav2Vec2ConformerForSequenceClassification,
Wav2Vec2ConformerForXVector,
Wav2Vec2ConformerModel,
Wav2Vec2ConformerPreTrainedModel,
)
from .models.wavlm import (
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
from .models.whisper import (
WhisperForAudioClassification,
WhisperForCausalLM,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
from .models.x_clip import (
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
from .models.xglm import (
XGLMForCausalLM,
XGLMModel,
XGLMPreTrainedModel,
)
from .models.xlm import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
from .models.xlm_roberta import (
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
from .models.xlm_roberta_xl import (
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
from .models.xlnet import (
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
from .models.xmod import (
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
from .models.yolos import (
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
from .models.yoso import (
YosoForMaskedLM,
YosoForMultipleChoice,
YosoForQuestionAnswering,
YosoForSequenceClassification,
YosoForTokenClassification,
YosoModel,
YosoPreTrainedModel,
)
from .models.zamba import (
ZambaForCausalLM,
ZambaForSequenceClassification,
ZambaModel,
ZambaPreTrainedModel,
)
from .models.zamba2 import (
Zamba2ForCausalLM,
Zamba2ForSequenceClassification,
Zamba2Model,
Zamba2PreTrainedModel,
)
from .models.zoedepth import (
ZoeDepthForDepthEstimation,
ZoeDepthPreTrainedModel,
)
# Optimization
from .optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
get_wsd_schedule,
)
from .pytorch_utils import Conv1D, apply_chunking_to_forward, prune_layer
# Trainer
from .trainer import Trainer
from .trainer_pt_utils import torch_distributed_zero_first
from .trainer_seq2seq import Seq2SeqTrainer
# TensorFlow
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
# Import the same objects as dummies to get them in the namespace.
# They will raise an import error if the user tries to instantiate / use them.
from .utils.dummy_tf_objects import *
else:
from .generation import (
TFForcedBOSTokenLogitsProcessor,
TFForcedEOSTokenLogitsProcessor,
TFForceTokensLogitsProcessor,
TFGenerationMixin,
TFLogitsProcessor,
TFLogitsProcessorList,
TFLogitsWarper,
TFMinLengthLogitsProcessor,
TFNoBadWordsLogitsProcessor,
TFNoRepeatNGramLogitsProcessor,
TFRepetitionPenaltyLogitsProcessor,
TFSuppressTokensAtBeginLogitsProcessor,
TFSuppressTokensLogitsProcessor,
TFTemperatureLogitsWarper,
TFTopKLogitsWarper,
TFTopPLogitsWarper,
)
from .keras_callbacks import KerasMetricCallback, PushToHubCallback
from .modeling_tf_utils import (
TFPreTrainedModel,
TFSequenceSummary,
TFSharedEmbeddings,
shape_list,
)
# TensorFlow model imports
from .models.albert import (
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
from .models.auto import (
TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
TF_MODEL_WITH_LM_HEAD_MAPPING,
TFAutoModel,
TFAutoModelForAudioClassification,
TFAutoModelForCausalLM,
TFAutoModelForDocumentQuestionAnswering,
TFAutoModelForImageClassification,
TFAutoModelForMaskedImageModeling,
TFAutoModelForMaskedLM,
TFAutoModelForMaskGeneration,
TFAutoModelForMultipleChoice,
TFAutoModelForNextSentencePrediction,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSemanticSegmentation,
TFAutoModelForSeq2SeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForSpeechSeq2Seq,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTextEncoding,
TFAutoModelForTokenClassification,
TFAutoModelForVision2Seq,
TFAutoModelForZeroShotImageClassification,
TFAutoModelWithLMHead,
)
from .models.bart import (
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBartModel,
TFBartPretrainedModel,
)
from .models.bert import (
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
from .models.blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
from .models.blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
from .models.blip import (
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
from .models.camembert import (
TFCamembertForCausalLM,
TFCamembertForMaskedLM,
TFCamembertForMultipleChoice,
TFCamembertForQuestionAnswering,
TFCamembertForSequenceClassification,
TFCamembertForTokenClassification,
TFCamembertModel,
TFCamembertPreTrainedModel,
)
from .models.clip import (
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
from .models.convbert import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
from .models.convnext import (
TFConvNextForImageClassification,
TFConvNextModel,
TFConvNextPreTrainedModel,
)
from .models.convnextv2 import (
TFConvNextV2ForImageClassification,
TFConvNextV2Model,
TFConvNextV2PreTrainedModel,
)
from .models.ctrl import (
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
from .models.cvt import (
TFCvtForImageClassification,
TFCvtModel,
TFCvtPreTrainedModel,
)
from .models.data2vec import (
TFData2VecVisionForImageClassification,
TFData2VecVisionForSemanticSegmentation,
TFData2VecVisionModel,
TFData2VecVisionPreTrainedModel,
)
from .models.deberta import (
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
from .models.deberta_v2 import (
TFDebertaV2ForMaskedLM,
TFDebertaV2ForMultipleChoice,
TFDebertaV2ForQuestionAnswering,
TFDebertaV2ForSequenceClassification,
TFDebertaV2ForTokenClassification,
TFDebertaV2Model,
TFDebertaV2PreTrainedModel,
)
from .models.deit import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
from .models.deprecated.efficientformer import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
from .models.deprecated.transfo_xl import (
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
from .models.distilbert import (
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
from .models.dpr import (
TFDPRContextEncoder,
TFDPRPretrainedContextEncoder,
TFDPRPretrainedQuestionEncoder,
TFDPRPretrainedReader,
TFDPRQuestionEncoder,
TFDPRReader,
)
from .models.electra import (
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
from .models.encoder_decoder import TFEncoderDecoderModel
from .models.esm import (
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
TFEsmPreTrainedModel,
)
from .models.flaubert import (
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertPreTrainedModel,
TFFlaubertWithLMHeadModel,
)
from .models.funnel import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
from .models.gpt2 import (
TFGPT2DoubleHeadsModel,
TFGPT2ForSequenceClassification,
TFGPT2LMHeadModel,
TFGPT2MainLayer,
TFGPT2Model,
TFGPT2PreTrainedModel,
)
from .models.gptj import (
TFGPTJForCausalLM,
TFGPTJForQuestionAnswering,
TFGPTJForSequenceClassification,
TFGPTJModel,
TFGPTJPreTrainedModel,
)
from .models.groupvit import (
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
from .models.hubert import (
TFHubertForCTC,
TFHubertModel,
TFHubertPreTrainedModel,
)
from .models.idefics import (
TFIdeficsForVisionText2Text,
TFIdeficsModel,
TFIdeficsPreTrainedModel,
)
from .models.layoutlm import (
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMMainLayer,
TFLayoutLMModel,
TFLayoutLMPreTrainedModel,
)
from .models.layoutlmv3 import (
TFLayoutLMv3ForQuestionAnswering,
TFLayoutLMv3ForSequenceClassification,
TFLayoutLMv3ForTokenClassification,
TFLayoutLMv3Model,
TFLayoutLMv3PreTrainedModel,
)
from .models.led import (
TFLEDForConditionalGeneration,
TFLEDModel,
TFLEDPreTrainedModel,
)
from .models.longformer import (
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
)
from .models.lxmert import (
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
from .models.marian import (
TFMarianModel,
TFMarianMTModel,
TFMarianPreTrainedModel,
)
from .models.mbart import (
TFMBartForConditionalGeneration,
TFMBartModel,
TFMBartPreTrainedModel,
)
from .models.mistral import (
TFMistralForCausalLM,
TFMistralForSequenceClassification,
TFMistralModel,
TFMistralPreTrainedModel,
)
from .models.mobilebert import (
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
from .models.mobilevit import (
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
from .models.mpnet import (
TFMPNetForMaskedLM,
TFMPNetForMultipleChoice,
TFMPNetForQuestionAnswering,
TFMPNetForSequenceClassification,
TFMPNetForTokenClassification,
TFMPNetMainLayer,
TFMPNetModel,
TFMPNetPreTrainedModel,
)
from .models.mt5 import (
TFMT5EncoderModel,
TFMT5ForConditionalGeneration,
TFMT5Model,
)
from .models.openai import (
TFOpenAIGPTDoubleHeadsModel,
TFOpenAIGPTForSequenceClassification,
TFOpenAIGPTLMHeadModel,
TFOpenAIGPTMainLayer,
TFOpenAIGPTModel,
TFOpenAIGPTPreTrainedModel,
)
from .models.opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
from .models.pegasus import (
TFPegasusForConditionalGeneration,
TFPegasusModel,
TFPegasusPreTrainedModel,
)
from .models.rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
from .models.regnet import (
TFRegNetForImageClassification,
TFRegNetModel,
TFRegNetPreTrainedModel,
)
from .models.rembert import (
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
from .models.resnet import (
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
from .models.roberta import (
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
from .models.roberta_prelayernorm import (
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
from .models.roformer import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
from .models.sam import (
TFSamModel,
TFSamPreTrainedModel,
)
from .models.segformer import (
TFSegformerDecodeHead,
TFSegformerForImageClassification,
TFSegformerForSemanticSegmentation,
TFSegformerModel,
TFSegformerPreTrainedModel,
)
from .models.speech_to_text import (
TFSpeech2TextForConditionalGeneration,
TFSpeech2TextModel,
TFSpeech2TextPreTrainedModel,
)
from .models.swiftformer import (
TFSwiftFormerForImageClassification,
TFSwiftFormerModel,
TFSwiftFormerPreTrainedModel,
)
from .models.swin import (
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
from .models.t5 import (
TFT5EncoderModel,
TFT5ForConditionalGeneration,
TFT5Model,
TFT5PreTrainedModel,
)
from .models.tapas import (
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
from .models.vision_encoder_decoder import TFVisionEncoderDecoderModel
from .models.vision_text_dual_encoder import TFVisionTextDualEncoderModel
from .models.vit import (
TFViTForImageClassification,
TFViTModel,
TFViTPreTrainedModel,
)
from .models.vit_mae import (
TFViTMAEForPreTraining,
TFViTMAEModel,
TFViTMAEPreTrainedModel,
)
from .models.wav2vec2 import (
TFWav2Vec2ForCTC,
TFWav2Vec2ForSequenceClassification,
TFWav2Vec2Model,
TFWav2Vec2PreTrainedModel,
)
from .models.whisper import (
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
from .models.xglm import (
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
from .models.xlm import (
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
from .models.xlm_roberta import (
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
from .models.xlnet import (
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
# Optimization
from .optimization_tf import (
AdamWeightDecay,
GradientAccumulator,
WarmUp,
create_optimizer,
)
try:
if not (
is_librosa_available()
and is_essentia_available()
and is_scipy_available()
and is_torch_available()
and is_pretty_midi_available()
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_essentia_and_librosa_and_pretty_midi_and_scipy_and_torch_objects import *
else:
from .models.pop2piano import (
Pop2PianoFeatureExtractor,
Pop2PianoProcessor,
Pop2PianoTokenizer,
)
try:
if not is_torchaudio_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torchaudio_objects import *
else:
from .models.musicgen_melody import MusicgenMelodyFeatureExtractor, MusicgenMelodyProcessor
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
# Import the same objects as dummies to get them in the namespace.
# They will raise an import error if the user tries to instantiate / use them.
from .utils.dummy_flax_objects import *
else:
from .generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxForceTokensLogitsProcessor,
FlaxGenerationMixin,
FlaxLogitsProcessor,
FlaxLogitsProcessorList,
FlaxLogitsWarper,
FlaxMinLengthLogitsProcessor,
FlaxSuppressTokensAtBeginLogitsProcessor,
FlaxSuppressTokensLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
FlaxWhisperTimeStampLogitsProcessor,
)
from .modeling_flax_utils import FlaxPreTrainedModel
# Flax model imports
from .models.albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
from .models.auto import (
FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
FLAX_MODEL_FOR_MASKED_LM_MAPPING,
FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
FLAX_MODEL_FOR_PRETRAINING_MAPPING,
FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
FLAX_MODEL_MAPPING,
FlaxAutoModel,
FlaxAutoModelForCausalLM,
FlaxAutoModelForImageClassification,
FlaxAutoModelForMaskedLM,
FlaxAutoModelForMultipleChoice,
FlaxAutoModelForNextSentencePrediction,
FlaxAutoModelForPreTraining,
FlaxAutoModelForQuestionAnswering,
FlaxAutoModelForSeq2SeqLM,
FlaxAutoModelForSequenceClassification,
FlaxAutoModelForSpeechSeq2Seq,
FlaxAutoModelForTokenClassification,
FlaxAutoModelForVision2Seq,
)
from .models.bart import (
FlaxBartDecoderPreTrainedModel,
FlaxBartForCausalLM,
FlaxBartForConditionalGeneration,
FlaxBartForQuestionAnswering,
FlaxBartForSequenceClassification,
FlaxBartModel,
FlaxBartPreTrainedModel,
)
from .models.beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
from .models.bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
from .models.big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
FlaxBigBirdPreTrainedModel,
)
from .models.blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
from .models.blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
from .models.bloom import (
FlaxBloomForCausalLM,
FlaxBloomModel,
FlaxBloomPreTrainedModel,
)
from .models.clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextModelWithProjection,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
from .models.dinov2 import (
FlaxDinov2ForImageClassification,
FlaxDinov2Model,
FlaxDinov2PreTrainedModel,
)
from .models.distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
from .models.electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
from .models.encoder_decoder import FlaxEncoderDecoderModel
from .models.gemma import (
FlaxGemmaForCausalLM,
FlaxGemmaModel,
FlaxGemmaPreTrainedModel,
)
from .models.gpt2 import (
FlaxGPT2LMHeadModel,
FlaxGPT2Model,
FlaxGPT2PreTrainedModel,
)
from .models.gpt_neo import (
FlaxGPTNeoForCausalLM,
FlaxGPTNeoModel,
FlaxGPTNeoPreTrainedModel,
)
from .models.gptj import (
FlaxGPTJForCausalLM,
FlaxGPTJModel,
FlaxGPTJPreTrainedModel,
)
from .models.llama import (
FlaxLlamaForCausalLM,
FlaxLlamaModel,
FlaxLlamaPreTrainedModel,
)
from .models.longt5 import (
FlaxLongT5ForConditionalGeneration,
FlaxLongT5Model,
FlaxLongT5PreTrainedModel,
)
from .models.marian import (
FlaxMarianModel,
FlaxMarianMTModel,
FlaxMarianPreTrainedModel,
)
from .models.mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
from .models.mistral import (
FlaxMistralForCausalLM,
FlaxMistralModel,
FlaxMistralPreTrainedModel,
)
from .models.mt5 import (
FlaxMT5EncoderModel,
FlaxMT5ForConditionalGeneration,
FlaxMT5Model,
)
from .models.opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
from .models.pegasus import (
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
FlaxPegasusPreTrainedModel,
)
from .models.regnet import (
FlaxRegNetForImageClassification,
FlaxRegNetModel,
FlaxRegNetPreTrainedModel,
)
from .models.resnet import (
FlaxResNetForImageClassification,
FlaxResNetModel,
FlaxResNetPreTrainedModel,
)
from .models.roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
from .models.roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
from .models.roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
from .models.speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
from .models.t5 import (
FlaxT5EncoderModel,
FlaxT5ForConditionalGeneration,
FlaxT5Model,
FlaxT5PreTrainedModel,
)
from .models.vision_encoder_decoder import FlaxVisionEncoderDecoderModel
from .models.vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
from .models.vit import (
FlaxViTForImageClassification,
FlaxViTModel,
FlaxViTPreTrainedModel,
)
from .models.wav2vec2 import (
FlaxWav2Vec2ForCTC,
FlaxWav2Vec2ForPreTraining,
FlaxWav2Vec2Model,
FlaxWav2Vec2PreTrainedModel,
)
from .models.whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
from .models.xglm import (
FlaxXGLMForCausalLM,
FlaxXGLMModel,
FlaxXGLMPreTrainedModel,
)
from .models.xlm_roberta import (
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
module_spec=__spec__,
extra_objects={"__version__": __version__},
)
if not is_tf_available() and not is_torch_available() and not is_flax_available():
logger.warning_advice(
"None of PyTorch, TensorFlow >= 2.0, or Flax have been found. "
"Models won't be available and only tokenizers, configuration "
"and file/data utilities can be used."
)
| transformers/src/transformers/__init__.py/0 | {
"file_path": "transformers/src/transformers/__init__.py",
"repo_id": "transformers",
"token_count": 160873
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speecht5 import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor
from ..utils import is_datasets_available
from .tools import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class TextToSpeechTool(PipelineTool):
default_checkpoint = "microsoft/speecht5_tts"
description = (
"This is a tool that reads an English text out loud. It returns a waveform object containing the sound."
)
name = "text_to_speech"
pre_processor_class = SpeechT5Processor
model_class = SpeechT5ForTextToSpeech
post_processor_class = SpeechT5HifiGan
inputs = {"text": {"type": "string", "description": "The text to read out loud (in English)"}}
output_type = "audio"
def setup(self):
if self.post_processor is None:
self.post_processor = "microsoft/speecht5_hifigan"
super().setup()
def encode(self, text, speaker_embeddings=None):
inputs = self.pre_processor(text=text, return_tensors="pt", truncation=True)
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("Datasets needs to be installed if not passing speaker embeddings.")
embeddings_dataset = load_dataset(
"Matthijs/cmu-arctic-xvectors", split="validation", trust_remote_code=True
)
speaker_embeddings = torch.tensor(embeddings_dataset[7305]["xvector"]).unsqueeze(0)
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def forward(self, inputs):
with torch.no_grad():
return self.model.generate_speech(**inputs)
def decode(self, outputs):
with torch.no_grad():
return self.post_processor(outputs).cpu().detach()
| transformers/src/transformers/agents/text_to_speech.py/0 | {
"file_path": "transformers/src/transformers/agents/text_to_speech.py",
"repo_id": "transformers",
"token_count": 892
} |
#!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from transformers import HfArgumentParser
from .add_fast_image_processor import AddFastImageProcessorCommand
from .add_new_model_like import AddNewModelLikeCommand
from .chat import ChatCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def main():
parser = HfArgumentParser(prog="Transformers CLI tool", usage="transformers-cli <command> [<args>]")
commands_parser = parser.add_subparsers(help="transformers-cli command helpers")
# Register commands
ChatCommand.register_subcommand(commands_parser)
ConvertCommand.register_subcommand(commands_parser)
DownloadCommand.register_subcommand(commands_parser)
EnvironmentCommand.register_subcommand(commands_parser)
RunCommand.register_subcommand(commands_parser)
ServeCommand.register_subcommand(commands_parser)
UserCommands.register_subcommand(commands_parser)
AddNewModelLikeCommand.register_subcommand(commands_parser)
LfsCommands.register_subcommand(commands_parser)
AddFastImageProcessorCommand.register_subcommand(commands_parser)
# Let's go
args = parser.parse_args()
if not hasattr(args, "func"):
parser.print_help()
exit(1)
# Run
service = args.func(args)
service.run()
if __name__ == "__main__":
main()
| transformers/src/transformers/commands/transformers_cli.py/0 | {
"file_path": "transformers/src/transformers/commands/transformers_cli.py",
"repo_id": "transformers",
"token_count": 628
} |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| transformers/src/transformers/data/processors/__init__.py/0 | {
"file_path": "transformers/src/transformers/data/processors/__init__.py",
"repo_id": "transformers",
"token_count": 287
} |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generation configuration class and utilities."""
import copy
import json
import os
import warnings
from abc import ABC, abstractmethod
from dataclasses import dataclass, is_dataclass
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
from .. import __version__
from ..configuration_utils import PretrainedConfig
from ..utils import (
GENERATION_CONFIG_NAME,
ExplicitEnum,
PushToHubMixin,
cached_file,
download_url,
extract_commit_hash,
is_remote_url,
is_torch_available,
logging,
)
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
logger = logging.get_logger(__name__)
METADATA_FIELDS = ("_from_model_config", "_commit_hash", "_original_object_hash", "transformers_version")
CACHE_CONFIG_MAPPING = {}
NEED_SETUP_CACHE_CLASSES_MAPPING = {}
QUANT_BACKEND_CLASSES_MAPPING = {}
ALL_CACHE_IMPLEMENTATIONS = []
if is_torch_available():
from ..cache_utils import (
HQQQuantizedCache,
HybridCache,
MambaCache,
OffloadedStaticCache,
QuantizedCacheConfig,
QuantoQuantizedCache,
SlidingWindowCache,
StaticCache,
StaticCacheConfig,
)
from .logits_process import SynthIDTextWatermarkLogitsProcessor, WatermarkLogitsProcessor
CACHE_CONFIG_MAPPING["quantized"] = QuantizedCacheConfig
CACHE_CONFIG_MAPPING["static"] = StaticCacheConfig
NEED_SETUP_CACHE_CLASSES_MAPPING = {
"static": StaticCache,
"offloaded_static": OffloadedStaticCache,
"sliding_window": SlidingWindowCache,
"hybrid": HybridCache,
"mamba": MambaCache,
}
QUANT_BACKEND_CLASSES_MAPPING = {"quanto": QuantoQuantizedCache, "HQQ": HQQQuantizedCache}
ALL_CACHE_IMPLEMENTATIONS = (
list(NEED_SETUP_CACHE_CLASSES_MAPPING.keys()) + list(CACHE_CONFIG_MAPPING.keys()) + ["offloaded"]
)
class GenerationMode(ExplicitEnum):
"""
Possible generation modes, downstream of the [`~generation.GenerationMixin.generate`] method.
"""
# Non-beam methods
CONTRASTIVE_SEARCH = "contrastive_search"
GREEDY_SEARCH = "greedy_search"
SAMPLE = "sample"
ASSISTED_GENERATION = "assisted_generation"
DOLA_GENERATION = "dola_generation"
# Beam methods
BEAM_SEARCH = "beam_search"
BEAM_SAMPLE = "beam_sample"
CONSTRAINED_BEAM_SEARCH = "constrained_beam_search"
GROUP_BEAM_SEARCH = "group_beam_search"
class GenerationConfig(PushToHubMixin):
# no-format
"""
Class that holds a configuration for a generation task. A `generate` call supports the following generation methods
for text-decoder, text-to-text, speech-to-text, and vision-to-text models:
- *greedy decoding* if `num_beams=1` and `do_sample=False`
- *contrastive search* if `penalty_alpha>0.` and `top_k>1`
- *multinomial sampling* if `num_beams=1` and `do_sample=True`
- *beam-search decoding* if `num_beams>1` and `do_sample=False`
- *beam-search multinomial sampling* if `num_beams>1` and `do_sample=True`
- *diverse beam-search decoding* if `num_beams>1` and `num_beam_groups>1`
- *constrained beam-search decoding* if `constraints!=None` or `force_words_ids!=None`
- *assisted decoding* if `assistant_model` or `prompt_lookup_num_tokens` is passed to `.generate()`
- *dola decoding* if `dola_layers` is passed to `.generate()`
To learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
<Tip>
A large number of these flags control the logits or the stopping criteria of the generation. Make sure you check
the [generate-related classes](https://huggingface.co/docs/transformers/internal/generation_utils) for a full
description of the possible manipulations, as well as examples of their usage.
</Tip>
Arg:
> Parameters that control the length of the output
max_length (`int`, *optional*, defaults to 20):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. Its effect is overridden by `max_new_tokens`, if also set.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
min_length (`int`, *optional*, defaults to 0):
The minimum length of the sequence to be generated. Corresponds to the length of the input prompt +
`min_new_tokens`. Its effect is overridden by `min_new_tokens`, if also set.
min_new_tokens (`int`, *optional*):
The minimum numbers of tokens to generate, ignoring the number of tokens in the prompt.
early_stopping (`bool` or `str`, *optional*, defaults to `False`):
Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values:
`True`, where the generation stops as soon as there are `num_beams` complete candidates; `False`, where an
heuristic is applied and the generation stops when is it very unlikely to find better candidates;
`"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical
beam search algorithm).
max_time (`float`, *optional*):
The maximum amount of time you allow the computation to run for in seconds. generation will still finish
the current pass after allocated time has been passed.
stop_strings (`str or List[str]`, *optional*):
A string or a list of strings that should terminate generation if the model outputs them.
> Parameters that control the generation strategy used
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
num_beams (`int`, *optional*, defaults to 1):
Number of beams for beam search. 1 means no beam search.
num_beam_groups (`int`, *optional*, defaults to 1):
Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
[this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
penalty_alpha (`float`, *optional*):
The values balance the model confidence and the degeneration penalty in contrastive search decoding.
dola_layers (`str` or `List[int]`, *optional*):
The layers to use for DoLa decoding. If `None`, DoLa decoding is not used. If a string, it must
be one of "low" or "high", which means using the lower part or higher part of the model layers, respectively.
"low" means the first half of the layers up to the first 20 layers, and "high" means the last half of the
layers up to the last 20 layers.
If a list of integers, it must contain the indices of the layers to use for candidate premature layers in DoLa.
The 0-th layer is the word embedding layer of the model. Set to `'low'` to improve long-answer reasoning tasks,
`'high'` to improve short-answer tasks. Check the [documentation](https://github.com/huggingface/transformers/blob/main/docs/source/en/generation_strategies.md)
or [the paper](https://arxiv.org/abs/2309.03883) for more details.
> Parameters that control the cache
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.
cache_implementation (`str`, *optional*, default to `None`):
Name of the cache class that will be instantiated in `generate`, for faster decoding. Possible values are:
- `"static"`: [`StaticCache`]
- `"offloaded_static"`: [`OffloadedStaticCache`]
- `"sliding_window"`: [`SlidingWindowCache`]
- `"hybrid"`: [`HybridCache`]
- `"mamba"`: [`MambaCache`]
- `"quantized"`: [`QuantizedCache`]
We support other cache types, but they must be manually instantiated and
passed to `generate` through the `past_key_values` argument. See our
[cache documentation](https://huggingface.co/docs/transformers/en/kv_cache) for further information.
cache_config (`CacheConfig` or `dict`, *optional*, default to `None`):
Arguments used in the key-value cache class can be passed in `cache_config`. Can be passed as a `Dict` and
it will be converted to its repsective `CacheConfig` internally.
Otherwise can be passed as a `CacheConfig` class matching the indicated `cache_implementation`.
return_legacy_cache (`bool`, *optional*, default to `True`):
Whether to return the legacy or new format of the cache when `DynamicCache` is used by default.
> Parameters for manipulation of the model output logits
temperature (`float`, *optional*, defaults to 1.0):
The value used to module the next token probabilities. This value is set in a model's `generation_config.json` file. If it isn't set, the default value is 1.0
top_k (`int`, *optional*, defaults to 50):
The number of highest probability vocabulary tokens to keep for top-k-filtering. This value is set in a model's `generation_config.json` file. If it isn't set, the default value is 50.
top_p (`float`, *optional*, defaults to 1.0):
If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to
`top_p` or higher are kept for generation. This value is set in a model's `generation_config.json` file. If it isn't set, the default value is 1.0
min_p (`float`, *optional*):
Minimum token probability, which will be scaled by the probability of the most likely token. It must be a
value between 0 and 1. Typical values are in the 0.01-0.2 range, comparably selective as setting `top_p` in
the 0.99-0.8 range (use the opposite of normal `top_p` values).
typical_p (`float`, *optional*, defaults to 1.0):
Local typicality measures how similar the conditional probability of predicting a target token next is to
the expected conditional probability of predicting a random token next, given the partial text already
generated. If set to float < 1, the smallest set of the most locally typical tokens with probabilities that
add up to `typical_p` or higher are kept for generation. See [this
paper](https://arxiv.org/pdf/2202.00666.pdf) for more details.
epsilon_cutoff (`float`, *optional*, defaults to 0.0):
If set to float strictly between 0 and 1, only tokens with a conditional probability greater than
`epsilon_cutoff` will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on the
size of the model. See [Truncation Sampling as Language Model
Desmoothing](https://arxiv.org/abs/2210.15191) for more details.
eta_cutoff (`float`, *optional*, defaults to 0.0):
Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly between
0 and 1, a token is only considered if it is greater than either `eta_cutoff` or `sqrt(eta_cutoff) *
exp(-entropy(softmax(next_token_logits)))`. The latter term is intuitively the expected next token
probability, scaled by `sqrt(eta_cutoff)`. In the paper, suggested values range from 3e-4 to 2e-3,
depending on the size of the model. See [Truncation Sampling as Language Model
Desmoothing](https://arxiv.org/abs/2210.15191) for more details.
diversity_penalty (`float`, *optional*, defaults to 0.0):
This value is subtracted from a beam's score if it generates a token same as any beam from other group at a
particular time. Note that `diversity_penalty` is only effective if `group beam search` is enabled.
repetition_penalty (`float`, *optional*, defaults to 1.0):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
encoder_repetition_penalty (`float`, *optional*, defaults to 1.0):
The paramater for encoder_repetition_penalty. An exponential penalty on sequences that are not in the
original input. 1.0 means no penalty.
length_penalty (`float`, *optional*, defaults to 1.0):
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
`length_penalty` < 0.0 encourages shorter sequences.
no_repeat_ngram_size (`int`, *optional*, defaults to 0):
If set to int > 0, all ngrams of that size can only occur once.
bad_words_ids (`List[List[int]]`, *optional*):
List of list of token ids that are not allowed to be generated. Check
[`~generation.NoBadWordsLogitsProcessor`] for further documentation and examples.
force_words_ids (`List[List[int]]` or `List[List[List[int]]]`, *optional*):
List of token ids that must be generated. If given a `List[List[int]]`, this is treated as a simple list of
words that must be included, the opposite to `bad_words_ids`. If given `List[List[List[int]]]`, this
triggers a [disjunctive constraint](https://github.com/huggingface/transformers/issues/14081), where one
can allow different forms of each word.
renormalize_logits (`bool`, *optional*, defaults to `False`):
Whether to renormalize the logits after applying all the logits processors (including the custom
ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the score logits
are normalized but some logit processors break the normalization.
constraints (`List[Constraint]`, *optional*):
Custom constraints that can be added to the generation to ensure that the output will contain the use of
certain tokens as defined by `Constraint` objects, in the most sensible way possible.
forced_bos_token_id (`int`, *optional*, defaults to `model.config.forced_bos_token_id`):
The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful for
multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be the target
language token.
forced_eos_token_id (`int` or List[int]`, *optional*, defaults to `model.config.forced_eos_token_id`):
The id of the token to force as the last generated token when `max_length` is reached. Optionally, use a
list to set multiple *end-of-sequence* tokens.
remove_invalid_values (`bool`, *optional*, defaults to `model.config.remove_invalid_values`):
Whether to remove possible *nan* and *inf* outputs of the model to prevent the generation method to crash.
Note that using `remove_invalid_values` can slow down generation.
exponential_decay_length_penalty (`tuple(int, float)`, *optional*):
This Tuple adds an exponentially increasing length penalty, after a certain amount of tokens have been
generated. The tuple shall consist of: `(start_index, decay_factor)` where `start_index` indicates where
penalty starts and `decay_factor` represents the factor of exponential decay
suppress_tokens (`List[int]`, *optional*):
A list of tokens that will be suppressed at generation. The `SupressTokens` logit processor will set their
log probs to `-inf` so that they are not sampled.
begin_suppress_tokens (`List[int]`, *optional*):
A list of tokens that will be suppressed at the beginning of the generation. The `SupressBeginTokens` logit
processor will set their log probs to `-inf` so that they are not sampled.
forced_decoder_ids (`List[List[int]]`, *optional*):
A list of pairs of integers which indicates a mapping from generation indices to token indices that will be
forced before sampling. For example, `[[1, 123]]` means the second generated token will always be a token
of index 123.
sequence_bias (`Dict[Tuple[int], float]`, *optional*)):
Dictionary that maps a sequence of tokens to its bias term. Positive biases increase the odds of the
sequence being selected, while negative biases do the opposite. Check
[`~generation.SequenceBiasLogitsProcessor`] for further documentation and examples.
token_healing (`bool`, *optional*, defaults to `False`):
Heal tail tokens of prompts by replacing them with their appropriate extensions.
This enhances the quality of completions for prompts affected by greedy tokenization bias.
guidance_scale (`float`, *optional*):
The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`.
Higher guidance scale encourages the model to generate samples that are more closely linked to the input
prompt, usually at the expense of poorer quality.
low_memory (`bool`, *optional*):
Switch to sequential beam search and sequential topk for contrastive search to reduce peak memory.
Used with beam search and contrastive search.
watermarking_config (`BaseWatermarkingConfig` or `dict`, *optional*):
Arguments used to watermark the model outputs by adding a small bias to randomly selected set of "green"
tokens. See the docs of [`SynthIDTextWatermarkingConfig`] and [`WatermarkingConfig`] for more
details. If passed as `Dict`, it will be converted to a `WatermarkingConfig` internally.
> Parameters that define the output variables of generate
num_return_sequences (`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
output_logits (`bool`, *optional*):
Whether or not to return the unprocessed prediction logit scores. See `logits` under returned tensors for
more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`~utils.ModelOutput`], as opposed to returning exclusively the generated
sequence. This flag must be set to `True` to return the generation cache (when `use_cache` is `True`)
or optional outputs (see flags starting with `output_`)
> Special tokens that can be used at generation time
pad_token_id (`int`, *optional*):
The id of the *padding* token.
bos_token_id (`int`, *optional*):
The id of the *beginning-of-sequence* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
> Generation parameters exclusive to encoder-decoder models
encoder_no_repeat_ngram_size (`int`, *optional*, defaults to 0):
If set to int > 0, all ngrams of that size that occur in the `encoder_input_ids` cannot occur in the
`decoder_input_ids`.
decoder_start_token_id (`int` or `List[int]`, *optional*):
If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token or a list of length
`batch_size`. Indicating a list enables different start ids for each element in the batch
(e.g. multilingual models with different target languages in one batch)
> Generation parameters exclusive to assistant generation
is_assistant (`bool`, *optional*, defaults to `False`):
Whether the model is an assistant (draft) model.
num_assistant_tokens (`int`, *optional*, defaults to 20):
Defines the number of _speculative tokens_ that shall be generated by the assistant model before being
checked by the target model at each iteration. Higher values for `num_assistant_tokens` make the generation
more _speculative_ : If the assistant model is performant larger speed-ups can be reached, if the assistant
model requires lots of corrections, lower speed-ups are reached.
num_assistant_tokens_schedule (`str`, *optional*, defaults to `"constant"`):
Defines the schedule at which max assistant tokens shall be changed during inference.
- `"heuristic"`: When all speculative tokens are correct, increase `num_assistant_tokens` by 2 else
reduce by 1. `num_assistant_tokens` value is persistent over multiple generation calls with the same assistant model.
- `"heuristic_transient"`: Same as `"heuristic"` but `num_assistant_tokens` is reset to its initial value after each generation call.
- `"constant"`: `num_assistant_tokens` stays unchanged during generation
assistant_confidence_threshold (`float`, *optional*, defaults to 0.4):
The confidence threshold for the assistant model. If the assistant model's confidence in its prediction for the current token is lower
than this threshold, the assistant model stops the current token generation iteration, even if the number of _speculative tokens_
(defined by `num_assistant_tokens`) is not yet reached. The assistant's confidence threshold is adjusted throughout the speculative iterations to reduce the number of unnecessary draft and target forward passes, biased towards avoiding false negatives.
`assistant_confidence_threshold` value is persistent over multiple generation calls with the same assistant model.
It is an unsupervised version of the dynamic speculation lookahead
from Dynamic Speculation Lookahead Accelerates Speculative Decoding of Large Language Models <https://arxiv.org/abs/2405.04304>.
prompt_lookup_num_tokens (`int`, *optional*):
The number of tokens to be output as candidate tokens.
max_matching_ngram_size (`int`, *optional*):
The maximum ngram size to be considered for matching in the prompt. Default to 2 if not provided.
assistant_early_exit(`int`, *optional*):
If set to a positive integer, early exit of the model will be used as an assistant. Can only be used with
models that support early exit (i.e. models where logits from intermediate layers can be interpreted by the LM head).
assistant_lookbehind(`int`, *optional*, defaults to 10):
If set to a positive integer, the re-encodeing process will additionally consider the last `assistant_lookbehind` assistant tokens
to correctly align tokens. Can only be used with different tokenizers in speculative decoding.
See this [blog](https://huggingface.co/blog/universal_assisted_generation) for more details.
target_lookbehind(`int`, *optional*, defaults to 10):
If set to a positive integer, the re-encodeing process will additionally consider the last `target_lookbehind` target tokens
to correctly align tokens. Can only be used with different tokenizers in speculative decoding.
See this [blog](https://huggingface.co/blog/universal_assisted_generation) for more details.
> Parameters related to performances and compilation
compile_config (CompileConfig, *optional*):
If using a static cache, this controls how `generate` will `compile` the forward pass for performance
gains.
> Wild card
generation_kwargs:
Additional generation kwargs will be forwarded to the `generate` function of the model. Kwargs that are not
present in `generate`'s signature will be used in the model forward pass.
"""
extra_output_flags = ("output_attentions", "output_hidden_states", "output_scores", "output_logits")
def __init__(self, **kwargs):
# Parameters that control the length of the output
self.max_length = kwargs.pop("max_length", 20)
self.max_new_tokens = kwargs.pop("max_new_tokens", None)
self.min_length = kwargs.pop("min_length", 0)
self.min_new_tokens = kwargs.pop("min_new_tokens", None)
self.early_stopping = kwargs.pop("early_stopping", False)
self.max_time = kwargs.pop("max_time", None)
self.stop_strings = kwargs.pop("stop_strings", None)
# Parameters that control the generation strategy used
self.do_sample = kwargs.pop("do_sample", False)
self.num_beams = kwargs.pop("num_beams", 1)
self.num_beam_groups = kwargs.pop("num_beam_groups", 1)
self.penalty_alpha = kwargs.pop("penalty_alpha", None)
self.dola_layers = kwargs.pop("dola_layers", None)
# Parameters that control the cache
self.use_cache = kwargs.pop("use_cache", True)
self.cache_implementation = kwargs.pop("cache_implementation", None)
self.cache_config = kwargs.pop("cache_config", None)
if self.cache_implementation is not None and self.cache_implementation in CACHE_CONFIG_MAPPING:
cache_config_class = CACHE_CONFIG_MAPPING[self.cache_implementation]
if isinstance(self.cache_config, dict):
self.cache_config = cache_config_class.from_dict(self.cache_config)
self.return_legacy_cache = kwargs.pop("return_legacy_cache", None)
# Parameters for manipulation of the model output logits
self.temperature = kwargs.pop("temperature", 1.0)
self.top_k = kwargs.pop("top_k", 50)
self.top_p = kwargs.pop("top_p", 1.0)
self.min_p = kwargs.pop("min_p", None)
self.typical_p = kwargs.pop("typical_p", 1.0)
self.epsilon_cutoff = kwargs.pop("epsilon_cutoff", 0.0)
self.eta_cutoff = kwargs.pop("eta_cutoff", 0.0)
self.diversity_penalty = kwargs.pop("diversity_penalty", 0.0)
self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0)
self.encoder_repetition_penalty = kwargs.pop("encoder_repetition_penalty", 1.0)
self.length_penalty = kwargs.pop("length_penalty", 1.0)
self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
self.bad_words_ids = kwargs.pop("bad_words_ids", None)
self.force_words_ids = kwargs.pop("force_words_ids", None)
self.renormalize_logits = kwargs.pop("renormalize_logits", False)
self.constraints = kwargs.pop("constraints", None)
self.forced_bos_token_id = kwargs.pop("forced_bos_token_id", None)
self.forced_eos_token_id = kwargs.pop("forced_eos_token_id", None)
self.remove_invalid_values = kwargs.pop("remove_invalid_values", False)
self.exponential_decay_length_penalty = kwargs.pop("exponential_decay_length_penalty", None)
self.suppress_tokens = kwargs.pop("suppress_tokens", None)
self.begin_suppress_tokens = kwargs.pop("begin_suppress_tokens", None)
self.forced_decoder_ids = kwargs.pop("forced_decoder_ids", None)
self.sequence_bias = kwargs.pop("sequence_bias", None)
self.token_healing = kwargs.pop("token_healing", False)
self.guidance_scale = kwargs.pop("guidance_scale", None)
self.low_memory = kwargs.pop("low_memory", None)
watermarking_config = kwargs.pop("watermarking_config", None)
if watermarking_config is None:
self.watermarking_config = None
elif isinstance(watermarking_config, BaseWatermarkingConfig):
self.watermarking_config = watermarking_config
else:
self.watermarking_config = WatermarkingConfig.from_dict(watermarking_config)
# Parameters that define the output variables of `generate`
self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
self.output_attentions = kwargs.pop("output_attentions", False)
self.output_hidden_states = kwargs.pop("output_hidden_states", False)
self.output_scores = kwargs.pop("output_scores", False)
self.output_logits = kwargs.pop("output_logits", None)
self.return_dict_in_generate = kwargs.pop("return_dict_in_generate", False)
# Special tokens that can be used at generation time
self.pad_token_id = kwargs.pop("pad_token_id", None)
self.bos_token_id = kwargs.pop("bos_token_id", None)
self.eos_token_id = kwargs.pop("eos_token_id", None)
# Generation parameters exclusive to encoder-decoder models
self.encoder_no_repeat_ngram_size = kwargs.pop("encoder_no_repeat_ngram_size", 0)
self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
# Assistant generation
self.is_assistant = False
self.num_assistant_tokens = kwargs.pop("num_assistant_tokens", 20)
self.num_assistant_tokens_schedule = kwargs.pop("num_assistant_tokens_schedule", "constant")
self.assistant_confidence_threshold = kwargs.pop("assistant_confidence_threshold", 0.4)
self.prompt_lookup_num_tokens = kwargs.pop("prompt_lookup_num_tokens", None)
self.max_matching_ngram_size = kwargs.pop("max_matching_ngram_size", None)
self.assistant_early_exit = kwargs.pop("assistant_early_exit", None)
## assistant generation for different tokenizers, the windows size for assistant/target model
self.assistant_lookbehind = kwargs.pop("assistant_lookbehind", 10)
self.target_lookbehind = kwargs.pop("target_lookbehind", 10)
# Performances
self.compile_config = kwargs.pop("compile_config", CompileConfig())
# Wild card
self.generation_kwargs = kwargs.pop("generation_kwargs", {})
# The remaining attributes do not parametrize `.generate()`, but are informative and/or used by the hub
# interface.
self._from_model_config = kwargs.pop("_from_model_config", False)
self._commit_hash = kwargs.pop("_commit_hash", None)
self.transformers_version = kwargs.pop("transformers_version", __version__)
# Additional attributes without default values
if not self._from_model_config:
# we don't want to copy values from the model config if we're initializing a `GenerationConfig` from a
# model's default configuration file
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
# Validate the values of the attributes
self.validate(is_init=True)
def __hash__(self):
return hash(self.to_json_string(ignore_metadata=True))
def __eq__(self, other):
if not isinstance(other, GenerationConfig):
return False
self_without_metadata = self.to_json_string(use_diff=False, ignore_metadata=True)
other_without_metadata = other.to_json_string(use_diff=False, ignore_metadata=True)
return self_without_metadata == other_without_metadata
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string(ignore_metadata=True)}"
def get_generation_mode(self, assistant_model: Optional["PreTrainedModel"] = None) -> GenerationMode:
"""
Returns the generation mode triggered by the [`GenerationConfig`] instance.
Arg:
assistant_model (`PreTrainedModel`, *optional*):
The assistant model to be used for assisted generation. If set, the generation mode will be
assisted generation.
Returns:
`GenerationMode`: The generation mode triggered by the instance.
"""
# TODO joao: find out a way of not depending on external fields (e.g. `assistant_model`), then make this a
# property and part of the `__repr__`
if self.constraints is not None or self.force_words_ids is not None:
generation_mode = GenerationMode.CONSTRAINED_BEAM_SEARCH
elif self.num_beams == 1:
if self.do_sample is False:
if (
self.top_k is not None
and self.top_k > 1
and self.penalty_alpha is not None
and self.penalty_alpha > 0
):
generation_mode = GenerationMode.CONTRASTIVE_SEARCH
else:
generation_mode = GenerationMode.GREEDY_SEARCH
else:
generation_mode = GenerationMode.SAMPLE
else:
if self.num_beam_groups > 1:
generation_mode = GenerationMode.GROUP_BEAM_SEARCH
elif self.do_sample is True:
generation_mode = GenerationMode.BEAM_SAMPLE
else:
generation_mode = GenerationMode.BEAM_SEARCH
# Assisted generation may extend some generation modes
if (
assistant_model is not None
or self.prompt_lookup_num_tokens is not None
or self.assistant_early_exit is not None
):
if generation_mode in ("greedy_search", "sample"):
generation_mode = GenerationMode.ASSISTED_GENERATION
else:
raise ValueError(
"You've set `assistant_model`, which triggers assisted generate. Currently, assisted generate "
"is only supported with Greedy Search and Sample."
)
# DoLa generation may extend some generation modes
if self.dola_layers is not None:
if generation_mode in ("greedy_search", "sample"):
generation_mode = GenerationMode.DOLA_GENERATION
else:
raise ValueError(
"You've set `dola_layers`, which triggers DoLa generate. Currently, DoLa generate "
"is only supported with Greedy Search and Sample."
)
return generation_mode
def validate(self, is_init=False):
"""
Validates the values of the attributes of the [`GenerationConfig`] instance. Raises exceptions in the presence
of parameterization that can be detected as incorrect from the configuration instance alone.
Note that some parameters not validated here are best validated at generate runtime, as they may depend on
other inputs and/or the model, such as parameters related to the generation length.
Arg:
is_init (`bool`, *optional*, defaults to `False`):
Whether the validation is performed during the initialization of the instance.
"""
# Validation of individual attributes
if self.early_stopping not in {True, False, "never"}:
raise ValueError(f"`early_stopping` must be a boolean or 'never', but is {self.early_stopping}.")
if self.max_new_tokens is not None and self.max_new_tokens <= 0:
raise ValueError(f"`max_new_tokens` must be greater than 0, but is {self.max_new_tokens}.")
if self.pad_token_id is not None and self.pad_token_id < 0:
warnings.warn(
f"`pad_token_id` should be positive but got {self.pad_token_id}. This will cause errors when batch "
"generating, if there is padding. Please set `pad_token_id` explicitly as "
"`model.generation_config.pad_token_id=PAD_TOKEN_ID` to avoid errors in generation"
)
# Validation of attribute relations:
fix_location = ""
if is_init:
fix_location = (
" This was detected when initializing the generation config instance, which means the corresponding "
"file may hold incorrect parameterization and should be fixed."
)
# 1. detect sampling-only parameterization when not in sampling mode
if self.do_sample is False:
greedy_wrong_parameter_msg = (
"`do_sample` is set to `False`. However, `{flag_name}` is set to `{flag_value}` -- this flag is only "
"used in sample-based generation modes. You should set `do_sample=True` or unset `{flag_name}`."
+ fix_location
)
if self.temperature is not None and self.temperature != 1.0:
warnings.warn(
greedy_wrong_parameter_msg.format(flag_name="temperature", flag_value=self.temperature),
UserWarning,
)
if self.top_p is not None and self.top_p != 1.0:
warnings.warn(
greedy_wrong_parameter_msg.format(flag_name="top_p", flag_value=self.top_p),
UserWarning,
)
if self.min_p is not None:
warnings.warn(
greedy_wrong_parameter_msg.format(flag_name="min_p", flag_value=self.min_p),
UserWarning,
)
if self.typical_p is not None and self.typical_p != 1.0:
warnings.warn(
greedy_wrong_parameter_msg.format(flag_name="typical_p", flag_value=self.typical_p),
UserWarning,
)
if (
self.top_k is not None and self.top_k != 50 and self.penalty_alpha is None
): # contrastive search uses top_k
warnings.warn(
greedy_wrong_parameter_msg.format(flag_name="top_k", flag_value=self.top_k),
UserWarning,
)
if self.epsilon_cutoff is not None and self.epsilon_cutoff != 0.0:
warnings.warn(
greedy_wrong_parameter_msg.format(flag_name="epsilon_cutoff", flag_value=self.epsilon_cutoff),
UserWarning,
)
if self.eta_cutoff is not None and self.eta_cutoff != 0.0:
warnings.warn(
greedy_wrong_parameter_msg.format(flag_name="eta_cutoff", flag_value=self.eta_cutoff),
UserWarning,
)
# 2. detect beam-only parameterization when not in beam mode
if self.num_beams is None:
warnings.warn("`num_beams` is set to None - defaulting to 1.", UserWarning)
self.num_beams = 1
if self.num_beams == 1:
single_beam_wrong_parameter_msg = (
"`num_beams` is set to 1. However, `{flag_name}` is set to `{flag_value}` -- this flag is only used "
"in beam-based generation modes. You should set `num_beams>1` or unset `{flag_name}`." + fix_location
)
if self.early_stopping is not False:
warnings.warn(
single_beam_wrong_parameter_msg.format(flag_name="early_stopping", flag_value=self.early_stopping),
UserWarning,
)
if self.num_beam_groups is not None and self.num_beam_groups != 1:
warnings.warn(
single_beam_wrong_parameter_msg.format(
flag_name="num_beam_groups", flag_value=self.num_beam_groups
),
UserWarning,
)
if self.diversity_penalty is not None and self.diversity_penalty != 0.0:
warnings.warn(
single_beam_wrong_parameter_msg.format(
flag_name="diversity_penalty", flag_value=self.diversity_penalty
),
UserWarning,
)
if self.length_penalty is not None and self.length_penalty != 1.0:
warnings.warn(
single_beam_wrong_parameter_msg.format(flag_name="length_penalty", flag_value=self.length_penalty),
UserWarning,
)
if self.constraints is not None:
warnings.warn(
single_beam_wrong_parameter_msg.format(flag_name="constraints", flag_value=self.constraints),
UserWarning,
)
# 3. detect incorrect paramaterization specific to advanced beam modes
else:
# constrained beam search
if self.constraints is not None or self.force_words_ids is not None:
constrained_wrong_parameter_msg = (
"one of `constraints`, `force_words_ids` is not `None`, triggering constrained beam search. However, "
"`{flag_name}` is set to `{flag_value}`, which is incompatible with this generation mode. Set "
"`constraints` and `force_words_ids` to `None` or unset `{flag_name}` to continue." + fix_location
)
if self.do_sample is True:
raise ValueError(
constrained_wrong_parameter_msg.format(flag_name="do_sample", flag_value=self.do_sample)
)
if self.num_beam_groups is not None and self.num_beam_groups != 1:
raise ValueError(
constrained_wrong_parameter_msg.format(
flag_name="num_beam_groups", flag_value=self.num_beam_groups
)
)
# group beam search
if self.diversity_penalty != 0.0 or self.num_beam_groups != 1:
group_error_prefix = (
"`diversity_penalty` is not 0.0 or `num_beam_groups` is not 1, triggering group beam search. In "
"this generation mode, "
)
if self.do_sample is True:
raise ValueError(group_error_prefix + "`do_sample` must be set to `False`")
if self.num_beams % self.num_beam_groups != 0:
raise ValueError(group_error_prefix + "`num_beams` should be divisible by `num_beam_groups`")
if self.diversity_penalty == 0.0:
raise ValueError(
group_error_prefix
+ "`diversity_penalty` should be greater than `0.0`, otherwise your groups will be identical."
)
# DoLa generation
if self.dola_layers is not None and (self.repetition_penalty is None or self.repetition_penalty < 1.2):
warnings.warn(
"`dola_layers` is set to trigger DoLa decoding, but `repetition_penalty` is set to a value of "
f"{self.repetition_penalty}, which could induce unwanted repetition. The recommended value for "
"DoLa decoding is `repetition_penalty>=1.2`.",
UserWarning,
)
# 4. check `num_return_sequences`
if self.num_return_sequences != 1:
if self.num_beams == 1:
if self.do_sample is False:
raise ValueError(
"Greedy methods without beam search do not support `num_return_sequences` different than 1 "
f"(got {self.num_return_sequences})."
)
elif self.num_return_sequences > self.num_beams:
raise ValueError(
f"`num_return_sequences` ({self.num_return_sequences}) has to be smaller or equal to `num_beams` "
f"({self.num_beams})."
)
# 5. check cache-related arguments
if self.cache_implementation is not None and self.cache_implementation not in ALL_CACHE_IMPLEMENTATIONS:
raise ValueError(
f"Invalid `cache_implementation` ({self.cache_implementation}). Choose one of: "
f"{ALL_CACHE_IMPLEMENTATIONS}"
)
if self.cache_config is not None:
cache_class = CACHE_CONFIG_MAPPING.get(self.cache_implementation)
if cache_class is None:
raise ValueError(
"You provided a `cache_config` but the cache implementation you are using "
f"({self.cache_implementation}) does not require any config. Make sure to use the "
"correct cache implementation matching your cache config."
)
if not isinstance(self.cache_config, cache_class):
self.cache_config = cache_class.from_dict(self.cache_config)
self.cache_config.validate()
if self.use_cache is False:
# In this case, all cache-related arguments should be unset. However, since `use_cache=False` is often used
# passed to `generate` directly to hot-fix cache issues, let's raise a warning instead of an error
# (otherwise a user might need to overwrite several parameters).
no_cache_warning = (
"You have set `use_cache` to `False`, but {cache_arg} is set to {cache_arg_value}. {cache_arg} will "
"have no effect."
)
for arg_name in ("cache_implementation", "cache_config", "return_legacy_cache"):
if getattr(self, arg_name) is not None:
logger.warning_once(
no_cache_warning.format(cache_arg=arg_name, cache_arg_value=getattr(self, arg_name)),
UserWarning,
)
# 6. check watermarking arguments
if self.watermarking_config is not None:
if not (
isinstance(self.watermarking_config, WatermarkingConfig)
or isinstance(self.watermarking_config, SynthIDTextWatermarkingConfig)
):
warnings.warn(
"`watermarking_config` as a dict is deprecated. Please construct `watermarking_config` object with "
"`WatermarkingConfig` or `SynthIDTextWatermarkingConfig` class.",
FutureWarning,
)
self.watermarking_config = WatermarkingConfig.from_dict(self.watermarking_config)
self.watermarking_config.validate()
# 7. performances arguments
if not isinstance(self.compile_config, CompileConfig):
raise ValueError(
f"You provided `compile_config` as an instance of {type(self.compile_config)}, but it must be an instance of `CompileConfig`."
)
# 8. other incorrect combinations
if self.return_dict_in_generate is not True:
for extra_output_flag in self.extra_output_flags:
if getattr(self, extra_output_flag) is True:
warnings.warn(
f"`return_dict_in_generate` is NOT set to `True`, but `{extra_output_flag}` is. When "
f"`return_dict_in_generate` is not `True`, `{extra_output_flag}` is ignored.",
UserWarning,
)
# 8. check common issue: passing `generate` arguments inside the generation config
generate_arguments = (
"logits_processor",
"stopping_criteria",
"prefix_allowed_tokens_fn",
"synced_gpus",
"assistant_model",
"streamer",
"negative_prompt_ids",
"negative_prompt_attention_mask",
)
for arg in generate_arguments:
if hasattr(self, arg):
raise ValueError(
f"Argument `{arg}` is not a valid argument of `GenerationConfig`. It should be passed to "
"`generate()` (or a pipeline) directly."
)
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
config_file_name: Optional[Union[str, os.PathLike]] = None,
push_to_hub: bool = False,
**kwargs,
):
r"""
Save a generation configuration object to the directory `save_directory`, so that it can be re-loaded using the
[`~GenerationConfig.from_pretrained`] class method.
Args:
save_directory (`str` or `os.PathLike`):
Directory where the configuration JSON file will be saved (will be created if it does not exist).
config_file_name (`str` or `os.PathLike`, *optional*, defaults to `"generation_config.json"`):
Name of the generation configuration JSON file to be saved in `save_directory`.
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs (`Dict[str, Any]`, *optional*):
Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
# At save time, validate the instance -- if any warning/exception is thrown, we refuse to save the instance.
# This strictness is enforced to prevent bad configurations from being saved and re-used.
try:
with warnings.catch_warnings(record=True) as caught_warnings:
self.validate()
if len(caught_warnings) > 0:
raise ValueError(str([w.message for w in caught_warnings]))
except ValueError as exc:
raise ValueError(
"The generation config instance is invalid -- `.validate()` throws warnings and/or exceptions. "
"Fix these issues to save the configuration.\n\nThrown during validation:\n" + str(exc)
)
use_auth_token = kwargs.pop("use_auth_token", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. "
"Please use `token` instead.",
FutureWarning,
)
if kwargs.get("token", None) is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
kwargs["token"] = use_auth_token
config_file_name = config_file_name if config_file_name is not None else GENERATION_CONFIG_NAME
if os.path.isfile(save_directory):
raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = self._create_repo(repo_id, **kwargs)
files_timestamps = self._get_files_timestamps(save_directory)
output_config_file = os.path.join(save_directory, config_file_name)
self.to_json_file(output_config_file, use_diff=True)
logger.info(f"Configuration saved in {output_config_file}")
if push_to_hub:
self._upload_modified_files(
save_directory,
repo_id,
files_timestamps,
commit_message=commit_message,
token=kwargs.get("token"),
)
@classmethod
def from_pretrained(
cls,
pretrained_model_name: Union[str, os.PathLike],
config_file_name: Optional[Union[str, os.PathLike]] = None,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = None,
revision: str = "main",
**kwargs,
) -> "GenerationConfig":
r"""
Instantiate a [`GenerationConfig`] from a generation configuration file.
Args:
pretrained_model_name (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a configuration file saved using the
[`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
config_file_name (`str` or `os.PathLike`, *optional*, defaults to `"generation_config.json"`):
Name of the generation configuration JSON file to be loaded from `pretrained_model_name`.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if
they exist.
resume_download:
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
<Tip>
To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>"`.
</Tip>
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
If `False`, then this function returns just the final configuration object.
If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of `kwargs` which has not been used to update `config` and is otherwise ignored.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.
kwargs (`Dict[str, Any]`, *optional*):
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
by the `return_unused_kwargs` keyword parameter.
Returns:
[`GenerationConfig`]: The configuration object instantiated from this pretrained model.
Examples:
```python
>>> from transformers import GenerationConfig
>>> # Download configuration from huggingface.co and cache.
>>> generation_config = GenerationConfig.from_pretrained("openai-community/gpt2")
>>> # E.g. config was saved using *save_pretrained('./test/saved_model/')*
>>> generation_config.save_pretrained("./test/saved_model/")
>>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/")
>>> # You can also specify configuration names to your generation configuration file
>>> generation_config.save_pretrained("./test/saved_model/", config_file_name="my_configuration.json")
>>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/", "my_configuration.json")
>>> # If you'd like to try a minor variation to an existing configuration, you can also pass generation
>>> # arguments to `.from_pretrained()`. Be mindful that typos and unused arguments will be ignored
>>> generation_config, unused_kwargs = GenerationConfig.from_pretrained(
... "openai-community/gpt2", top_k=1, foo=False, do_sample=True, return_unused_kwargs=True
... )
>>> generation_config.top_k
1
>>> unused_kwargs
{'foo': False}
```"""
config_file_name = config_file_name if config_file_name is not None else GENERATION_CONFIG_NAME
resume_download = kwargs.pop("resume_download", None)
proxies = kwargs.pop("proxies", None)
use_auth_token = kwargs.pop("use_auth_token", None)
subfolder = kwargs.pop("subfolder", "")
from_pipeline = kwargs.pop("_from_pipeline", None)
from_auto_class = kwargs.pop("_from_auto", False)
commit_hash = kwargs.pop("_commit_hash", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
token = use_auth_token
user_agent = {"file_type": "config", "from_auto_class": from_auto_class}
if from_pipeline is not None:
user_agent["using_pipeline"] = from_pipeline
config_path = os.path.join(pretrained_model_name, config_file_name)
config_path = str(config_path)
is_local = os.path.exists(config_path)
if os.path.isfile(os.path.join(subfolder, config_path)):
# Special case when config_path is a local file
resolved_config_file = config_path
is_local = True
elif is_remote_url(config_path):
configuration_file = config_path
resolved_config_file = download_url(config_path)
else:
configuration_file = config_file_name
try:
# Load from local folder or from cache or download from model Hub and cache
resolved_config_file = cached_file(
pretrained_model_name,
configuration_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
revision=revision,
subfolder=subfolder,
_commit_hash=commit_hash,
)
commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
except EnvironmentError:
# Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
# the original exception.
raise
except Exception:
# For any other exception, we throw a generic error.
raise EnvironmentError(
f"Can't load the configuration of '{pretrained_model_name}'. If you were trying to load it"
" from 'https://huggingface.co/models', make sure you don't have a local directory with the same"
f" name. Otherwise, make sure '{pretrained_model_name}' is the correct path to a directory"
f" containing a {configuration_file} file"
)
try:
# Load config dict
config_dict = cls._dict_from_json_file(resolved_config_file)
config_dict["_commit_hash"] = commit_hash
except (json.JSONDecodeError, UnicodeDecodeError):
raise EnvironmentError(
f"It looks like the config file at '{resolved_config_file}' is not a valid JSON file."
)
if is_local:
logger.info(f"loading configuration file {resolved_config_file}")
else:
logger.info(f"loading configuration file {configuration_file} from cache at {resolved_config_file}")
if kwargs.get("return_unused_kwargs") is True:
config, unused_kwargs = cls.from_dict(config_dict, **kwargs)
config._original_object_hash = hash(config) # Hash to detect whether the instance was modified
return config, unused_kwargs
else:
config = cls.from_dict(config_dict, **kwargs)
config._original_object_hash = hash(config) # Hash to detect whether the instance was modified
return config
@classmethod
def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return json.loads(text)
@classmethod
def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "GenerationConfig":
"""
Instantiates a [`GenerationConfig`] from a Python dictionary of parameters.
Args:
config_dict (`Dict[str, Any]`):
Dictionary that will be used to instantiate the configuration object.
kwargs (`Dict[str, Any]`):
Additional parameters from which to initialize the configuration object.
Returns:
[`GenerationConfig`]: The configuration object instantiated from those parameters.
"""
return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
# Those arguments may be passed along for our internal telemetry.
# We remove them so they don't appear in `return_unused_kwargs`.
kwargs.pop("_from_auto", None)
kwargs.pop("_from_pipeline", None)
# The commit hash might have been updated in the `config_dict`, we don't want the kwargs to erase that update.
if "_commit_hash" in kwargs and "_commit_hash" in config_dict:
kwargs["_commit_hash"] = config_dict["_commit_hash"]
# The line below allows model-specific config to be loaded as well through kwargs, with safety checks.
# See https://github.com/huggingface/transformers/pull/21269
config = cls(**{**config_dict, **kwargs})
unused_kwargs = config.update(**kwargs)
logger.info(f"Generate config {config}")
if return_unused_kwargs:
return config, unused_kwargs
else:
return config
def dict_torch_dtype_to_str(self, d: Dict[str, Any]) -> None:
"""
Checks whether the passed dictionary and its nested dicts have a *torch_dtype* key and if it's not None,
converts torch.dtype to a string of just the type. For example, `torch.float32` get converted into *"float32"*
string, which can then be stored in the json format.
"""
if d.get("torch_dtype", None) is not None and not isinstance(d["torch_dtype"], str):
d["torch_dtype"] = str(d["torch_dtype"]).split(".")[1]
for value in d.values():
if isinstance(value, dict):
self.dict_torch_dtype_to_str(value)
def to_diff_dict(self) -> Dict[str, Any]:
"""
Removes all attributes from config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.
Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance,
"""
config_dict = self.to_dict()
# get the default config dict
default_config_dict = GenerationConfig().to_dict()
serializable_config_dict = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if key not in default_config_dict or key == "transformers_version" or value != default_config_dict[key]:
serializable_config_dict[key] = value
self.dict_torch_dtype_to_str(serializable_config_dict)
return serializable_config_dict
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary.
Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
"""
output = copy.deepcopy(self.__dict__)
# Fields to ignore at serialization time
if "_commit_hash" in output:
del output["_commit_hash"]
if "_original_object_hash" in output:
del output["_original_object_hash"]
if "compile_config" in output:
del output["compile_config"]
# Transformers version when serializing this file
output["transformers_version"] = __version__
self.dict_torch_dtype_to_str(output)
return output
def to_json_string(self, use_diff: bool = True, ignore_metadata: bool = False) -> str:
"""
Serializes this instance to a JSON string.
Args:
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default `GenerationConfig()`
is serialized to JSON string.
ignore_metadata (`bool`, *optional*, defaults to `False`):
Whether to ignore the metadata fields present in the instance
Returns:
`str`: String containing all the attributes that make up this configuration instance in JSON format.
"""
if use_diff is True:
config_dict = self.to_diff_dict()
else:
config_dict = self.to_dict()
if ignore_metadata:
for metadata_field in METADATA_FIELDS:
config_dict.pop(metadata_field, None)
def convert_keys_to_string(obj):
if isinstance(obj, dict):
return {str(key): convert_keys_to_string(value) for key, value in obj.items()}
elif isinstance(obj, list):
return [convert_keys_to_string(item) for item in obj]
else:
return obj
def convert_dataclass_to_dict(obj):
if isinstance(obj, dict):
return {key: convert_dataclass_to_dict(value) for key, value in obj.items()}
elif is_dataclass(obj):
return obj.to_dict()
else:
return obj
config_dict = convert_keys_to_string(config_dict)
config_dict = convert_dataclass_to_dict(config_dict)
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
def to_json_file(self, json_file_path: Union[str, os.PathLike], use_diff: bool = True):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file in which this configuration instance's parameters will be saved.
use_diff (`bool`, *optional*, defaults to `True`):
If set to `True`, only the difference between the config instance and the default `GenerationConfig()`
is serialized to JSON file.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
writer.write(self.to_json_string(use_diff=use_diff))
@classmethod
def from_model_config(cls, model_config: PretrainedConfig) -> "GenerationConfig":
"""
Instantiates a [`GenerationConfig`] from a [`PretrainedConfig`]. This function is useful to convert legacy
[`PretrainedConfig`] objects, which may contain generation parameters, into a stand-alone [`GenerationConfig`].
Args:
model_config (`PretrainedConfig`):
The model config that will be used to instantiate the generation config.
Returns:
[`GenerationConfig`]: The configuration object instantiated from those parameters.
"""
config_dict = model_config.to_dict()
config_dict.pop("_from_model_config", None)
# Removes all `None` from the model config dict -- this lets the generation config defaults to take hold
config_dict = {key: value for key, value in config_dict.items() if value is not None}
generation_config = cls.from_dict(config_dict, return_unused_kwargs=False, _from_model_config=True)
# Special case: some models have generation attributes set in the decoder. Use them if still unset in the
# generation config (which in turn is defined from the outer attributes of model config).
decoder_config = model_config.get_text_config(decoder=True)
if decoder_config is not model_config:
default_generation_config = GenerationConfig()
decoder_config_dict = decoder_config.to_dict()
for attr in generation_config.to_dict().keys():
is_unset = getattr(generation_config, attr) == getattr(default_generation_config, attr)
if attr in decoder_config_dict and is_unset:
setattr(generation_config, attr, decoder_config_dict[attr])
# If any `output_...` flag is set to `True`, we ensure `return_dict_in_generate` is set to `True`.
if generation_config.return_dict_in_generate is False:
if any(
getattr(generation_config, extra_output_flag, False)
for extra_output_flag in generation_config.extra_output_flags
):
generation_config.return_dict_in_generate = True
# Hash to detect whether the instance was modified
generation_config._original_object_hash = hash(generation_config)
return generation_config
def update(self, **kwargs):
"""
Updates attributes of this class instance with attributes from `kwargs` if they match existing attributes,
returning all the unused kwargs.
Args:
kwargs (`Dict[str, Any]`):
Dictionary of attributes to tentatively update this class.
Returns:
`Dict[str, Any]`: Dictionary containing all the key-value pairs that were not used to update the instance.
"""
to_remove = []
for key, value in kwargs.items():
if hasattr(self, key):
setattr(self, key, value)
to_remove.append(key)
# Confirm that the updated instance is still valid
self.validate()
# Remove all the attributes that were updated, without modifying the input dict
unused_kwargs = {key: value for key, value in kwargs.items() if key not in to_remove}
return unused_kwargs
@dataclass
class BaseWatermarkingConfig(ABC):
"""Generic watermarking config"""
@classmethod
def from_dict(cls, config_dict, **kwargs):
"""
Constructs a BaseWatermarkingConfig instance from a dictionary of parameters.
Args:
config_dict (Dict[str, Any]): Dictionary containing configuration parameters.
**kwargs: Additional keyword arguments to override dictionary values.
Returns:
BaseWatermarkingConfig: Instance of BaseWatermarkingConfig constructed from the dictionary.
"""
config = cls(**config_dict)
to_remove = []
for key, value in kwargs.items():
if hasattr(config, key):
setattr(config, key, value)
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
return config
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
"""
Save this instance to a JSON file.
Args:
json_file_path (Union[str, os.PathLike]): Path to the JSON file in which this configuration instance's parameters will be saved.
"""
with open(json_file_path, "w", encoding="utf-8") as writer:
config_dict = self.to_dict()
json_string = json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
writer.write(json_string)
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary.
Returns:
Dict[str, Any]: Dictionary of all the attributes that make up this configuration instance.
"""
output = copy.deepcopy(self.__dict__)
return output
def __iter__(self):
for attr, value in copy.deepcopy(self.__dict__).items():
yield attr, value
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
def to_json_string(self):
"""
Serializes this instance to a JSON formatted string.
Returns:
str: JSON formatted string representing the configuration instance.
"""
return json.dumps(self.__dict__, indent=2) + "\n"
def update(self, **kwargs):
"""
Update the configuration attributes with new values.
Args:
**kwargs: Keyword arguments representing configuration attributes and their new values.
"""
for key, value in kwargs.items():
if hasattr(self, key):
setattr(self, key, value)
@abstractmethod
def validate(self): ...
@abstractmethod
def construct_processor(self, vocab_size): ...
@dataclass
class WatermarkingConfig(BaseWatermarkingConfig):
"""
Class that holds arguments for watermark generation and should be passed into `GenerationConfig` during `generate`.
See [this paper](https://arxiv.org/abs/2306.04634) for more details on the arguments.
Accepts the following keys:
- greenlist_ratio (`float`):
Used for watermarking. The ratio of "green" tokens used to the vocabulary size. Defaults to 0.25.
- bias (`float`):
Used with watermarking. The bias added to the selected "green" tokens' logits. Defaults to 2.0.
- hashing_key (`int`):
Hashing key used for watermarking. Defaults to 15485863 (the millionth prime).
- seeding_scheme (`str`):
Algorithm to use for watermarking. Accepts values:
- "lefthash" (default): "green" tokens selection depend on the last token (Algorithm 2 from the paper)
- "selfhash": "green" tokens selection depends on the current token itself (Algorithm 3 from the paper)
The downside of this scheme is that it considers all possible next tokens and can be slower than "lefthash".
- context_width(`int`):
The context length of previous tokens to use in seeding. Higher context length makes watermarking more robust.
"""
def __init__(
self,
greenlist_ratio: Optional[float] = 0.25,
bias: Optional[float] = 2.0,
hashing_key: Optional[int] = 15485863,
seeding_scheme: Optional[str] = "lefthash",
context_width: Optional[int] = 1,
):
self.greenlist_ratio = greenlist_ratio
self.bias = bias
self.hashing_key = hashing_key
self.seeding_scheme = seeding_scheme
self.context_width = context_width
def validate(self):
watermark_missing_arg_msg = (
"Some of the keys in `watermarking_config` are defined incorrectly. `{key}` should be {correct_value}` "
"but found {found_value}"
)
if self.seeding_scheme not in ["selfhash", "lefthash"]:
raise ValueError(
watermark_missing_arg_msg.format(
key="seeding_scheme",
correct_value="[`selfhash`, `lefthash`]",
found_value=self.seeding_scheme,
),
)
if not 0.0 <= self.greenlist_ratio <= 1.0:
raise ValueError(
watermark_missing_arg_msg.format(
key="greenlist_ratio",
correct_value="in range between 0.0 and 1.0",
found_value=self.seeding_scheme,
),
)
if not self.context_width >= 1:
raise ValueError(
watermark_missing_arg_msg.format(
key="context_width",
correct_value="a positive integer",
found_value=self.context_width,
),
)
def construct_processor(self, vocab_size: int, device) -> "WatermarkLogitsProcessor":
return WatermarkLogitsProcessor(
vocab_size=vocab_size,
device=device,
greenlist_ratio=self.greenlist_ratio,
bias=self.bias,
hashing_key=self.hashing_key,
seeding_scheme=self.seeding_scheme,
context_width=self.context_width,
)
@dataclass
class SynthIDTextWatermarkingConfig(BaseWatermarkingConfig):
"""
Class that holds arguments for watermark generation and should be passed into `GenerationConfig` during `generate`.
See [this paper](https://www.nature.com/articles/s41586-024-08025-4) for more details on the arguments.
Args:
ngram_len (`int`):
Ngram length.
keys (`List[int]`):
A sequence of watermarking keys, one for each depth.
context_history_size (`int`, *optional*, defaults to 1024):
Size of the tensor to keep track of seen contexts.
sampling_table_seed (`int`, *optional*, defaults to 0):
Random seed to generate the sampling table.
sampling_table_size (`int`, *optional*, defaults to 65536):
Size of the sampling table.
skip_first_ngram_calls (`bool`, *optional*, defaults to `False`):
Whether to skip first ngram calls.
debug_mode (`bool`, optional, *optional*, defaults to `False`):
Logits are modified to uniform one got before watermarking modification is applied. This is to test the
implementation.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, SynthIDTextWatermarkingConfig
>>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b', padding_side="left")
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b')
>>> # SynthID Text configuration
>>> watermarking_config = SynthIDTextWatermarkingConfig(
... keys=[654, 400, 836, 123, 340, 443, 597, 160, 57],
... ngram_len=5,
... )
>>> # Generation with watermarking
>>> tokenized_prompts = tokenizer(["Once upon a time, "], return_tensors="pt", padding=True)
>>> output_sequences = model.generate(
... **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True, max_new_tokens=10
... )
>>> watermarked_text = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
```
"""
def __init__(
self,
ngram_len: int,
keys: List[int],
context_history_size: int = 1024,
sampling_table_seed: int = 0,
sampling_table_size: int = 2**16,
skip_first_ngram_calls: bool = False,
debug_mode: bool = False,
):
self.ngram_len = ngram_len
self.keys = keys
self.sampling_table_size = sampling_table_size
self.sampling_table_seed = sampling_table_seed
self.context_history_size = context_history_size
self.skip_first_ngram_calls = skip_first_ngram_calls
self.debug_mode = debug_mode
def validate(self):
watermark_missing_arg_msg = (
"Some of the keys in `watermarking_config` are defined incorrectly. `{key}` should be {correct_value}` "
"but found {found_value}"
)
if self.sampling_table_size > 2**24:
raise ValueError(
watermark_missing_arg_msg.format(
key="sampling_table_size",
correct_value="< 2**24",
found_value=self.sampling_table_size,
),
)
def construct_processor(self, vocab_size: int, device) -> "WatermarkLogitsProcessor":
return SynthIDTextWatermarkLogitsProcessor(
ngram_len=self.ngram_len,
keys=self.keys,
sampling_table_size=self.sampling_table_size,
sampling_table_seed=self.sampling_table_seed,
context_history_size=self.context_history_size,
device=device,
skip_first_ngram_calls=self.skip_first_ngram_calls,
debug_mode=self.debug_mode,
)
@dataclass
class CompileConfig:
"""
Class that holds arguments relative to `torch.compile` behavior, when using automatic compilation in `generate`.
See [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) for more details on the arguments.
Args:
fullgraph (`bool`, *optional*, defaults to `True`):
If `True`, requires that the whole forward be capturable in a single graph.
dynamic (`bool` or `None`, *optional*):
Whether to try to use dynamic shape graphs.
backend (`str` or `Callable`, *optional*, defaults to `"inductor"`):
Backend to be used.
mode (`str`, *optional*, defaults to `"reduce-overhead"`):
Controls balance between performance and overhead.
options (`dict`, *optional*):
A dictionary of options to pass to the backend.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, CompileConfig
>>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b')
>>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b').cuda()
>>> # Automatic compile configuration, used with static cache
>>> compile_config = CompileConfig(dynamic=True)
>>> # Generation with static cache and compile config
>>> input = tokenizer.encode("Hello there, how", return_tensors="pt").cuda()
>>> output = model.generate(
... input, do_sample=False, max_new_tokens=300, cache_implementation="static", compile_config=compile_config
... )
>>> output_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
```
"""
fullgraph: bool = True
dynamic: Optional[bool] = None
backend: Union[str, Callable] = "inductor"
mode: str = "reduce-overhead"
options: Optional[dict] = None
# Used to flag our `generate` call to compile on e.g. CPU. Often not optimal, but useful for testing purposes.
_compile_all_devices = None
def to_dict(self) -> Dict[str, Any]:
"""Serializes this instance to a Python dictionary."""
return copy.deepcopy({key: value for key, value in self.__dict__.items() if key != "_compile_all_devices"})
| transformers/src/transformers/generation/configuration_utils.py/0 | {
"file_path": "transformers/src/transformers/generation/configuration_utils.py",
"repo_id": "transformers",
"token_count": 35024
} |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import base64
import os
from contextlib import redirect_stdout
from dataclasses import dataclass
from io import BytesIO
from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
import requests
from packaging import version
from .utils import (
ExplicitEnum,
TensorType,
is_av_available,
is_cv2_available,
is_decord_available,
is_jax_tensor,
is_numpy_array,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_torchvision_available,
is_vision_available,
is_yt_dlp_available,
logging,
requires_backends,
to_numpy,
)
from .utils.constants import ( # noqa: F401
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
)
if is_vision_available():
import PIL.Image
import PIL.ImageOps
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PILImageResampling = PIL.Image.Resampling
else:
PILImageResampling = PIL.Image
if is_torchvision_available():
from torchvision import io as torchvision_io
from torchvision.transforms import InterpolationMode
pil_torch_interpolation_mapping = {
PILImageResampling.NEAREST: InterpolationMode.NEAREST,
PILImageResampling.BOX: InterpolationMode.BOX,
PILImageResampling.BILINEAR: InterpolationMode.BILINEAR,
PILImageResampling.HAMMING: InterpolationMode.HAMMING,
PILImageResampling.BICUBIC: InterpolationMode.BICUBIC,
PILImageResampling.LANCZOS: InterpolationMode.LANCZOS,
}
if is_decord_available():
from decord import VideoReader, cpu
if is_av_available():
import av
if is_cv2_available():
import cv2
if is_yt_dlp_available():
from yt_dlp import YoutubeDL
if TYPE_CHECKING:
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
ImageInput = Union[
"PIL.Image.Image", np.ndarray, "torch.Tensor", List["PIL.Image.Image"], List[np.ndarray], List["torch.Tensor"]
] # noqa
VideoInput = Union[
List["PIL.Image.Image"],
"np.ndarray",
"torch.Tensor",
List["np.ndarray"],
List["torch.Tensor"],
List[List["PIL.Image.Image"]],
List[List["np.ndarrray"]],
List[List["torch.Tensor"]],
] # noqa
class ChannelDimension(ExplicitEnum):
FIRST = "channels_first"
LAST = "channels_last"
class AnnotationFormat(ExplicitEnum):
COCO_DETECTION = "coco_detection"
COCO_PANOPTIC = "coco_panoptic"
class AnnotionFormat(ExplicitEnum):
COCO_DETECTION = AnnotationFormat.COCO_DETECTION.value
COCO_PANOPTIC = AnnotationFormat.COCO_PANOPTIC.value
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
def is_pil_image(img):
return is_vision_available() and isinstance(img, PIL.Image.Image)
class ImageType(ExplicitEnum):
PIL = "pillow"
TORCH = "torch"
NUMPY = "numpy"
TENSORFLOW = "tensorflow"
JAX = "jax"
def get_image_type(image):
if is_pil_image(image):
return ImageType.PIL
if is_torch_tensor(image):
return ImageType.TORCH
if is_numpy_array(image):
return ImageType.NUMPY
if is_tf_tensor(image):
return ImageType.TENSORFLOW
if is_jax_tensor(image):
return ImageType.JAX
raise ValueError(f"Unrecognised image type {type(image)}")
def is_valid_image(img):
return is_pil_image(img) or is_numpy_array(img) or is_torch_tensor(img) or is_tf_tensor(img) or is_jax_tensor(img)
def is_valid_list_of_images(images: List):
return images and all(is_valid_image(image) for image in images)
def valid_images(imgs):
# If we have an list of images, make sure every image is valid
if isinstance(imgs, (list, tuple)):
for img in imgs:
if not valid_images(img):
return False
# If not a list of tuple, we have been given a single image or batched tensor of images
elif not is_valid_image(imgs):
return False
return True
def is_batched(img):
if isinstance(img, (list, tuple)):
return is_valid_image(img[0])
return False
def is_scaled_image(image: np.ndarray) -> bool:
"""
Checks to see whether the pixel values have already been rescaled to [0, 1].
"""
if image.dtype == np.uint8:
return False
# It's possible the image has pixel values in [0, 255] but is of floating type
return np.min(image) >= 0 and np.max(image) <= 1
def make_list_of_images(images, expected_ndims: int = 3) -> List[ImageInput]:
"""
Ensure that the output is a list of images. If the input is a single image, it is converted to a list of length 1.
If the input is a batch of images, it is converted to a list of images.
Args:
images (`ImageInput`):
Image of images to turn into a list of images.
expected_ndims (`int`, *optional*, defaults to 3):
Expected number of dimensions for a single input image. If the input image has a different number of
dimensions, an error is raised.
"""
if is_batched(images):
return images
# Either the input is a single image, in which case we create a list of length 1
if is_pil_image(images):
# PIL images are never batched
return [images]
if is_valid_image(images):
if images.ndim == expected_ndims + 1:
# Batch of images
images = list(images)
elif images.ndim == expected_ndims:
# Single image
images = [images]
else:
raise ValueError(
f"Invalid image shape. Expected either {expected_ndims + 1} or {expected_ndims} dimensions, but got"
f" {images.ndim} dimensions."
)
return images
raise ValueError(
"Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or "
f"jax.ndarray, but got {type(images)}."
)
def make_flat_list_of_images(
images: Union[List[ImageInput], ImageInput],
) -> ImageInput:
"""
Ensure that the output is a flat list of images. If the input is a single image, it is converted to a list of length 1.
If the input is a nested list of images, it is converted to a flat list of images.
Args:
images (`Union[List[ImageInput], ImageInput]`):
The input image.
Returns:
list: A list of images or a 4d array of images.
"""
# If the input is a nested list of images, we flatten it
if (
isinstance(images, (list, tuple))
and all(isinstance(images_i, (list, tuple)) for images_i in images)
and all(is_valid_list_of_images(images_i) for images_i in images)
):
return [img for img_list in images for img in img_list]
if isinstance(images, (list, tuple)) and is_valid_list_of_images(images):
if is_pil_image(images[0]) or images[0].ndim == 3:
return images
if images[0].ndim == 4:
return [img for img_list in images for img in img_list]
if is_valid_image(images):
if is_pil_image(images) or images.ndim == 3:
return [images]
if images.ndim == 4:
return list(images)
raise ValueError(f"Could not make a flat list of images from {images}")
def make_nested_list_of_images(
images: Union[List[ImageInput], ImageInput],
) -> ImageInput:
"""
Ensure that the output is a nested list of images.
Args:
images (`Union[List[ImageInput], ImageInput]`):
The input image.
Returns:
list: A list of list of images or a list of 4d array of images.
"""
# If it's a list of batches, it's already in the right format
if (
isinstance(images, (list, tuple))
and all(isinstance(images_i, (list, tuple)) for images_i in images)
and all(is_valid_list_of_images(images_i) for images_i in images)
):
return images
# If it's a list of images, it's a single batch, so convert it to a list of lists
if isinstance(images, (list, tuple)) and is_valid_list_of_images(images):
if is_pil_image(images[0]) or images[0].ndim == 3:
return [images]
if images[0].ndim == 4:
return [list(image) for image in images]
# If it's a single image, convert it to a list of lists
if is_valid_image(images):
if is_pil_image(images) or images.ndim == 3:
return [[images]]
if images.ndim == 4:
return [list(images)]
raise ValueError("Invalid input type. Must be a single image, a list of images, or a list of batches of images.")
def make_batched_videos(videos) -> VideoInput:
"""
Ensure that the input is a list of videos.
Args:
videos (`VideoInput`):
Video or videos to turn into a list of videos.
Returns:
list: A list of videos.
"""
if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
# case 1: nested batch of videos so we flatten it
if not is_pil_image(videos[0][0]) and videos[0][0].ndim == 4:
videos = [video for batch_list in videos for video in batch_list]
# case 2: list of videos represented as list of video frames
return videos
elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
if is_pil_image(videos[0]) or videos[0].ndim == 3:
return [videos]
elif videos[0].ndim == 4:
return [list(video) for video in videos]
elif is_valid_image(videos):
if is_pil_image(videos) or videos.ndim == 3:
return [[videos]]
elif videos.ndim == 4:
return [list(videos)]
raise ValueError(f"Could not make batched video from {videos}")
def to_numpy_array(img) -> np.ndarray:
if not is_valid_image(img):
raise ValueError(f"Invalid image type: {type(img)}")
if is_vision_available() and isinstance(img, PIL.Image.Image):
return np.array(img)
return to_numpy(img)
def infer_channel_dimension_format(
image: np.ndarray, num_channels: Optional[Union[int, Tuple[int, ...]]] = None
) -> ChannelDimension:
"""
Infers the channel dimension format of `image`.
Args:
image (`np.ndarray`):
The image to infer the channel dimension of.
num_channels (`int` or `Tuple[int, ...]`, *optional*, defaults to `(1, 3)`):
The number of channels of the image.
Returns:
The channel dimension of the image.
"""
num_channels = num_channels if num_channels is not None else (1, 3)
num_channels = (num_channels,) if isinstance(num_channels, int) else num_channels
if image.ndim == 3:
first_dim, last_dim = 0, 2
elif image.ndim == 4:
first_dim, last_dim = 1, 3
else:
raise ValueError(f"Unsupported number of image dimensions: {image.ndim}")
if image.shape[first_dim] in num_channels and image.shape[last_dim] in num_channels:
logger.warning(
f"The channel dimension is ambiguous. Got image shape {image.shape}. Assuming channels are the first dimension."
)
return ChannelDimension.FIRST
elif image.shape[first_dim] in num_channels:
return ChannelDimension.FIRST
elif image.shape[last_dim] in num_channels:
return ChannelDimension.LAST
raise ValueError("Unable to infer channel dimension format")
def get_channel_dimension_axis(
image: np.ndarray, input_data_format: Optional[Union[ChannelDimension, str]] = None
) -> int:
"""
Returns the channel dimension axis of the image.
Args:
image (`np.ndarray`):
The image to get the channel dimension axis of.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the image. If `None`, will infer the channel dimension from the image.
Returns:
The channel dimension axis of the image.
"""
if input_data_format is None:
input_data_format = infer_channel_dimension_format(image)
if input_data_format == ChannelDimension.FIRST:
return image.ndim - 3
elif input_data_format == ChannelDimension.LAST:
return image.ndim - 1
raise ValueError(f"Unsupported data format: {input_data_format}")
def get_image_size(image: np.ndarray, channel_dim: ChannelDimension = None) -> Tuple[int, int]:
"""
Returns the (height, width) dimensions of the image.
Args:
image (`np.ndarray`):
The image to get the dimensions of.
channel_dim (`ChannelDimension`, *optional*):
Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image.
Returns:
A tuple of the image's height and width.
"""
if channel_dim is None:
channel_dim = infer_channel_dimension_format(image)
if channel_dim == ChannelDimension.FIRST:
return image.shape[-2], image.shape[-1]
elif channel_dim == ChannelDimension.LAST:
return image.shape[-3], image.shape[-2]
else:
raise ValueError(f"Unsupported data format: {channel_dim}")
def get_image_size_for_max_height_width(
image_size: Tuple[int, int],
max_height: int,
max_width: int,
) -> Tuple[int, int]:
"""
Computes the output image size given the input image and the maximum allowed height and width. Keep aspect ratio.
Important, even if image_height < max_height and image_width < max_width, the image will be resized
to at least one of the edges be equal to max_height or max_width.
For example:
- input_size: (100, 200), max_height: 50, max_width: 50 -> output_size: (25, 50)
- input_size: (100, 200), max_height: 200, max_width: 500 -> output_size: (200, 400)
Args:
image_size (`Tuple[int, int]`):
The image to resize.
max_height (`int`):
The maximum allowed height.
max_width (`int`):
The maximum allowed width.
"""
height, width = image_size
height_scale = max_height / height
width_scale = max_width / width
min_scale = min(height_scale, width_scale)
new_height = int(height * min_scale)
new_width = int(width * min_scale)
return new_height, new_width
def is_valid_annotation_coco_detection(annotation: Dict[str, Union[List, Tuple]]) -> bool:
if (
isinstance(annotation, dict)
and "image_id" in annotation
and "annotations" in annotation
and isinstance(annotation["annotations"], (list, tuple))
and (
# an image can have no annotations
len(annotation["annotations"]) == 0 or isinstance(annotation["annotations"][0], dict)
)
):
return True
return False
def is_valid_annotation_coco_panoptic(annotation: Dict[str, Union[List, Tuple]]) -> bool:
if (
isinstance(annotation, dict)
and "image_id" in annotation
and "segments_info" in annotation
and "file_name" in annotation
and isinstance(annotation["segments_info"], (list, tuple))
and (
# an image can have no segments
len(annotation["segments_info"]) == 0 or isinstance(annotation["segments_info"][0], dict)
)
):
return True
return False
def valid_coco_detection_annotations(annotations: Iterable[Dict[str, Union[List, Tuple]]]) -> bool:
return all(is_valid_annotation_coco_detection(ann) for ann in annotations)
def valid_coco_panoptic_annotations(annotations: Iterable[Dict[str, Union[List, Tuple]]]) -> bool:
return all(is_valid_annotation_coco_panoptic(ann) for ann in annotations)
def load_image(image: Union[str, "PIL.Image.Image"], timeout: Optional[float] = None) -> "PIL.Image.Image":
"""
Loads `image` to a PIL Image.
Args:
image (`str` or `PIL.Image.Image`):
The image to convert to the PIL Image format.
timeout (`float`, *optional*):
The timeout value in seconds for the URL request.
Returns:
`PIL.Image.Image`: A PIL Image.
"""
requires_backends(load_image, ["vision"])
if isinstance(image, str):
if image.startswith("http://") or image.startswith("https://"):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
image = PIL.Image.open(BytesIO(requests.get(image, timeout=timeout).content))
elif os.path.isfile(image):
image = PIL.Image.open(image)
else:
if image.startswith("data:image/"):
image = image.split(",")[1]
# Try to load as base64
try:
b64 = base64.decodebytes(image.encode())
image = PIL.Image.open(BytesIO(b64))
except Exception as e:
raise ValueError(
f"Incorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got {image}. Failed with {e}"
)
elif isinstance(image, PIL.Image.Image):
image = image
else:
raise TypeError(
"Incorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image."
)
image = PIL.ImageOps.exif_transpose(image)
image = image.convert("RGB")
return image
def get_uniform_frame_indices(total_num_frames: int, num_frames: Optional[int] = None):
"""
Creates a numpy array for uniform sampling of `num_frame` frames from `total_num_frames`
when loading a video.
Args:
total_num_frames (`int`):
Total number of frames that a video has.
num_frames (`int`, *optional*):
Number of frames to sample uniformly. If not specified, all frames are sampled.
Returns:
np.ndarray: np array of frame indices that will be sampled.
"""
if num_frames is not None:
indices = np.arange(0, total_num_frames, total_num_frames / num_frames).astype(int)
else:
indices = np.arange(0, total_num_frames).astype(int)
return indices
def read_video_opencv(video_path: str, num_frames: Optional[int] = None):
"""
Decode the video with open-cv decoder.
Args:
video_path (`str`):
Path to the video file.
num_frames (`int`, *optional*):
Number of frames to sample uniformly. If not specified, all frames are sampled.
Returns:
np.ndarray: np array of decoded frames of shape (num_frames, height, width, 3).
"""
video = cv2.VideoCapture(video_path)
total_num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
indices = get_uniform_frame_indices(total_num_frames, num_frames=num_frames)
index = 0
frames = []
while video.isOpened():
success, frame = video.read()
if index in indices:
height, width, channel = frame.shape
frames.append(frame[0:height, 0:width, 0:channel])
if success:
index += 1
if index >= total_num_frames:
break
video.release()
return np.stack(frames)
def read_video_decord(video_path: str, num_frames: Optional[int] = None):
"""
Decode the video with Decord decoder.
Args:
video_path (`str`):
Path to the video file.
num_frames (`int`, *optional*):
Number of frames to sample uniformly. If not specified, all frames are sampled.
Returns:
np.ndarray: np array of decoded frames of shape (num_frames, height, width, 3).
"""
vr = VideoReader(uri=video_path, ctx=cpu(0)) # decord has problems with gpu
indices = get_uniform_frame_indices(total_num_frames=len(vr), num_frames=num_frames)
frames = vr.get_batch(indices).asnumpy()
return frames
def read_video_pyav(video_path: str, num_frames: Optional[int] = None):
"""
Decode the video with PyAV decoder.
Args:
video_path (`str`):
Path to the video file.
num_frames (`int`, *optional*):
Number of frames to sample uniformly. If not specified, all frames are sampled.
Returns:
np.ndarray: np array of decoded frames of shape (num_frames, height, width, 3).
"""
container = av.open(video_path)
# sample uniformly "num_frames" frames from the video
total_num_frames = container.streams.video[0].frames
indices = get_uniform_frame_indices(total_num_frames, num_frames=num_frames)
frames = []
container.seek(0)
end_index = indices[-1]
for i, frame in enumerate(container.decode(video=0)):
if i > end_index:
break
if i >= 0 and i in indices:
frames.append(frame)
return np.stack([x.to_ndarray(format="rgb24") for x in frames])
def read_video_torchvision(video_path: str, num_frames: Optional[int] = None):
"""
Decode the video with torchvision decoder.
Args:
video_path (`str`):
Path to the video file.
num_frames (`int`, *optional*):
Number of frames to sample uniformly. If not specified, all frames are sampled.
Returns:
np.ndarray: np array of decoded frames of shape (num_frames, height, width, 3).
"""
video, _, info = torchvision_io.read_video(
video_path,
start_pts=0.0,
end_pts=None,
pts_unit="sec",
output_format="TCHW",
)
if num_frames is not None:
idx = torch.linspace(0, video.size(0) - 1, num_frames, dtype=torch.int64)
return video[idx]
return video
VIDEO_DECODERS = {
"decord": read_video_decord,
"opencv": read_video_opencv,
"pyav": read_video_pyav,
"torchvision": read_video_torchvision,
}
def load_video(video: Union[str, "VideoInput"], num_frames: Optional[int] = None, backend: str = "opencv") -> np.array:
"""
Loads `video` to a numpy array.
Args:
video (`str` or `VideoInput`):
The video to convert to the numpy array format. Can be a link to video or local path.
num_frames (`int`, *optional*):
Number of frames to sample uniformly. If not passed, the whole video is loaded.
backend (`str`, *optional*, defaults to `"opencv"`):
The backend to use when loading the video. Can be any of ["decord", "pyav", "opencv", "torchvision"]. Defaults to "opencv".
Returns:
`np.array`: A numpy array of shape (num_frames, channels, height, width).
"""
if video.startswith("https://www.youtube.com") or video.startswith("http://www.youtube.com"):
if not is_yt_dlp_available():
raise ImportError("To load a video from YouTube url you have to install `yt_dlp` first.")
buffer = BytesIO()
with redirect_stdout(buffer), YoutubeDL() as f:
f.download([video])
bytes_obj = buffer.getvalue()
file_obj = BytesIO(bytes_obj)
elif video.startswith("http://") or video.startswith("https://"):
file_obj = BytesIO(requests.get(video).content)
elif os.path.isfile(video):
file_obj = video
elif is_valid_image(video) or (isinstance(video, (list, tuple) and is_valid_image(video[0]))):
file_obj = None
else:
raise TypeError("Incorrect format used for video. Should be an url linking to an video or a local path.")
# can also load with decord, but not cv2/torchvision
# both will fail in case of url links
video_is_url = video.startswith("http://") or video.startswith("https://")
if video_is_url and backend in ["opencv", "torchvision"]:
raise ValueError(
"If you are trying to load a video from URL, you can decode the video only with `pyav` or `decord` as backend"
)
if file_obj is None:
return video
if (
(not is_decord_available() and backend == "decord")
or (not is_av_available() and backend == "pyav")
or (not is_cv2_available() and backend == "opencv")
or (not is_torchvision_available() and backend == "torchvision")
):
raise ImportError(
f"You chose backend={backend} for loading the video but the required library is not found in your environment "
f"Make sure to install {backend} before loading the video."
)
video_decoder = VIDEO_DECODERS[backend]
video = video_decoder(file_obj)
return video
def load_images(
images: Union[List, Tuple, str, "PIL.Image.Image"], timeout: Optional[float] = None
) -> Union["PIL.Image.Image", List["PIL.Image.Image"], List[List["PIL.Image.Image"]]]:
"""Loads images, handling different levels of nesting.
Args:
images: A single image, a list of images, or a list of lists of images to load.
timeout: Timeout for loading images.
Returns:
A single image, a list of images, a list of lists of images.
"""
if isinstance(images, (list, tuple)):
if len(images) and isinstance(images[0], (list, tuple)):
return [[load_image(image, timeout=timeout) for image in image_group] for image_group in images]
else:
return [load_image(image, timeout=timeout) for image in images]
else:
return load_image(images, timeout=timeout)
def validate_preprocess_arguments(
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_pad: Optional[bool] = None,
size_divisibility: Optional[int] = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[Dict[str, int]] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: Optional["PILImageResampling"] = None,
):
"""
Checks validity of typically used arguments in an `ImageProcessor` `preprocess` method.
Raises `ValueError` if arguments incompatibility is caught.
Many incompatibilities are model-specific. `do_pad` sometimes needs `size_divisor`,
sometimes `size_divisibility`, and sometimes `size`. New models and processors added should follow
existing arguments when possible.
"""
if do_rescale and rescale_factor is None:
raise ValueError("`rescale_factor` must be specified if `do_rescale` is `True`.")
if do_pad and size_divisibility is None:
# Here, size_divisor might be passed as the value of size
raise ValueError(
"Depending on the model, `size_divisibility`, `size_divisor`, `pad_size` or `size` must be specified if `do_pad` is `True`."
)
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("`image_mean` and `image_std` must both be specified if `do_normalize` is `True`.")
if do_center_crop and crop_size is None:
raise ValueError("`crop_size` must be specified if `do_center_crop` is `True`.")
if do_resize and (size is None or resample is None):
raise ValueError("`size` and `resample` must be specified if `do_resize` is `True`.")
def validate_fast_preprocess_arguments(
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_pad: Optional[bool] = None,
size_divisibility: Optional[int] = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[Dict[str, int]] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: Optional["PILImageResampling"] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
):
"""
Checks validity of typically used arguments in an `ImageProcessorFast` `preprocess` method.
Raises `ValueError` if arguments incompatibility is caught.
"""
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_pad=do_pad,
size_divisibility=size_divisibility,
do_center_crop=do_center_crop,
crop_size=crop_size,
do_resize=do_resize,
size=size,
resample=resample,
)
# Extra checks for ImageProcessorFast
if return_tensors is not None and return_tensors != "pt":
raise ValueError("Only returning PyTorch tensors is currently supported.")
if data_format != ChannelDimension.FIRST:
raise ValueError("Only channel first data format is currently supported.")
# In the future we can add a TF implementation here when we have TF models.
class ImageFeatureExtractionMixin:
"""
Mixin that contain utilities for preparing image features.
"""
def _ensure_format_supported(self, image):
if not isinstance(image, (PIL.Image.Image, np.ndarray)) and not is_torch_tensor(image):
raise ValueError(
f"Got type {type(image)} which is not supported, only `PIL.Image.Image`, `np.array` and "
"`torch.Tensor` are."
)
def to_pil_image(self, image, rescale=None):
"""
Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if
needed.
Args:
image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`):
The image to convert to the PIL Image format.
rescale (`bool`, *optional*):
Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will
default to `True` if the image type is a floating type, `False` otherwise.
"""
self._ensure_format_supported(image)
if is_torch_tensor(image):
image = image.numpy()
if isinstance(image, np.ndarray):
if rescale is None:
# rescale default to the array being of floating type.
rescale = isinstance(image.flat[0], np.floating)
# If the channel as been moved to first dim, we put it back at the end.
if image.ndim == 3 and image.shape[0] in [1, 3]:
image = image.transpose(1, 2, 0)
if rescale:
image = image * 255
image = image.astype(np.uint8)
return PIL.Image.fromarray(image)
return image
def convert_rgb(self, image):
"""
Converts `PIL.Image.Image` to RGB format.
Args:
image (`PIL.Image.Image`):
The image to convert.
"""
self._ensure_format_supported(image)
if not isinstance(image, PIL.Image.Image):
return image
return image.convert("RGB")
def rescale(self, image: np.ndarray, scale: Union[float, int]) -> np.ndarray:
"""
Rescale a numpy image by scale amount
"""
self._ensure_format_supported(image)
return image * scale
def to_numpy_array(self, image, rescale=None, channel_first=True):
"""
Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first
dimension.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to convert to a NumPy array.
rescale (`bool`, *optional*):
Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will
default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise.
channel_first (`bool`, *optional*, defaults to `True`):
Whether or not to permute the dimensions of the image to put the channel dimension first.
"""
self._ensure_format_supported(image)
if isinstance(image, PIL.Image.Image):
image = np.array(image)
if is_torch_tensor(image):
image = image.numpy()
rescale = isinstance(image.flat[0], np.integer) if rescale is None else rescale
if rescale:
image = self.rescale(image.astype(np.float32), 1 / 255.0)
if channel_first and image.ndim == 3:
image = image.transpose(2, 0, 1)
return image
def expand_dims(self, image):
"""
Expands 2-dimensional `image` to 3 dimensions.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to expand.
"""
self._ensure_format_supported(image)
# Do nothing if PIL image
if isinstance(image, PIL.Image.Image):
return image
if is_torch_tensor(image):
image = image.unsqueeze(0)
else:
image = np.expand_dims(image, axis=0)
return image
def normalize(self, image, mean, std, rescale=False):
"""
Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array
if it's a PIL Image.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to normalize.
mean (`List[float]` or `np.ndarray` or `torch.Tensor`):
The mean (per channel) to use for normalization.
std (`List[float]` or `np.ndarray` or `torch.Tensor`):
The standard deviation (per channel) to use for normalization.
rescale (`bool`, *optional*, defaults to `False`):
Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will
happen automatically.
"""
self._ensure_format_supported(image)
if isinstance(image, PIL.Image.Image):
image = self.to_numpy_array(image, rescale=True)
# If the input image is a PIL image, it automatically gets rescaled. If it's another
# type it may need rescaling.
elif rescale:
if isinstance(image, np.ndarray):
image = self.rescale(image.astype(np.float32), 1 / 255.0)
elif is_torch_tensor(image):
image = self.rescale(image.float(), 1 / 255.0)
if isinstance(image, np.ndarray):
if not isinstance(mean, np.ndarray):
mean = np.array(mean).astype(image.dtype)
if not isinstance(std, np.ndarray):
std = np.array(std).astype(image.dtype)
elif is_torch_tensor(image):
import torch
if not isinstance(mean, torch.Tensor):
if isinstance(mean, np.ndarray):
mean = torch.from_numpy(mean)
else:
mean = torch.tensor(mean)
if not isinstance(std, torch.Tensor):
if isinstance(std, np.ndarray):
std = torch.from_numpy(std)
else:
std = torch.tensor(std)
if image.ndim == 3 and image.shape[0] in [1, 3]:
return (image - mean[:, None, None]) / std[:, None, None]
else:
return (image - mean) / std
def resize(self, image, size, resample=None, default_to_square=True, max_size=None):
"""
Resizes `image`. Enforces conversion of input to PIL.Image.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to resize.
size (`int` or `Tuple[int, int]`):
The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be
matched to this.
If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If
`size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to
this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).
resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`):
The filter to user for resampling.
default_to_square (`bool`, *optional*, defaults to `True`):
How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a
square (`size`,`size`). If set to `False`, will replicate
[`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize)
with support for resizing only the smallest edge and providing an optional `max_size`.
max_size (`int`, *optional*, defaults to `None`):
The maximum allowed for the longer edge of the resized image: if the longer edge of the image is
greater than `max_size` after being resized according to `size`, then the image is resized again so
that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller
edge may be shorter than `size`. Only used if `default_to_square` is `False`.
Returns:
image: A resized `PIL.Image.Image`.
"""
resample = resample if resample is not None else PILImageResampling.BILINEAR
self._ensure_format_supported(image)
if not isinstance(image, PIL.Image.Image):
image = self.to_pil_image(image)
if isinstance(size, list):
size = tuple(size)
if isinstance(size, int) or len(size) == 1:
if default_to_square:
size = (size, size) if isinstance(size, int) else (size[0], size[0])
else:
width, height = image.size
# specified size only for the smallest edge
short, long = (width, height) if width <= height else (height, width)
requested_new_short = size if isinstance(size, int) else size[0]
if short == requested_new_short:
return image
new_short, new_long = requested_new_short, int(requested_new_short * long / short)
if max_size is not None:
if max_size <= requested_new_short:
raise ValueError(
f"max_size = {max_size} must be strictly greater than the requested "
f"size for the smaller edge size = {size}"
)
if new_long > max_size:
new_short, new_long = int(max_size * new_short / new_long), max_size
size = (new_short, new_long) if width <= height else (new_long, new_short)
return image.resize(size, resample=resample)
def center_crop(self, image, size):
"""
Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the
size given, it will be padded (so the returned result has the size asked).
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)):
The image to resize.
size (`int` or `Tuple[int, int]`):
The size to which crop the image.
Returns:
new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels,
height, width).
"""
self._ensure_format_supported(image)
if not isinstance(size, tuple):
size = (size, size)
# PIL Image.size is (width, height) but NumPy array and torch Tensors have (height, width)
if is_torch_tensor(image) or isinstance(image, np.ndarray):
if image.ndim == 2:
image = self.expand_dims(image)
image_shape = image.shape[1:] if image.shape[0] in [1, 3] else image.shape[:2]
else:
image_shape = (image.size[1], image.size[0])
top = (image_shape[0] - size[0]) // 2
bottom = top + size[0] # In case size is odd, (image_shape[0] + size[0]) // 2 won't give the proper result.
left = (image_shape[1] - size[1]) // 2
right = left + size[1] # In case size is odd, (image_shape[1] + size[1]) // 2 won't give the proper result.
# For PIL Images we have a method to crop directly.
if isinstance(image, PIL.Image.Image):
return image.crop((left, top, right, bottom))
# Check if image is in (n_channels, height, width) or (height, width, n_channels) format
channel_first = True if image.shape[0] in [1, 3] else False
# Transpose (height, width, n_channels) format images
if not channel_first:
if isinstance(image, np.ndarray):
image = image.transpose(2, 0, 1)
if is_torch_tensor(image):
image = image.permute(2, 0, 1)
# Check if cropped area is within image boundaries
if top >= 0 and bottom <= image_shape[0] and left >= 0 and right <= image_shape[1]:
return image[..., top:bottom, left:right]
# Otherwise, we may need to pad if the image is too small. Oh joy...
new_shape = image.shape[:-2] + (max(size[0], image_shape[0]), max(size[1], image_shape[1]))
if isinstance(image, np.ndarray):
new_image = np.zeros_like(image, shape=new_shape)
elif is_torch_tensor(image):
new_image = image.new_zeros(new_shape)
top_pad = (new_shape[-2] - image_shape[0]) // 2
bottom_pad = top_pad + image_shape[0]
left_pad = (new_shape[-1] - image_shape[1]) // 2
right_pad = left_pad + image_shape[1]
new_image[..., top_pad:bottom_pad, left_pad:right_pad] = image
top += top_pad
bottom += top_pad
left += left_pad
right += left_pad
new_image = new_image[
..., max(0, top) : min(new_image.shape[-2], bottom), max(0, left) : min(new_image.shape[-1], right)
]
return new_image
def flip_channel_order(self, image):
"""
Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of
`image` to a NumPy array if it's a PIL Image.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should
be first.
"""
self._ensure_format_supported(image)
if isinstance(image, PIL.Image.Image):
image = self.to_numpy_array(image)
return image[::-1, :, :]
def rotate(self, image, angle, resample=None, expand=0, center=None, translate=None, fillcolor=None):
"""
Returns a rotated copy of `image`. This method returns a copy of `image`, rotated the given number of degrees
counter clockwise around its centre.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to rotate. If `np.ndarray` or `torch.Tensor`, will be converted to `PIL.Image.Image` before
rotating.
Returns:
image: A rotated `PIL.Image.Image`.
"""
resample = resample if resample is not None else PIL.Image.NEAREST
self._ensure_format_supported(image)
if not isinstance(image, PIL.Image.Image):
image = self.to_pil_image(image)
return image.rotate(
angle, resample=resample, expand=expand, center=center, translate=translate, fillcolor=fillcolor
)
def validate_annotations(
annotation_format: AnnotationFormat,
supported_annotation_formats: Tuple[AnnotationFormat, ...],
annotations: List[Dict],
) -> None:
if annotation_format not in supported_annotation_formats:
raise ValueError(f"Unsupported annotation format: {format} must be one of {supported_annotation_formats}")
if annotation_format is AnnotationFormat.COCO_DETECTION:
if not valid_coco_detection_annotations(annotations):
raise ValueError(
"Invalid COCO detection annotations. Annotations must a dict (single image) or list of dicts "
"(batch of images) with the following keys: `image_id` and `annotations`, with the latter "
"being a list of annotations in the COCO format."
)
if annotation_format is AnnotationFormat.COCO_PANOPTIC:
if not valid_coco_panoptic_annotations(annotations):
raise ValueError(
"Invalid COCO panoptic annotations. Annotations must a dict (single image) or list of dicts "
"(batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with "
"the latter being a list of annotations in the COCO format."
)
def validate_kwargs(valid_processor_keys: List[str], captured_kwargs: List[str]):
unused_keys = set(captured_kwargs).difference(set(valid_processor_keys))
if unused_keys:
unused_key_str = ", ".join(unused_keys)
# TODO raise a warning here instead of simply logging?
logger.warning(f"Unused or unrecognized kwargs: {unused_key_str}.")
@dataclass(frozen=True)
class SizeDict:
"""
Hashable dictionary to store image size information.
"""
height: int = None
width: int = None
longest_edge: int = None
shortest_edge: int = None
max_height: int = None
max_width: int = None
def __getitem__(self, key):
if hasattr(self, key):
return getattr(self, key)
raise KeyError(f"Key {key} not found in SizeDict.")
| transformers/src/transformers/image_utils.py/0 | {
"file_path": "transformers/src/transformers/image_utils.py",
"repo_id": "transformers",
"token_count": 19540
} |
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