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/**
* @module generation/parameters
*/
/**
* @typedef {Object} GenerationFunctionParameters
* @property {import('../utils/tensor.js').Tensor} [inputs=null] (`Tensor` of varying shape depending on the modality, *optional*):
* The sequence used as a prompt for the generation or as model inputs to the encoder. If `null` the
* method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
* should be in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
* `input_ids`, `input_values`, `input_features`, or `pixel_values`.
* @property {import('./configuration_utils.js').GenerationConfig} [generation_config=null] (`GenerationConfig`, *optional*):
* The generation configuration to be used as base parametrization for the generation call.
* `**kwargs` passed to generate matching the attributes of `generation_config` will override them.
* If `generation_config` is not provided, the default will be used, which has the following loading
* priority:
* - (1) from the `generation_config.json` model file, if it exists;
* - (2) from the model configuration. Please note that unspecified parameters will inherit [`GenerationConfig`]'s
* default values, whose documentation should be checked to parameterize generation.
* @property {import('./logits_process.js').LogitsProcessorList} [logits_processor=null] (`LogitsProcessorList`, *optional*):
* Custom logits processors that complement the default logits processors built from arguments and
* generation config. If a logit processor is passed that is already created with the arguments or a
* generation config an error is thrown. This feature is intended for advanced users.
* @property {import('./stopping_criteria.js').StoppingCriteriaList} [stopping_criteria=null] (`StoppingCriteriaList`, *optional*):
* Custom stopping criteria that complements the default stopping criteria built from arguments and a
* generation config. If a stopping criteria is passed that is already created with the arguments or a
* generation config an error is thrown. This feature is intended for advanced users.
* @property {import('./streamers.js').BaseStreamer} [streamer=null] (`BaseStreamer`, *optional*):
* Streamer object that will be used to stream the generated sequences. Generated tokens are passed
* through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
* @property {number[]} [decoder_input_ids=null] (`number[]`, *optional*):
* If the model is an encoder-decoder model, this argument is used to pass the `decoder_input_ids`.
* @param {any} [kwargs] (`Dict[str, any]`, *optional*):
*/
| transformers.js/src/generation/parameters.js/0 | {
"file_path": "transformers.js/src/generation/parameters.js",
"repo_id": "transformers.js",
"token_count": 701
} |
import {
ImageProcessor,
} from "../../base/image_processors_utils.js";
export class DonutImageProcessor extends ImageProcessor {
pad_image(pixelData, imgDims, padSize, options = {}) {
const [imageHeight, imageWidth, imageChannels] = imgDims;
let image_mean = this.image_mean;
if (!Array.isArray(this.image_mean)) {
image_mean = new Array(imageChannels).fill(image_mean);
}
let image_std = this.image_std;
if (!Array.isArray(image_std)) {
image_std = new Array(imageChannels).fill(image_mean);
}
const constant_values = image_mean.map((x, i) => - x / image_std[i]);
return super.pad_image(pixelData, imgDims, padSize, {
center: true,
// Since normalization is done after padding, we need to use certain constant values to ensure the same behaviour is observed.
// For more information, see https://github.com/huggingface/transformers/blob/main/src/transformers/models/donut/image_processing_donut.py#L433-L451
constant_values,
...options,
});
}
}
export class DonutFeatureExtractor extends DonutImageProcessor { }
| transformers.js/src/models/donut/image_processing_donut.js/0 | {
"file_path": "transformers.js/src/models/donut/image_processing_donut.js",
"repo_id": "transformers.js",
"token_count": 482
} |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
import { RawImage } from "../../utils/image.js";
const IMAGE_TOKEN = "<|image|>";
const IMAGE_TOKEN_PATTERN = /<\|image_\d+\|>/g;
export class Phi3VProcessor extends Processor {
static image_processor_class = AutoImageProcessor
static tokenizer_class = AutoTokenizer
/**
*
* @param {string|string[]} text
* @param {RawImage|RawImage[]} images
* @param { { padding?: boolean, truncation?: boolean, num_crops?: number } | undefined } options
* @returns {Promise<any>}
*/
async _call(text, images = null, {
padding = true,
truncation = true,
num_crops = null,
} = {}) {
if (!Array.isArray(text)) {
text = [text];
}
let text_inputs, image_inputs;
if (images) {
image_inputs = await this.image_processor(images, { num_crops });
const { num_img_tokens } = image_inputs;
// The original implementation adds a bos_token before the image tokens
// TODO: Check if this affects performance, since it looks like a bug in the original implementation
const prompt_chunks = text.map((t, i) => t.split(IMAGE_TOKEN_PATTERN).join(IMAGE_TOKEN.repeat(num_img_tokens[i])));
text_inputs = this.tokenizer(prompt_chunks, { padding, truncation });
// The model expects image tokens to be negative, so we negate the image token ids
const image_token_id = this.tokenizer.model.convert_tokens_to_ids([IMAGE_TOKEN])[0];
text_inputs.input_ids.map_(id => (id == image_token_id) ? -id : id);
} else {
text_inputs = this.tokenizer(text);
}
return {
...text_inputs,
...image_inputs,
}
}
}
| transformers.js/src/models/phi3_v/processing_phi3_v.js/0 | {
"file_path": "transformers.js/src/models/phi3_v/processing_phi3_v.js",
"repo_id": "transformers.js",
"token_count": 827
} |
import {
ImageProcessor,
} from "../../base/image_processors_utils.js";
export class Swin2SRImageProcessor extends ImageProcessor {
pad_image(pixelData, imgDims, padSize, options = {}) {
// NOTE: In this case, `padSize` represents the size of the sliding window for the local attention.
// In other words, the image is padded so that its width and height are multiples of `padSize`.
const [imageHeight, imageWidth, imageChannels] = imgDims;
return super.pad_image(pixelData, imgDims, {
// NOTE: For Swin2SR models, the original python implementation adds padding even when the image's width/height is already
// a multiple of `pad_size`. However, this is most likely a bug (PR: https://github.com/mv-lab/swin2sr/pull/19).
// For this reason, we only add padding when the image's width/height is not a multiple of `pad_size`.
width: imageWidth + (padSize - imageWidth % padSize) % padSize,
height: imageHeight + (padSize - imageHeight % padSize) % padSize,
}, {
mode: 'symmetric',
center: false,
constant_values: -1,
...options,
})
}
} | transformers.js/src/models/swin2sr/image_processing_swin2sr.js/0 | {
"file_path": "transformers.js/src/models/swin2sr/image_processing_swin2sr.js",
"repo_id": "transformers.js",
"token_count": 462
} |
/**
* @file Entry point for the Transformers.js library. Only the exports from this file
* are available to the end user, and are grouped as follows:
*
* 1. [Pipelines](./pipelines)
* 2. [Environment variables](./env)
* 3. [Models](./models)
* 4. [Tokenizers](./tokenizers)
* 5. [Processors](./processors)
*
* @module transformers
*/
export { env } from './env.js';
export * from './pipelines.js';
export * from './models.js';
export * from './tokenizers.js';
export * from './configs.js';
export * from './utils/audio.js';
export * from './utils/image.js';
export * from './utils/tensor.js';
export * from './utils/maths.js';
export { FeatureExtractor } from './base/feature_extraction_utils.js';
export * from './models/feature_extractors.js';
export * from './models/auto/feature_extraction_auto.js';
export { ImageProcessor } from './base/image_processors_utils.js';
export * from './models/image_processors.js';
export * from './models/auto/image_processing_auto.js';
export { Processor } from './base/processing_utils.js';
export * from './models/processors.js';
export * from './models/auto/processing_auto.js';
export * from './generation/streamers.js';
export * from './generation/stopping_criteria.js';
export * from './generation/logits_process.js';
| transformers.js/src/transformers.js/0 | {
"file_path": "transformers.js/src/transformers.js",
"repo_id": "transformers.js",
"token_count": 422
} |
import { CodeGenTokenizer, CodeGenForCausalLM } 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("CodeGenForCausalLM", () => {
const model_id = "hf-internal-testing/tiny-random-CodeGenForCausalLM";
/** @type {CodeGenForCausalLM} */
let model;
/** @type {CodeGenTokenizer} */
let tokenizer;
beforeAll(async () => {
model = await CodeGenForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
tokenizer = await CodeGenTokenizer.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([[258n, 863n, 79n, 437n, 334n, 450n, 294n, 621n, 375n, 385n]]);
},
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, 0n, 258n, 863n, 79n, 437n, 334n, 450n, 294n, 621n],
[258n, 863n, 79n, 269n, 813n, 759n, 113n, 295n, 574n, 987n],
]);
},
MAX_TEST_EXECUTION_TIME,
);
afterAll(async () => {
await model?.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/models/codegen/test_modeling_codegen.js/0 | {
"file_path": "transformers.js/tests/models/codegen/test_modeling_codegen.js",
"repo_id": "transformers.js",
"token_count": 768
} |
import { GemmaTokenizer, Gemma2ForCausalLM } 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("Gemma2ForCausalLM", () => {
const model_id = "hf-internal-testing/tiny-random-Gemma2ForCausalLM";
/** @type {Gemma2ForCausalLM} */
let model;
/** @type {GemmaTokenizer} */
let tokenizer;
beforeAll(async () => {
model = await Gemma2ForCausalLM.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, 127534n, 160055n, 160055n, 160055n, 160055n, 160055n, 160055n, 160055n]]);
},
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, 127534n, 127534n, 215341n, 215341n, 215341n, 215341n, 215341n],
[2n, 17534n, 2134n, 107508n, 160055n, 160055n, 160055n, 160055n, 160055n, 160055n],
]);
},
MAX_TEST_EXECUTION_TIME,
);
afterAll(async () => {
await model?.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/models/gemma2/test_modeling_gemma2.js/0 | {
"file_path": "transformers.js/tests/models/gemma2/test_modeling_gemma2.js",
"repo_id": "transformers.js",
"token_count": 796
} |
import { AutoProcessor, VLChatProcessor } from "../../../src/transformers.js";
import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
export default () => {
describe("VLChatProcessor", () => {
const model_id = "onnx-community/Janus-1.3B-ONNX";
/** @type {VLChatProcessor} */
let processor;
beforeAll(async () => {
processor = await AutoProcessor.from_pretrained(model_id);
}, MAX_PROCESSOR_LOAD_TIME);
it(
"Image and text",
async () => {
// Prepare inputs
const conversation = [
{
role: "User",
content: "<image_placeholder>\nConvert the formula into latex code.",
images: ["https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/quadratic_formula.png"],
},
];
const { input_ids, attention_mask, images_seq_mask, images_emb_mask, pixel_values, original_sizes, reshaped_input_sizes } = await processor(conversation);
const num_tokens = 631;
const { num_image_tokens } = processor.config; // 576
const { image_size } = processor.image_processor.config; // 384
expect(input_ids.dims).toEqual([1, num_tokens]);
expect(attention_mask.dims).toEqual([1, num_tokens]);
expect(images_seq_mask.dims).toEqual([1, num_tokens]);
expect(images_seq_mask.to("float32").mean().item()).toBeCloseTo(num_image_tokens / num_tokens, 6);
expect(images_emb_mask.dims).toEqual([1, 1, num_image_tokens]);
expect(images_emb_mask.to("float32").mean().item()).toBeCloseTo(1);
expect(pixel_values.dims).toEqual([1, 1, 3, image_size, image_size]);
expect(pixel_values.mean().item()).toBeCloseTo(0.5999642610549927, 6);
expect(original_sizes).toEqual([[206, 767]]);
expect(reshaped_input_sizes).toEqual([[103, image_size]]);
},
MAX_TEST_EXECUTION_TIME,
);
});
};
| transformers.js/tests/models/janus/test_processor_janus.js/0 | {
"file_path": "transformers.js/tests/models/janus/test_processor_janus.js",
"repo_id": "transformers.js",
"token_count": 849
} |
import { T5Tokenizer, MusicgenForConditionalGeneration, full } 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("MusicgenForConditionalGeneration", () => {
const model_id = "hf-internal-testing/tiny-random-MusicgenForConditionalGeneration";
// Example adapted from https://huggingface.co/docs/transformers/model_doc/musicgen#text-conditional-generation
const texts = ["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"];
/** @type {MusicgenForConditionalGeneration} */
let model;
/** @type {T5Tokenizer} */
let tokenizer;
beforeAll(async () => {
model = await MusicgenForConditionalGeneration.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
tokenizer = await T5Tokenizer.from_pretrained(model_id);
}, MAX_MODEL_LOAD_TIME);
it(
"forward",
async () => {
// Example from https://huggingface.co/docs/transformers/model_doc/musicgen#transformers.MusicgenForConditionalGeneration.forward.example
const inputs = tokenizer(texts, { padding: true });
const pad_token_id = BigInt(model.generation_config.pad_token_id);
const decoder_input_ids = full([inputs.input_ids.dims[0] * model.config.decoder.num_codebooks, 1], pad_token_id);
const { logits } = await model({ ...inputs, decoder_input_ids });
expect(logits.dims).toEqual([8, 1, 99]);
expect(logits.mean().item()).toBeCloseTo(-0.0018370470497757196, 4);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"batch_size=1",
async () => {
const inputs = tokenizer(texts[0]);
const audio_values = await model.generate({ ...inputs, max_length: 10 });
expect(audio_values.dims).toEqual([1, 1, 1920]);
expect(audio_values.mean().item()).toBeCloseTo(0.16644205152988434, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"batch_size>1",
async () => {
const inputs = tokenizer(texts, { padding: true });
const audio_values = await model.generate({ ...inputs, max_length: 10 });
expect(audio_values.dims).toEqual([2, 1, 1920]);
expect(audio_values.mean().item()).toBeCloseTo(0.16644206643104553, 5);
},
MAX_TEST_EXECUTION_TIME,
);
afterAll(async () => {
await model?.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/models/musicgen/test_modeling_musicgen.js/0 | {
"file_path": "transformers.js/tests/models/musicgen/test_modeling_musicgen.js",
"repo_id": "transformers.js",
"token_count": 1011
} |
import { Qwen2VLProcessor, Qwen2VLForConditionalGeneration, 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: "text", text: "Hello" }],
},
];
// Example adapted from https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct
const CONVERSATION_WITH_IMAGE = [
{
role: "user",
content: [{ type: "image" }, { type: "text", text: "Describe this image." }],
},
];
// Empty white image
const dims = [224, 224, 3];
const image = new RawImage(new Uint8ClampedArray(dims[0] * dims[1] * dims[2]).fill(255), ...dims);
describe("Qwen2VLForConditionalGeneration", () => {
const model_id = "hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration";
/** @type {Qwen2VLForConditionalGeneration} */
let model;
/** @type {Qwen2VLProcessor} */
let processor;
beforeAll(async () => {
model = await Qwen2VLForConditionalGeneration.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
processor = await Qwen2VLProcessor.from_pretrained(model_id);
}, MAX_MODEL_LOAD_TIME);
it(
"forward",
async () => {
const text = processor.apply_chat_template(CONVERSATION_WITH_IMAGE, {
add_generation_prompt: true,
});
const inputs = await processor(text, image);
const { logits } = await model(inputs);
expect(logits.dims).toEqual([1, 89, 152064]);
expect(logits.mean().item()).toBeCloseTo(-0.0011299321195110679, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"text-only (batch_size=1)",
async () => {
const text = processor.apply_chat_template(CONVERSATION, {
add_generation_prompt: true,
});
const inputs = await processor(text);
const generate_ids = await model.generate({
...inputs,
max_new_tokens: 10,
});
const new_tokens = generate_ids.slice(null, [inputs.input_ids.dims.at(-1), null]);
expect(new_tokens.tolist()).toEqual([[24284n, 63986n, 108860n, 84530n, 8889n, 23262n, 128276n, 64948n, 136757n, 138348n]]);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"text + image (batch_size=1)",
async () => {
const text = processor.apply_chat_template(CONVERSATION_WITH_IMAGE, {
add_generation_prompt: true,
});
const inputs = await processor(text, image);
const generate_ids = await model.generate({
...inputs,
max_new_tokens: 10,
});
const new_tokens = generate_ids.slice(null, [inputs.input_ids.dims.at(-1), null]);
expect(new_tokens.tolist()).toEqual([[24284n, 35302n, 60575n, 38679n, 113390n, 115118n, 137596n, 38241n, 96726n, 142301n]]);
},
MAX_TEST_EXECUTION_TIME,
);
afterAll(async () => {
await model?.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/models/qwen2_vl/test_modeling_qwen2_vl.js/0 | {
"file_path": "transformers.js/tests/models/qwen2_vl/test_modeling_qwen2_vl.js",
"repo_id": "transformers.js",
"token_count": 1367
} |
import { AutoFeatureExtractor, WeSpeakerFeatureExtractor } from "../../../src/transformers.js";
import { MAX_FEATURE_EXTRACTOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
export default () => {
// WeSpeakerFeatureExtractor
describe("WeSpeakerFeatureExtractor", () => {
const model_id = "onnx-community/wespeaker-voxceleb-resnet34-LM";
/** @type {WeSpeakerFeatureExtractor} */
let feature_extractor;
beforeAll(async () => {
feature_extractor = await AutoFeatureExtractor.from_pretrained(model_id);
}, MAX_FEATURE_EXTRACTOR_LOAD_TIME);
it(
"default",
async () => {
const audio = new Float32Array(16000).map((_, i) => Math.sin(i / 100));
const { input_features } = await feature_extractor(audio);
const { dims, data } = input_features;
expect(dims).toEqual([1, 98, 80]);
expect(input_features.mean().item()).toBeCloseTo(5.461731689138105e-8);
expect(data[0]).toBeCloseTo(-0.19300270080566406);
expect(data[1]).toBeCloseTo(-0.05825042724609375);
expect(data[78]).toBeCloseTo(0.2683420181274414);
expect(data[79]).toBeCloseTo(0.26250171661376953);
expect(data[80]).toBeCloseTo(0.19062232971191406);
expect(data.at(-2)).toBeCloseTo(-0.43694400787353516);
expect(data.at(-1)).toBeCloseTo(-0.4266204833984375);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"pad to `min_num_frames`",
async () => {
const audio = new Float32Array(3).map((_, i) => Math.sin(i / 100));
const { input_features } = await feature_extractor(audio);
const { dims, data } = input_features;
expect(dims).toEqual([1, 9, 80]);
expect(input_features.mean().item()).toBeCloseTo(-0.0000010093053181966146);
expect(data[0]).toBeCloseTo(20.761859893798828);
expect(data[1]).toBeCloseTo(21.02924346923828);
expect(data[78]).toBeCloseTo(19.083993911743164);
expect(data[79]).toBeCloseTo(18.003454208374023);
expect(data[80]).toBeCloseTo(-2.595233917236328);
expect(data.at(-2)).toBeCloseTo(-2.385499954223633);
expect(data.at(-1)).toBeCloseTo(-2.2504329681396484);
},
MAX_TEST_EXECUTION_TIME,
);
});
};
| transformers.js/tests/models/wespeaker_resnet/test_feature_extraction_wespeaker_resnet.js/0 | {
"file_path": "transformers.js/tests/models/wespeaker_resnet/test_feature_extraction_wespeaker_resnet.js",
"repo_id": "transformers.js",
"token_count": 1014
} |
import { pipeline, ImageToImagePipeline } 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-to-image";
export default () => {
describe("Image to Image", () => {
const model_id = "hf-internal-testing/tiny-random-Swin2SRForImageSuperResolution";
/** @type {ImageToImagePipeline} */
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 ImageToImagePipeline", () => {
expect(pipe).toBeInstanceOf(ImageToImagePipeline);
});
describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe(images[0]);
expect(output.size).toEqual([64, 64]);
expect(output.channels).toEqual(3);
expect(output.data.reduce((a, b) => a + b, 0) / output.data.length).toBeCloseTo(110.107421875, 3);
},
MAX_TEST_EXECUTION_TIME,
);
});
describe("batch_size>1", () => {
it(
"default",
async () => {
const output = await pipe(images);
expect(output[0].size).toEqual([64, 64]);
expect(output[0].channels).toEqual(3);
expect(output[0].data.reduce((a, b) => a + b, 0) / output[0].data.length).toBeCloseTo(110.107421875, 3);
expect(output[1].size).toEqual([64, 64]);
expect(output[1].channels).toEqual(3);
expect(output[1].data.reduce((a, b) => a + b, 0) / output[1].data.length).toBeCloseTo(110.60196940104167, 3);
},
MAX_TEST_EXECUTION_TIME,
);
});
afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/pipelines/test_pipelines_image_to_image.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_image_to_image.js",
"repo_id": "transformers.js",
"token_count": 898
} |
import fs from "fs";
import path from "path";
import { fileURLToPath } from "url";
export async function loadAudio(url) {
// NOTE: Since the Web Audio API is not available in Node.js, we will need to use the `wavefile` library to obtain the raw audio data.
// For more information, see: https://huggingface.co/docs/transformers.js/guides/node-audio-processing
let wavefile = (await import("wavefile")).default;
// Load audio data
let buffer = Buffer.from(await fetch(url).then((x) => x.arrayBuffer()));
// Read .wav file and convert it to required format
let wav = new wavefile.WaveFile(buffer);
wav.toBitDepth("32f"); // Pipeline expects input as a Float32Array
wav.toSampleRate(16000); // Whisper expects audio with a sampling rate of 16000
let audioData = wav.getSamples();
if (Array.isArray(audioData)) {
if (audioData.length > 1) {
const SCALING_FACTOR = Math.sqrt(2);
// Merge channels (into first channel to save memory)
for (let i = 0; i < audioData[0].length; ++i) {
audioData[0][i] = (SCALING_FACTOR * (audioData[0][i] + audioData[1][i])) / 2;
}
}
// Select first channel
audioData = audioData[0];
}
return audioData;
}
/**
* Deep equality test (for arrays and objects) with tolerance for floating point numbers
* @param {any} val1 The first item
* @param {any} val2 The second item
* @param {number} tol Tolerance for floating point numbers
*/
export function compare(val1, val2, tol = 0.1) {
if (val1 !== null && val2 !== null && typeof val1 === "object" && typeof val2 === "object") {
// Both are non-null objects
if (Array.isArray(val1) && Array.isArray(val2)) {
expect(val1).toHaveLength(val2.length);
for (let i = 0; i < val1.length; ++i) {
compare(val1[i], val2[i], tol);
}
} else {
expect(Object.keys(val1)).toHaveLength(Object.keys(val2).length);
for (let key in val1) {
compare(val1[key], val2[key], tol);
}
}
} else {
// At least one of them is not an object
// First check that both have the same type
expect(typeof val1).toEqual(typeof val2);
if (typeof val1 === "number" && (!Number.isInteger(val1) || !Number.isInteger(val2))) {
// If both are numbers and at least one of them is not an integer
expect(val1).toBeCloseTo(val2, -Math.log10(tol));
} else {
// Perform equality test
expect(val1).toEqual(val2);
}
}
}
const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);
const models_dir = path.join(__dirname, "models");
const pipelines_dir = path.join(__dirname, "pipelines");
/**
* Helper function to collect all unit tests, which can be found in files
* of the form: `tests/models/<model_type>/test_<filename>_<model_type>.js`.
* @param {string} filename
* @returns {Promise<[string, Function][]>}
*/
export async function collect_tests(filename) {
const model_types = fs.readdirSync(models_dir);
const all_tests = [];
for (const model_type of model_types) {
const dir = path.join(models_dir, model_type);
if (!fs.existsSync(dir) || !fs.statSync(dir).isDirectory()) {
continue;
}
const file = path.join(dir, `test_${filename}_${model_type}.js`);
if (!fs.existsSync(file)) {
continue;
}
const items = await import(file);
all_tests.push([model_type, items]);
}
return all_tests;
}
/**
* Helper function to collect and execute all unit tests, which can be found in files
* of the form: `tests/models/<model_type>/test_<filename>_<model_type>.js`.
* @param {string} title The title of the test
* @param {string} filename The name of the test
*/
export async function collect_and_execute_tests(title, filename) {
// 1. Collect all tests
const all_tests = await collect_tests(filename);
// 2. Execute tests
describe(title, () => all_tests.forEach(([name, test]) => describe(name, test.default)));
}
/**
* Helper function to collect all pipeline tests, which can be found in files
* of the form: `tests/pipelines/test_pipeline_<pipeline_id>.js`.
*/
export async function collect_and_execute_pipeline_tests(title) {
// 1. Collect all tests
const all_tests = [];
const pipeline_types = fs.readdirSync(pipelines_dir);
for (const filename of pipeline_types) {
const file = path.join(pipelines_dir, filename);
const items = await import(file);
all_tests.push(items);
}
// 2. Execute tests
describe(title, () => all_tests.forEach((test) => test.default()));
}
| transformers.js/tests/test_utils.js/0 | {
"file_path": "transformers.js/tests/test_utils.js",
"repo_id": "transformers.js",
"token_count": 1612
} |
import re
import argparse
def parse_pytest_output(file_path):
skipped_tests = {}
skipped_count = 0
with open(file_path, 'r') as file:
for line in file:
match = re.match(r'^SKIPPED \[(\d+)\] (tests/.*): (.*)$', line)
if match:
skipped_count += 1
test_file, test_line, reason = match.groups()
skipped_tests[reason] = skipped_tests.get(reason, []) + [(test_file, test_line)]
for k,v in sorted(skipped_tests.items(), key=lambda x:len(x[1])):
print(f"{len(v):4} skipped because: {k}")
print("Number of skipped tests:", skipped_count)
def parse_pytest_failure_output(file_path):
failed_tests = {}
failed_count = 0
with open(file_path, 'r') as file:
for line in file:
match = re.match(r'^FAILED (tests/.*) - (.*): (.*)$', line)
if match:
failed_count += 1
_, error, reason = match.groups()
failed_tests[reason] = failed_tests.get(reason, []) + [error]
for k,v in sorted(failed_tests.items(), key=lambda x:len(x[1])):
print(f"{len(v):4} failed because `{v[0]}` -> {k}")
print("Number of failed tests:", failed_count)
if failed_count>0:
exit(1)
def parse_pytest_errors_output(file_path):
print(file_path)
error_tests = {}
error_count = 0
with open(file_path, 'r') as file:
for line in file:
match = re.match(r'^ERROR (tests/.*) - (.*): (.*)$', line)
if match:
error_count += 1
_, test_error, reason = match.groups()
error_tests[reason] = error_tests.get(reason, []) + [test_error]
for k,v in sorted(error_tests.items(), key=lambda x:len(x[1])):
print(f"{len(v):4} errored out because of `{v[0]}` -> {k}")
print("Number of errors:", error_count)
if error_count>0:
exit(1)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--file", help="file to parse")
parser.add_argument("--skip", action="store_true", help="show skipped reasons")
parser.add_argument("--fail", action="store_true", help="show failed tests")
parser.add_argument("--errors", action="store_true", help="show failed tests")
args = parser.parse_args()
if args.skip:
parse_pytest_output(args.file)
if args.fail:
parse_pytest_failure_output(args.file)
if args.errors:
parse_pytest_errors_output(args.file)
if __name__ == "__main__":
main()
| transformers/.circleci/parse_test_outputs.py/0 | {
"file_path": "transformers/.circleci/parse_test_outputs.py",
"repo_id": "transformers",
"token_count": 1145
} |
import argparse
import subprocess
def main(config_dir, config_name, args):
subprocess.run(["optimum-benchmark", "--config-dir", f"{config_dir}", "--config-name", f"{config_name}"] + ["hydra/job_logging=disabled", "hydra/hydra_logging=disabled"] + args)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config-dir", type=str, required=True, help="The path to the config directory.")
parser.add_argument("--config-name", type=str, required=True, help="The config name.")
args, unknown = parser.parse_known_args()
main(args.config_dir, args.config_name, unknown)
| transformers/benchmark/optimum_benchmark_wrapper.py/0 | {
"file_path": "transformers/benchmark/optimum_benchmark_wrapper.py",
"repo_id": "transformers",
"token_count": 216
} |
<!---
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.
-->
# Generating the documentation
To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
you can install them with the following command, at the root of the code repository:
```bash
pip install -e ".[docs]"
```
Then you need to install our special tool that builds the documentation:
```bash
pip install git+https://github.com/huggingface/doc-builder
```
---
**NOTE**
You only need to generate the documentation to inspect it locally (if you're planning changes and want to
check how they look before committing for instance). You don't have to commit the built documentation.
---
## Building the documentation
Once you have setup the `doc-builder` and additional packages, you can generate the documentation by
typing the following command:
```bash
doc-builder build transformers docs/source/en/ --build_dir ~/tmp/test-build
```
You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate
the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
Markdown editor.
## Previewing the documentation
To preview the docs, first install the `watchdog` module with:
```bash
pip install watchdog
```
Then run the following command:
```bash
doc-builder preview {package_name} {path_to_docs}
```
For example:
```bash
doc-builder preview transformers docs/source/en/
```
The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
---
**NOTE**
The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
---
## Adding a new element to the navigation bar
Accepted files are Markdown (.md).
Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml) file.
## Renaming section headers and moving sections
It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
```
Sections that were moved:
[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
```
and of course, if you moved it to another file, then:
```
Sections that were moved:
[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
```
Use the relative style to link to the new file so that the versioned docs continue to work.
For an example of a rich moved section set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).
## Writing Documentation - Specification
The `huggingface/transformers` documentation follows the
[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
although we can write them directly in Markdown.
### Adding a new tutorial
Adding a new tutorial or section is done in two steps:
- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
- Link that file in `./source/_toctree.yml` on the correct toc-tree.
Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or
four.
### Translating
When translating, refer to the guide at [./TRANSLATING.md](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md).
### Adding a new model
When adding a new model:
- Create a file `xxx.md` or under `./source/model_doc` (don't hesitate to copy an existing file as template).
- Link that file in `./source/_toctree.yml`.
- Write a short overview of the model:
- Overview with paper & authors
- Paper abstract
- Tips and tricks and how to use it best
- Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and
every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow.
The order is generally:
- Configuration
- Tokenizer
- PyTorch base model
- PyTorch head models
- TensorFlow base model
- TensorFlow head models
- Flax base model
- Flax head models
These classes should be added using our Markdown syntax. Usually as follows:
```
## XXXConfig
[[autodoc]] XXXConfig
```
This will include every public method of the configuration that is documented. If for some reason you wish for a method
not to be displayed in the documentation, you can do so by specifying which methods should be in the docs:
```
## XXXTokenizer
[[autodoc]] XXXTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
```
If you just want to add a method that is not documented (for instance magic methods like `__call__` are not documented
by default) you can put the list of methods to add in a list that contains `all`:
```
## XXXTokenizer
[[autodoc]] XXXTokenizer
- all
- __call__
```
### Writing source documentation
Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
and objects like True, None, or any strings should usually be put in `code`.
When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or
function to be in the main package.
If you want to create a link to some internal class or function, you need to
provide its path. For instance: \[\`utils.ModelOutput\`\]. This will be converted into a link with
`utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are
linking to in the description, add a ~: \[\`~utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description.
The same works for methods so you can either use \[\`XXXClass.method\`\] or \[\`~XXXClass.method\`\].
#### Defining arguments in a method
Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
description:
```
Args:
n_layers (`int`): The number of layers of the model.
```
If the description is too long to fit in one line, another indentation is necessary before writing the description
after the argument.
Here's an example showcasing everything so far:
```
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and
[`~PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
```
For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
following signature:
```
def my_function(x: str = None, a: float = 1):
```
then its documentation should look like this:
```
Args:
x (`str`, *optional*):
This argument controls ...
a (`float`, *optional*, defaults to 1):
This argument is used to ...
```
Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even
if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
however, write as many lines as you want in the indented description (see the example above with `input_ids`).
#### Writing a multi-line code block
Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
````
```
# first line of code
# second line
# etc
```
````
We follow the [doctest](https://docs.python.org/3/library/doctest.html) syntax for the examples to automatically test
the results to stay consistent with the library.
#### Writing a return block
The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.
The first line should be the type of the return, followed by a line return. No need to indent further for the elements
building the return.
Here's an example of a single value return:
```python
Returns:
`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
```
Here's an example of a tuple return, comprising several objects:
```python
Returns:
`tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
- ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
- **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
```
#### Adding an image
Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.
## Styling the docstring
We have an automatic script running with the `make style` comment that will make sure that:
- the docstrings fully take advantage of the line width
- all code examples are formatted using black, like the code of the Transformers library
This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's
recommended to commit your changes before running `make style`, so you can revert the changes done by that script
easily.
# Testing documentation examples
Good documentation often comes with an example of how a specific function or class should be used.
Each model class should contain at least one example showcasing
how to use this model class in inference. *E.g.* the class [Wav2Vec2ForCTC](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC)
includes an example of how to transcribe speech to text in the
[docstring of its forward function](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC.forward).
## Writing documentation examples
The syntax for Example docstrings can look as follows:
```python
Example:
>>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_ids = torch.argmax(logits, dim=-1)
>>> # transcribe speech
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription[0]
'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'
```
The docstring should give a minimal, clear example of how the respective model
is to be used in inference and also include the expected (ideally sensible)
output.
Often, readers will try out the example before even going through the function
or class definitions. Therefore, it is of utmost importance that the example
works as expected.
## Docstring testing
To do so each example should be included in the doctests.
We use pytests' [doctest integration](https://docs.pytest.org/doctest.html) to verify that all of our examples run correctly.
For Transformers, the doctests are run on a daily basis via GitHub Actions as can be
seen [here](https://github.com/huggingface/transformers/actions/workflows/doctests.yml).
### For Python files
Run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance:
```bash
pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py -sv --doctest-continue-on-failure
```
If you want to isolate a specific docstring, just add `::` after the file name then type the whole path of the function/class/method whose docstring you want to test. For instance, here is how to just test the forward method of `Wav2Vec2ForCTC`:
```bash
pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py::transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward -sv --doctest-continue-on-failure
```
### For Markdown files
You can test locally a given file with this command (here testing the quicktour):
```bash
pytest --doctest-modules docs/source/quicktour.md -sv --doctest-continue-on-failure --doctest-glob="*.md"
```
### Writing doctests
Here are a few tips to help you debug the doctests and make them pass:
- The outputs of the code need to match the expected output **exactly**, so make sure you have the same outputs. In particular doctest will see a difference between single quotes and double quotes, or a missing parenthesis. The only exceptions to that rule are:
* whitespace: one give whitespace (space, tabulation, new line) is equivalent to any number of whitespace, so you can add new lines where there are spaces to make your output more readable.
* numerical values: you should never put more than 4 or 5 digits to expected results as different setups or library versions might get you slightly different results. `doctest` is configured to ignore any difference lower than the precision to which you wrote (so 1e-4 if you write 4 digits).
- Don't leave a block of code that is very long to execute. If you can't make it fast, you can either not use the doctest syntax on it (so that it's ignored), or if you want to use the doctest syntax to show the results, you can add a comment `# doctest: +SKIP` at the end of the lines of code too long to execute
- Each line of code that produces a result needs to have that result written below. You can ignore an output if you don't want to show it in your code example by adding a comment ` # doctest: +IGNORE_RESULT` at the end of the line of code producing it.
| transformers/docs/README.md/0 | {
"file_path": "transformers/docs/README.md",
"repo_id": "transformers",
"token_count": 4825
} |
# GGUF وتفاعلها مع المحولات
تُستخدم صيغة ملف GGUF لتخزين النماذج للاستدلال باستخدام [GGML](https://github.com/ggerganov/ggml) والمكتبات الأخرى التي تعتمد عليه، مثل [llama.cpp](https://github.com/ggerganov/llama.cpp) أو [whisper.cpp](https://github.com/ggerganov/whisper.cpp) الشهيرة جدًا.
إنها صيغة ملف [مدعومة من قبل Hugging Face Hub](https://huggingface.co/docs/hub/en/gguf) مع ميزات تسمح بالفحص السريع للموترات والبيانات الوصفية داخل الملف.
تم تصميم تنسيق الملف هذا كـ "تنسيق ملف واحد" حيث يحتوي ملف واحد عادةً على كل من سمات التكوين ومفردات المجزىء اللغوي والخصائص الأخرى، بالإضافة إلى جميع الموترات التي سيتم تحميلها في النموذج. تأتي هذه الملفات بتنسيقات مختلفة وفقًا لنوع التكميم في الملف. نلقي نظرة موجزة على بعضها [هنا](https://huggingface.co/docs/hub/en/gguf#quantization-types).
## الدعم داخل المحولات
أضفنا القدرة على تحميل ملفات `gguf` داخل `المحولات` لتوفير قدرات تدريب/ضبط إضافية لنماذج gguf، قبل إعادة تحويل تلك النماذج إلى `gguf` لاستخدامها داخل نظام `ggml`. عند تحميل نموذج، نقوم أولاً بإلغاء تكميمه إلى fp32، قبل تحميل الأوزان لاستخدامها في PyTorch.
> [!NOTE]
> لا يزال الدعم تجريبيًا للغاية ونرحب بالمساهمات من أجل ترسيخه عبر أنواع التكميم وبنى النماذج.
فيما يلي، بنيات النماذج وأنواع التكميم المدعومة:
### أنواع التكميم المدعومة
تُحدد أنواع التكميم المدعومة مبدئيًا وفقًا لملفات التكميم الشائعة التي تمت مشاركتها على Hub.
- F32
- F16
- BF16
- Q4_0
- Q4_1
- Q5_0
- Q5_1
- Q8_0
- Q2_K
- Q3_K
- Q4_K
- Q5_K
- Q6_K
- IQ1_S
- IQ1_M
- IQ2_XXS
- IQ2_XS
- IQ2_S
- IQ3_XXS
- IQ3_S
- IQ4_XS
- IQ4_NL
> [!NOTE]
> لدعم إلغاء تكميم gguf، يلزم تثبيت `gguf>=0.10.0`.
### بنيات النماذج المدعومة
في الوقت الحالي، بنيات النماذج المدعومة هي البنيات التي كانت شائعة جدًا على Hub، وهي:
- LLaMa
- Mistral
- Qwen2
- Qwen2Moe
- Phi3
- Bloom
- Falcon
- StableLM
- GPT2
- Starcoder2
- T5
## مثال الاستخدام
لتحميل ملفات `gguf` في `transformers`، يجب تحديد معامل `gguf_file` فى دالة `from_pretrained` لكل من المُجزّئ اللغوية والنموذج. فيما يلي كيفية تحميل المُجزّئ اللغوي ونموذج، يمكن تحميلهما من نفس الملف:
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
filename = "tinyllama-1.1b-chat-v1.0.Q6_K.gguf"
tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename)
```
الآن لديك إمكانية الوصول إلى النسخة الكامل غير المكممة للنموذج في بيئة PyTorch، حيث يمكنك دمجه مع مجموعة كبيرة من الأدوات الأخرى.
لإعادة التحويل إلى ملف `gguf`، نوصي باستخدام ملف [`convert-hf-to-gguf.py`](https://github.com/ggerganov/llama.cpp/blob/master/convert-hf-to-gguf.py) من llama.cpp.
فيما يلي كيفية إكمال البرنامج النصي أعلاه لحفظ النموذج وإعادة تصديره مرة أخرى إلى `gguf`:
```py
tokenizer.save_pretrained('directory')
model.save_pretrained('directory')
!python ${path_to_llama_cpp}/convert-hf-to-gguf.py ${directory}
```
| transformers/docs/source/ar/gguf.md/0 | {
"file_path": "transformers/docs/source/ar/gguf.md",
"repo_id": "transformers",
"token_count": 2249
} |
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# الترجمة(Translation)
[[open-in-colab]]
<Youtube id="1JvfrvZgi6c"/>
الترجمة هي عملية تحويل سلسلة نصية من لغة إلى أخرى. وهي إحدى المهام التي يمكن صياغتها كمسألة تسلسل إلى تسلسل، وهو إطار عمل قوي لإنتاج مخرجات من مدخلات، مثل الترجمة أو التلخيص. تُستخدم أنظمة الترجمة عادةً للترجمة بين نصوص لغات مختلفة، ويمكن استخدامها أيضًا لترجمة الكلام أو لمهام تجمع بين النصوص والكلام، مثل تحويل النص إلى كلام أو تحويل الكلام إلى نص.
سيوضح لك هذا الدليل كيفية:
1. ضبط دقيق لنموذج [T5](https://huggingface.co/google-t5/t5-small) على المجموعة الفرعية الإنجليزية-الفرنسية من مجموعة بيانات [OPUS Books](https://huggingface.co/datasets/opus_books) لترجمة النص الإنجليزي إلى الفرنسية.
2. استخدام النموذج المضبوط بدقة للاستدلال.
<Tip>
لمشاهدة جميع البنى والنسخ المتوافقة مع هذه المهمة، نوصي بالتحقق من [صفحة المهمة](https://huggingface.co/tasks/translation).
</Tip>
قبل البدء، تأكد من تثبيت جميع المكتبات الضرورية:
```bash
pip install transformers datasets evaluate sacrebleu
```
نشجعك على تسجيل الدخول إلى حساب Hugging Face الخاص بك حتى تتمكن من تحميل نموذجك ومشاركته مع المجتمع. عند الطلب، أدخل الرمز المميز الخاص بك لتسجيل الدخول:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## تحميل مجموعة بيانات OPUS Books
ابدأ بتحميل المجموعة الفرعية الإنجليزية-الفرنسية من مجموعة بيانات [OPUS Books](https://huggingface.co/datasets/opus_books) من مكتبة 🤗 Datasets:
```py
>>> from datasets import load_dataset
>>> books = load_dataset("opus_books", "en-fr")
```
قسّم مجموعة البيانات إلى مجموعة تدريب ومجموعة اختبار باستخدام طريقة [`~datasets.Dataset.train_test_split`]:
```py
>>> books = books["train"].train_test_split(test_size=0.2)
```
ثم ألقِ نظرة على مثال:
```py
>>> books["train"][0]
{'id': '90560',
'translation': {'en': 'But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.',
'fr': 'Mais ce plateau élevé ne mesurait que quelques toises, et bientôt nous fûmes rentrés dans notre élément.'}}
```
`translation`: ترجمة إنجليزية وفرنسية للنص.
## المعالجة المسبقة(Preprocess)
<Youtube id="XAR8jnZZuUs"/>
الخطوة التالية هي تحميل مُجزئ T5 لمعالجة أزواج اللغة الإنجليزية-الفرنسية:
```py
>>> from transformers import AutoTokenizer
>>> checkpoint = "google-t5/t5-small"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
```
يجب أن تقوم دالة المعالجة المسبقة التي تُريد إنشاءها بما يلي:
1. إضافة بادئة إلى المُدخل بمُوجه حتى يعرف T5 أن هذه مهمة ترجمة. تتطلب بعض النماذج القادرة على أداء مهام متعددة توجيهًا لمهام مُحددة.
2. تعيين اللغة الهدف (الفرنسية) في معامل `text_target` لضمان معالجة المُجزئ للنص بشكل صحيح. إذا لم تُعيّن `text_target`، فسيُعالج المُجزئ النص على أنه إنجليزي.
3. اقتطاع التسلسلات بحيث لا يزيد طولها عن الحد الأقصى الذي يحدده معامل `max_length`.
```py
>>> source_lang = "en"
>>> target_lang = "fr"
>>> prefix = "translate English to French: "
>>> def preprocess_function(examples):
... inputs = [prefix + example[source_lang] for example in examples["translation"]]
... targets = [example[target_lang] for example in examples["translation"]]
... model_inputs = tokenizer(inputs, text_target=targets, max_length=128, truncation=True)
... return model_inputs
```
لتطبيق دالة المعالجة المسبقة على مجموعة البيانات بأكملها، استخدم طريقة [`~datasets.Dataset.map`] من 🤗 Datasets. يمكنك تسريع دالة `map` عن طريق تعيين `batched=True` لمعالجة عناصر متعددة من مجموعة البيانات في وقت واحد:
```py
>>> tokenized_books = books.map(preprocess_function, batched=True)
```
الآن أنشئ دفعة من الأمثلة باستخدام [`DataCollatorForSeq2Seq`]. من الأكثر كفاءة *الحشو الديناميكي* للجمل إلى أطول طول في دفعة أثناء التجميع، بدلاً من حشو مجموعة البيانات بأكملها إلى الحد الأقصى للطول.
<frameworkcontent>
<pt>
```py
>>> from transformers import DataCollatorForSeq2Seq
>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)
```
</pt>
<tf>
```py
>>> from transformers import DataCollatorForSeq2Seq
>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint, return_tensors="tf")
```
</tf>
</frameworkcontent>
## التقييم (Evaluate)
غالباً ما يكون تضمين مقياس أثناء التدريب مفيداً لتقييم أداء نموذجك. يمكنك تحميل طريقة تقييم بسرعة باستخدام مكتبة 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index). لهذه المهمة، حمّل مقياس [SacreBLEU](https://huggingface.co/spaces/evaluate-metric/sacrebleu) (راجع [الجولة السريعة](https://huggingface.co/docs/evaluate/a_quick_tour) لـ 🤗 Evaluate لمعرفة المزيد حول كيفية تحميل وحساب مقياس):
```py
>>> import evaluate
>>> metric = evaluate.load("sacrebleu")
```
ثم أنشئ دالة تُمرر تنبؤاتك وتسمياتك إلى [`~evaluate.EvaluationModule.compute`] لحساب درجة SacreBLEU:
```py
>>> import numpy as np
>>> def postprocess_text(preds, labels):
... preds = [pred.strip() for pred in preds]
... labels = [[label.strip()] for label in labels]
... return preds, labels
>>> def compute_metrics(eval_preds):
... preds, labels = eval_preds
... if isinstance(preds, tuple):
... preds = preds[0]
... decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
... labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
... decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
... decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
... result = metric.compute(predictions=decoded_preds, references=decoded_labels)
... result = {"bleu": result["score"]}
... prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
... result["gen_len"] = np.mean(prediction_lens)
... result = {k: round(v, 4) for k, v in result.items()}
... return result
```
دالة `compute_metrics` الخاصة بك جاهزة الآن، وسوف تعود إليها عند إعداد التدريب.
## التدريب (Train)
<frameworkcontent>
<pt>
<Tip>
إذا لم تكن معتادًا على ضبط دقيق نموذج باستخدام [`Trainer`], فألقِ نظرة على البرنامج التعليمي الأساسي [هنا](../training#train-with-pytorch-trainer)!
</Tip>
أنت جاهز لبدء تدريب نموذجك الآن! حمّل T5 باستخدام [`AutoModelForSeq2SeqLM`]:
```py
>>> from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
>>> model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
```
في هذه المرحلة، تبقى ثلاث خطوات فقط:
1. حدد مُعاملات للتدريب في [`Seq2SeqTrainingArguments`]. المُعامل الوحيدة المطلوبة هي `output_dir` التي تحدد مكان حفظ النموذج الخاص بك. ستقوم بدفع هذا النموذج إلى Hub عن طريق تعيين `push_to_hub=True` (يجب عليك تسجيل الدخول إلى Hugging Face لتحميل نموذجك). في نهاية كل حقبة، سيقوم [`Trainer`] بتقييم مقياس SacreBLEU وحفظ نقطة تدقيق التدريب.
2. مرر مُعاملات التدريب إلى [`Seq2SeqTrainer`] جنبًا إلى جنب مع النموذج ومجموعة البيانات والمعالج اللغوي وجامع البيانات ووظيفة `compute_metrics`.
3. نفّذ [`~Trainer.train`] لضبط نموذجك.
```py
>>> training_args = Seq2SeqTrainingArguments(
... output_dir="my_awesome_opus_books_model",
... eval_strategy="epoch",
... learning_rate=2e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,
... weight_decay=0.01,
... save_total_limit=3,
... num_train_epochs=2,
... predict_with_generate=True,
... fp16=True, #change to bf16=True for XPU
... push_to_hub=True,
... )
>>> trainer = Seq2SeqTrainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_books["train"],
... eval_dataset=tokenized_books["test"],
... processing_class=tokenizer,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
بمجرد اكتمال التدريب، شارك نموذجك مع Hub باستخدام طريقة [`~transformers.Trainer.push_to_hub`] حتى يتمكن الجميع من استخدام نموذجك:
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
إذا لم تكن معتادًا على ضبط نموذج باستخدام Keras، فألق نظرة على البرنامج التعليمي الأساسي [هنا](../training#train-a-tensorflow-model-with-keras)!
</Tip>
لضبط نموذج في TensorFlow، ابدأ بإعداد دالة مُحسِّن وجدول معدل تعلم وبعض المعلمات الفائقة للتدريب:
```py
>>> from transformers import AdamWeightDecay
>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
```
ثم يمكنك تحميل T5 باستخدام [`TFAutoModelForSeq2SeqLM`]:
```py
>>> from transformers import TFAutoModelForSeq2SeqLM
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)
```
حوّل مجموعات البيانات الخاصة بك إلى تنسيق `tf.data.Dataset` باستخدام [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> tf_train_set = model.prepare_tf_dataset(
... tokenized_books["train"],
... shuffle=True,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> tf_test_set = model.prepare_tf_dataset(
... tokenized_books["test"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
قم بتكوين النموذج للتدريب باستخدام [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). لاحظ أن جميع نماذج Transformers تحتوي على دالة خسارة ذات صلة بالمهمة بشكل افتراضي، لذلك لا تحتاج إلى تحديد واحدة إلا إذا كنت ترغب في ذلك:
```py
>>> import tensorflow as tf
>>> model.compile(optimizer=optimizer) # No loss argument!
```
آخر شيئين يجب إعدادهما قبل بدء التدريب هما حساب مقياس SacreBLEU من التوقعات، وتوفير طريقة لدفع نموذجك إلى Hub. يتم كلاهما باستخدام [استدعاءات Keras](../main_classes/keras_callbacks).
مرر دالة `compute_metrics` الخاصة بك إلى [`~transformers.KerasMetricCallback`]:
```py
>>> from transformers.keras_callbacks import KerasMetricCallback
>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_test_set)
```
حدد مكان دفع نموذجك ومعالجك اللغوي في [`~transformers.PushToHubCallback`]:
```py
>>> from transformers.keras_callbacks import PushToHubCallback
>>> push_to_hub_callback = PushToHubCallback(
... output_dir="my_awesome_opus_books_model",
... tokenizer=tokenizer,
... )
```
ثم اجمع استدعاءاتك معًا:
```py
>>> callbacks = [metric_callback, push_to_hub_callback]
```
أخيرًا، أنت جاهز لبدء تدريب نموذجك! اتصل بـ [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) مع مجموعات بيانات التدريب والتحقق من الصحة وعدد الحقب واستدعاءاتك لضبط النموذج:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=callbacks)
```
بمجرد اكتمال التدريب، يتم تحميل نموذجك تلقائيًا إلى Hub حتى يتمكن الجميع من استخدامه!
</tf>
</frameworkcontent>
<Tip>
للحصول على مثال أكثر تعمقًا لكيفية ضبط نموذج للترجمة، ألق نظرة على [دفتر ملاحظات PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb) المقابل
أو [دفتر ملاحظات TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb).
</Tip>
## الاستدلال (Inference)
رائع، الآن بعد أن قمت بضبط نموذج، يمكنك استخدامه للاستدلال!
أحضر بعض النصوص التي ترغب في ترجمتها إلى لغة أخرى. بالنسبة لـ T5، تحتاج إلى إضافة بادئة إلى مدخلاتك اعتمادًا على المهمة التي تعمل عليها. للترجمة من الإنجليزية إلى الفرنسية، يجب عليك إضافة بادئة إلى مدخلاتك كما هو موضح أدناه:
```py
>>> text = "translate English to French: Legumes share resources with nitrogen-fixing bacteria."
```
أبسط طريقة لتجربة نموذجك المضبوط للاستدلال هي استخدامه في [`pipeline`]. قم بإنشاء مثيل لـ `pipeline` للترجمة باستخدام نموذجك، ومرر النص الخاص بك إليه:
```py
>>> from transformers import pipeline
# تغيير `xx` إلى لغة الإدخال و `yy` إلى لغة المخرجات المطلوبة.
# أمثلة: "en" للغة الإنجليزية، "fr" للغة الفرنسية، "de" للغة الألمانية، "es" للغة الإسبانية، "zh" للغة الصينية، إلخ؛ translation_en_to_fr تترجم من الإنجليزية إلى الفرنسية
# يمكنك عرض جميع قوائم اللغات هنا - https://huggingface.co/languages
>>> translator = pipeline("translation_xx_to_yy", model="username/my_awesome_opus_books_model")
>>> translator(text)
[{'translation_text': 'Legumes partagent des ressources avec des bactéries azotantes.'}]
```
يمكنك أيضًا تكرار نتائج `pipeline` يدويًا إذا أردت:
<frameworkcontent>
<pt>
قم بتحويل النص إلى رموز وإرجاع `input_ids` كموترات PyTorch:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("username/my_awesome_opus_books_model")
>>> inputs = tokenizer(text, return_tensors="pt").input_ids
```
استخدم الدالة [`~generation.GenerationMixin.generate`] لإنشاء الترجمة. لمزيد من التفاصيل حول استراتيجيات توليد النصوص المختلفة والمعلمات للتحكم في التوليد، تحقق من واجهة برمجة تطبيقات [توليد النصوص](../main_classes/text_generation).
```py
>>> from transformers import AutoModelForSeq2SeqLM
>>> model = AutoModelForSeq2SeqLM.from_pretrained("username/my_awesome_opus_books_model")
>>> outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
```
فك تشفير معرفات الرموز المولدة مرة أخرى إلى نص:
```py
>>> tokenizer.decode(outputs[0], skip_special_tokens=True)
'Les lignées partagent des ressources avec des bactéries enfixant l'azote.'
```
</pt>
<tf>
قم بتحويل النص إلى رموز وإرجاع `input_ids` كموترات TensorFlow:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("username/my_awesome_opus_books_model")
>>> inputs = tokenizer(text, return_tensors="tf").input_ids
```
استخدم طريقة [`~transformers.generation_tf_utils.TFGenerationMixin.generate`] لإنشاء الترجمة. لمزيد من التفاصيل حول استراتيجيات توليد النصوص المختلفة والمعلمات للتحكم في التوليد، تحقق من واجهة برمجة تطبيقات [توليد النصوص](../main_classes/text_generation).
```py
>>> from transformers import TFAutoModelForSeq2SeqLM
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("username/my_awesome_opus_books_model")
>>> outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
```
فك تشفير معرفات الرموز المولدة مرة أخرى إلى نص:
```py
>>> tokenizer.decode(outputs[0], skip_special_tokens=True)
'Les lugumes partagent les ressources avec des bactéries fixatrices d'azote.'
```
</tf>
</frameworkcontent> | transformers/docs/source/ar/tasks/translation.md/0 | {
"file_path": "transformers/docs/source/ar/tasks/translation.md",
"repo_id": "transformers",
"token_count": 8915
} |
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# Distributed training with 🤗 Accelerate
As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude. At Hugging Face, we created the [🤗 Accelerate](https://huggingface.co/docs/accelerate) library to help users easily train a 🤗 Transformers model on any type of distributed setup, whether it is multiple GPU's on one machine or multiple GPU's across several machines. In this tutorial, learn how to customize your native PyTorch training loop to enable training in a distributed environment.
## Setup
Get started by installing 🤗 Accelerate:
```bash
pip install accelerate
```
Then import and create an [`~accelerate.Accelerator`] object. The [`~accelerate.Accelerator`] will automatically detect your type of distributed setup and initialize all the necessary components for training. You don't need to explicitly place your model on a device.
```py
>>> from accelerate import Accelerator
>>> accelerator = Accelerator()
```
## Prepare to accelerate
The next step is to pass all the relevant training objects to the [`~accelerate.Accelerator.prepare`] method. This includes your training and evaluation DataLoaders, a model and an optimizer:
```py
>>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
... train_dataloader, eval_dataloader, model, optimizer
... )
```
## Backward
The last addition is to replace the typical `loss.backward()` in your training loop with 🤗 Accelerate's [`~accelerate.Accelerator.backward`] method:
```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)
```
As you can see in the following code, you only need to add four additional lines of code to your training loop to enable distributed training!
```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)
```
## Train
Once you've added the relevant lines of code, launch your training in a script or a notebook like Colaboratory.
### Train with a script
If you are running your training from a script, run the following command to create and save a configuration file:
```bash
accelerate config
```
Then launch your training with:
```bash
accelerate launch train.py
```
### Train with a notebook
🤗 Accelerate can also run in a notebook if you're planning on using Colaboratory's TPUs. Wrap all the code responsible for training in a function, and pass it to [`~accelerate.notebook_launcher`]:
```py
>>> from accelerate import notebook_launcher
>>> notebook_launcher(training_function)
```
For more information about 🤗 Accelerate and its rich features, refer to the [documentation](https://huggingface.co/docs/accelerate).
| transformers/docs/source/en/accelerate.md/0 | {
"file_path": "transformers/docs/source/en/accelerate.md",
"repo_id": "transformers",
"token_count": 1516
} |
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# DeepSpeed
[DeepSpeed](https://www.deepspeed.ai/) is a PyTorch optimization library that makes distributed training memory-efficient and fast. At its core is the [Zero Redundancy Optimizer (ZeRO)](https://hf.co/papers/1910.02054) which enables training large models at scale. ZeRO works in several stages:
* ZeRO-1, optimizer state partitioning across GPUs
* ZeRO-2, gradient partitioning across GPUs
* ZeRO-3, parameter partitioning across GPUs
In GPU-limited environments, ZeRO also enables offloading optimizer memory and computation from the GPU to the CPU to fit and train really large models on a single GPU. DeepSpeed is integrated with the Transformers [`Trainer`] class for all ZeRO stages and offloading. All you need to do is provide a config file or you can use a provided template. For inference, Transformers support ZeRO-3 and offloading since it allows loading huge models.
This guide will walk you through how to deploy DeepSpeed training, the features you can enable, how to setup the config files for different ZeRO stages, offloading, inference, and using DeepSpeed without the [`Trainer`].
## Installation
DeepSpeed is available to install from PyPI or Transformers (for more detailed installation options, take a look at the DeepSpeed [installation details](https://www.deepspeed.ai/tutorials/advanced-install/) or the GitHub [README](https://github.com/deepspeedai/DeepSpeed#installation)).
<Tip>
If you're having difficulties installing DeepSpeed, check the [DeepSpeed CUDA installation](../debugging#deepspeed-cuda-installation) guide. While DeepSpeed has a pip installable PyPI package, it is highly recommended to [install it from source](https://www.deepspeed.ai/tutorials/advanced-install/#install-deepspeed-from-source) to best match your hardware and to support certain features, like 1-bit Adam, which aren’t available in the PyPI distribution.
</Tip>
<hfoptions id="install">
<hfoption id="PyPI">
```bash
pip install deepspeed
```
</hfoption>
<hfoption id="Transformers">
```bash
pip install transformers[deepspeed]
```
</hfoption>
</hfoptions>
## Memory requirements
Before you begin, it is a good idea to check whether you have enough GPU and CPU memory to fit your model. DeepSpeed provides a tool for estimating the required CPU/GPU memory. For example, to estimate the memory requirements for the [bigscience/T0_3B](bigscience/T0_3B) model on a single GPU:
```bash
$ python -c 'from transformers import AutoModel; \
from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \
model = AutoModel.from_pretrained("bigscience/T0_3B"); \
estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=1, num_nodes=1)'
[...]
Estimated memory needed for params, optim states and gradients for a:
HW: Setup with 1 node, 1 GPU per node.
SW: Model with 2783M total params, 65M largest layer params.
per CPU | per GPU | Options
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0
62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=1
62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=0
0.37GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=1
15.56GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=0
```
This means you either need a single 80GB GPU without CPU offload or a 8GB GPU and a ~60GB CPU to offload to (these are just the memory requirements for the parameters, optimizer states and gradients, and you'll need a bit more for the CUDA kernels and activations). You should also consider the tradeoff between cost and speed because it'll be cheaper to rent or buy a smaller GPU but it'll take longer to train your model.
If you have enough GPU memory make sure you disable CPU/NVMe offload to make everything faster.
## Select a ZeRO stage
After you've installed DeepSpeed and have a better idea of your memory requirements, the next step is selecting a ZeRO stage to use. In order of fastest and most memory-efficient:
| Fastest | Memory efficient |
|------------------|------------------|
| ZeRO-1 | ZeRO-3 + offload |
| ZeRO-2 | ZeRO-3 |
| ZeRO-2 + offload | ZeRO-2 + offload |
| ZeRO-3 | ZeRO-2 |
| ZeRO-3 + offload | ZeRO-1 |
To find what works best for you, start with the fastest approach and if you run out of memory, try the next stage which is slower but more memory efficient. Feel free to work in whichever direction you prefer (starting with the most memory efficient or fastest) to discover the appropriate balance between speed and memory usage.
A general process you can use is (start with batch size of 1):
1. enable gradient checkpointing
2. try ZeRO-2
3. try ZeRO-2 and offload the optimizer
4. try ZeRO-3
5. try ZeRO-3 and offload parameters to the CPU
6. try ZeRO-3 and offload parameters and the optimizer to the CPU
7. try lowering various default values like a narrower search beam if you're using the [`~GenerationMixin.generate`] method
8. try mixed half-precision (fp16 on older GPU architectures and bf16 on Ampere) over full-precision weights
9. add more hardware if possible or enable Infinity to offload parameters and the optimizer to a NVMe
10. once you're not running out of memory, measure effective throughput and then try to increase the batch size as large as you can to maximize GPU efficiency
11. lastly, try to optimize your training setup by disabling some offload features or use a faster ZeRO stage and increasing/decreasing the batch size to find the best tradeoff between speed and memory usage
## DeepSpeed configuration file
DeepSpeed works with the [`Trainer`] class by way of a config file containing all the parameters for configuring how you want setup your training run. When you execute your training script, DeepSpeed logs the configuration it received from [`Trainer`] to the console so you can see exactly what configuration was used.
<Tip>
Find a complete list of DeepSpeed configuration options on the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference. You can also find more practical examples of various DeepSpeed configuration examples on the [DeepSpeedExamples](https://github.com/deepspeedai/DeepSpeedExamples) repository or the main [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) repository. To quickly find specific examples, you can:
```bash
git clone https://github.com/deepspeedai/DeepSpeedExamples
cd DeepSpeedExamples
find . -name '*json'
# find examples with the Lamb optimizer
grep -i Lamb $(find . -name '*json')
```
</Tip>
The DeepSpeed configuration file is passed as a path to a JSON file if you're training from the command line interface or as a nested `dict` object if you're using the [`Trainer`] in a notebook setting.
<hfoptions id="pass-config">
<hfoption id="path to file">
```py
TrainingArguments(..., deepspeed="path/to/deepspeed_config.json")
```
</hfoption>
<hfoption id="nested dict">
```py
ds_config_dict = dict(scheduler=scheduler_params, optimizer=optimizer_params)
args = TrainingArguments(..., deepspeed=ds_config_dict)
trainer = Trainer(model, args, ...)
```
</hfoption>
</hfoptions>
### DeepSpeed and Trainer parameters
There are three types of configuration parameters:
1. Some of the configuration parameters are shared by [`Trainer`] and DeepSpeed, and it can be difficult to identify errors when there are conflicting definitions. To make it easier, these shared configuration parameters are configured from the [`Trainer`] command line arguments.
2. Some configuration parameters that are automatically derived from the model configuration so you don't need to manually adjust these values. The [`Trainer`] uses a configuration value `auto` to determine set the most correct or efficient value. You could set your own configuration parameters explicitly, but you must take care to ensure the [`Trainer`] arguments and DeepSpeed configuration parameters agree. Mismatches may cause the training to fail in very difficult to detect ways!
3. Some configuration parameters specific to DeepSpeed only which need to be manually set based on your training needs.
You could also modify the DeepSpeed configuration and edit [`TrainingArguments`] from it:
1. Create or load a DeepSpeed configuration to use as the main configuration
2. Create a [`TrainingArguments`] object based on these DeepSpeed configuration values
Some values, such as `scheduler.params.total_num_steps` are calculated by the [`Trainer`] during training.
### ZeRO configuration
There are three configurations, each corresponding to a different ZeRO stage. Stage 1 is not as interesting for scalability, and this guide focuses on stages 2 and 3. The `zero_optimization` configuration contains all the options for what to enable and how to configure them. For a more detailed explanation of each parameter, take a look at the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference.
<Tip warning={true}>
DeepSpeed doesn’t validate parameter names and any typos fallback on the parameter's default setting. You can watch the DeepSpeed engine startup log messages to see what values it is going to use.
</Tip>
The following configurations must be setup with DeepSpeed because the [`Trainer`] doesn't provide equivalent command line arguments.
<hfoptions id="zero-config">
<hfoption id="ZeRO-1">
ZeRO-1 shards the optimizer states across GPUs, and you can expect a tiny speed up. The ZeRO-1 config can be setup like this:
```yml
{
"zero_optimization": {
"stage": 1
}
}
```
</hfoption>
<hfoption id="ZeRO-2">
ZeRO-2 shards the optimizer and gradients across GPUs. This stage is primarily used for training since its features are not relevant to inference. Some important parameters to configure for better performance include:
* `offload_optimizer` should be enabled to reduce GPU memory usage.
* `overlap_comm` when set to `true` trades off increased GPU memory usage to lower allreduce latency. This feature uses 4.5x the `allgather_bucket_size` and `reduce_bucket_size` values. In this example, they're set to `5e8` which means it requires 9GB of GPU memory. If your GPU memory is 8GB or less, you should reduce `overlap_comm` to lower the memory requirements and prevent an out-of-memory (OOM) error.
* `allgather_bucket_size` and `reduce_bucket_size` trade off available GPU memory for communication speed. The smaller their values, the slower communication is and the more GPU memory is available. You can balance, for example, whether a bigger batch size is more important than a slightly slower training time.
* `round_robin_gradients` is available in DeepSpeed 0.4.4 for CPU offloading. It parallelizes gradient copying to CPU memory among ranks by fine-grained gradient partitioning. Performance benefit grows with gradient accumulation steps (more copying between optimizer steps) or GPU count (increased parallelism).
```yml
{
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"contiguous_gradients": true
"round_robin_gradients": true
}
}
```
</hfoption>
<hfoption id="ZeRO-3">
ZeRO-3 shards the optimizer, gradient, and parameters across GPUs. Unlike ZeRO-2, ZeRO-3 can also be used for inference, in addition to training, because it allows large models to be loaded on multiple GPUs. Some important parameters to configure include:
* `device: "cpu"` can help if you're running out of GPU memory and if you have free CPU memory available. This allows offloading model parameters to the CPU.
* `pin_memory: true` can improve throughput, but less memory becomes available for other processes because the pinned memory is reserved for the specific process that requested it and it's typically accessed much faster than normal CPU memory.
* `stage3_max_live_parameters` is the upper limit on how many full parameters you want to keep on the GPU at any given time. Reduce this value if you encounter an OOM error.
* `stage3_max_reuse_distance` is a value for determining when a parameter is used again in the future, and it helps decide whether to throw the parameter away or to keep it. If the parameter is going to be reused (if the value is less than `stage3_max_reuse_distance`), then it is kept to reduce communication overhead. This is super helpful when activation checkpointing is enabled and you want to keep the parameter in the forward recompute until the backward pass. But reduce this value if you encounter an OOM error.
* `stage3_gather_16bit_weights_on_model_save` consolidates fp16 weights when a model is saved. For large models and multiple GPUs, this is expensive in terms of memory and speed. You should enable it if you're planning on resuming training.
* `sub_group_size` controls which parameters are updated during the optimizer step. Parameters are grouped into buckets of `sub_group_size` and each bucket is updated one at a time. When used with NVMe offload, `sub_group_size` determines when model states are moved in and out of CPU memory from during the optimization step. This prevents running out of CPU memory for extremely large models. `sub_group_size` can be left to its default value if you aren't using NVMe offload, but you may want to change it if you:
1. Run into an OOM error during the optimizer step. In this case, reduce `sub_group_size` to reduce memory usage of the temporary buffers.
2. The optimizer step is taking a really long time. In this case, increase `sub_group_size` to improve bandwidth utilization as a result of increased data buffers.
* `reduce_bucket_size`, `stage3_prefetch_bucket_size`, and `stage3_param_persistence_threshold` are dependent on a model's hidden size. It is recommended to set these values to `auto` and allow the [`Trainer`] to automatically assign the values.
```yml
{
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
}
}
```
You can use the [`deepspeed.zero.Init`](https://deepspeed.readthedocs.io/en/latest/zero3.html#deepspeed.zero.Init) context manager to initialize a model faster:
```py
from transformers import T5ForConditionalGeneration, T5Config
import deepspeed
with deepspeed.zero.Init():
config = T5Config.from_pretrained("google-t5/t5-small")
model = T5ForConditionalGeneration(config)
```
For pretrained models, the DeepSped config file needs to have `is_deepspeed_zero3_enabled: true` setup in [`TrainingArguments`] and it needs a ZeRO configuration enabled. The [`TrainingArguments`] object must be created **before** calling the model [`~PreTrainedModel.from_pretrained`].
```py
from transformers import AutoModel, Trainer, TrainingArguments
training_args = TrainingArguments(..., deepspeed=ds_config)
model = AutoModel.from_pretrained("google-t5/t5-small")
trainer = Trainer(model=model, args=training_args, ...)
```
You'll need ZeRO-3 if the fp16 weights don't fit on a single GPU. If you're able to load fp16 weights, then make sure you specify `torch_dtype=torch.float16` in [`~PreTrainedModel.from_pretrained`].
Another consideration for ZeRO-3 is if you have multiple GPUs, no single GPU has all the parameters unless it's the parameters for the currently executing layer. To access all parameters from all the layers at once, such as loading pretrained model weights in [`~PreTrainedModel.from_pretrained`], one layer is loaded at a time and immediately partitioned to all GPUs. This is because for very large models, it isn't possible to load the weights on one GPU and then distribute them across the other GPUs due to memory limitations.
If you encounter a model parameter weight that looks like the following, where `tensor([1.])` or the parameter size is 1 instead of a larger multi-dimensional shape, this means the parameter is partitioned and this is a ZeRO-3 placeholder.
```py
tensor([1.0], device="cuda:0", dtype=torch.float16, requires_grad=True)
```
<Tip>
For more information about initializing large models with ZeRO-3 and accessing the parameters, take a look at the [Constructing Massive Models](https://deepspeed.readthedocs.io/en/latest/zero3.html#constructing-massive-models) and [Gathering Parameters](https://deepspeed.readthedocs.io/en/latest/zero3.html#gathering-parameters) guides.
</Tip>
</hfoption>
</hfoptions>
### NVMe configuration
[ZeRO-Infinity](https://hf.co/papers/2104.07857) allows offloading model states to the CPU and/or NVMe to save even more memory. Smart partitioning and tiling algorithms allow each GPU to send and receive very small amounts of data during offloading such that a modern NVMe can fit an even larger total memory pool than is available to your training process. ZeRO-Infinity requires ZeRO-3.
Depending on the CPU and/or NVMe memory available, you can offload both the [optimizer states](https://www.deepspeed.ai/docs/config-json/#optimizer-offloading) and [parameters](https://www.deepspeed.ai/docs/config-json/#parameter-offloading), just one of them, or none. You should also make sure the `nvme_path` is pointing to an NVMe device, because while it still works with a normal hard drive or solid state drive, it'll be significantly slower. With a modern NVMe, you can expect peak transfer speeds of ~3.5GB/s for read and ~3GB/s for write operations. Lastly, [run a benchmark](https://github.com/deepspeedai/DeepSpeed/issues/998) on your training setup to determine the optimal `aio` configuration.
The example ZeRO-3/Infinity configuration file below sets most of the parameter values to `auto`, but you could also manually add these values.
```yml
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "nvme",
"nvme_path": "/local_nvme",
"pin_memory": true,
"buffer_count": 4,
"fast_init": false
},
"offload_param": {
"device": "nvme",
"nvme_path": "/local_nvme",
"pin_memory": true,
"buffer_count": 5,
"buffer_size": 1e8,
"max_in_cpu": 1e9
},
"aio": {
"block_size": 262144,
"queue_depth": 32,
"thread_count": 1,
"single_submit": false,
"overlap_events": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
```
## DeepSpeed features
There are a number of important parameters to specify in the DeepSpeed configuration file which are briefly described in this section.
### Activation/gradient checkpointing
Activation and gradient checkpointing trades speed for more GPU memory which allows you to overcome scenarios where your GPU is out of memory or to increase your batch size for better performance. To enable this feature:
1. For a Hugging Face model, set `model.gradient_checkpointing_enable()` or `--gradient_checkpointing` in the [`Trainer`].
2. For a non-Hugging Face model, use the DeepSpeed [Activation Checkpointing API](https://deepspeed.readthedocs.io/en/latest/activation-checkpointing.html). You could also replace the Transformers modeling code and replace `torch.utils.checkpoint` with the DeepSpeed API. This approach is more flexible because you can offload the forward activations to the CPU memory instead of recalculating them.
### Optimizer and scheduler
DeepSpeed and Transformers optimizer and scheduler can be mixed and matched as long as you don't enable `offload_optimizer`. When `offload_optimizer` is enabled, you could use a non-DeepSpeed optimizer (except for LAMB) as long as it has both a CPU and GPU implementation.
<Tip warning={true}>
The optimizer and scheduler parameters for the config file can be set from the command line to avoid hard to find errors. For example, if the learning rate is set to a different value in another place you can override it from the command line. Aside from the optimizer and scheduler parameters, you'll need to ensure your [`Trainer`] command line arguments match the DeepSpeed configuration.
</Tip>
<hfoptions id="opt-sched">
<hfoption id="optimizer">
DeepSpeed offers several [optimizers](https://www.deepspeed.ai/docs/config-json/#optimizer-parameters) (Adam, AdamW, OneBitAdam, and LAMB) but you can also import other optimizers from PyTorch. If you don't configure the optimizer in the config, the [`Trainer`] automatically selects AdamW and either uses the supplied values or the default values for the following parameters from the command line: `lr`, `adam_beta1`, `adam_beta2`, `adam_epsilon`, `weight_decay`.
You can set the parameters to `"auto"` or manually input your own desired values.
```yaml
{
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
}
}
```
You can also use an unsupported optimizer by adding the following to the top level configuration.
```yaml
{
"zero_allow_untested_optimizer": true
}
```
From DeepSpeed==0.8.3 on, if you want to use offload, you'll also need to the following to the top level configuration because offload works best with DeepSpeed's CPU Adam optimizer.
```yaml
{
"zero_force_ds_cpu_optimizer": false
}
```
</hfoption>
<hfoption id="scheduler">
DeepSpeed supports the LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR learning rate [schedulers](https://www.deepspeed.ai/docs/config-json/#scheduler-parameters).
Transformers and DeepSpeed provide two of the same schedulers:
* WarmupLR is the same as `--lr_scheduler_type constant_with_warmup` in Transformers
* WarmupDecayLR is the same as `--lr_scheduler_type linear` in Transformers (this is the default scheduler used in Transformers)
If you don't configure the scheduler in the config, the [`Trainer`] automatically selects WarmupDecayLR and either uses the supplied values or the default values for the following parameters from the command line: `warmup_min_lr`, `warmup_max_lr`, `warmup_num_steps`, `total_num_steps` (automatically calculated during run time if `max_steps` is not provided).
You can set the parameters to `"auto"` or manually input your own desired values.
```yaml
{
"scheduler": {
"type": "WarmupDecayLR",
"params": {
"total_num_steps": "auto",
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
}
}
```
</hfoption>
</hfoptions>
### Precision
Deepspeed supports fp32, fp16, and bf16 mixed precision.
<hfoptions id="precision">
<hfoption id="fp32">
If your model doesn't work well with mixed precision, for example if it wasn't pretrained in mixed precision, you may encounter overflow or underflow issues which can cause NaN loss. For these cases, you should use full fp32 precision by explicitly disabling the default fp16 mode.
```yaml
{
"fp16": {
"enabled": false
}
}
```
For Ampere GPUs and PyTorch > 1.7, it automatically switches to the more efficient [tf32](https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) format for some operations but the results are still in fp32. You can control it from the [`Trainer`] by setting `--tf32` to enable it, and `--tf32 0` or `--no_tf32` to disable it.
</hfoption>
<hfoption id="fp16">
To configure PyTorch AMP-like fp16 mixed precision reduces memory usage and accelerates training speed. [`Trainer`] automatically enables or disables fp16 based on the value of `args.fp16_backend`, and the rest of the config can be set by you. fp16 is enabled from the command line when the following arguments are passed: `--fp16`, `--fp16_backend amp` or `--fp16_full_eval`.
```yaml
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
}
}
```
For additional DeepSpeed fp16 training options, take a look at the [FP16 Training Options](https://www.deepspeed.ai/docs/config-json/#fp16-training-options) reference.
To configure Apex-like fp16 mixed precision, setup the config as shown below with `"auto"` or your own values. [`Trainer`] automatically configure `amp` based on the values of `args.fp16_backend` and `args.fp16_opt_level`. It can also be enabled from the command line when the following arguments are passed: `--fp16`, `--fp16_backend apex` or `--fp16_opt_level 01`.
```yaml
{
"amp": {
"enabled": "auto",
"opt_level": "auto"
}
}
```
</hfoption>
<hfoption id="bf16">
To use bf16, you'll need at least DeepSpeed==0.6.0. bf16 has the same dynamic range as fp32 and doesn’t require loss scaling. However, if you use [gradient accumulation](#gradient-accumulation) with bf16, gradients are accumulated in bf16 which may not be desired because this format's low precision can lead to lossy accumulation.
bf16 can be setup in the config file or enabled from the command line when the following arguments are passed: `--bf16` or `--bf16_full_eval`.
```yaml
{
"bf16": {
"enabled": "auto"
}
}
```
</hfoption>
</hfoptions>
### Batch size
The batch size can be auto-configured or explicitly set. If you choose to use the `"auto"` option, [`Trainer`] sets `train_micro_batch_size_per_gpu` to the value of args.`per_device_train_batch_size` and `train_batch_size` to `args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps`.
```yaml
{
"train_micro_batch_size_per_gpu": "auto",
"train_batch_size": "auto"
}
```
### Gradient accumulation
Gradient accumulation can be auto-configured or explicitly set. If you choose to use the `"auto"` option, [`Trainer`] sets it to the value of `args.gradient_accumulation_steps`.
```yaml
{
"gradient_accumulation_steps": "auto"
}
```
### Gradient clipping
Gradient clipping can be auto-configured or explicitly set. If you choose to use the `"auto"` option, [`Trainer`] sets it to the value of `args.max_grad_norm`.
```yaml
{
"gradient_clipping": "auto"
}
```
### Communication data type
For communication collectives like reduction, gathering and scattering operations, a separate data type is used.
All gather and scatter operations are performed in the same data type the data is in. For example, if you're training with bf16, the data is also gathered in bf16 because gathering is a non-lossy operation.
Reduce operations are lossy, for example when gradients are averaged across multiple GPUs. When the communication is done in fp16 or bf16, it is more likely to be lossy because adding multiple numbers in low precision isn't exact. This is especially the case with bf16 which has a lower precision than fp16. For this reason, fp16 is the default for reduction operations because the loss is minimal when averaging gradients.
You can choose the communication data type by setting the `communication_data_type` parameter in the config file. For example, choosing fp32 adds a small amount of overhead but ensures the reduction operation is accumulated in fp32 and when it is ready, it is downcasted to whichever half-precision dtype you're training in.
```yaml
{
"communication_data_type": "fp32"
}
```
### Universal Checkpointing
[Universal Checkpointing](https://www.deepspeed.ai/tutorials/universal-checkpointing) is an efficient and flexible feature for saving and loading model checkpoints. It enables seamless model training continuation and fine-tuning across different model architectures, parallelism techniques, and training configurations.
Resume training with a universal checkpoint by setting [load_universal](https://www.deepspeed.ai/docs/config-json/#checkpoint-options) to `true` in the config file.
```yaml
{
"checkpoint": {
"load_universal": true
}
}
```
## Deployment
DeepSpeed can be deployed by different launchers such as [torchrun](https://pytorch.org/docs/stable/elastic/run.html), the `deepspeed` launcher, or [Accelerate](https://huggingface.co/docs/accelerate/basic_tutorials/launch#using-accelerate-launch). To deploy, add `--deepspeed ds_config.json` to the [`Trainer`] command line. It’s recommended to use DeepSpeed’s [`add_config_arguments`](https://deepspeed.readthedocs.io/en/latest/initialize.html#argument-parsing) utility to add any necessary command line arguments to your code.
This guide will show you how to deploy DeepSpeed with the `deepspeed` launcher for different training setups. You can check out this [post](https://github.com/huggingface/transformers/issues/8771#issuecomment-759248400) for more practical usage examples.
<hfoptions id="deploy">
<hfoption id="multi-GPU">
To deploy DeepSpeed on multiple GPUs, add the `--num_gpus` parameter. If you want to use all available GPUs, you don't need to add `--num_gpus`. The example below uses 2 GPUs.
```bash
deepspeed --num_gpus=2 examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero3.json \
--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config "ro-en" \
--source_lang en --target_lang ro
```
</hfoption>
<hfoption id="single-GPU">
To deploy DeepSpeed on a single GPU, add the `--num_gpus` parameter. It isn't necessary to explicitly set this value if you only have 1 GPU because DeepSpeed deploys all GPUs it can see on a given node.
```bash
deepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero2.json \
--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config "ro-en" \
--source_lang en --target_lang ro
```
DeepSpeed is still useful with just 1 GPU because you can:
1. Offload some computations and memory to the CPU to make more GPU resources available to your model to use a larger batch size or fit a very large model that normally won't fit.
2. Minimize memory fragmentation with it's smart GPU memory management system which also allows you to fit bigger models and data batches.
<Tip>
Set the `allgather_bucket_size` and `reduce_bucket_size` values to 2e8 in the [ZeRO-2](#zero-configuration) configuration file to get better performance on a single GPU.
</Tip>
</hfoption>
</hfoptions>
### Multi-node deployment
A node is one or more GPUs for running a workload. A more powerful setup is a multi-node setup which can be launched with the `deepspeed` launcher. For this guide, let's assume there are two nodes with 8 GPUs each. The first node can be accessed `ssh hostname1` and the second node with `ssh hostname2`. Both nodes must be able to communicate with each other locally over ssh without a password.
By default, DeepSpeed expects your multi-node environment to use a shared storage. If this is not the case and each node can only see the local filesystem, you need to adjust the config file to include a [`checkpoint`](https://www.deepspeed.ai/docs/config-json/#checkpoint-options) to allow loading without access to a shared filesystem:
```yaml
{
"checkpoint": {
"use_node_local_storage": true
}
}
```
You could also use the [`Trainer`]'s `--save_on_each_node` argument to automatically add the above `checkpoint` to your config.
<hfoptions id="multinode">
<hfoption id="torchrun">
For [torchrun](https://pytorch.org/docs/stable/elastic/run.html), you have to ssh to each node and run the following command on both of them. The launcher waits until both nodes are synchronized before launching the training.
```bash
torchrun --nproc_per_node=8 --nnode=2 --node_rank=0 --master_addr=hostname1 \
--master_port=9901 your_program.py <normal cl args> --deepspeed ds_config.json
```
</hfoption>
<hfoption id="deepspeed">
For the `deepspeed` launcher, start by creating a `hostfile`.
```bash
hostname1 slots=8
hostname2 slots=8
```
Then you can launch the training with the following command. The `deepspeed` launcher automatically launches the command on both nodes at once.
```bash
deepspeed --num_gpus 8 --num_nodes 2 --hostfile hostfile --master_addr hostname1 --master_port=9901 \
your_program.py <normal cl args> --deepspeed ds_config.json
```
Check out the [Resource Configuration (multi-node)](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node) guide for more details about configuring multi-node compute resources.
</hfoption>
</hfoptions>
### SLURM
In a SLURM environment, you'll need to adapt your SLURM script to your specific SLURM environment. An example SLURM script may look like:
```bash
#SBATCH --job-name=test-nodes # name
#SBATCH --nodes=2 # nodes
#SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
#SBATCH --cpus-per-task=10 # number of cores per tasks
#SBATCH --gres=gpu:8 # number of gpus
#SBATCH --time 20:00:00 # maximum execution time (HH:MM:SS)
#SBATCH --output=%x-%j.out # output file name
export GPUS_PER_NODE=8
export MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
export MASTER_PORT=9901
srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
--nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
--master_addr $MASTER_ADDR --master_port $MASTER_PORT \
your_program.py <normal cl args> --deepspeed ds_config.json'
```
Then you can schedule your multi-node deployment with the following command which launches training simultaneously on all nodes.
```bash
sbatch launch.slurm
```
### Notebook
The `deepspeed` launcher doesn't support deployment from a notebook so you'll need to emulate the distributed environment. However, this only works for 1 GPU. If you want to use more than 1 GPU, you must use a multi-process environment for DeepSpeed to work. This means you have to use the `deepspeed` launcher which can't be emulated as shown here.
```py
# DeepSpeed requires a distributed environment even when only one process is used.
# This emulates a launcher in the notebook
import os
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "9994" # modify if RuntimeError: Address already in use
os.environ["RANK"] = "0"
os.environ["LOCAL_RANK"] = "0"
os.environ["WORLD_SIZE"] = "1"
# Now proceed as normal, plus pass the DeepSpeed config file
training_args = TrainingArguments(..., deepspeed="ds_config_zero3.json")
trainer = Trainer(...)
trainer.train()
```
If you want to create the config file on the fly in the notebook in the current directory, you could have a dedicated cell.
```py
%%bash
cat <<'EOT' > ds_config_zero3.json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
EOT
```
If the training script is in a file and not in a notebook cell, you can launch `deepspeed` normally from the shell in a notebook cell. For example, to launch `run_translation.py`:
```py
!git clone https://github.com/huggingface/transformers
!cd transformers; deepspeed examples/pytorch/translation/run_translation.py ...
```
You could also use `%%bash` magic and write multi-line code to run the shell program, but you won't be able to view the logs until training is complete. With `%%bash` magic, you don't need to emulate a distributed environment.
```py
%%bash
git clone https://github.com/huggingface/transformers
cd transformers
deepspeed examples/pytorch/translation/run_translation.py ...
```
## Save model weights
DeepSpeed stores the main full precision fp32 weights in custom checkpoint optimizer files (the glob pattern looks like `global_step*/*optim_states.pt`) and are saved under the normal checkpoint.
<hfoptions id="save">
<hfoption id="fp16">
A model trained with ZeRO-2 saves the pytorch_model.bin weights in fp16. To save the model weights in fp16 for a model trained with ZeRO-3, you need to set `"stage3_gather_16bit_weights_on_model_save": true` because the model weights are partitioned across multiple GPUs. Otherwise, the [`Trainer`] won't save the weights in fp16 and it won't create a pytorch_model.bin file. This is because DeepSpeed's state_dict contains a placeholder instead of the real weights and you won't be able to load them.
```yaml
{
"zero_optimization": {
"stage3_gather_16bit_weights_on_model_save": true
}
}
```
</hfoption>
<hfoption id="fp32">
The full precision weights shouldn't be saved during training because it can require a lot of memory. It is usually best to save the fp32 weights offline after training is complete. But if you have a lot of free CPU memory, it is possible to save the fp32 weights during training. This section covers both online and offline approaches.
### Online
You must have saved at least one checkpoint to load the latest checkpoint as shown in the following:
```py
from transformers.trainer_utils import get_last_checkpoint
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
checkpoint_dir = get_last_checkpoint(trainer.args.output_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
```
If you've enabled the `--load_best_model_at_end` parameter to track the best checkpoint in [`TrainingArguments`], you can finish training first and save the final model explicitly. Then you can reload it as shown below:
```py
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
checkpoint_dir = os.path.join(trainer.args.output_dir, "checkpoint-final")
trainer.deepspeed.save_checkpoint(checkpoint_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
```
<Tip>
Once `load_state_dict_from_zero_checkpoint` is run, the model is no longer usable in DeepSpeed in the context of the same application. You'll need to initialize the DeepSpeed engine again since `model.load_state_dict(state_dict)` removes all the DeepSpeed magic from it. Only use this at the very end of training.
</Tip>
You can also extract and load the state_dict of the fp32 weights:
```py
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
model = model.cpu()
model.load_state_dict(state_dict)
```
### Offline
DeepSpeed provides a zero_to_fp32.py script at the top-level of the checkpoint folder for extracting weights at any point. This is a standalone script and you don't need a configuration file or [`Trainer`].
For example, if your checkpoint folder looked like this:
```bash
$ ls -l output_dir/checkpoint-1/
-rw-rw-r-- 1 stas stas 1.4K Mar 27 20:42 config.json
drwxrwxr-x 2 stas stas 4.0K Mar 25 19:52 global_step1/
-rw-rw-r-- 1 stas stas 12 Mar 27 13:16 latest
-rw-rw-r-- 1 stas stas 827K Mar 27 20:42 optimizer.pt
-rw-rw-r-- 1 stas stas 231M Mar 27 20:42 pytorch_model.bin
-rw-rw-r-- 1 stas stas 623 Mar 27 20:42 scheduler.pt
-rw-rw-r-- 1 stas stas 1.8K Mar 27 20:42 special_tokens_map.json
-rw-rw-r-- 1 stas stas 774K Mar 27 20:42 spiece.model
-rw-rw-r-- 1 stas stas 1.9K Mar 27 20:42 tokenizer_config.json
-rw-rw-r-- 1 stas stas 339 Mar 27 20:42 trainer_state.json
-rw-rw-r-- 1 stas stas 2.3K Mar 27 20:42 training_args.bin
-rwxrw-r-- 1 stas stas 5.5K Mar 27 13:16 zero_to_fp32.py*
```
To reconstruct the fp32 weights from the DeepSpeed checkpoint (ZeRO-2 or ZeRO-3) subfolder `global_step1`, run the following command to create and consolidate the full fp32 weights from multiple GPUs into a single pytorch_model.bin file. The script automatically discovers the subfolder containing the checkpoint.
```py
python zero_to_fp32.py . pytorch_model.bin
```
<Tip>
Run `python zero_to_fp32.py -h` for more usage details. The script requires 2x the general RAM of the final fp32 weights.
</Tip>
</hfoption>
</hfoptions>
## ZeRO Inference
[ZeRO Inference](https://www.deepspeed.ai/2022/09/09/zero-inference.html) places the model weights in CPU or NVMe memory to avoid burdening the GPU which makes it possible to run inference with huge models on a GPU. Inference doesn't require any large additional amounts of memory for the optimizer states and gradients so you can fit much larger batches and/or sequence lengths on the same hardware.
ZeRO Inference shares the same configuration file as [ZeRO-3](#zero-configuration), and ZeRO-2 and ZeRO-1 configs won't work because they don't provide any benefits for inference.
To run ZeRO Inference, pass your usual training arguments to the [`TrainingArguments`] class and add the `--do_eval` argument.
```bash
deepspeed --num_gpus=2 your_program.py <normal cl args> --do_eval --deepspeed ds_config.json
```
## Non-Trainer DeepSpeed integration
DeepSpeed also works with Transformers without the [`Trainer`] class. This is handled by the [`HfDeepSpeedConfig`] which only takes care of gathering ZeRO-3 parameters and splitting a model across multiple GPUs when you call [`~PreTrainedModel.from_pretrained`].
<Tip>
If you want everything automatically taken care of for you, try using DeepSpeed with the [`Trainer`]! You'll need to follow the [DeepSpeed documentation](https://www.deepspeed.ai/), and manually configure the parameter values in the config file (you can't use the `"auto"` value).
</Tip>
To efficiently deploy ZeRO-3, you must instantiate the [`HfDeepSpeedConfig`] object before the model and keep that object alive:
<hfoptions id="models">
<hfoption id="pretrained model">
```py
from transformers.integrations import HfDeepSpeedConfig
from transformers import AutoModel
import deepspeed
ds_config = {...} # deepspeed config object or path to the file
# must run before instantiating the model to detect zero 3
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
model = AutoModel.from_pretrained("openai-community/gpt2")
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
```
</hfoption>
<hfoption id="non-pretrained model">
[`HfDeepSpeedConfig`] is not required for ZeRO-1 or ZeRO-2.
```py
from transformers.integrations import HfDeepSpeedConfig
from transformers import AutoModel, AutoConfig
import deepspeed
ds_config = {...} # deepspeed config object or path to the file
# must run before instantiating the model to detect zero 3
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
config = AutoConfig.from_pretrained("openai-community/gpt2")
model = AutoModel.from_config(config)
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
```
</hfoption>
</hfoptions>
### Non-Trainer ZeRO Inference
To run ZeRO Inference without the [`Trainer`] in cases where you can’t fit a model onto a single GPU, try using additional GPUs or/and offloading to CPU memory. The important nuance to understand here is that the way ZeRO is designed, you can process different inputs on different GPUs in parallel.
Make sure to:
* disable CPU offload if you have enough GPU memory (since it slows things down).
* enable bf16 if you have an Ampere or newer GPU to make things faster. If you don’t have one of these GPUs, you may enable fp16 as long as you don’t use a model pretrained in bf16 (T5 models) because it may lead to an overflow error.
Take a look at the following script to get a better idea of how to run ZeRO Inference without the [`Trainer`] on a model that won't fit on a single GPU.
```py
#!/usr/bin/env python
# This script demonstrates how to use Deepspeed ZeRO in an inference mode when one can't fit a model
# into a single GPU
#
# 1. Use 1 GPU with CPU offload
# 2. Or use multiple GPUs instead
#
# First you need to install deepspeed: pip install deepspeed
#
# Here we use a 3B "bigscience/T0_3B" model which needs about 15GB GPU RAM - so 1 largish or 2
# small GPUs can handle it. or 1 small GPU and a lot of CPU memory.
#
# To use a larger model like "bigscience/T0" which needs about 50GB, unless you have an 80GB GPU -
# you will need 2-4 gpus. And then you can adapt the script to handle more gpus if you want to
# process multiple inputs at once.
#
# The provided deepspeed config also activates CPU memory offloading, so chances are that if you
# have a lot of available CPU memory and you don't mind a slowdown you should be able to load a
# model that doesn't normally fit into a single GPU. If you have enough GPU memory the program will
# run faster if you don't want offload to CPU - so disable that section then.
#
# To deploy on 1 gpu:
#
# deepspeed --num_gpus 1 t0.py
# or:
# python -m torch.distributed.run --nproc_per_node=1 t0.py
#
# To deploy on 2 gpus:
#
# deepspeed --num_gpus 2 t0.py
# or:
# python -m torch.distributed.run --nproc_per_node=2 t0.py
from transformers import AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM
from transformers.integrations import HfDeepSpeedConfig
import deepspeed
import os
import torch
os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warnings about parallelism in tokenizers
# distributed setup
local_rank = int(os.getenv("LOCAL_RANK", "0"))
world_size = int(os.getenv("WORLD_SIZE", "1"))
torch.cuda.set_device(local_rank)
deepspeed.init_distributed()
model_name = "bigscience/T0_3B"
config = AutoConfig.from_pretrained(model_name)
model_hidden_size = config.d_model
# batch size has to be divisible by world_size, but can be bigger than world_size
train_batch_size = 1 * world_size
# ds_config notes
#
# - enable bf16 if you use Ampere or higher GPU - this will run in mixed precision and will be
# faster.
#
# - for older GPUs you can enable fp16, but it'll only work for non-bf16 pretrained models - e.g.
# all official t5 models are bf16-pretrained
#
# - set offload_param.device to "none" or completely remove the `offload_param` section if you don't
# - want CPU offload
#
# - if using `offload_param` you can manually finetune stage3_param_persistence_threshold to control
# - which params should remain on gpus - the larger the value the smaller the offload size
#
# For in-depth info on Deepspeed config see
# https://huggingface.co/docs/transformers/main/main_classes/deepspeed
# keeping the same format as json for consistency, except it uses lower case for true/false
# fmt: off
ds_config = {
"fp16": {
"enabled": False
},
"bf16": {
"enabled": False
},
"zero_optimization": {
"stage": 3,
"offload_param": {
"device": "cpu",
"pin_memory": True
},
"overlap_comm": True,
"contiguous_gradients": True,
"reduce_bucket_size": model_hidden_size * model_hidden_size,
"stage3_prefetch_bucket_size": 0.9 * model_hidden_size * model_hidden_size,
"stage3_param_persistence_threshold": 10 * model_hidden_size
},
"steps_per_print": 2000,
"train_batch_size": train_batch_size,
"train_micro_batch_size_per_gpu": 1,
"wall_clock_breakdown": False
}
# fmt: on
# next line instructs transformers to partition the model directly over multiple gpus using
# deepspeed.zero.Init when model's `from_pretrained` method is called.
#
# **it has to be run before loading the model AutoModelForSeq2SeqLM.from_pretrained(model_name)**
#
# otherwise the model will first be loaded normally and only partitioned at forward time which is
# less efficient and when there is little CPU RAM may fail
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
# now a model can be loaded.
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# initialise Deepspeed ZeRO and store only the engine object
ds_engine = deepspeed.initialize(model=model, config_params=ds_config)[0]
ds_engine.module.eval() # inference
# Deepspeed ZeRO can process unrelated inputs on each GPU. So for 2 gpus you process 2 inputs at once.
# If you use more GPUs adjust for more.
# And of course if you have just one input to process you then need to pass the same string to both gpus
# If you use only one GPU, then you will have only rank 0.
rank = torch.distributed.get_rank()
if rank == 0:
text_in = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"
elif rank == 1:
text_in = "Is this review positive or negative? Review: this is the worst restaurant ever"
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer.encode(text_in, return_tensors="pt").to(device=local_rank)
with torch.no_grad():
outputs = ds_engine.module.generate(inputs, synced_gpus=True)
text_out = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"rank{rank}:\n in={text_in}\n out={text_out}")
```
Save the script as t0.py and launch it:
```bash
$ deepspeed --num_gpus 2 t0.py
rank0:
in=Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy
out=Positive
rank1:
in=Is this review positive or negative? Review: this is the worst restaurant ever
out=negative
```
This is a very basic example and you'll want to adapt it to your use case.
### Generate
Using multiple GPUs with ZeRO-3 for generation requires synchronizing the GPUs by setting `synced_gpus=True` in the [`~GenerationMixin.generate`] method. Otherwise, if one GPU is finished generating before another one, the whole system hangs because the remaining GPUs haven't received the weight shard from the GPU that finished first.
For Transformers>=4.28, if `synced_gpus` is automatically set to `True` if multiple GPUs are detected during generation.
## Troubleshoot
When you encounter an issue, you should consider whether DeepSpeed is the cause of the problem because often it isn't (unless it's super obviously and you can see DeepSpeed modules in the exception)! The first step should be to retry your setup without DeepSpeed, and if the problem persists, then you can report the issue. If the issue is a core DeepSpeed problem and unrelated to the Transformers integration, open an Issue on the [DeepSpeed repository](https://github.com/deepspeedai/DeepSpeed).
For issues related to the Transformers integration, please provide the following information:
* the full DeepSpeed config file
* the command line arguments of the [`Trainer`], or [`TrainingArguments`] arguments if you're scripting the [`Trainer`] setup yourself (don't dump the [`TrainingArguments`] which has dozens of irrelevant entries)
* the outputs of:
```bash
python -c 'import torch; print(f"torch: {torch.__version__}")'
python -c 'import transformers; print(f"transformers: {transformers.__version__}")'
python -c 'import deepspeed; print(f"deepspeed: {deepspeed.__version__}")'
```
* a link to a Google Colab notebook to reproduce the issue
* if impossible, a standard and non-custom dataset we can use and also try to use an existing example to reproduce the issue with
The following sections provide a guide for resolving two of the most common issues.
### DeepSpeed process killed at startup
When the DeepSpeed process is killed during launch without a traceback, that usually means the program tried to allocate more CPU memory than your system has or your process tried to allocate more CPU memory than allowed leading the OS kernel to terminate the process. In this case, check whether your configuration file has either `offload_optimizer`, `offload_param` or both configured to offload to the CPU.
If you have NVMe and ZeRO-3 setup, experiment with offloading to the NVMe ([estimate](https://deepspeed.readthedocs.io/en/latest/memory.html) the memory requirements for your model).
### NaN loss
NaN loss often occurs when a model is pretrained in bf16 and then you try to use it with fp16 (especially relevant for TPU trained models). To resolve this, use fp32 or bf16 if your hardware supports it (TPU, Ampere GPUs or newer).
The other issue may be related to using fp16. For example, if this is your fp16 configuration:
```yaml
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
}
}
```
You might see the following `OVERFLOW!` messages in the logs:
```bash
0%| | 0/189 [00:00<?, ?it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 262144
1%|▌ | 1/189 [00:00<01:26, 2.17it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 131072.0
1%|█▏
[...]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
14%|████████████████▌ | 27/189 [00:14<01:13, 2.21it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
15%|█████████████████▏ | 28/189 [00:14<01:13, 2.18it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
15%|█████████████████▊ | 29/189 [00:15<01:13, 2.18it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
[...]
```
This means the DeepSpeed loss scaler is unable to find a scaling coefficient to overcome loss overflow. To fix it, try a higher `initial_scale_power` value (32 usually works).
## Resources
DeepSpeed ZeRO is a powerful technology for training and loading very large models for inference with limited GPU resources, making it more accessible to everyone. To learn more about DeepSpeed, feel free to read the [blog posts](https://www.microsoft.com/en-us/research/search/?q=deepspeed), [documentation](https://www.deepspeed.ai/getting-started/), and [GitHub repository](https://github.com/deepspeedai/DeepSpeed).
The following papers are also a great resource for learning more about ZeRO:
* [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://hf.co/papers/1910.02054)
* [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://hf.co/papers/2101.06840)
* [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://hf.co/papers/2104.07857)
| transformers/docs/source/en/deepspeed.md/0 | {
"file_path": "transformers/docs/source/en/deepspeed.md",
"repo_id": "transformers",
"token_count": 18928
} |
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# Audio Spectrogram Transformer
## Overview
The Audio Spectrogram Transformer model was proposed in [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
The Audio Spectrogram Transformer applies a [Vision Transformer](vit) to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results
for audio classification.
The abstract from the paper is the following:
*In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-attention mechanism on top of the CNN, forming a CNN-attention hybrid model. However, it is unclear whether the reliance on a CNN is necessary, and if neural networks purely based on attention are sufficient to obtain good performance in audio classification. In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/audio_spectogram_transformer_architecture.png"
alt="drawing" width="600"/>
<small> Audio Spectrogram Transformer architecture. Taken from the <a href="https://arxiv.org/abs/2104.01778">original paper</a>.</small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/YuanGongND/ast).
## Usage tips
- When fine-tuning the Audio Spectrogram Transformer (AST) on your own dataset, it's recommended to take care of the input normalization (to make
sure the input has mean of 0 and std of 0.5). [`ASTFeatureExtractor`] takes care of this. Note that it uses the AudioSet
mean and std by default. You can check [`ast/src/get_norm_stats.py`](https://github.com/YuanGongND/ast/blob/master/src/get_norm_stats.py) to see how
the authors compute the stats for a downstream dataset.
- Note that the AST needs a low learning rate (the authors use a 10 times smaller learning rate compared to their CNN model proposed in the
[PSLA paper](https://arxiv.org/abs/2102.01243)) and converges quickly, so please search for a suitable learning rate and learning rate scheduler for your task.
### 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 ASTForAudioClassification
model = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593", 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 `MIT/ast-finetuned-audioset-10-10-0.4593` model, we saw the following speedups during inference.
| Batch size | Average inference time (ms), eager mode | Average inference time (ms), sdpa model | Speed up, Sdpa / Eager (x) |
|--------------|-------------------------------------------|-------------------------------------------|------------------------------|
| 1 | 27 | 6 | 4.5 |
| 2 | 12 | 6 | 2 |
| 4 | 21 | 8 | 2.62 |
| 8 | 40 | 14 | 2.86 |
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with the Audio Spectrogram Transformer.
<PipelineTag pipeline="audio-classification"/>
- A notebook illustrating inference with AST for audio classification can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/AST).
- [`ASTForAudioClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb).
- See also: [Audio classification](../tasks/audio_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.
## ASTConfig
[[autodoc]] ASTConfig
## ASTFeatureExtractor
[[autodoc]] ASTFeatureExtractor
- __call__
## ASTModel
[[autodoc]] ASTModel
- forward
## ASTForAudioClassification
[[autodoc]] ASTForAudioClassification
- forward
| transformers/docs/source/en/model_doc/audio-spectrogram-transformer.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/audio-spectrogram-transformer.md",
"repo_id": "transformers",
"token_count": 2176
} |
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# Big Transfer (BiT)
## Overview
The BiT model was proposed in [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
BiT is a simple recipe for scaling up pre-training of [ResNet](resnet)-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.
The abstract from the paper is the following:
*Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes -- from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.*
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/google-research/big_transfer).
## Usage tips
- BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by [group normalization](https://arxiv.org/abs/1803.08494),
2) [weight standardization](https://arxiv.org/abs/1903.10520) is used for convolutional layers. The authors show that the combination of both is useful for training with large batch sizes, and has a significant
impact on transfer learning.
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BiT.
<PipelineTag pipeline="image-classification"/>
- [`BitForImageClassification`] 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.
## BitConfig
[[autodoc]] BitConfig
## BitImageProcessor
[[autodoc]] BitImageProcessor
- preprocess
## BitModel
[[autodoc]] BitModel
- forward
## BitForImageClassification
[[autodoc]] BitForImageClassification
- forward | transformers/docs/source/en/model_doc/bit.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bit.md",
"repo_id": "transformers",
"token_count": 1005
} |
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# CLIPSeg
## Overview
The CLIPSeg model was proposed in [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke
and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen [CLIP](clip) model for zero-shot and one-shot image segmentation.
The abstract from the paper is the following:
*Image segmentation is usually addressed by training a
model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive
as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system
that can generate image segmentations based on arbitrary
prompts at test time. A prompt can be either a text or an
image. This approach enables us to create a unified model
(trained once) for three common segmentation tasks, which
come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation.
We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense
prediction. After training on an extended version of the
PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on
an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail.
This novel hybrid input allows for dynamic adaptation not
only to the three segmentation tasks mentioned above, but
to any binary segmentation task where a text or image query
can be formulated. Finally, we find our system to adapt well
to generalized queries involving affordances or properties*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/clipseg_architecture.png"
alt="drawing" width="600"/>
<small> CLIPSeg overview. Taken from the <a href="https://arxiv.org/abs/2112.10003">original paper.</a> </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/timojl/clipseg).
## Usage tips
- [`CLIPSegForImageSegmentation`] adds a decoder on top of [`CLIPSegModel`]. The latter is identical to [`CLIPModel`].
- [`CLIPSegForImageSegmentation`] can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text
(provided to the model as `input_ids`) or an image (provided to the model as `conditional_pixel_values`). One can also provide custom
conditional embeddings (provided to the model as `conditional_embeddings`).
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIPSeg. 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.
<PipelineTag pipeline="image-segmentation"/>
- A notebook that illustrates [zero-shot image segmentation with CLIPSeg](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/CLIPSeg/Zero_shot_image_segmentation_with_CLIPSeg.ipynb).
## CLIPSegConfig
[[autodoc]] CLIPSegConfig
- from_text_vision_configs
## CLIPSegTextConfig
[[autodoc]] CLIPSegTextConfig
## CLIPSegVisionConfig
[[autodoc]] CLIPSegVisionConfig
## CLIPSegProcessor
[[autodoc]] CLIPSegProcessor
## CLIPSegModel
[[autodoc]] CLIPSegModel
- forward
- get_text_features
- get_image_features
## CLIPSegTextModel
[[autodoc]] CLIPSegTextModel
- forward
## CLIPSegVisionModel
[[autodoc]] CLIPSegVisionModel
- forward
## CLIPSegForImageSegmentation
[[autodoc]] CLIPSegForImageSegmentation
- forward | transformers/docs/source/en/model_doc/clipseg.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/clipseg.md",
"repo_id": "transformers",
"token_count": 1222
} |
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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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# Falcon
## Overview
Falcon is a class of causal decoder-only models built by [TII](https://www.tii.ae/). The largest Falcon checkpoints
have been trained on >=1T tokens of text, with a particular emphasis on the [RefinedWeb](https://arxiv.org/abs/2306.01116)
corpus. They are made available under the Apache 2.0 license.
Falcon's architecture is modern and optimized for inference, with multi-query attention and support for efficient
attention variants like `FlashAttention`. Both 'base' models trained only as causal language models as well as
'instruct' models that have received further fine-tuning are available.
Falcon models are (as of 2023) some of the largest and most powerful open-source language models,
and consistently rank highly in the [OpenLLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
## Converting custom checkpoints
<Tip>
Falcon models were initially added to the Hugging Face Hub as custom code checkpoints. However, Falcon is now fully
supported in the Transformers library. If you fine-tuned a model from a custom code checkpoint, we recommend converting
your checkpoint to the new in-library format, as this should give significant improvements to stability and
performance, especially for generation, as well as removing the need to use `trust_remote_code=True`!
</Tip>
You can convert custom code checkpoints to full Transformers checkpoints using the `convert_custom_code_checkpoint.py`
script located in the
[Falcon model directory](https://github.com/huggingface/transformers/tree/main/src/transformers/models/falcon)
of the Transformers library. To use this script, simply call it with
`python convert_custom_code_checkpoint.py --checkpoint_dir my_model`. This will convert your checkpoint in-place, and
you can immediately load it from the directory afterwards with e.g. `from_pretrained()`. If your model hasn't been
uploaded to the Hub, we recommend making a backup before attempting the conversion, just in case!
## FalconConfig
[[autodoc]] FalconConfig
- all
## FalconModel
[[autodoc]] FalconModel
- forward
## FalconForCausalLM
[[autodoc]] FalconForCausalLM
- forward
## FalconForSequenceClassification
[[autodoc]] FalconForSequenceClassification
- forward
## FalconForTokenClassification
[[autodoc]] FalconForTokenClassification
- forward
## FalconForQuestionAnswering
[[autodoc]] FalconForQuestionAnswering
- forward
| transformers/docs/source/en/model_doc/falcon.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/falcon.md",
"repo_id": "transformers",
"token_count": 837
} |
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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.
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# GLM
## Overview
The GLM Model was proposed
in [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://arxiv.org/html/2406.12793v1)
by GLM Team, THUDM & ZhipuAI.
The abstract from the paper is the following:
*We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report
primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent our most
capable models that are trained with all the insights and lessons gained from the preceding three generations of
ChatGLM. To date, the GLM-4 models are pre-trained on ten trillions of tokens mostly in Chinese and English, along with
a small set of corpus from 24 languages, and aligned primarily for Chinese and English usage. The high-quality alignment
is achieved via a multi-stage post-training process, which involves supervised fine-tuning and learning from human
feedback. Evaluations show that GLM-4 1) closely rivals or outperforms GPT-4 in terms of general metrics such as MMLU,
GSM8K, MATH, BBH, GPQA, and HumanEval, 2) gets close to GPT-4-Turbo in instruction following as measured by IFEval, 3)
matches GPT-4 Turbo (128K) and Claude 3 for long context tasks, and 4) outperforms GPT-4 in Chinese alignments as
measured by AlignBench. The GLM-4 All Tools model is further aligned to understand user intent and autonomously decide
when and which tool(s) to use—including web browser, Python interpreter, text-to-image model, and user-defined
functions—to effectively complete complex tasks. In practical applications, it matches and even surpasses GPT-4 All
Tools in tasks like accessing online information via web browsing and solving math problems using Python interpreter.
Over the course, we have open-sourced a series of models, including ChatGLM-6B (three generations), GLM-4-9B (128K, 1M),
GLM-4V-9B, WebGLM, and CodeGeeX, attracting over 10 million downloads on Hugging face in the year 2023 alone.*
Tips:
- This model was contributed by [THUDM](https://huggingface.co/THUDM). The most recent code can be
found [here](https://github.com/thudm/GLM-4).
## Usage tips
`GLM-4` can be found on the [Huggingface Hub](https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7)
In the following, we demonstrate how to use `glm-4-9b-chat` 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("THUDM/glm-4-9b-chat", device_map="auto", trust_remote_code=True)
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat")
>>> 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]
```
## GlmConfig
[[autodoc]] GlmConfig
## GlmModel
[[autodoc]] GlmModel
- forward
## GlmForCausalLM
[[autodoc]] GlmForCausalLM
- forward
## GlmForSequenceClassification
[[autodoc]] GlmForSequenceClassification
- forward
## GlmForTokenClassification
[[autodoc]] GlmForTokenClassification
- forward
| transformers/docs/source/en/model_doc/glm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/glm.md",
"repo_id": "transformers",
"token_count": 1351
} |
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# Jukebox
<Tip warning={true}>
This model is in maintenance mode only, we don'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: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.
</Tip>
## Overview
The Jukebox model was proposed in [Jukebox: A generative model for music](https://arxiv.org/pdf/2005.00341.pdf)
by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford,
Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditioned on
an artist, genres and lyrics.
The abstract from the paper is the following:
*We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multiscale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples, along with model weights and code.*
As shown on the following figure, Jukebox is made of 3 `priors` which are decoder only models. They follow the architecture described in [Generating Long Sequences with Sparse Transformers](https://arxiv.org/abs/1904.10509), modified to support longer context length.
First, a autoencoder is used to encode the text lyrics. Next, the first (also called `top_prior`) prior attends to the last hidden states extracted from the lyrics encoder. The priors are linked to the previous priors respectively via an `AudioConditioner` module. The`AudioConditioner` upsamples the outputs of the previous prior to raw tokens at a certain audio frame per second resolution.
The metadata such as *artist, genre and timing* are passed to each prior, in the form of a start token and positional embedding for the timing data. The hidden states are mapped to the closest codebook vector from the VQVAE in order to convert them to raw audio.
This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ).
The original code can be found [here](https://github.com/openai/jukebox).
## Usage tips
- This model only supports inference. This is for a few reasons, mostly because it requires a crazy amount of memory to train. Feel free to open a PR and add what's missing to have a full integration with the hugging face trainer!
- This model is very slow, and takes 8h to generate a minute long audio using the 5b top prior on a V100 GPU. In order automaticallay handle the device on which the model should execute, use `accelerate`.
- Contrary to the paper, the order of the priors goes from `0` to `1` as it felt more intuitive : we sample starting from `0`.
- Primed sampling (conditioning the sampling on raw audio) requires more memory than ancestral sampling and should be used with `fp16` set to `True`.
This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ).
The original code can be found [here](https://github.com/openai/jukebox).
## JukeboxConfig
[[autodoc]] JukeboxConfig
## JukeboxPriorConfig
[[autodoc]] JukeboxPriorConfig
## JukeboxVQVAEConfig
[[autodoc]] JukeboxVQVAEConfig
## JukeboxTokenizer
[[autodoc]] JukeboxTokenizer
- save_vocabulary
## JukeboxModel
[[autodoc]] JukeboxModel
- ancestral_sample
- primed_sample
- continue_sample
- upsample
- _sample
## JukeboxPrior
[[autodoc]] JukeboxPrior
- sample
- forward
## JukeboxVQVAE
[[autodoc]] JukeboxVQVAE
- forward
- encode
- decode
| transformers/docs/source/en/model_doc/jukebox.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/jukebox.md",
"repo_id": "transformers",
"token_count": 1309
} |
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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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# Longformer
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=longformer">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-longformer-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/longformer-base-4096-finetuned-squadv1">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
</div>
## Overview
The Longformer model was presented in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
The abstract from the paper is the following:
*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales
quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention
mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or
longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local
windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we
evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In
contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our
pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
WikiHop and TriviaQA.*
This model was contributed by [beltagy](https://huggingface.co/beltagy). The Authors' code can be found [here](https://github.com/allenai/longformer).
## Usage tips
- Since the Longformer is based on RoBERTa, it doesn't have `token_type_ids`. You don't need to indicate which
token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or
`</s>`).
- A transformer model replacing the attention matrices by sparse matrices to go faster. Often, the local context (e.g., what are the two tokens left and right?) is enough to take action for a given token. Some preselected input tokens are still given global attention, but the attention matrix has way less parameters, resulting in a speed-up. See the local attention section for more information.
## Longformer Self Attention
Longformer self attention employs self attention on both a "local" context and a "global" context. Most tokens only
attend "locally" to each other meaning that each token attends to its \\(\frac{1}{2} w\\) previous tokens and
\\(\frac{1}{2} w\\) succeeding tokens with \\(w\\) being the window length as defined in
`config.attention_window`. Note that `config.attention_window` can be of type `List` to define a
different \\(w\\) for each layer. A selected few tokens attend "globally" to all other tokens, as it is
conventionally done for all tokens in `BertSelfAttention`.
Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices. Also note
that every "locally" attending token not only attends to tokens within its window \\(w\\), but also to all "globally"
attending tokens so that global attention is *symmetric*.
The user can define which tokens attend "locally" and which tokens attend "globally" by setting the tensor
`global_attention_mask` at run-time appropriately. All Longformer models employ the following logic for
`global_attention_mask`:
- 0: the token attends "locally",
- 1: the token attends "globally".
For more information please also refer to [`~LongformerModel.forward`] method.
Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually
represents the memory and time bottleneck, can be reduced from \\(\mathcal{O}(n_s \times n_s)\\) to
\\(\mathcal{O}(n_s \times w)\\), with \\(n_s\\) being the sequence length and \\(w\\) being the average window
size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of
"locally" attending tokens.
For more information, please refer to the official [paper](https://arxiv.org/pdf/2004.05150.pdf).
## Training
[`LongformerForMaskedLM`] is trained the exact same way [`RobertaForMaskedLM`] is
trained and should be used as follows:
```python
input_ids = tokenizer.encode("This is a sentence from [MASK] training data", return_tensors="pt")
mlm_labels = tokenizer.encode("This is a sentence from the training data", return_tensors="pt")
loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
```
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## LongformerConfig
[[autodoc]] LongformerConfig
## LongformerTokenizer
[[autodoc]] LongformerTokenizer
## LongformerTokenizerFast
[[autodoc]] LongformerTokenizerFast
## Longformer specific outputs
[[autodoc]] models.longformer.modeling_longformer.LongformerBaseModelOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling
[[autodoc]] models.longformer.modeling_longformer.LongformerMaskedLMOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerSequenceClassifierOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerTokenClassifierOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutputWithPooling
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput
[[autodoc]] models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput
<frameworkcontent>
<pt>
## LongformerModel
[[autodoc]] LongformerModel
- forward
## LongformerForMaskedLM
[[autodoc]] LongformerForMaskedLM
- forward
## LongformerForSequenceClassification
[[autodoc]] LongformerForSequenceClassification
- forward
## LongformerForMultipleChoice
[[autodoc]] LongformerForMultipleChoice
- forward
## LongformerForTokenClassification
[[autodoc]] LongformerForTokenClassification
- forward
## LongformerForQuestionAnswering
[[autodoc]] LongformerForQuestionAnswering
- forward
</pt>
<tf>
## TFLongformerModel
[[autodoc]] TFLongformerModel
- call
## TFLongformerForMaskedLM
[[autodoc]] TFLongformerForMaskedLM
- call
## TFLongformerForQuestionAnswering
[[autodoc]] TFLongformerForQuestionAnswering
- call
## TFLongformerForSequenceClassification
[[autodoc]] TFLongformerForSequenceClassification
- call
## TFLongformerForTokenClassification
[[autodoc]] TFLongformerForTokenClassification
- call
## TFLongformerForMultipleChoice
[[autodoc]] TFLongformerForMultipleChoice
- call
</tf>
</frameworkcontent>
| transformers/docs/source/en/model_doc/longformer.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/longformer.md",
"repo_id": "transformers",
"token_count": 2395
} |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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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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# MegatronBERT
## Overview
The MegatronBERT model was proposed in [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model
Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley,
Jared Casper and Bryan Catanzaro.
The abstract from the paper is the following:
*Recent work in language modeling demonstrates that training large transformer models advances the state of the art in
Natural Language Processing applications. However, very large models can be quite difficult to train due to memory
constraints. In this work, we present our techniques for training very large transformer models and implement a simple,
efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our
approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model
parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We
illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain
15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline
that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance
the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9
billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in
BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we
achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA
accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy
of 89.4%).*
This model was contributed by [jdemouth](https://huggingface.co/jdemouth). The original code can be found [here](https://github.com/NVIDIA/Megatron-LM).
That repository contains a multi-GPU and multi-node implementation of the Megatron Language models. In particular,
it contains a hybrid model parallel approach using "tensor parallel" and "pipeline parallel" techniques.
## Usage tips
We have provided pretrained [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m) checkpoints
for use to evaluate or finetuning downstream tasks.
To access these checkpoints, first [sign up](https://ngc.nvidia.com/signup) for and setup the NVIDIA GPU Cloud (NGC)
Registry CLI. Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1).
Alternatively, you can directly download the checkpoints using:
BERT-345M-uncased:
```bash
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_uncased/zip
-O megatron_bert_345m_v0_1_uncased.zip
```
BERT-345M-cased:
```bash
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O
megatron_bert_345m_v0_1_cased.zip
```
Once you have obtained the checkpoints from NVIDIA GPU Cloud (NGC), you have to convert them to a format that will
easily be loaded by Hugging Face Transformers and our port of the BERT code.
The following commands allow you to do the conversion. We assume that the folder `models/megatron_bert` contains
`megatron_bert_345m_v0_1_{cased, uncased}.zip` and that the commands are run from inside that folder:
```bash
python3 $PATH_TO_TRANSFORMERS/models/megatron_bert/convert_megatron_bert_checkpoint.py megatron_bert_345m_v0_1_uncased.zip
```
```bash
python3 $PATH_TO_TRANSFORMERS/models/megatron_bert/convert_megatron_bert_checkpoint.py megatron_bert_345m_v0_1_cased.zip
```
## 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)
## MegatronBertConfig
[[autodoc]] MegatronBertConfig
## MegatronBertModel
[[autodoc]] MegatronBertModel
- forward
## MegatronBertForMaskedLM
[[autodoc]] MegatronBertForMaskedLM
- forward
## MegatronBertForCausalLM
[[autodoc]] MegatronBertForCausalLM
- forward
## MegatronBertForNextSentencePrediction
[[autodoc]] MegatronBertForNextSentencePrediction
- forward
## MegatronBertForPreTraining
[[autodoc]] MegatronBertForPreTraining
- forward
## MegatronBertForSequenceClassification
[[autodoc]] MegatronBertForSequenceClassification
- forward
## MegatronBertForMultipleChoice
[[autodoc]] MegatronBertForMultipleChoice
- forward
## MegatronBertForTokenClassification
[[autodoc]] MegatronBertForTokenClassification
- forward
## MegatronBertForQuestionAnswering
[[autodoc]] MegatronBertForQuestionAnswering
- forward
| transformers/docs/source/en/model_doc/megatron-bert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/megatron-bert.md",
"repo_id": "transformers",
"token_count": 1735
} |
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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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# Persimmon
## Overview
The Persimmon model was created by [ADEPT](https://www.adept.ai/blog/persimmon-8b), and authored by Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani.
The authors introduced Persimmon-8B, a decoder model based on the classic transformers architecture, with query and key normalization. Persimmon-8B is a fully permissively-licensed model with approximately 8 billion parameters, released under the Apache license. Some of the key attributes of Persimmon-8B are long context size (16K), performance, and capabilities for multimodal extensions.
The authors showcase their approach to model evaluation, focusing on practical text generation, mirroring how users interact with language models. The work also includes a comparative analysis, pitting Persimmon-8B against other prominent models (MPT 7B Instruct and Llama 2 Base 7B 1-Shot), across various evaluation tasks. The results demonstrate Persimmon-8B's competitive performance, even with limited training data.
In terms of model details, the work outlines the architecture and training methodology of Persimmon-8B, providing insights into its design choices, sequence length, and dataset composition. The authors present a fast inference code that outperforms traditional implementations through operator fusion and CUDA graph utilization while maintaining code coherence. They express their anticipation of how the community will leverage this contribution to drive innovation, hinting at further upcoming releases as part of an ongoing series of developments.
This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ).
The original code can be found [here](https://github.com/persimmon-ai-labs/adept-inference).
## Usage tips
<Tip warning={true}>
The `Persimmon` models were trained using `bfloat16`, but the original inference uses `float16` The checkpoints uploaded on the hub use `torch_dtype = 'float16'` which will be
used by the `AutoModel` API to cast the checkpoints from `torch.float32` to `torch.float16`.
The `dtype` of the online weights is mostly irrelevant, unless you are using `torch_dtype="auto"` when initializing a model using `model = AutoModelForCausalLM.from_pretrained("path", torch_dtype = "auto")`. The reason is that the model will first be downloaded ( using the `dtype` of the checkpoints online) then it will be cast to the default `dtype` of `torch` (becomes `torch.float32`). Users should specify the `torch_dtype` they want, and if they don't it will be `torch.float32`.
Finetuning the model in `float16` is not recommended and known to produce `nan`, as such the model should be fine-tuned in `bfloat16`.
</Tip>
Tips:
- To convert the model, you need to clone the original repository using `git clone https://github.com/persimmon-ai-labs/adept-inference`, then get the checkpoints:
```bash
git clone https://github.com/persimmon-ai-labs/adept-inference
wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_base_model_release.tar
tar -xvf 8b_base_model_release.tar
python src/transformers/models/persimmon/convert_persimmon_weights_to_hf.py --input_dir /path/to/downloaded/persimmon/weights/ --output_dir /output/path \
--pt_model_path /path/to/8b_chat_model_release/iter_0001251/mp_rank_00/model_optim_rng.pt
--ada_lib_path /path/to/adept-inference
```
For the chat model:
```bash
wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_chat_model_release.tar
tar -xvf 8b_base_model_release.tar
```
Thereafter, models can be loaded via:
```py
from transformers import PersimmonForCausalLM, PersimmonTokenizer
model = PersimmonForCausalLM.from_pretrained("/output/path")
tokenizer = PersimmonTokenizer.from_pretrained("/output/path")
```
- Perismmon uses a `sentencepiece` based tokenizer, with a `Unigram` model. It supports bytefallback, which is only available in `tokenizers==0.14.0` for the fast tokenizer.
The `LlamaTokenizer` is used as it is a standard wrapper around sentencepiece. The `chat` template will be updated with the templating functions in a follow up PR!
- The authors suggest to use the following prompt format for the chat mode: `f"human: {prompt}\n\nadept:"`
## PersimmonConfig
[[autodoc]] PersimmonConfig
## PersimmonModel
[[autodoc]] PersimmonModel
- forward
## PersimmonForCausalLM
[[autodoc]] PersimmonForCausalLM
- forward
## PersimmonForSequenceClassification
[[autodoc]] PersimmonForSequenceClassification
- forward
## PersimmonForTokenClassification
[[autodoc]] PersimmonForTokenClassification
- forward
| transformers/docs/source/en/model_doc/persimmon.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/persimmon.md",
"repo_id": "transformers",
"token_count": 1594
} |
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# RT-DETRv2
## Overview
The RT-DETRv2 model was proposed in [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer](https://arxiv.org/abs/2407.17140) by Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu.
RT-DETRv2 refines RT-DETR by introducing selective multi-scale feature extraction, a discrete sampling operator for broader deployment compatibility, and improved training strategies like dynamic data augmentation and scale-adaptive hyperparameters. These changes enhance flexibility and practicality while maintaining real-time performance.
The abstract from the paper is the following:
*In this report, we present RT-DETRv2, an improved Real-Time DEtection TRansformer (RT-DETR). RT-DETRv2 builds upon the previous state-of-the-art real-time detector, RT-DETR, and opens up a set of bag-of-freebies for flexibility and practicality, as well as optimizing the training strategy to achieve enhanced performance. To improve the flexibility, we suggest setting a distinct number of sampling points for features at different scales in the deformable attention to achieve selective multi-scale feature extraction by the decoder. To enhance practicality, we propose an optional discrete sampling operator to replace the grid_sample operator that is specific to RT-DETR compared to YOLOs. This removes the deployment constraints typically associated with DETRs. For the training strategy, we propose dynamic data augmentation and scale-adaptive hyperparameters customization to improve performance without loss of speed.*
This model was contributed by [jadechoghari](https://huggingface.co/jadechoghari).
The original code can be found [here](https://github.com/lyuwenyu/RT-DETR).
## Usage tips
This second version of RT-DETR improves how the decoder finds objects in an image.
- **better sampling** – adjusts offsets so the model looks at the right areas
- **flexible attention** – can use smooth (bilinear) or fixed (discrete) sampling
- **optimized processing** – improves how attention weights mix information
```py
>>> import torch
>>> import requests
>>> from PIL import Image
>>> from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor
>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd")
>>> model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)
>>> for result in results:
... for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
... score, label = score.item(), label_id.item()
... box = [round(i, 2) for i in box.tolist()]
... print(f"{model.config.id2label[label]}: {score:.2f} {box}")
cat: 0.97 [341.14, 25.11, 639.98, 372.89]
cat: 0.96 [12.78, 56.35, 317.67, 471.34]
remote: 0.95 [39.96, 73.12, 175.65, 117.44]
sofa: 0.86 [-0.11, 2.97, 639.89, 473.62]
sofa: 0.82 [-0.12, 1.78, 639.87, 473.52]
remote: 0.79 [333.65, 76.38, 370.69, 187.48]
```
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with RT-DETRv2.
<PipelineTag pipeline="object-detection"/>
- Scripts for finetuning [`RTDetrV2ForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
- See also: [Object detection task guide](../tasks/object_detection).
- Notebooks for [inference](https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/RT_DETR_v2_inference.ipynb) and [fine-tuning](https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/RT_DETR_v2_finetune_on_a_custom_dataset.ipynb) RT-DETRv2 on a custom dataset (🌎).
## RTDetrV2Config
[[autodoc]] RTDetrV2Config
## RTDetrV2Model
[[autodoc]] RTDetrV2Model
- forward
## RTDetrV2ForObjectDetection
[[autodoc]] RTDetrV2ForObjectDetection
- forward
| transformers/docs/source/en/model_doc/rt_detr_v2.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/rt_detr_v2.md",
"repo_id": "transformers",
"token_count": 1602
} |
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# StableLM
## Overview
`StableLM 3B 4E1T` was proposed in [`StableLM 3B 4E1T`: Technical Report](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Stability AI and is the first model in a series of multi-epoch pre-trained language models.
### Model Details
`StableLM 3B 4E1T` is a decoder-only base language model pre-trained on 1 trillion tokens of diverse English and code datasets for four epochs.
The model architecture is transformer-based with partial Rotary Position Embeddings, SwiGLU activation, LayerNorm, etc.
We also provide `StableLM Zephyr 3B`, an instruction fine-tuned version of the model that can be used for chat-based applications.
### Usage Tips
- The architecture is similar to LLaMA but with RoPE applied to 25% of head embedding dimensions, LayerNorm instead of RMSNorm, and optional QKV bias terms.
- `StableLM 3B 4E1T`-based models uses the same tokenizer as [`GPTNeoXTokenizerFast`].
`StableLM 3B 4E1T` and `StableLM Zephyr 3B` can be found on the [Huggingface Hub](https://huggingface.co/stabilityai)
The following code snippet demonstrates how to use `StableLM 3B 4E1T` for inference:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> device = "cuda" # the device to load the model onto
>>> set_seed(0)
>>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
>>> model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t")
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> model_inputs = tokenizer("The weather is always wonderful in", return_tensors="pt").to(model.device)
>>> generated_ids = model.generate(**model_inputs, max_length=32, do_sample=True)
>>> responses = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
>>> responses
['The weather is always wonderful in Costa Rica, which makes it a prime destination for retirees. That’s where the Pensionado program comes in, offering']
```
## Combining StableLM and Flash Attention 2
First, make sure to install the latest version of Flash Attention v2.
```bash
pip install -U flash-attn --no-build-isolation
```
Also make sure that your hardware is compatible with Flash-Attention 2. Read more about it in the official documentation of the [`flash-attn`](https://github.com/Dao-AILab/flash-attention) repository. Note: you must load your model in half-precision (e.g. `torch.bfloat16`).
Now, to run the model with Flash Attention 2, refer to the snippet below:
```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> device = "cuda" # the device to load the model onto
>>> set_seed(0)
>>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
>>> model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2") # doctest: +SKIP
>>> model.to(device) # doctest: +SKIP
>>> model_inputs = tokenizer("The weather is always wonderful in", return_tensors="pt").to(model.device)
>>> generated_ids = model.generate(**model_inputs, max_length=32, do_sample=True) # doctest: +SKIP
>>> responses = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) # doctest: +SKIP
>>> responses # doctest: +SKIP
['The weather is always wonderful in Costa Rica, which makes it a prime destination for retirees. That’s where the Pensionado program comes in, offering']
```
## StableLmConfig
[[autodoc]] StableLmConfig
## StableLmModel
[[autodoc]] StableLmModel
- forward
## StableLmForCausalLM
[[autodoc]] StableLmForCausalLM
- forward
## StableLmForSequenceClassification
[[autodoc]] StableLmForSequenceClassification
- forward
## StableLmForTokenClassification
[[autodoc]] StableLmForTokenClassification
- forward
| transformers/docs/source/en/model_doc/stablelm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/stablelm.md",
"repo_id": "transformers",
"token_count": 1478
} |
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# Wav2Vec2-Conformer
## Overview
The Wav2Vec2-Conformer was added to an updated version of [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
The official results of the model can be found in Table 3 and Table 4 of the paper.
The Wav2Vec2-Conformer weights were released by the Meta AI team within the [Fairseq library](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md#pre-trained-models).
This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
The original code can be found [here](https://github.com/pytorch/fairseq/tree/main/examples/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
- Wav2Vec2-Conformer follows the same architecture as Wav2Vec2, but replaces the *Attention*-block with a *Conformer*-block
as introduced in [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100).
- For the same number of layers, Wav2Vec2-Conformer requires more parameters than Wav2Vec2, but also yields
an improved word error rate.
- Wav2Vec2-Conformer uses the same tokenizer and feature extractor as Wav2Vec2.
- Wav2Vec2-Conformer can use either no relative position embeddings, Transformer-XL-like position embeddings, or
rotary position embeddings by setting the correct `config.position_embeddings_type`.
## Resources
- [Audio classification task guide](../tasks/audio_classification)
- [Automatic speech recognition task guide](../tasks/asr)
## Wav2Vec2ConformerConfig
[[autodoc]] Wav2Vec2ConformerConfig
## Wav2Vec2Conformer specific outputs
[[autodoc]] models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForPreTrainingOutput
## Wav2Vec2ConformerModel
[[autodoc]] Wav2Vec2ConformerModel
- forward
## Wav2Vec2ConformerForCTC
[[autodoc]] Wav2Vec2ConformerForCTC
- forward
## Wav2Vec2ConformerForSequenceClassification
[[autodoc]] Wav2Vec2ConformerForSequenceClassification
- forward
## Wav2Vec2ConformerForAudioFrameClassification
[[autodoc]] Wav2Vec2ConformerForAudioFrameClassification
- forward
## Wav2Vec2ConformerForXVector
[[autodoc]] Wav2Vec2ConformerForXVector
- forward
## Wav2Vec2ConformerForPreTraining
[[autodoc]] Wav2Vec2ConformerForPreTraining
- forward
| transformers/docs/source/en/model_doc/wav2vec2-conformer.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/wav2vec2-conformer.md",
"repo_id": "transformers",
"token_count": 1104
} |
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# YOLOS
## Overview
The YOLOS model was proposed in [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
YOLOS proposes to just leverage the plain [Vision Transformer (ViT)](vit) for object detection, inspired by DETR. It turns out that a base-sized encoder-only Transformer can also achieve 42 AP on COCO, similar to DETR and much more complex frameworks such as Faster R-CNN.
The abstract from the paper is the following:
*Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yolos_architecture.png"
alt="drawing" width="600"/>
<small> YOLOS architecture. Taken from the <a href="https://arxiv.org/abs/2106.00666">original paper</a>.</small>
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/hustvl/YOLOS).
## 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 AutoModelForObjectDetection
model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base", 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 `hustvl/yolos-base` model, we saw the following speedups during inference.
| Batch size | Average inference time (ms), eager mode | Average inference time (ms), sdpa model | Speed up, Sdpa / Eager (x) |
|--------------|-------------------------------------------|-------------------------------------------|------------------------------|
| 1 | 106 | 76 | 1.39 |
| 2 | 154 | 90 | 1.71 |
| 4 | 222 | 116 | 1.91 |
| 8 | 368 | 168 | 2.19 |
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with YOLOS.
<PipelineTag pipeline="object-detection"/>
- All example notebooks illustrating inference + fine-tuning [`YolosForObjectDetection`] on a custom dataset can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS).
- Scripts for finetuning [`YolosForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
- See also: [Object detection task guide](../tasks/object_detection)
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.
<Tip>
Use [`YolosImageProcessor`] for preparing images (and optional targets) for the model. Contrary to [DETR](detr), YOLOS doesn't require a `pixel_mask` to be created.
</Tip>
## YolosConfig
[[autodoc]] YolosConfig
## YolosImageProcessor
[[autodoc]] YolosImageProcessor
- preprocess
- pad
- post_process_object_detection
## YolosFeatureExtractor
[[autodoc]] YolosFeatureExtractor
- __call__
- pad
- post_process_object_detection
## YolosModel
[[autodoc]] YolosModel
- forward
## YolosForObjectDetection
[[autodoc]] YolosForObjectDetection
- forward
| transformers/docs/source/en/model_doc/yolos.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/yolos.md",
"repo_id": "transformers",
"token_count": 2101
} |
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# GPU inference
GPUs are the standard choice of hardware for machine learning, unlike CPUs, because they are optimized for memory bandwidth and parallelism. To keep up with the larger sizes of modern models or to run these large models on existing and older hardware, there are several optimizations you can use to speed up GPU inference. In this guide, you'll learn how to use FlashAttention-2 (a more memory-efficient attention mechanism), BetterTransformer (a PyTorch native fastpath execution), and bitsandbytes to quantize your model to a lower precision. Finally, learn how to use 🤗 Optimum to accelerate inference with ONNX Runtime on Nvidia and AMD GPUs.
<Tip>
The majority of the optimizations described here also apply to multi-GPU setups!
</Tip>
## FlashAttention-2
<Tip>
FlashAttention-2 is experimental and may change considerably in future versions.
</Tip>
[FlashAttention-2](https://huggingface.co/papers/2205.14135) is a faster and more efficient implementation of the standard attention mechanism that can significantly speedup inference by:
1. additionally parallelizing the attention computation over sequence length
2. partitioning the work between GPU threads to reduce communication and shared memory reads/writes between them
FlashAttention-2 is currently supported for the following architectures:
* [Aria](https://huggingface.co/docs/transformers/model_doc/aria#transformers.AriaForConditionalGeneration)
* [Bark](https://huggingface.co/docs/transformers/model_doc/bark#transformers.BarkModel)
* [Bamba](https://huggingface.co/docs/transformers/model_doc/bamba#transformers.BambaModel)
* [Bart](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartModel)
* [Chameleon](https://huggingface.co/docs/transformers/model_doc/chameleon#transformers.Chameleon)
* [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPModel)
* [Cohere](https://huggingface.co/docs/transformers/model_doc/cohere#transformers.CohereModel)
* [Cohere2](https://huggingface.co/docs/transformers/model_doc/cohere2#transformers.Cohere2Model)
* [GLM](https://huggingface.co/docs/transformers/model_doc/glm#transformers.GLMModel)
* [Dbrx](https://huggingface.co/docs/transformers/model_doc/dbrx#transformers.DbrxModel)
* [DiffLlama](https://huggingface.co/docs/transformers/model_doc/diffllama#transformers.DiffLlamaModel)
* [DistilBert](https://huggingface.co/docs/transformers/model_doc/distilbert#transformers.DistilBertModel)
* [Emu3](https://huggingface.co/docs/transformers/model_doc/emu3)
* [Gemma](https://huggingface.co/docs/transformers/model_doc/gemma#transformers.GemmaModel)
* [Gemma2](https://huggingface.co/docs/transformers/model_doc/gemma2#transformers.Gemma2Model)
* [GotOcr2](https://huggingface.co/docs/transformers/model_doc/got_ocr2#transformers.GotOcr2ForConditionalGeneration)
* [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)
* [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode#transformers.GPTBigCodeModel)
* [GPTNeo](https://huggingface.co/docs/transformers/model_doc/gpt_neo#transformers.GPTNeoModel)
* [GPTNeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox#transformers.GPTNeoXModel)
* [GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj#transformers.GPTJModel)
* [Granite](https://huggingface.co/docs/transformers/model_doc/granite#transformers.GraniteModel)
* [GraniteMoe](https://huggingface.co/docs/transformers/model_doc/granitemoe#transformers.GraniteMoeModel)
* [Idefics2](https://huggingface.co/docs/transformers/model_doc/idefics2#transformers.Idefics2Model)
* [Idefics3](https://huggingface.co/docs/transformers/model_doc/idefics3#transformers.Idefics3Model)
* [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
* [JetMoe](https://huggingface.co/docs/transformers/model_doc/jetmoe#transformers.JetMoeModel)
* [Jamba](https://huggingface.co/docs/transformers/model_doc/jamba#transformers.JambaModel)
* [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
* [Llava](https://huggingface.co/docs/transformers/model_doc/llava)
* [Llava-NeXT](https://huggingface.co/docs/transformers/model_doc/llava_next)
* [Llava-NeXT-Video](https://huggingface.co/docs/transformers/model_doc/llava_next_video)
* [LLaVA-Onevision](https://huggingface.co/docs/transformers/model_doc/llava_onevision)
* [Moonshine](https://huggingface.co/docs/transformers/model_doc/moonshine#transformers.MoonshineModel)
* [Mimi](https://huggingface.co/docs/transformers/model_doc/mimi)
* [VipLlava](https://huggingface.co/docs/transformers/model_doc/vipllava)
* [VideoLlava](https://huggingface.co/docs/transformers/model_doc/video_llava)
* [M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)
* [MBart](https://huggingface.co/docs/transformers/model_doc/mbart#transformers.MBartModel)
* [Mistral](https://huggingface.co/docs/transformers/model_doc/mistral#transformers.MistralModel)
* [Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral#transformers.MixtralModel)
* [ModernBert](https://huggingface.co/docs/transformers/model_doc/modernbert#transformers.ModernBert)
* [Moshi](https://huggingface.co/docs/transformers/model_doc/moshi#transformers.MoshiModel)
* [Musicgen](https://huggingface.co/docs/transformers/model_doc/musicgen#transformers.MusicgenModel)
* [MusicGen Melody](https://huggingface.co/docs/transformers/model_doc/musicgen_melody#transformers.MusicgenMelodyModel)
* [Nemotron](https://huggingface.co/docs/transformers/model_doc/nemotron)
* [NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)
* [OLMo](https://huggingface.co/docs/transformers/model_doc/olmo#transformers.OlmoModel)
* [OLMo2](https://huggingface.co/docs/transformers/model_doc/olmo2#transformers.Olmo2Model)
* [OLMoE](https://huggingface.co/docs/transformers/model_doc/olmoe#transformers.OlmoeModel)
* [OPT](https://huggingface.co/docs/transformers/model_doc/opt#transformers.OPTModel)
* [PaliGemma](https://huggingface.co/docs/transformers/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration)
* [Phi](https://huggingface.co/docs/transformers/model_doc/phi#transformers.PhiModel)
* [Phi3](https://huggingface.co/docs/transformers/model_doc/phi3#transformers.Phi3Model)
* [PhiMoE](https://huggingface.co/docs/transformers/model_doc/phimoe#transformers.PhimoeModel)
* [StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm#transformers.StableLmModel)
* [Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2#transformers.Starcoder2Model)
* [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
* [Qwen2Audio](https://huggingface.co/docs/transformers/model_doc/qwen2_audio#transformers.Qwen2AudioEncoder)
* [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel)
* [Qwen2VL](https://huggingface.co/docs/transformers/model_doc/qwen2_vl#transformers.Qwen2VLModel)
* [Qwen2.5VL](https://huggingface.co/docs/transformers/model_doc/qwen2_5_vl#transformers.Qwen2_5_VLModel)
* [RAG](https://huggingface.co/docs/transformers/model_doc/rag#transformers.RagModel)
* [SpeechEncoderDecoder](https://huggingface.co/docs/transformers/model_doc/speech_encoder_decoder#transformers.SpeechEncoderDecoderModel)
* [VisionEncoderDecoder](https://huggingface.co/docs/transformers/model_doc/vision_encoder_decoder#transformers.VisionEncoderDecoderModel)
* [VisionTextDualEncoder](https://huggingface.co/docs/transformers/model_doc/vision_text_dual_encoder#transformers.VisionTextDualEncoderModel)
* [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperModel)
* [Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2Model)
* [Hubert](https://huggingface.co/docs/transformers/model_doc/hubert#transformers.HubertModel)
* [data2vec_audio](https://huggingface.co/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioModel)
* [Sew](https://huggingface.co/docs/transformers/main/en/model_doc/sew#transformers.SEWModel)
* [SigLIP](https://huggingface.co/docs/transformers/model_doc/siglip)
* [UniSpeech](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech#transformers.UniSpeechModel)
* [unispeech_sat](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel)
* [helium](https://huggingface.co/docs/transformers/main/en/model_doc/heliumtransformers.HeliumModel)
* [Zamba2](https://huggingface.co/docs/transformers/model_doc/zamba2)
You can request to add FlashAttention-2 support for another model by opening a GitHub Issue or Pull Request.
Before you begin, make sure you have FlashAttention-2 installed.
<hfoptions id="install">
<hfoption id="NVIDIA">
```bash
pip install flash-attn --no-build-isolation
```
We strongly suggest referring to the detailed [installation instructions](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features) to learn more about supported hardware and data types!
</hfoption>
<hfoption id="AMD">
FlashAttention-2 is also supported on AMD GPUs and current support is limited to **Instinct MI210**, **Instinct MI250** and **Instinct MI300**. We strongly suggest using this [Dockerfile](https://github.com/huggingface/optimum-amd/tree/main/docker/transformers-pytorch-amd-gpu-flash/Dockerfile) to use FlashAttention-2 on AMD GPUs.
</hfoption>
</hfoptions>
To enable FlashAttention-2, pass the argument `attn_implementation="flash_attention_2"` to [`~AutoModelForCausalLM.from_pretrained`]:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
```
<Tip>
FlashAttention-2 can only be used when the model's dtype is `fp16` or `bf16`. Make sure to cast your model to the appropriate dtype and load them on a supported device before using FlashAttention-2.
<br>
You can also set `use_flash_attention_2=True` to enable FlashAttention-2 but it is deprecated in favor of `attn_implementation="flash_attention_2"`.
</Tip>
FlashAttention-2 can be combined with other optimization techniques like quantization to further speedup inference. For example, you can combine FlashAttention-2 with 8-bit or 4-bit quantization:
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
model_id = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# load in 8bit
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_8bit=True,
attn_implementation="flash_attention_2",
)
# load in 4bit
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_4bit=True,
attn_implementation="flash_attention_2",
)
```
### Expected speedups
You can benefit from considerable speedups for inference, especially for inputs with long sequences. However, since FlashAttention-2 does not support computing attention scores with padding tokens, you must manually pad/unpad the attention scores for batched inference when the sequence contains padding tokens. This leads to a significant slowdown for batched generations with padding tokens.
To overcome this, you should use FlashAttention-2 without padding tokens in the sequence during training (by packing a dataset or [concatenating sequences](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py#L516) until reaching the maximum sequence length).
For a single forward pass on [tiiuae/falcon-7b](https://hf.co/tiiuae/falcon-7b) with a sequence length of 4096 and various batch sizes without padding tokens, the expected speedup is:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/falcon-7b-inference-large-seqlen.png">
</div>
For a single forward pass on [meta-llama/Llama-7b-hf](https://hf.co/meta-llama/Llama-7b-hf) with a sequence length of 4096 and various batch sizes without padding tokens, the expected speedup is:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-7b-inference-large-seqlen.png">
</div>
For sequences with padding tokens (generating with padding tokens), you need to unpad/pad the input sequences to correctly compute the attention scores. With a relatively small sequence length, a single forward pass creates overhead leading to a small speedup (in the example below, 30% of the input is filled with padding tokens):
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-2-small-seqlen-padding.png">
</div>
But for larger sequence lengths, you can expect even more speedup benefits:
<Tip>
FlashAttention is more memory efficient, meaning you can train on much larger sequence lengths without running into out-of-memory issues. You can potentially reduce memory usage up to 20x for larger sequence lengths. Take a look at the [flash-attention](https://github.com/Dao-AILab/flash-attention) repository for more details.
</Tip>
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/llama-2-large-seqlen-padding.png">
</div>
## PyTorch scaled dot product attention
PyTorch's [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html) (SDPA) can also call FlashAttention and memory-efficient attention kernels under the hood. SDPA support is currently being added natively in Transformers and is used by default for `torch>=2.1.1` when an implementation is available. You may also set `attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
For now, Transformers supports SDPA inference and training for the following architectures:
* [Albert](https://huggingface.co/docs/transformers/model_doc/albert#transformers.AlbertModel)
* [Aria](https://huggingface.co/docs/transformers/model_doc/aria#transformers.AriaForConditionalGeneration)
* [Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer#transformers.ASTModel)
* [Bamba](https://huggingface.co/docs/transformers/model_doc/bamba#transformers.BambaModel)
* [Bart](https://huggingface.co/docs/transformers/model_doc/bart#transformers.BartModel)
* [Beit](https://huggingface.co/docs/transformers/model_doc/beit#transformers.BeitModel)
* [Bert](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertModel)
* [BioGpt](https://huggingface.co/docs/transformers/model_doc/biogpt#transformers.BioGptModel)
* [CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert#transformers.CamembertModel)
* [Chameleon](https://huggingface.co/docs/transformers/model_doc/chameleon#transformers.Chameleon)
* [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPModel)
* [GLM](https://huggingface.co/docs/transformers/model_doc/glm#transformers.GLMModel)
* [Cohere](https://huggingface.co/docs/transformers/model_doc/cohere#transformers.CohereModel)
* [Cohere2](https://huggingface.co/docs/transformers/model_doc/cohere2#transformers.Cohere2Model)
* [data2vec_audio](https://huggingface.co/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecAudioModel)
* [data2vec_vision](https://huggingface.co/docs/transformers/main/en/model_doc/data2vec#transformers.Data2VecVisionModel)
* [Dbrx](https://huggingface.co/docs/transformers/model_doc/dbrx#transformers.DbrxModel)
* [DeiT](https://huggingface.co/docs/transformers/model_doc/deit#transformers.DeiTModel)
* [DiffLlama](https://huggingface.co/docs/transformers/model_doc/diffllama#transformers.DiffLlamaModel)
* [Dinov2](https://huggingface.co/docs/transformers/en/model_doc/dinov2)
* [Dinov2_with_registers](https://huggingface.co/docs/transformers/en/model_doc/dinov2)
* [DistilBert](https://huggingface.co/docs/transformers/model_doc/distilbert#transformers.DistilBertModel)
* [Dpr](https://huggingface.co/docs/transformers/model_doc/dpr#transformers.DprReader)
* [EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder_decoder#transformers.EncoderDecoderModel)
* [Emu3](https://huggingface.co/docs/transformers/model_doc/emu3)
* [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon#transformers.FalconModel)
* [Gemma](https://huggingface.co/docs/transformers/model_doc/gemma#transformers.GemmaModel)
* [Gemma2](https://huggingface.co/docs/transformers/model_doc/gemma2#transformers.Gemma2Model)
* [GotOcr2](https://huggingface.co/docs/transformers/model_doc/got_ocr2#transformers.GotOcr2ForConditionalGeneration)
* [Granite](https://huggingface.co/docs/transformers/model_doc/granite#transformers.GraniteModel)
* [GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)
* [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode#transformers.GPTBigCodeModel)
* [GPTNeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox#transformers.GPTNeoXModel)
* [Hubert](https://huggingface.co/docs/transformers/model_doc/hubert#transformers.HubertModel)
* [Idefics](https://huggingface.co/docs/transformers/model_doc/idefics#transformers.IdeficsModel)
* [Idefics2](https://huggingface.co/docs/transformers/model_doc/idefics2#transformers.Idefics2Model)
* [Idefics3](https://huggingface.co/docs/transformers/model_doc/idefics3#transformers.Idefics3Model)
* [I-JEPA](https://huggingface.co/docs/transformers/model_doc/ijepa#transformers.IJepaModel)
* [GraniteMoe](https://huggingface.co/docs/transformers/model_doc/granitemoe#transformers.GraniteMoeModel)
* [JetMoe](https://huggingface.co/docs/transformers/model_doc/jetmoe#transformers.JetMoeModel)
* [Jamba](https://huggingface.co/docs/transformers/model_doc/jamba#transformers.JambaModel)
* [Llama](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)
* [Llava](https://huggingface.co/docs/transformers/model_doc/llava)
* [Llava-NeXT](https://huggingface.co/docs/transformers/model_doc/llava_next)
* [Llava-NeXT-Video](https://huggingface.co/docs/transformers/model_doc/llava_next_video)
* [LLaVA-Onevision](https://huggingface.co/docs/transformers/model_doc/llava_onevision)
* [M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100#transformers.M2M100Model)
* [Moonshine](https://huggingface.co/docs/transformers/model_doc/moonshine#transformers.MoonshineModel)
* [Mimi](https://huggingface.co/docs/transformers/model_doc/mimi)
* [Mistral](https://huggingface.co/docs/transformers/model_doc/mistral#transformers.MistralModel)
* [Mllama](https://huggingface.co/docs/transformers/model_doc/mllama#transformers.MllamaForConditionalGeneration)
* [Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral#transformers.MixtralModel)
* [ModernBert](https://huggingface.co/docs/transformers/model_doc/modernbert#transformers.ModernBert)
* [Moshi](https://huggingface.co/docs/transformers/model_doc/moshi#transformers.MoshiModel)
* [Musicgen](https://huggingface.co/docs/transformers/model_doc/musicgen#transformers.MusicgenModel)
* [MusicGen Melody](https://huggingface.co/docs/transformers/model_doc/musicgen_melody#transformers.MusicgenMelodyModel)
* [NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)
* [OLMo](https://huggingface.co/docs/transformers/model_doc/olmo#transformers.OlmoModel)
* [OLMo2](https://huggingface.co/docs/transformers/model_doc/olmo2#transformers.Olmo2Model)
* [OLMoE](https://huggingface.co/docs/transformers/model_doc/olmoe#transformers.OlmoeModel)
* [OPT](https://huggingface.co/docs/transformers/en/model_doc/opt)
* [PaliGemma](https://huggingface.co/docs/transformers/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration)
* [Phi](https://huggingface.co/docs/transformers/model_doc/phi#transformers.PhiModel)
* [Phi3](https://huggingface.co/docs/transformers/model_doc/phi3#transformers.Phi3Model)
* [PhiMoE](https://huggingface.co/docs/transformers/model_doc/phimoe#transformers.PhimoeModel)
* [Idefics](https://huggingface.co/docs/transformers/model_doc/idefics#transformers.IdeficsModel)
* [mBart](https://huggingface.co/docs/transformers/model_doc/mbart#transformers.MBartModel)
* [Moonshine](https://huggingface.co/docs/transformers/model_doc/moonshine#transformers.MoonshineModel)
* [Mistral](https://huggingface.co/docs/transformers/model_doc/mistral#transformers.MistralModel)
* [Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral#transformers.MixtralModel)
* [StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm#transformers.StableLmModel)
* [Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2#transformers.Starcoder2Model)
* [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
* [Qwen2Audio](https://huggingface.co/docs/transformers/model_doc/qwen2_audio#transformers.Qwen2AudioEncoder)
* [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel)
* [Qwen2.5VL](https://huggingface.co/docs/transformers/model_doc/qwen2_5_vl#transformers.Qwen2_5_VLModel)
* [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaModel)
* [Sew](https://huggingface.co/docs/transformers/main/en/model_doc/sew#transformers.SEWModel)
* [SigLIP](https://huggingface.co/docs/transformers/model_doc/siglip)
* [StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm#transformers.StableLmModel)
* [Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2#transformers.Starcoder2Model)
* [UniSpeech](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech#transformers.UniSpeechModel)
* [unispeech_sat](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel)
* [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaModel)
* [Qwen2VL](https://huggingface.co/docs/transformers/model_doc/qwen2_vl#transformers.Qwen2VLModel)
* [Musicgen](https://huggingface.co/docs/transformers/model_doc/musicgen#transformers.MusicgenModel)
* [MusicGen Melody](https://huggingface.co/docs/transformers/model_doc/musicgen_melody#transformers.MusicgenMelodyModel)
* [Nemotron](https://huggingface.co/docs/transformers/model_doc/nemotron)
* [SpeechEncoderDecoder](https://huggingface.co/docs/transformers/model_doc/speech_encoder_decoder#transformers.SpeechEncoderDecoderModel)
* [VideoLlava](https://huggingface.co/docs/transformers/model_doc/video_llava)
* [VipLlava](https://huggingface.co/docs/transformers/model_doc/vipllava)
* [VisionEncoderDecoder](https://huggingface.co/docs/transformers/model_doc/vision_encoder_decoder#transformers.VisionEncoderDecoderModel)
* [ViT](https://huggingface.co/docs/transformers/model_doc/vit#transformers.ViTModel)
* [ViTHybrid](https://huggingface.co/docs/transformers/model_doc/vit_hybrid#transformers.ViTHybridModel)
* [ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae#transformers.ViTMAEModel)
* [ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn#transformers.ViTMSNModel)
* [VisionTextDualEncoder](https://huggingface.co/docs/transformers/model_doc/vision_text_dual_encoder#transformers.VisionTextDualEncoderModel)
* [VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae#transformers.VideoMAEModell)
* [ViViT](https://huggingface.co/docs/transformers/model_doc/vivit#transformers.VivitModel)
* [wav2vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2Model)
* [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperModel)
* [XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaModel)
* [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel)
* [YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos#transformers.YolosModel)
* [helium](https://huggingface.co/docs/transformers/main/en/model_doc/heliumtransformers.HeliumModel)
* [Zamba2](https://huggingface.co/docs/transformers/model_doc/zamba2)
<Tip>
FlashAttention can only be used for models with the `fp16` or `bf16` torch type, so make sure to cast your model to the appropriate type first. The memory-efficient attention backend is able to handle `fp32` models.
</Tip>
<Tip>
SDPA does not support certain sets of attention parameters, such as `head_mask` and `output_attentions=True`.
In that case, you should see a warning message and we will fall back to the (slower) eager implementation.
</Tip>
By default, SDPA selects the most performant kernel available but you can check whether a backend is available in a given setting (hardware, problem size) with [`torch.nn.attention.sdpa_kernel`](https://pytorch.org/docs/stable/generated/torch.nn.attention.sdpa_kernel.html) as a context manager:
```diff
import torch
+ from torch.nn.attention import SDPBackend, sdpa_kernel
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16).to("cuda")
input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+ with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
If you see a bug with the traceback below, try using the nightly version of PyTorch which may have broader coverage for FlashAttention:
```bash
RuntimeError: No available kernel. Aborting execution.
# install PyTorch nightly
pip3 install -U --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118
```
## BetterTransformer
<Tip warning={true}>
Some BetterTransformer features are being upstreamed to Transformers with default support for native `torch.nn.scaled_dot_product_attention`. BetterTransformer still has a wider coverage than the Transformers SDPA integration, but you can expect more and more architectures to natively support SDPA in Transformers.
</Tip>
<Tip>
Check out our benchmarks with BetterTransformer and scaled dot product attention in the [Out of the box acceleration and memory savings of 🤗 decoder models with PyTorch 2.0](https://pytorch.org/blog/out-of-the-box-acceleration/) and learn more about the fastpath execution in the [BetterTransformer](https://medium.com/pytorch/bettertransformer-out-of-the-box-performance-for-huggingface-transformers-3fbe27d50ab2) blog post.
</Tip>
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 (SDPA)](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention), and it calls optimized kernels like [FlashAttention](https://huggingface.co/papers/2205.14135) under the hood.
Before you start, make sure you have 🤗 Optimum [installed](https://huggingface.co/docs/optimum/installation).
Then you can enable BetterTransformer with the [`PreTrainedModel.to_bettertransformer`] method:
```python
model = model.to_bettertransformer()
```
You can return the original Transformers model with the [`~PreTrainedModel.reverse_bettertransformer`] method. You should use this before saving your model to use the canonical Transformers modeling:
```py
model = model.reverse_bettertransformer()
model.save_pretrained("saved_model")
```
## bitsandbytes
bitsandbytes is a quantization library that includes support for 4-bit and 8-bit quantization. Quantization reduces your model size compared to its native full precision version, making it easier to fit large models onto GPUs with limited memory.
Make sure you have bitsandbytes and 🤗 Accelerate installed:
```bash
# these versions support 8-bit and 4-bit
pip install bitsandbytes>=0.39.0 accelerate>=0.20.0
# install Transformers
pip install transformers
```
### 4-bit
To load a model in 4-bit for inference, use the `load_in_4bit` parameter. The `device_map` parameter is optional, but we recommend setting it to `"auto"` to allow 🤗 Accelerate to automatically and efficiently allocate the model given the available resources in the environment.
```py
from transformers import AutoModelForCausalLM
model_name = "bigscience/bloom-1b7"
model_4bit = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto", load_in_4bit=True)
```
To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 2GB of memory to the first GPU and 5GB of memory to the second GPU:
```py
max_memory_mapping = {0: "2GB", 1: "5GB"}
model_name = "bigscience/bloom-3b"
model_4bit = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype="auto", device_map="auto", load_in_4bit=True, max_memory=max_memory_mapping
)
```
### 8-bit
<Tip>
If you're curious and interested in learning more about the concepts underlying 8-bit quantization, read the [Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using Hugging Face Transformers, Accelerate and bitsandbytes](https://huggingface.co/blog/hf-bitsandbytes-integration) blog post.
</Tip>
To load a model in 8-bit for inference, use the `load_in_8bit` parameter. The `device_map` parameter is optional, but we recommend setting it to `"auto"` to allow 🤗 Accelerate to automatically and efficiently allocate the model given the available resources in the environment:
```py
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
model_name = "bigscience/bloom-1b7"
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", quantization_config=BitsAndBytesConfig(load_in_8bit=True))
```
If you're loading a model in 8-bit for text generation, you should use the [`~transformers.GenerationMixin.generate`] method instead of the [`Pipeline`] function which is not optimized for 8-bit models and will be slower. Some sampling strategies, like nucleus sampling, are also not supported by the [`Pipeline`] for 8-bit models. You should also place all inputs on the same device as the model:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "bigscience/bloom-1b7"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", quantization_config=BitsAndBytesConfig(load_in_8bit=True))
prompt = "Hello, my llama is cute"
inputs = tokenizer(prompt, return_tensors="pt").to(model_8bit.device)
generated_ids = model_8bit.generate(**inputs)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
```
To load a model in 8-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 2GB of memory to the first GPU and 5GB of memory to the second GPU:
```py
max_memory_mapping = {0: "2GB", 1: "5GB"}
model_name = "bigscience/bloom-3b"
model_8bit = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype="auto", device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping
)
```
<Tip>
Feel free to try running a 11 billion parameter [T5 model](https://colab.research.google.com/drive/1YORPWx4okIHXnjW7MSAidXN29mPVNT7F?usp=sharing) or the 3 billion parameter [BLOOM model](https://colab.research.google.com/drive/1qOjXfQIAULfKvZqwCen8-MoWKGdSatZ4?usp=sharing) for inference on Google Colab's free tier GPUs!
</Tip>
## 🤗 Optimum
<Tip>
Learn more details about using ORT with 🤗 Optimum in the [Accelerated inference on NVIDIA GPUs](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/gpu#accelerated-inference-on-nvidia-gpus) and [Accelerated inference on AMD GPUs](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/amdgpu#accelerated-inference-on-amd-gpus) guides. This section only provides a brief and simple example.
</Tip>
ONNX Runtime (ORT) is a model accelerator that supports accelerated inference on Nvidia GPUs, and AMD GPUs that use [ROCm](https://www.amd.com/en/products/software/rocm.html) stack. ORT uses optimization techniques like fusing common operations into a single node and constant folding to reduce the number of computations performed and speedup inference. ORT also places the most computationally intensive operations on the GPU and the rest on the CPU to intelligently distribute the workload between the two devices.
ORT is supported by 🤗 Optimum which can be used in 🤗 Transformers. You'll need to use an [`~optimum.onnxruntime.ORTModel`] for the task you're solving, and specify the `provider` parameter which can be set to either [`CUDAExecutionProvider`](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/gpu#cudaexecutionprovider), [`ROCMExecutionProvider`](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/amdgpu) or [`TensorrtExecutionProvider`](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/gpu#tensorrtexecutionprovider). If you want to load a model that was not yet exported to ONNX, you can set `export=True` to convert your model on-the-fly to the ONNX format:
```py
from optimum.onnxruntime import ORTModelForSequenceClassification
ort_model = ORTModelForSequenceClassification.from_pretrained(
"distilbert/distilbert-base-uncased-finetuned-sst-2-english",
export=True,
provider="CUDAExecutionProvider",
)
```
Now you're free to use the model for inference:
```py
from optimum.pipelines import pipeline
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased-finetuned-sst-2-english")
pipeline = pipeline(task="text-classification", model=ort_model, tokenizer=tokenizer, device="cuda:0")
result = pipeline("Both the music and visual were astounding, not to mention the actors performance.")
```
## Combine optimizations
It is often possible to combine several of the optimization techniques described above to get the best inference performance possible for your model. For example, you can load a model in 4-bit, and then enable BetterTransformer with FlashAttention:
```py
import torch
from torch.nn.attention import SDPBackend, sdpa_kernel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
# load model in 4-bit
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype="auto", quantization_config=quantization_config)
input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
# enable FlashAttention
with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
| transformers/docs/source/en/perf_infer_gpu_one.md/0 | {
"file_path": "transformers/docs/source/en/perf_infer_gpu_one.md",
"repo_id": "transformers",
"token_count": 12327
} |
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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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# AWQ
<Tip>
Try AWQ quantization with this [notebook](https://colab.research.google.com/drive/1HzZH89yAXJaZgwJDhQj9LqSBux932BvY)!
</Tip>
[Activation-aware Weight Quantization (AWQ)](https://hf.co/papers/2306.00978) doesn't quantize all the weights in a model, and instead, it preserves a small percentage of weights that are important for LLM performance. This significantly reduces quantization loss such that you can run models in 4-bit precision without experiencing any performance degradation.
There are several libraries for quantizing models with the AWQ algorithm, such as [llm-awq](https://github.com/mit-han-lab/llm-awq), [autoawq](https://github.com/casper-hansen/AutoAWQ) or [optimum-intel](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc). Transformers supports loading models quantized with the llm-awq and autoawq libraries. This guide will show you how to load models quantized with autoawq, but the process is similar for llm-awq quantized models.
Make sure you have autoawq installed:
```bash
pip install autoawq
```
AWQ-quantized models can be identified by checking the `quantization_config` attribute in the model's [config.json](https://huggingface.co/TheBloke/zephyr-7B-alpha-AWQ/blob/main/config.json) file:
```json
{
"_name_or_path": "/workspace/process/huggingfaceh4_zephyr-7b-alpha/source",
"architectures": [
"MistralForCausalLM"
],
...
...
...
"quantization_config": {
"quant_method": "awq",
"zero_point": true,
"group_size": 128,
"bits": 4,
"version": "gemm"
}
}
```
A quantized model is loaded with the [`~PreTrainedModel.from_pretrained`] method. If you loaded your model on the CPU, make sure to move it to a GPU device first. Use the `device_map` parameter to specify where to place the model:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "TheBloke/zephyr-7B-alpha-AWQ"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:0")
```
Loading an AWQ-quantized model automatically sets other weights to fp16 by default for performance reasons. If you want to load these other weights in a different format, use the `torch_dtype` parameter:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "TheBloke/zephyr-7B-alpha-AWQ"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
```
AWQ quantization can also be combined with [FlashAttention-2](../perf_infer_gpu_one#flashattention-2) to further accelerate inference:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-alpha-AWQ", attn_implementation="flash_attention_2", device_map="cuda:0")
```
## Fused modules
Fused modules offers improved accuracy and performance and it is supported out-of-the-box for AWQ modules for [Llama](https://huggingface.co/meta-llama) and [Mistral](https://huggingface.co/mistralai/Mistral-7B-v0.1) architectures, but you can also fuse AWQ modules for unsupported architectures.
<Tip warning={true}>
Fused modules cannot be combined with other optimization techniques such as FlashAttention-2.
</Tip>
<hfoptions id="fuse">
<hfoption id="supported architectures">
To enable fused modules for supported architectures, create an [`AwqConfig`] and set the parameters `fuse_max_seq_len` and `do_fuse=True`. The `fuse_max_seq_len` parameter is the total sequence length and it should include the context length and the expected generation length. You can set it to a larger value to be safe.
For example, to fuse the AWQ modules of the [TheBloke/Mistral-7B-OpenOrca-AWQ](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ) model.
```python
import torch
from transformers import AwqConfig, AutoModelForCausalLM
model_id = "TheBloke/Mistral-7B-OpenOrca-AWQ"
quantization_config = AwqConfig(
bits=4,
fuse_max_seq_len=512,
do_fuse=True,
)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config).to(0)
```
The [TheBloke/Mistral-7B-OpenOrca-AWQ](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ) model was benchmarked with `batch_size=1` with and without fused modules.
<figcaption class="text-center text-gray-500 text-lg">Unfused module</figcaption>
| Batch Size | Prefill Length | Decode Length | Prefill tokens/s | Decode tokens/s | Memory (VRAM) |
|-------------:|-----------------:|----------------:|-------------------:|------------------:|:----------------|
| 1 | 32 | 32 | 60.0984 | 38.4537 | 4.50 GB (5.68%) |
| 1 | 64 | 64 | 1333.67 | 31.6604 | 4.50 GB (5.68%) |
| 1 | 128 | 128 | 2434.06 | 31.6272 | 4.50 GB (5.68%) |
| 1 | 256 | 256 | 3072.26 | 38.1731 | 4.50 GB (5.68%) |
| 1 | 512 | 512 | 3184.74 | 31.6819 | 4.59 GB (5.80%) |
| 1 | 1024 | 1024 | 3148.18 | 36.8031 | 4.81 GB (6.07%) |
| 1 | 2048 | 2048 | 2927.33 | 35.2676 | 5.73 GB (7.23%) |
<figcaption class="text-center text-gray-500 text-lg">Fused module</figcaption>
| Batch Size | Prefill Length | Decode Length | Prefill tokens/s | Decode tokens/s | Memory (VRAM) |
|-------------:|-----------------:|----------------:|-------------------:|------------------:|:----------------|
| 1 | 32 | 32 | 81.4899 | 80.2569 | 4.00 GB (5.05%) |
| 1 | 64 | 64 | 1756.1 | 106.26 | 4.00 GB (5.05%) |
| 1 | 128 | 128 | 2479.32 | 105.631 | 4.00 GB (5.06%) |
| 1 | 256 | 256 | 1813.6 | 85.7485 | 4.01 GB (5.06%) |
| 1 | 512 | 512 | 2848.9 | 97.701 | 4.11 GB (5.19%) |
| 1 | 1024 | 1024 | 3044.35 | 87.7323 | 4.41 GB (5.57%) |
| 1 | 2048 | 2048 | 2715.11 | 89.4709 | 5.57 GB (7.04%) |
The speed and throughput of fused and unfused modules were also tested with the [optimum-benchmark](https://github.com/huggingface/optimum-benchmark) library.
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/fused_forward_memory_plot.png" alt="generate throughput per batch size" />
<figcaption class="mt-2 text-center text-sm text-gray-500">forward peak memory/batch size</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/fused_generate_throughput_plot.png" alt="forward latency per batch size" />
<figcaption class="mt-2 text-center text-sm text-gray-500">generate throughput/batch size</figcaption>
</div>
</div>
</hfoption>
<hfoption id="unsupported architectures">
For architectures that don't support fused modules yet, you need to create a custom fusing mapping to define which modules need to be fused with the `modules_to_fuse` parameter. For example, to fuse the AWQ modules of the [TheBloke/Yi-34B-AWQ](https://huggingface.co/TheBloke/Yi-34B-AWQ) model.
```python
import torch
from transformers import AwqConfig, AutoModelForCausalLM
model_id = "TheBloke/Yi-34B-AWQ"
quantization_config = AwqConfig(
bits=4,
fuse_max_seq_len=512,
modules_to_fuse={
"attention": ["q_proj", "k_proj", "v_proj", "o_proj"],
"layernorm": ["ln1", "ln2", "norm"],
"mlp": ["gate_proj", "up_proj", "down_proj"],
"use_alibi": False,
"num_attention_heads": 56,
"num_key_value_heads": 8,
"hidden_size": 7168
}
)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config).to(0)
```
The parameter `modules_to_fuse` should include:
- `"attention"`: The names of the attention layers to fuse in the following order: query, key, value and output projection layer. If you don't want to fuse these layers, pass an empty list.
- `"layernorm"`: The names of all the LayerNorm layers you want to replace with a custom fused LayerNorm. If you don't want to fuse these layers, pass an empty list.
- `"mlp"`: The names of the MLP layers you want to fuse into a single MLP layer in the order: (gate (dense, layer, post-attention) / up / down layers).
- `"use_alibi"`: If your model uses ALiBi positional embedding.
- `"num_attention_heads"`: The number of attention heads.
- `"num_key_value_heads"`: The number of key value heads that should be used to implement Grouped Query Attention (GQA). If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA), otherwise GQA is used.
- `"hidden_size"`: The dimension of the hidden representations.
</hfoption>
</hfoptions>
## ExLlama-v2 support
Recent versions of `autoawq` supports ExLlama-v2 kernels for faster prefill and decoding. To get started, first install the latest version of `autoawq` by running:
```bash
pip install git+https://github.com/casper-hansen/AutoAWQ.git
```
Get started by passing an `AwqConfig()` with `version="exllama"`.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig
quantization_config = AwqConfig(version="exllama")
model = AutoModelForCausalLM.from_pretrained(
"TheBloke/Mistral-7B-Instruct-v0.1-AWQ",
quantization_config=quantization_config,
device_map="auto",
)
input_ids = torch.randint(0, 100, (1, 128), dtype=torch.long, device="cuda")
output = model(input_ids)
print(output.logits)
tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-AWQ")
input_ids = tokenizer.encode("How to make a cake", return_tensors="pt").to(model.device)
output = model.generate(input_ids, do_sample=True, max_length=50, pad_token_id=50256)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
<Tip warning={true}>
Note this feature is supported on AMD GPUs.
</Tip>
## CPU support
Recent versions of `autoawq` supports CPU with ipex op optimizations. To get started, first install the latest version of `autoawq` by running:
```bash
pip install intel-extension-for-pytorch
pip install git+https://github.com/casper-hansen/AutoAWQ.git
```
Get started by passing an `AwqConfig()` with `version="ipex"`.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig
quantization_config = AwqConfig(version="ipex")
model = AutoModelForCausalLM.from_pretrained(
"TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ",
quantization_config=quantization_config,
device_map="cpu",
)
input_ids = torch.randint(0, 100, (1, 128), dtype=torch.long, device="cpu")
output = model(input_ids)
print(output.logits)
tokenizer = AutoTokenizer.from_pretrained("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ")
input_ids = tokenizer.encode("How to make a cake", return_tensors="pt")
pad_token_id = tokenizer.eos_token_id
output = model.generate(input_ids, do_sample=True, max_length=50, pad_token_id=pad_token_id)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
<Tip warning={true}>
Note this feature is supported on Intel CPUs.
</Tip> | transformers/docs/source/en/quantization/awq.md/0 | {
"file_path": "transformers/docs/source/en/quantization/awq.md",
"repo_id": "transformers",
"token_count": 4825
} |
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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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# Train with a script
Along with the 🤗 Transformers [notebooks](./notebooks), there are also example scripts demonstrating how to train a model for a task with [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch), [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow), or [JAX/Flax](https://github.com/huggingface/transformers/tree/main/examples/flax).
You will also find scripts we've used in our [research projects](https://github.com/huggingface/transformers/tree/main/examples/research_projects) and [legacy examples](https://github.com/huggingface/transformers/tree/main/examples/legacy) which are mostly community contributed. These scripts are not actively maintained and require a specific version of 🤗 Transformers that will most likely be incompatible with the latest version of the library.
The example scripts are not expected to work out-of-the-box on every problem, and you may need to adapt the script to the problem you're trying to solve. To help you with this, most of the scripts fully expose how data is preprocessed, allowing you to edit it as necessary for your use case.
For any feature you'd like to implement in an example script, please discuss it on the [forum](https://discuss.huggingface.co/) or in an [issue](https://github.com/huggingface/transformers/issues) before submitting a Pull Request. While we welcome bug fixes, it is unlikely we will merge a Pull Request that adds more functionality at the cost of readability.
This guide will show you how to run an example summarization training script in [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) and [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization). All examples are expected to work with both frameworks unless otherwise specified.
## Setup
To successfully run the latest version of the example scripts, you have to **install 🤗 Transformers from source** in a new virtual environment:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
pip install .
```
For older versions of the example scripts, click on the toggle below:
<details>
<summary>Examples for older versions of 🤗 Transformers</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>
Then switch your current clone of 🤗 Transformers to a specific version, like v3.5.1 for example:
```bash
git checkout tags/v3.5.1
```
After you've setup the correct library version, navigate to the example folder of your choice and install the example specific requirements:
```bash
pip install -r requirements.txt
```
## Run a script
<frameworkcontent>
<pt>
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset with the [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/google-t5/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
```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>
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset using Keras on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/google-t5/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
```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>
## Distributed training and mixed precision
The [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) supports distributed training and mixed precision, which means you can also use it in a script. To enable both of these features:
- Add the `fp16` or `bf16` argument to enable mixed precision. XPU devices only supports `bf16` for mixed precision training.
- Set the number of GPUs to use with the `nproc_per_node` argument.
```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 scripts utilize a [`MirroredStrategy`](https://www.tensorflow.org/guide/distributed_training#mirroredstrategy) for distributed training, and you don't need to add any additional arguments to the training script. The TensorFlow script will use multiple GPUs by default if they are available.
## Run a script on a TPU
<frameworkcontent>
<pt>
Tensor Processing Units (TPUs) are specifically designed to accelerate performance. PyTorch supports TPUs with the [XLA](https://www.tensorflow.org/xla) deep learning compiler (see [here](https://github.com/pytorch/xla/blob/master/README.md) for more details). To use a TPU, launch the `xla_spawn.py` script and use the `num_cores` argument to set the number of TPU cores you want to use.
```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) are specifically designed to accelerate performance. TensorFlow scripts utilize a [`TPUStrategy`](https://www.tensorflow.org/guide/distributed_training#tpustrategy) for training on TPUs. To use a TPU, pass the name of the TPU resource to the `tpu` argument.
```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>
## Run a script with 🤗 Accelerate
🤗 [Accelerate](https://huggingface.co/docs/accelerate) is a PyTorch-only library that offers a unified method for training a model on several types of setups (CPU-only, multiple GPUs, TPUs) while maintaining complete visibility into the PyTorch training loop. Make sure you have 🤗 Accelerate installed if you don't already have it:
> Note: As Accelerate is rapidly developing, the git version of accelerate must be installed to run the scripts
```bash
pip install git+https://github.com/huggingface/accelerate
```
Instead of the `run_summarization.py` script, you need to use the `run_summarization_no_trainer.py` script. 🤗 Accelerate supported scripts will have a `task_no_trainer.py` file in the folder. Begin by running the following command to create and save a configuration file:
```bash
accelerate config
```
Test your setup to make sure it is configured correctly:
```bash
accelerate test
```
Now you are ready to launch the training:
```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
```
## Use a custom dataset
The summarization script supports custom datasets as long as they are a CSV or JSON Line file. When you use your own dataset, you need to specify several additional arguments:
- `train_file` and `validation_file` specify the path to your training and validation files.
- `text_column` is the input text to summarize.
- `summary_column` is the target text to output.
A summarization script using a custom dataset would look like this:
```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
```
## Test a script
It is often a good idea to run your script on a smaller number of dataset examples to ensure everything works as expected before committing to an entire dataset which may take hours to complete. Use the following arguments to truncate the dataset to a maximum number of samples:
- `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
```
Not all example scripts support the `max_predict_samples` argument. If you aren't sure whether your script supports this argument, add the `-h` argument to check:
```bash
examples/pytorch/summarization/run_summarization.py -h
```
## Resume training from checkpoint
Another helpful option to enable is resuming training from a previous checkpoint. This will ensure you can pick up where you left off without starting over if your training gets interrupted. There are two methods to resume training from a checkpoint.
The first method uses the `output_dir previous_output_dir` argument to resume training from the latest checkpoint stored in `output_dir`. In this case, you should remove `overwrite_output_dir`:
```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
```
The second method uses the `resume_from_checkpoint path_to_specific_checkpoint` argument to resume training from a specific checkpoint folder.
```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
```
## Share your model
All scripts can upload your final model to the [Model Hub](https://huggingface.co/models). Make sure you are logged into Hugging Face before you begin:
```bash
huggingface-cli login
```
Then add the `push_to_hub` argument to the script. This argument will create a repository with your Hugging Face username and the folder name specified in `output_dir`.
To give your repository a specific name, use the `push_to_hub_model_id` argument to add it. The repository will be automatically listed under your namespace.
The following example shows how to upload a model with a specific repository name:
```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/en/run_scripts.md/0 | {
"file_path": "transformers/docs/source/en/run_scripts.md",
"repo_id": "transformers",
"token_count": 5934
} |
<!--Copyright 2024 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.
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# Mask Generation
Mask generation is the task of generating semantically meaningful masks for an image.
This task is very similar to [image segmentation](semantic_segmentation), but many differences exist. Image segmentation models are trained on labeled datasets and are limited to the classes they have seen during training; they return a set of masks and corresponding classes, given an image.
Mask generation models are trained on large amounts of data and operate in two modes.
- Prompting mode: In this mode, the model takes in an image and a prompt, where a prompt can be a 2D point location (XY coordinates) in the image within an object or a bounding box surrounding an object. In prompting mode, the model only returns the mask over the object
that the prompt is pointing out.
- Segment Everything mode: In segment everything, given an image, the model generates every mask in the image. To do so, a grid of points is generated and overlaid on the image for inference.
Mask generation task is supported by [Segment Anything Model (SAM)](model_doc/sam). It's a powerful model that consists of a Vision Transformer-based image encoder, a prompt encoder, and a two-way transformer mask decoder. Images and prompts are encoded, and the decoder takes these embeddings and generates valid masks.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/sam.png" alt="SAM Architecture"/>
</div>
SAM serves as a powerful foundation model for segmentation as it has large data coverage. It is trained on
[SA-1B](https://ai.meta.com/datasets/segment-anything/), a dataset with 1 million images and 1.1 billion masks.
In this guide, you will learn how to:
- Infer in segment everything mode with batching,
- Infer in point prompting mode,
- Infer in box prompting mode.
First, let's install `transformers`:
```bash
pip install -q transformers
```
## Mask Generation Pipeline
The easiest way to infer mask generation models is to use the `mask-generation` pipeline.
```python
>>> from transformers import pipeline
>>> checkpoint = "facebook/sam-vit-base"
>>> mask_generator = pipeline(model=checkpoint, task="mask-generation")
```
Let's see the image.
```python
from PIL import Image
import requests
img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"
image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg" alt="Example Image"/>
</div>
Let's segment everything. `points-per-batch` enables parallel inference of points in segment everything mode. This enables faster inference, but consumes more memory. Moreover, SAM only enables batching over points and not the images. `pred_iou_thresh` is the IoU confidence threshold where only the masks above that certain threshold are returned.
```python
masks = mask_generator(image, points_per_batch=128, pred_iou_thresh=0.88)
```
The `masks` looks like the following:
```bash
{'masks': [array([[False, False, False, ..., True, True, True],
[False, False, False, ..., True, True, True],
[False, False, False, ..., True, True, True],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]]),
array([[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
'scores': tensor([0.9972, 0.9917,
...,
}
```
We can visualize them like this:
```python
import matplotlib.pyplot as plt
plt.imshow(image, cmap='gray')
for i, mask in enumerate(masks["masks"]):
plt.imshow(mask, cmap='viridis', alpha=0.1, vmin=0, vmax=1)
plt.axis('off')
plt.show()
```
Below is the original image in grayscale with colorful maps overlaid. Very impressive.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee_segmented.png" alt="Visualized"/>
</div>
## Model Inference
### Point Prompting
You can also use the model without the pipeline. To do so, initialize the model and
the processor.
```python
from transformers import SamModel, SamProcessor
import torch
from accelerate.test_utils.testing import get_backend
# automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
device, _, _ = get_backend()
model = SamModel.from_pretrained("facebook/sam-vit-base").to(device)
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
```
To do point prompting, pass the input point to the processor, then take the processor output
and pass it to the model for inference. To post-process the model output, pass the outputs and
`original_sizes` and `reshaped_input_sizes` we take from the processor's initial output. We need to pass these
since the processor resizes the image, and the output needs to be extrapolated.
```python
input_points = [[[2592, 1728]]] # point location of the bee
inputs = processor(image, input_points=input_points, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())
```
We can visualize the three masks in the `masks` output.
```python
import matplotlib.pyplot as plt
import numpy as np
fig, axes = plt.subplots(1, 4, figsize=(15, 5))
axes[0].imshow(image)
axes[0].set_title('Original Image')
mask_list = [masks[0][0][0].numpy(), masks[0][0][1].numpy(), masks[0][0][2].numpy()]
for i, mask in enumerate(mask_list, start=1):
overlayed_image = np.array(image).copy()
overlayed_image[:,:,0] = np.where(mask == 1, 255, overlayed_image[:,:,0])
overlayed_image[:,:,1] = np.where(mask == 1, 0, overlayed_image[:,:,1])
overlayed_image[:,:,2] = np.where(mask == 1, 0, overlayed_image[:,:,2])
axes[i].imshow(overlayed_image)
axes[i].set_title(f'Mask {i}')
for ax in axes:
ax.axis('off')
plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/masks.png" alt="Visualized"/>
</div>
### Box Prompting
You can also do box prompting in a similar fashion to point prompting. You can simply pass the input box in the format of a list
`[x_min, y_min, x_max, y_max]` format along with the image to the `processor`. Take the processor output and directly pass it
to the model, then post-process the output again.
```python
# bounding box around the bee
box = [2350, 1600, 2850, 2100]
inputs = processor(
image,
input_boxes=[[[box]]],
return_tensors="pt"
).to("cuda")
with torch.no_grad():
outputs = model(**inputs)
mask = processor.image_processor.post_process_masks(
outputs.pred_masks.cpu(),
inputs["original_sizes"].cpu(),
inputs["reshaped_input_sizes"].cpu()
)[0][0][0].numpy()
```
You can visualize the bounding box around the bee as shown below.
```python
import matplotlib.patches as patches
fig, ax = plt.subplots()
ax.imshow(image)
rectangle = patches.Rectangle((2350, 1600), 500, 500, linewidth=2, edgecolor='r', facecolor='none')
ax.add_patch(rectangle)
ax.axis("off")
plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/bbox.png" alt="Visualized Bbox"/>
</div>
You can see the inference output below.
```python
fig, ax = plt.subplots()
ax.imshow(image)
ax.imshow(mask, cmap='viridis', alpha=0.4)
ax.axis("off")
plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/box_inference.png" alt="Visualized Inference"/>
</div>
| transformers/docs/source/en/tasks/mask_generation.md/0 | {
"file_path": "transformers/docs/source/en/tasks/mask_generation.md",
"repo_id": "transformers",
"token_count": 2873
} |
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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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# Zero-shot image classification
[[open-in-colab]]
Zero-shot image classification is a task that involves classifying images into different categories using a model that was
not explicitly trained on data containing labeled examples from those specific categories.
Traditionally, image classification requires training a model on a specific set of labeled images, and this model learns to
"map" certain image features to labels. When there's a need to use such model for a classification task that introduces a
new set of labels, fine-tuning is required to "recalibrate" the model.
In contrast, zero-shot or open vocabulary image classification models are typically multi-modal models that have been trained on a large
dataset of images and associated descriptions. These models learn aligned vision-language representations that can be used for many downstream tasks including zero-shot image classification.
This is a more flexible approach to image classification that allows models to generalize to new and unseen categories
without the need for additional training data and enables users to query images with free-form text descriptions of their target objects .
In this guide you'll learn how to:
* create a zero-shot image classification pipeline
* run zero-shot image classification inference by hand
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install -q "transformers[torch]" pillow
```
## Zero-shot image classification pipeline
The simplest way to try out inference with a model supporting zero-shot image classification is to use the corresponding [`pipeline`].
Instantiate a pipeline from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?pipeline_tag=zero-shot-image-classification&sort=downloads):
```python
>>> from transformers import pipeline
>>> checkpoint = "openai/clip-vit-large-patch14"
>>> detector = pipeline(model=checkpoint, task="zero-shot-image-classification")
```
Next, choose an image you'd like to classify.
```py
>>> from PIL import Image
>>> import requests
>>> url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&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/owl.jpg" alt="Photo of an owl"/>
</div>
Pass the image and the candidate object labels to the pipeline. Here we pass the image directly; other suitable options
include a local path to an image or an image url.
The candidate labels can be simple words like in this example, or more descriptive.
```py
>>> predictions = detector(image, candidate_labels=["fox", "bear", "seagull", "owl"])
>>> predictions
[{'score': 0.9996670484542847, 'label': 'owl'},
{'score': 0.000199399160919711, 'label': 'seagull'},
{'score': 7.392891711788252e-05, 'label': 'fox'},
{'score': 5.96074532950297e-05, 'label': 'bear'}]
```
## Zero-shot image classification by hand
Now that you've seen how to use the zero-shot image classification pipeline, let's take a look how you can run zero-shot
image classification manually.
Start by loading the model and associated processor from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?pipeline_tag=zero-shot-image-classification&sort=downloads).
Here we'll use the same checkpoint as before:
```py
>>> from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
>>> model = AutoModelForZeroShotImageClassification.from_pretrained(checkpoint)
>>> processor = AutoProcessor.from_pretrained(checkpoint)
```
Let's take a different image to switch things up.
```py
>>> from PIL import Image
>>> import requests
>>> url = "https://unsplash.com/photos/xBRQfR2bqNI/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjc4Mzg4ODEx&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/car.jpg" alt="Photo of a car"/>
</div>
Use the processor to prepare the inputs for the model. The processor combines an image processor that prepares the
image for the model by resizing and normalizing it, and a tokenizer that takes care of the text inputs.
```py
>>> candidate_labels = ["tree", "car", "bike", "cat"]
# follows the pipeline prompt template to get same results
>>> candidate_labels = [f'This is a photo of {label}.' for label in candidate_labels]
>>> inputs = processor(images=image, text=candidate_labels, return_tensors="pt", padding=True)
```
Pass the inputs through the model, and post-process the results:
```py
>>> import torch
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> logits = outputs.logits_per_image[0]
>>> probs = logits.softmax(dim=-1).numpy()
>>> scores = probs.tolist()
>>> result = [
... {"score": score, "label": candidate_label}
... for score, candidate_label in sorted(zip(probs, candidate_labels), key=lambda x: -x[0])
... ]
>>> result
[{'score': 0.998572, 'label': 'car'},
{'score': 0.0010570387, 'label': 'bike'},
{'score': 0.0003393686, 'label': 'tree'},
{'score': 3.1572064e-05, 'label': 'cat'}]
``` | transformers/docs/source/en/tasks/zero_shot_image_classification.md/0 | {
"file_path": "transformers/docs/source/en/tasks/zero_shot_image_classification.md",
"repo_id": "transformers",
"token_count": 1801
} |
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# Modelado de lenguaje
El modelado de lenguaje predice palabras en un enunciado. Hay dos formas de modelado de lenguaje.
<Youtube id="Vpjb1lu0MDk"/>
El modelado de lenguaje causal predice el siguiente token en una secuencia de tokens, y el modelo solo puede considerar los tokens a la izquierda.
<Youtube id="mqElG5QJWUg"/>
El modelado de lenguaje por enmascaramiento predice un token enmascarado en una secuencia, y el modelo puede considerar los tokens bidireccionalmente.
Esta guía te mostrará cómo realizar fine-tuning [DistilGPT2](https://huggingface.co/distilbert/distilgpt2) para modelos de lenguaje causales y [DistilRoBERTa](https://huggingface.co/distilbert/distilroberta-base) para modelos de lenguaje por enmascaramiento en el [r/askscience](https://www.reddit.com/r/askscience/) subdataset [ELI5](https://huggingface.co/datasets/eli5).
<Tip>
Mira la [página de tarea](https://huggingface.co/tasks/text-generation) para generación de texto y la [página de tarea](https://huggingface.co/tasks/fill-mask) para modelos de lenguajes por enmascaramiento para obtener más información sobre los modelos, datasets, y métricas asociadas.
</Tip>
## Carga el dataset ELI5
Carga solo los primeros 5000 registros desde la biblioteca 🤗 Datasets, dado que es bastante grande:
```py
>>> from datasets import load_dataset
>>> eli5 = load_dataset("eli5", split="train_asks[:5000]")
```
Divide este dataset en subdatasets para el entrenamiento y el test:
```py
eli5 = eli5.train_test_split(test_size=0.2)
```
Luego observa un ejemplo:
```py
>>> eli5["train"][0]
{'answers': {'a_id': ['c3d1aib', 'c3d4lya'],
'score': [6, 3],
'text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
"Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"]},
'answers_urls': {'url': []},
'document': '',
'q_id': 'nyxfp',
'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']},
'subreddit': 'askscience',
'title': 'Few questions about this space walk photograph.',
'title_urls': {'url': []}}
```
Observa que `text` es un subcampo anidado dentro del diccionario `answers`. Cuando preproceses el dataset, deberás extraer el subcampo `text` en una columna aparte.
## Preprocesamiento
<Youtube id="ma1TrR7gE7I"/>
Para modelados de lenguaje causales carga el tokenizador DistilGPT2 para procesar el subcampo `text`:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
```
<Youtube id="8PmhEIXhBvI"/>
Para modelados de lenguaje por enmascaramiento carga el tokenizador DistilRoBERTa, en lugar de DistilGPT2:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilroberta-base")
```
Extrae el subcampo `text` desde su estructura anidado con el método [`flatten`](https://huggingface.co/docs/datasets/process#flatten):
```py
>>> eli5 = eli5.flatten()
>>> eli5["train"][0]
{'answers.a_id': ['c3d1aib', 'c3d4lya'],
'answers.score': [6, 3],
'answers.text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
"Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"],
'answers_urls.url': [],
'document': '',
'q_id': 'nyxfp',
'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'],
'subreddit': 'askscience',
'title': 'Few questions about this space walk photograph.',
'title_urls.url': []}
```
Cada subcampo es ahora una columna separada, como lo indica el prefijo `answers`. Observa que `answers.text` es una lista. En lugar de tokenizar cada enunciado por separado, convierte la lista en un string para tokenizarlos conjuntamente.
Así es como puedes crear una función de preprocesamiento para convertir la lista en una cadena y truncar las secuencias para que no superen la longitud máxima de input de DistilGPT2:
```py
>>> def preprocess_function(examples):
... return tokenizer([" ".join(x) for x in examples["answers.text"]], truncation=True)
```
Usa de 🤗 Datasets la función [`map`](https://huggingface.co/docs/datasets/process#map) para aplicar la función de preprocesamiento sobre el dataset en su totalidad. Puedes acelerar la función `map` configurando el argumento `batched=True` para procesar múltiples elementos del dataset a la vez y aumentar la cantidad de procesos con `num_proc`. Elimina las columnas que no necesitas:
```py
>>> tokenized_eli5 = eli5.map(
... preprocess_function,
... batched=True,
... num_proc=4,
... remove_columns=eli5["train"].column_names,
... )
```
Ahora necesitas una segunda función de preprocesamiento para capturar el texto truncado de cualquier ejemplo demasiado largo para evitar cualquier pérdida de información. Esta función de preprocesamiento debería:
- Concatenar todo el texto.
- Dividir el texto concatenado en trozos más pequeños definidos por un `block_size`.
```py
>>> block_size = 128
>>> def group_texts(examples):
... concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
... total_length = len(concatenated_examples[list(examples.keys())[0]])
... total_length = (total_length // block_size) * block_size
... 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
```
Aplica la función `group_texts` sobre todo el dataset:
```py
>>> lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)
```
Para modelados de lenguaje causales, usa [`DataCollatorForLanguageModeling`] para crear un lote de ejemplos. Esto también *rellenará dinámicamente* tu texto a la dimensión del elemento más largo del lote para que de esta manera tengan largo uniforme. Si bien es posible rellenar tu texto en la función `tokenizer` mediante el argumento `padding=True`, el rellenado dinámico es más eficiente.
<frameworkcontent>
<pt>
Puedes usar el token de final de secuencia como el token de relleno y asignar `mlm=False`. Esto usará los inputs como etiquetas movidas un elemento hacia la derecha:
```py
>>> from transformers import DataCollatorForLanguageModeling
>>> tokenizer.pad_token = tokenizer.eos_token
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
```
Para modelados de lenguaje por enmascaramiento usa el mismo [`DataCollatorForLanguageModeling`] excepto que deberás especificar `mlm_probability` para enmascarar tokens aleatoriamente cada vez que iteras sobre los datos.
```py
>>> from transformers import DataCollatorForLanguageModeling
>>> tokenizer.pad_token = tokenizer.eos_token
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)
```
</pt>
<tf>
Puedes usar el token de final de secuencia como el token de relleno y asignar `mlm=False`. Esto usará los inputs como etiquetas movidas un elemento hacia la derecha:
```py
>>> from transformers import DataCollatorForLanguageModeling
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")
```
Para modelados de lenguajes por enmascaramiento usa el mismo [`DataCollatorForLanguageModeling`] excepto que deberás especificar `mlm_probability` para enmascarar tokens aleatoriamente cada vez que iteras sobre los datos.
```py
>>> from transformers import DataCollatorForLanguageModeling
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")
```
</tf>
</frameworkcontent>
## Modelado de lenguaje causal
El modelado de lenguaje causal es frecuentemente utilizado para generación de texto. Esta sección te muestra cómo realizar fine-tuning a [DistilGPT2](https://huggingface.co/distilbert/distilgpt2) para generar nuevo texto.
### Entrenamiento
<frameworkcontent>
<pt>
Carga DistilGPT2 con [`AutoModelForCausalLM`]:
```py
>>> from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
```
<Tip>
Si no estás familiarizado con el proceso de realizar fine-tuning sobre un modelo con [`Trainer`], considera el tutorial básico [aquí](../training#finetune-with-trainer)!
</Tip>
A este punto, solo faltan tres pasos:
1. Definir tus hiperparámetros de entrenamiento en [`TrainingArguments`].
2. Pasarle los argumentos de entrenamiento a [`Trainer`] junto con el modelo, dataset, y el data collator.
3. Realiza la llamada [`~Trainer.train`] para realizar el fine-tuning sobre tu modelo.
```py
>>> training_args = TrainingArguments(
... output_dir="./results",
... eval_strategy="epoch",
... learning_rate=2e-5,
... weight_decay=0.01,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=lm_dataset["train"],
... eval_dataset=lm_dataset["test"],
... data_collator=data_collator,
... )
>>> trainer.train()
```
</pt>
<tf>
Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator:
```py
>>> tf_train_set = lm_dataset["train"].to_tf_dataset(
... columns=["attention_mask", "input_ids", "labels"],
... dummy_labels=True,
... shuffle=True,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> tf_test_set = lm_dataset["test"].to_tf_dataset(
... columns=["attention_mask", "input_ids", "labels"],
... dummy_labels=True,
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
<Tip>
Si no estás familiarizado con realizar fine-tuning de tus modelos con Keras, considera el tutorial básico [aquí](training#finetune-with-keras)!
</Tip>
Crea la función optimizadora, la tasa de aprendizaje, y algunos hiperparámetros de entrenamiento:
```py
>>> from transformers import create_optimizer, AdamWeightDecay
>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
```
Carga DistilGPT2 con [`TFAutoModelForCausalLM`]:
```py
>>> from transformers import TFAutoModelForCausalLM
>>> model = TFAutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
```
Configura el modelo para entrenamiento con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
```py
>>> import tensorflow as tf
>>> model.compile(optimizer=optimizer)
```
Llama a [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) para realizar el fine-tuning del modelo:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
```
</tf>
</frameworkcontent>
## Modelado de lenguaje por enmascaramiento
El modelado de lenguaje por enmascaramiento es también conocido como una tarea de rellenar la máscara, pues predice un token enmascarado dada una secuencia. Los modelos de lenguaje por enmascaramiento requieren una buena comprensión del contexto de una secuencia entera, en lugar de solo el contexto a la izquierda. Esta sección te enseña como realizar el fine-tuning de [DistilRoBERTa](https://huggingface.co/distilbert/distilroberta-base) para predecir una palabra enmascarada.
### Entrenamiento
<frameworkcontent>
<pt>
Carga DistilRoBERTa con [`AutoModelForMaskedlM`]:
```py
>>> from transformers import AutoModelForMaskedLM
>>> model = AutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base")
```
<Tip>
Si no estás familiarizado con el proceso de realizar fine-tuning sobre un modelo con [`Trainer`], considera el tutorial básico [aquí](../training#finetune-with-trainer)!
</Tip>
A este punto, solo faltan tres pasos:
1. Definir tus hiperparámetros de entrenamiento en [`TrainingArguments`].
2. Pasarle los argumentos de entrenamiento a [`Trainer`] junto con el modelo, dataset, y el data collator.
3. Realiza la llamada [`~Trainer.train`] para realizar el fine-tuning de tu modelo.
```py
>>> training_args = TrainingArguments(
... output_dir="./results",
... eval_strategy="epoch",
... learning_rate=2e-5,
... num_train_epochs=3,
... weight_decay=0.01,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=lm_dataset["train"],
... eval_dataset=lm_dataset["test"],
... data_collator=data_collator,
... )
>>> trainer.train()
```
</pt>
<tf>
Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator:
```py
>>> tf_train_set = lm_dataset["train"].to_tf_dataset(
... columns=["attention_mask", "input_ids", "labels"],
... dummy_labels=True,
... shuffle=True,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> tf_test_set = lm_dataset["test"].to_tf_dataset(
... columns=["attention_mask", "input_ids", "labels"],
... dummy_labels=True,
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
<Tip>
Si no estás familiarizado con realizar fine-tuning de tus modelos con Keras, considera el tutorial básico [aquí](training#finetune-with-keras)!
</Tip>
Crea la función optimizadora, la tasa de aprendizaje, y algunos hiperparámetros de entrenamiento:
```py
>>> from transformers import create_optimizer, AdamWeightDecay
>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
```
Carga DistilRoBERTa con [`TFAutoModelForMaskedLM`]:
```py
>>> from transformers import TFAutoModelForMaskedLM
>>> model = TFAutoModelForCausalLM.from_pretrained("distilbert/distilroberta-base")
```
Configura el modelo para entrenamiento con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
```py
>>> import tensorflow as tf
>>> model.compile(optimizer=optimizer)
```
Llama a [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) para realizar el fine-tuning del modelo:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
```
</tf>
</frameworkcontent>
<Tip>
Para un ejemplo más profundo sobre cómo realizar el fine-tuning sobre un modelo de lenguaje causal, considera
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)
o [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
</Tip> | transformers/docs/source/es/tasks/language_modeling.md/0 | {
"file_path": "transformers/docs/source/es/tasks/language_modeling.md",
"repo_id": "transformers",
"token_count": 6287
} |
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# Entraîner avec un script
En plus des [notebooks](./notebooks) de 🤗 Transformers, il existe également des exemples de scripts démontrant comment entraîner un modèle pour une tâche avec [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch), [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow) ou [JAX/Flax](https://github.com/huggingface/transformers/tree/main/examples/flax).
Vous trouverez également des scripts que nous avons utilisé dans nos [projets de recherche](https://github.com/huggingface/transformers/tree/main/examples/research_projects) et des [exemples "legacy"](https://github.com/huggingface/transformers/tree/main/examples/legacy) qui sont des contributions de la communauté. Ces scripts ne sont pas activement maintenus et nécessitent une version spécifique de 🤗 Transformers qui sera probablement incompatible avec la dernière version de la librairie.
Les exemples de scripts ne sont pas censés fonctionner immédiatement pour chaque problème, et il se peut que vous ayez besoin d'adapter le script au problème que vous essayez de résoudre. Pour vous aider dans cette tâche, la plupart des scripts exposent entièrement la manière dont les données sont prétraitées, vous permettant de les modifier selon vos besoins.
Pour toute fonctionnalité que vous souhaitez implémenter dans un script d'exemple, veuillez en discuter sur le [forum](https://discuss.huggingface.co/) ou dans une [issue](https://github.com/huggingface/transformers/issues) avant de soumettre une Pull Request. Bien que nous acceptions les corrections de bugs, il est peu probable que nous fusionnions une Pull Request (opération "merge" dans Git) ajoutant plus de fonctionnalités au détriment de la lisibilité.
Ce guide vous montrera comment exécuter un script d'entraînement de résumé en exemple avec [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) et [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization). Tous les exemples sont censés fonctionner avec les deux frameworks, sauf indication contraire.
## Configuration
Pour exécuter avec succès la dernière version des scripts d'exemple, vous devez **installer 🤗 Transformers à partir du code source** dans un nouvel environnement virtuel :
```bash
git clone https://github.com/huggingface/transformers
cd transformers
pip install .
```
Pour les versions plus anciennes des exemples de scripts, cliquez sur le bouton ci-dessous :
<details>
<summary>Exemples pour les anciennes versions de Transformers 🤗</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>
Ensuite, changez votre clone actuel de 🤗 Transformers pour une version spécifique, comme par exemple v3.5.1 :
```bash
git checkout tags/v3.5.1
```
Après avoir configuré la bonne version de la librairie, accédez au dossier d'exemple de votre choix et installez les prérequis spécifiques à l'exemple.
```bash
pip install -r requirements.txt
```
## Exécuter un script
<frameworkcontent>
<pt>
Le script d'exemple télécharge et prétraite un jeu de données à partir de la bibliothèque 🤗 [Datasets](https://huggingface.co/docs/datasets/). Ensuite, le script affine un ensemble de données à l'aide de [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) sur une architecture qui prend en charge la tâche de résumé. L'exemple suivant montre comment ajuster le modèle [T5-small](https://huggingface.co/google-t5/t5-small) sur les données [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail). Le modèle T5 nécessite un argument supplémentaire `source_prefix` en raison de la façon dont il a été entraîné. Cette invite permet à T5 de savoir qu'il s'agit d'une tâche de résumé.
```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>
Le script d'exemple télécharge et prétraite un jeu de données à partir de la bibliothèque 🤗 [Datasets](https://huggingface.co/docs/datasets/). Ensuite, le script ajuste un modèle à l'aide de Keras sur une architecture qui prend en charge la tâche de résumé. L'exemple suivant montre comment ajuster le modèle [T5-small](https://huggingface.co/google-t5/t5-small) sur le jeu de données [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail). Le modèle T5 nécessite un argument supplémentaire source_prefix en raison de la façon dont il a été entraîné. Cette invite permet à T5 de savoir qu'il s'agit d'une tâche de résumé.
```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>
## Entraînement distribué et précision mixte
[Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) prend en charge l'entraînement distribué et la précision mixte, ce qui signifie que vous pouvez également les utiliser dans un script. Pour activer ces deux fonctionnalités :
- Ajoutez l'argument fp16 pour activer la précision mixte.
- Définissez le nombre de GPU à utiliser avec l'argument `nproc_per_node`.
```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
```
Les scripts TensorFlow utilisent une Strategie en Miroir [`MirroredStrategy`](https://www.tensorflow.org/guide/distributed_training#mirroredstrategy) pour l'entraînement distribué, et vous n'avez pas besoin d'ajouter d'arguments supplémentaires au script d'entraînement. Le script TensorFlow utilisera plusieurs GPU par défaut s'ils sont disponibles.
## Exécuter un script sur un TPU
<frameworkcontent>
<pt>
Les unités de traitement de tenseurs (UTT) (TPU) sont spécialement conçues pour accélérer les performances. PyTorch prend en charge les TPU avec le compilateur de deep learning [XLA](https://www.tensorflow.org/xla). Pour utiliser un TPU, lancez le script xla_spawn.py et utilisez l'argument num_cores pour définir le nombre de cœurs TPU que vous souhaitez utilise
```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>
Les scripts TensorFlow utilisent une [`TPUStrategy`](https://www.tensorflow.org/guide/distributed_training#tpustrategy) pour l'entraînement sur TPU. Pour utiliser un TPU, passez le nom de la ressource TPU à l'argument tpu.
```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>
## Exécuter un script avec 🤗 Accelerate
🤗 [Accelerate](https://huggingface.co/docs/accelerate) est une bibliothèque uniquement pour PyTorch qui offre une méthode unifiée pour entraîner un modèle sur plusieurs types de configurations (CPU uniquement, plusieurs GPU, TPU) tout en maintenant une visibilité complète sur la boucle d'entraînement PyTorch. Assurez-vous que vous avez installé 🤗 Accelerate si ce n'est pas déjà le cas.
> Note : Comme Accelerate est en développement rapide, la version git d'accelerate doit être installée pour exécuter les scripts.
```bash
pip install git+https://github.com/huggingface/accelerate
```
Au lieu du script `run_summarization.py`, vous devez utiliser le script `run_summarization_no_trainer.py`. Les scripts compatibles avec 🤗 Accelerate auront un fichier `task_no_trainer.py` dans le dossier. Commencez par exécuter la commande suivante pour créer et enregistrer un fichier de configuration.
```bash
accelerate config
```
Testez votre configuration pour vous assurer qu'elle est correctement configurée :
```bash
accelerate test
```
Maintenant, vous êtes prêt à lancer l'entraînement :
```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
```
## Utiliser un jeu de données personnalisé
Le script de résumé prend en charge les jeux de données personnalisés tant qu'ils sont au format CSV ou JSON Line. Lorsque vous utilisez votre propre jeu de données, vous devez spécifier plusieurs arguments supplémentaires :
- `train_file` et `validation_file` spécifient le chemin vers vos fichiers d'entraînement et de validation.
- `text_column` est le texte d'entrée à résumer.
- `summary_column` est le texte cible à produire.
Un exemple de script de résumé utilisant un ensemble de données personnalisé ressemblerait à ceci :
```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
```
## Tester un script
Il est souvent judicieux d'exécuter votre script sur un plus petit nombre d'exemples de jeu de données pour s'assurer que tout fonctionne comme prévu avant de s'engager sur un jeu de données complet qui pourrait prendre des heures à traiter. Utilisez les arguments suivants pour tronquer le jeu de données à un nombre maximal d'échantillons :
- `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
```
Tous les scripts d'exemple ne prennent pas en charge l'argument `max_predict_samples`. Si vous n'êtes pas sûr que votre script prenne en charge cet argument, ajoutez l'argument `-h` pour vérifier.
```bash
examples/pytorch/summarization/run_summarization.py -h
```
## Reprendre l'entraînement à partir d'un point de contrôle
Une autre option utile est de reprendre l'entraînement à partir d'un point de contrôle précédent. Cela vous permettra de reprendre là où vous vous étiez arrêté sans recommencer si votre entraînement est interrompu. Il existe deux méthodes pour reprendre l'entraînement à partir d'un point de contrôle.
La première méthode utilise l'argument `output_dir previous_output_dir` pour reprendre l'entraînement à partir du dernier point de contrôle stocké dans `output_dir`. Dans ce cas, vous devez supprimer l'argument `overwrite_output_dir`.
```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
```
La seconde méthode utilise l'argument `resume_from_checkpoint path_to_specific_checkpoint` pour reprendre l'entraînement à partir d'un dossier de point de contrôle spécifique.
```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
```
## Partage ton modèle
Tous les scripts peuvent télécharger votre modèle final sur le Model Hub. Assurez-vous que vous êtes connecté à Hugging Face avant de commencer :
```bash
huggingface-cli login
```
Ensuite, ajoutez l'argument `push_to_hub` au script. Cet argument créera un dépôt avec votre nom d'utilisateur Hugging Face et le nom du dossier spécifié dans `output_dir`.
Pour donner un nom spécifique à votre dépôt, utilisez l'argument `push_to_hub_model_id` pour l'ajouter. Le dépôt sera automatiquement listé sous votre namespace.
L'exemple suivant montre comment télécharger un modèle avec un nom de dépôt spécifique :
```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/fr/run_scripts_fr.md/0 | {
"file_path": "transformers/docs/source/fr/run_scripts_fr.md",
"repo_id": "transformers",
"token_count": 7046
} |
<!--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
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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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-->
# Convertire checkpoint di Tensorflow
È disponibile un'interfaccia a linea di comando per convertire gli originali checkpoint di Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM
in modelli che possono essere caricati utilizzando i metodi `from_pretrained` della libreria.
<Tip>
A partire dalla versione 2.3.0 lo script di conversione è parte di transformers CLI (**transformers-cli**), disponibile in ogni installazione
di transformers >=2.3.0.
La seguente documentazione riflette il formato dei comandi di **transformers-cli convert**.
</Tip>
## BERT
Puoi convertire qualunque checkpoint Tensorflow di BERT (in particolare
[i modeli pre-allenati rilasciati da Google](https://github.com/google-research/bert#pre-trained-models))
in un file di salvataggio Pytorch utilizzando lo script
[convert_bert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py).
Questo CLI prende come input un checkpoint di Tensorflow (tre files che iniziano con `bert_model.ckpt`) ed il relativo
file di configurazione (`bert_config.json`), crea un modello Pytorch per questa configurazione, carica i pesi dal
checkpoint di Tensorflow nel modello di Pytorch e salva il modello che ne risulta in un file di salvataggio standard di Pytorch che
può essere importato utilizzando `from_pretrained()` (vedi l'esempio nel
[quicktour](quicktour) , [run_glue.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_glue.py) ).
Devi soltanto lanciare questo script di conversione **una volta** per ottenere un modello Pytorch. Dopodichè, potrai tralasciare
il checkpoint di Tensorflow (i tre files che iniziano con `bert_model.ckpt`), ma assicurati di tenere il file di configurazione
(`bert_config.json`) ed il file di vocabolario (`vocab.txt`) in quanto queste componenti sono necessarie anche per il modello di Pytorch.
Per lanciare questo specifico script di conversione avrai bisogno di un'installazione di Tensorflow e di Pytorch
(`pip install tensorflow`). Il resto della repository richiede soltanto Pytorch.
Questo è un esempio del processo di conversione per un modello `BERT-Base Uncased` pre-allenato:
```bash
export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
transformers-cli convert --model_type bert \
--tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
--config $BERT_BASE_DIR/bert_config.json \
--pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
```
Puoi scaricare i modelli pre-allenati di Google per la conversione [qua](https://github.com/google-research/bert#pre-trained-models).
## ALBERT
Per il modello ALBERT, converti checkpoint di Tensoflow in Pytorch utilizzando lo script
[convert_albert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/main/src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py).
Il CLI prende come input un checkpoint di Tensorflow (tre files che iniziano con `model.ckpt-best`) e i relativi file di
configurazione (`albert_config.json`), dopodichè crea e salva un modello Pytorch. Per lanciare questa conversione
avrai bisogno di un'installazione di Tensorflow e di Pytorch.
Ecco un esempio del procedimento di conversione di un modello `ALBERT Base` pre-allenato:
```bash
export ALBERT_BASE_DIR=/path/to/albert/albert_base
transformers-cli convert --model_type albert \
--tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
--config $ALBERT_BASE_DIR/albert_config.json \
--pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
```
Puoi scaricare i modelli pre-allenati di Google per la conversione [qui](https://github.com/google-research/albert#pre-trained-models).
## OpenAI GPT
Ecco un esempio del processo di conversione di un modello OpenAI GPT pre-allenato, assumendo che il tuo checkpoint di NumPy
sia salvato nello stesso formato dei modelli pre-allenati OpenAI (vedi [qui](https://github.com/openai/finetune-transformer-lm)):
```bash
export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
transformers-cli convert --model_type gpt \
--tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
[--config OPENAI_GPT_CONFIG] \
[--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \
```
## OpenAI GPT-2
Ecco un esempio del processo di conversione di un modello OpenAI GPT-2 pre-allenato (vedi [qui](https://github.com/openai/gpt-2)):
```bash
export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/openai-community/gpt2/pretrained/weights
transformers-cli convert --model_type gpt2 \
--tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
[--config OPENAI_GPT2_CONFIG] \
[--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
```
## XLNet
Ecco un esempio del processo di conversione di un modello XLNet pre-allenato:
```bash
export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
transformers-cli convert --model_type xlnet \
--tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
--config $TRANSFO_XL_CONFIG_PATH \
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
[--finetuning_task_name XLNET_FINETUNED_TASK] \
```
## XLM
Ecco un esempio del processo di conversione di un modello XLM pre-allenato:
```bash
export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
transformers-cli convert --model_type xlm \
--tf_checkpoint $XLM_CHECKPOINT_PATH \
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT
[--config XML_CONFIG] \
[--finetuning_task_name XML_FINETUNED_TASK]
```
## T5
Ecco un esempio del processo di conversione di un modello T5 pre-allenato:
```bash
export T5=/path/to/t5/uncased_L-12_H-768_A-12
transformers-cli convert --model_type t5 \
--tf_checkpoint $T5/t5_model.ckpt \
--config $T5/t5_config.json \
--pytorch_dump_output $T5/pytorch_model.bin
```
| transformers/docs/source/it/converting_tensorflow_models.md/0 | {
"file_path": "transformers/docs/source/it/converting_tensorflow_models.md",
"repo_id": "transformers",
"token_count": 2405
} |
<!--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
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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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# Addestramento su Hardware Specializzato
<Tip>
Nota: Molte delle strategie introdotte nella [sezione sulla GPU singola](perf_train_gpu_one) (come mixed precision training o gradient accumulation) e [sezione multi-GPU](perf_train_gpu_many) sono generiche e applicabili all'addestramento di modelli in generale quindi assicurati di dargli un'occhiata prima di immergerti in questa sezione.
</Tip>
Questo documento sarà presto completato con informazioni su come effettuare la formazione su hardware specializzato.
| transformers/docs/source/it/perf_train_special.md/0 | {
"file_path": "transformers/docs/source/it/perf_train_special.md",
"repo_id": "transformers",
"token_count": 341
} |
<!--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.
-->
# Chat Templates
## Introduction
LLM（Language Model）のますます一般的な使用事例の1つは「チャット」です。
チャットのコンテキストでは、通常の言語モデルのように単一のテキストストリングを継続するのではなく、モデルは1つ以上の「メッセージ」からなる会話を継続します。
各メッセージには「ロール」とメッセージテキストが含まれます。
最も一般的に、これらのロールはユーザーからのメッセージには「ユーザー」、モデルからのメッセージには「アシスタント」が割り当てられます。
一部のモデルは「システム」ロールもサポートしています。
システムメッセージは通常会話の開始時に送信され、モデルの動作方法に関する指示が含まれます。
すべての言語モデル、チャット用に微調整されたモデルを含むすべてのモデルは、トークンのリニアシーケンスで動作し、ロールに特有の特別な処理を持ちません。
つまり、ロール情報は通常、メッセージ間に制御トークンを追加して注入され、メッセージの境界と関連するロールを示すことで提供されます。
残念ながら、トークンの使用方法については（まだ！）標準が存在せず、異なるモデルはチャット用のフォーマットや制御トークンが大きく異なる形式でトレーニングされています。
これはユーザーにとって実際の問題になる可能性があります。正しいフォーマットを使用しないと、モデルは入力に混乱し、パフォーマンスが本来よりも遥かに低下します。
これが「チャットテンプレート」が解決しようとする問題です。
チャット会話は通常、各辞書が「ロール」と「コンテンツ」のキーを含み、単一のチャットメッセージを表すリストとして表現されます。
チャットテンプレートは、指定されたモデルの会話を単一のトークン化可能なシーケンスにどのようにフォーマットするかを指定するJinjaテンプレートを含む文字列です。
トークナイザとこの情報を保存することにより、モデルが期待する形式の入力データを取得できるようになります。
さっそく、`BlenderBot` モデルを使用した例を示して具体的にしましょう。`BlenderBot` のデフォルトテンプレートは非常にシンプルで、ほとんどが対話のラウンド間に空白を追加するだけです。
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> chat = [
... {"role": "user", "content": "Hello, how are you?"},
... {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
... {"role": "user", "content": "I'd like to show off how chat templating works!"},
... ]
>>> tokenizer.apply_chat_template(chat, tokenize=False)
" Hello, how are you? I'm doing great. How can I help you today? I'd like to show off how chat templating works!</s>"
```
指定された通り、チャット全体が単一の文字列にまとめられています。デフォルトの設定である「tokenize=True」を使用すると、
その文字列もトークン化されます。しかし、より複雑なテンプレートが実際にどのように機能するかを確認するために、
「meta-llama/Llama-2-7b-chat-hf」モデルを使用してみましょう。ただし、このモデルはゲート付きアクセスを持っており、
このコードを実行する場合は[リポジトリでアクセスをリクエスト](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)する必要があります。
```python
>> from transformers import AutoTokenizer
>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
>> chat = [
... {"role": "user", "content": "Hello, how are you?"},
... {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
... {"role": "user", "content": "I'd like to show off how chat templating works!"},
... ]
>> tokenizer.use_default_system_prompt = False
>> tokenizer.apply_chat_template(chat, tokenize=False)
"<s>[INST] Hello, how are you? [/INST] I'm doing great. How can I help you today? </s><s>[INST] I'd like to show off how chat templating works! [/INST]"
```
今回、トークナイザは制御トークン [INST] と [/INST] を追加しました。これらはユーザーメッセージの開始と終了を示すためのものです（ただし、アシスタントメッセージには適用されません！）
## How do chat templates work?
モデルのチャットテンプレートは、`tokenizer.chat_template`属性に格納されています。チャットテンプレートが設定されていない場合、そのモデルクラスのデフォルトテンプレートが代わりに使用されます。`BlenderBot`のテンプレートを見てみましょう:
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer.chat_template
"{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}"
```
これは少し抑圧的ですね。可読性を高めるために、新しい行とインデントを追加しましょう。
各ブロックの直前の空白と、ブロックの直後の最初の改行は、デフォルトでJinjaの `trim_blocks` および `lstrip_blocks` フラグを使用して削除します。
これにより、インデントと改行を含むテンプレートを書いても正常に機能することができます。
```
{% for message in messages %}
{% if message['role'] == 'user' %}
{{ ' ' }}
{% endif %}
{{ message['content'] }}
{% if not loop.last %}
{{ ' ' }}
{% endif %}
{% endfor %}
{{ eos_token }}
```
これが初めて見る方へ、これは[Jinjaテンプレート](https://jinja.palletsprojects.com/en/3.1.x/templates/)です。
Jinjaはテキストを生成するためのシンプルなコードを記述できるテンプレート言語です。多くの点で、コードと
構文はPythonに似ています。純粋なPythonでは、このテンプレートは次のようになるでしょう：
```python
for idx, message in enumerate(messages):
if message['role'] == 'user':
print(' ')
print(message['content'])
if not idx == len(messages) - 1: # Check for the last message in the conversation
print(' ')
print(eos_token)
```
実際に、このテンプレートは次の3つのことを行います：
1. 各メッセージに対して、メッセージがユーザーメッセージである場合、それの前に空白を追加し、それ以外の場合は何も表示しません。
2. メッセージの内容を追加します。
3. メッセージが最後のメッセージでない場合、その後に2つのスペースを追加します。最後のメッセージの後にはEOSトークンを表示します。
これは非常にシンプルなテンプレートです。制御トークンを追加しないし、モデルに対する指示を伝える一般的な方法である「システム」メッセージをサポートしていません。
ただし、Jinjaはこれらのことを行うための多くの柔軟性を提供しています！
LLaMAがフォーマットする方法に類似した入力をフォーマットするためのJinjaテンプレートを見てみましょう
（実際のLLaMAテンプレートはデフォルトのシステムメッセージの処理や、一般的なシステムメッセージの処理が若干異なるため、
実際のコードではこのテンプレートを使用しないでください！）
```
{% for message in messages %}
{% if message['role'] == 'user' %}
{{ bos_token + '[INST] ' + message['content'] + ' [/INST]' }}
{% elif message['role'] == 'system' %}
{{ '<<SYS>>\\n' + message['content'] + '\\n<</SYS>>\\n\\n' }}
{% elif message['role'] == 'assistant' %}
{{ ' ' + message['content'] + ' ' + eos_token }}
{% endif %}
{% endfor %}
```
願わくば、少し見つめていただければ、このテンプレートが何を行っているかがわかるかもしれません。
このテンプレートは、各メッセージの「役割」に基づいて特定のトークンを追加します。これらのトークンは、メッセージを送信した人を表すものです。
ユーザー、アシスタント、およびシステムメッセージは、それらが含まれるトークンによってモデルによって明確に区別されます。
## How do I create a chat template?
簡単です。単純にJinjaテンプレートを書いて、`tokenizer.chat_template`を設定します。
他のモデルから既存のテンプレートを始点にして、必要に応じて編集すると便利かもしれません！
例えば、上記のLLaMAテンプレートを取って、アシスタントメッセージに"[ASST]"と"[/ASST]"を追加できます。
```
{% for message in messages %}
{% if message['role'] == 'user' %}
{{ bos_token + '[INST] ' + message['content'].strip() + ' [/INST]' }}
{% elif message['role'] == 'system' %}
{{ '<<SYS>>\\n' + message['content'].strip() + '\\n<</SYS>>\\n\\n' }}
{% elif message['role'] == 'assistant' %}
{{ '[ASST] ' + message['content'] + ' [/ASST]' + eos_token }}
{% endif %}
{% endfor %}
```
次に、単に`tokenizer.chat_template`属性を設定してください。
次回、[`~PreTrainedTokenizer.apply_chat_template`]を使用する際に、新しいテンプレートが使用されます！
この属性は`tokenizer_config.json`ファイルに保存されるため、[`~utils.PushToHubMixin.push_to_hub`]を使用して
新しいテンプレートをHubにアップロードし、みんなが正しいテンプレートを使用していることを確認できます！
```python
template = tokenizer.chat_template
template = template.replace("SYS", "SYSTEM") # Change the system token
tokenizer.chat_template = template # Set the new template
tokenizer.push_to_hub("model_name") # Upload your new template to the Hub!
```
[`~PreTrainedTokenizer.apply_chat_template`] メソッドは、あなたのチャットテンプレートを使用するために `TextGenerationPipeline` クラスによって呼び出されます。
したがって、正しいチャットテンプレートを設定すると、あなたのモデルは自動的に [`TextGenerationPipeline`] と互換性があるようになります。
## What are "default" templates?
チャットテンプレートの導入前に、チャットの処理はモデルクラスレベルでハードコードされていました。
後方互換性のために、このクラス固有の処理をデフォルトテンプレートとして保持し、クラスレベルで設定されています。
モデルにチャットテンプレートが設定されていない場合、ただしモデルクラスのデフォルトテンプレートがある場合、
`TextGenerationPipeline`クラスや`apply_chat_template`などのメソッドはクラステンプレートを使用します。
トークナイザのデフォルトのチャットテンプレートを確認するには、`tokenizer.default_chat_template`属性をチェックしてください。
これは、後方互換性のために純粋に行っていることで、既存のワークフローを壊さないようにしています。
モデルにとってクラステンプレートが適切である場合でも、デフォルトテンプレートをオーバーライドして
`chat_template`属性を明示的に設定することを強くお勧めします。これにより、ユーザーにとって
モデルがチャット用に正しく構成されていることが明確になり、デフォルトテンプレートが変更されたり廃止された場合に備えることができます。
## What template should I use?
すでにチャットのトレーニングを受けたモデルのテンプレートを設定する場合、テンプレートがトレーニング中にモデルが見たメッセージのフォーマットとまったく一致することを確認する必要があります。
そうでない場合、性能の低下を経験する可能性が高いです。これはモデルをさらにトレーニングしている場合でも同様です - チャットトークンを一定に保つと、おそらく最高の性能が得られます。
これはトークン化と非常に類似しており、通常はトレーニング中に使用されたトークン化と正確に一致する場合に、推論またはファインチューニングの際に最良の性能が得られます。
一方、ゼロからモデルをトレーニングするか、チャットのためにベース言語モデルをファインチューニングする場合、適切なテンプレートを選択する自由度があります。
LLM（Language Model）はさまざまな入力形式を処理できるほどスマートです。クラス固有のテンプレートがないモデル用のデフォルトテンプレートは、一般的なユースケースに対して良い柔軟な選択肢です。
これは、`ChatMLフォーマット`に従ったもので、多くのユースケースに適しています。次のようになります：
```
{% for message in messages %}
{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}
{% endfor %}
```
If you like this one, here it is in one-liner form, ready to copy into your code:
```python
tokenizer.chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}"
```
このテンプレートは、各メッセージを「``」トークンで囲み、役割を文字列として単純に記述します。
これにより、トレーニングで使用する役割に対する柔軟性が得られます。出力は以下のようになります：
```
<|im_start|>system
You are a helpful chatbot that will do its best not to say anything so stupid that people tweet about it.<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
I'm doing great!<|im_end|>
```
「ユーザー」、「システム」、および「アシスタント」の役割は、チャットの標準です。
特に、`TextGenerationPipeline`との連携をスムーズに行う場合には、これらの役割を使用することをお勧めします。ただし、これらの役割に制約はありません。テンプレートは非常に柔軟で、任意の文字列を役割として使用できます。
## I want to use chat templates! How should I get started?
チャットモデルを持っている場合、そのモデルの`tokenizer.chat_template`属性を設定し、[`~PreTrainedTokenizer.apply_chat_template`]を使用してテストする必要があります。
これはモデルの所有者でない場合でも適用されます。モデルのリポジトリが空のチャットテンプレートを使用している場合、またはデフォルトのクラステンプレートを使用している場合でも、
この属性を適切に設定できるように[プルリクエスト](https://huggingface.co/docs/hub/repositories-pull-requests-discussions)を開いてください。
一度属性が設定されれば、それで完了です！ `tokenizer.apply_chat_template`は、そのモデルに対して正しく動作するようになります。これは、
`TextGenerationPipeline` などの場所でも自動的にサポートされます。
モデルがこの属性を持つことを確認することで、オープンソースモデルの全コミュニティがそのフルパワーを使用できるようになります。
フォーマットの不一致はこの分野に悩み続け、パフォーマンスに黙って影響を与えてきました。それを終わらせる時が来ました！
| transformers/docs/source/ja/chat_templating.md/0 | {
"file_path": "transformers/docs/source/ja/chat_templating.md",
"repo_id": "transformers",
"token_count": 6992
} |
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# Optimization
`.optimization` モジュールは以下を提供します。
- モデルの微調整に使用できる重み減衰が修正されたオプティマイザー、および
- `_LRSchedule` から継承するスケジュール オブジェクトの形式のいくつかのスケジュール:
- 複数のバッチの勾配を累積するための勾配累積クラス
## AdamW (PyTorch)
[[autodoc]] AdamW
## AdaFactor (PyTorch)
[[autodoc]] Adafactor
## AdamWeightDecay (TensorFlow)
[[autodoc]] AdamWeightDecay
[[autodoc]] create_optimizer
## Schedules
### Learning Rate Schedules (Pytorch)
[[autodoc]] SchedulerType
[[autodoc]] get_scheduler
[[autodoc]] get_constant_schedule
[[autodoc]] get_constant_schedule_with_warmup
<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_constant_schedule.png"/>
[[autodoc]] get_cosine_schedule_with_warmup
<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_schedule.png"/>
[[autodoc]] get_cosine_with_hard_restarts_schedule_with_warmup
<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_hard_restarts_schedule.png"/>
[[autodoc]] get_linear_schedule_with_warmup
<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_linear_schedule.png"/>
[[autodoc]] get_polynomial_decay_schedule_with_warmup
[[autodoc]] get_inverse_sqrt_schedule
### Warmup (TensorFlow)
[[autodoc]] WarmUp
## Gradient Strategies
### GradientAccumulator (TensorFlow)
[[autodoc]] GradientAccumulator
| transformers/docs/source/ja/main_classes/optimizer_schedules.md/0 | {
"file_path": "transformers/docs/source/ja/main_classes/optimizer_schedules.md",
"repo_id": "transformers",
"token_count": 851
} |
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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
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# BARThez
## Overview
BARThez モデルは、Moussa Kamal Eddine、Antoine J.-P によって [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) で提案されました。ティクシエ、ミカリス・ヴァジルジャンニス、10月23日、
2020年。
論文の要約:
*帰納的転移学習は、自己教師あり学習によって可能になり、自然言語処理全体を実行します。
(NLP) 分野は、BERT や BART などのモデルにより、無数の自然言語に新たな最先端技術を確立し、嵐を巻き起こしています。
タスクを理解すること。いくつかの注目すべき例外はありますが、利用可能なモデルと研究のほとんどは、
英語を対象に実施されました。この作品では、フランス語用の最初の BART モデルである BARTez を紹介します。
（我々の知る限りに）。 BARThez は、過去の研究から得た非常に大規模な単一言語フランス語コーパスで事前トレーニングされました
BART の摂動スキームに合わせて調整しました。既存の BERT ベースのフランス語モデルとは異なり、
CamemBERT と FlauBERT、BARThez は、エンコーダだけでなく、
そのデコーダは事前トレーニングされています。 FLUE ベンチマークからの識別タスクに加えて、BARThez を新しい評価に基づいて評価します。
この論文とともにリリースする要約データセット、OrangeSum。また、すでに行われている事前トレーニングも継続します。
BARTHez のコーパス上で多言語 BART を事前訓練し、結果として得られるモデル (mBARTHez と呼ぶ) が次のことを示します。
バニラの BARThez を大幅に強化し、CamemBERT や FlauBERT と同等かそれを上回ります。*
このモデルは [moussakam](https://huggingface.co/moussakam) によって寄稿されました。著者のコードは[ここ](https://github.com/moussaKam/BARThez)にあります。
<Tip>
BARThez の実装は、トークン化を除いて BART と同じです。詳細については、[BART ドキュメント](bart) を参照してください。
構成クラスとそのパラメータ。 BARThez 固有のトークナイザーについては以下に記載されています。
</Tip>
### Resources
- BARThez は、BART と同様の方法でシーケンス間のタスクを微調整できます。以下を確認してください。
[examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/README.md)。
## BarthezTokenizer
[[autodoc]] BarthezTokenizer
## BarthezTokenizerFast
[[autodoc]] BarthezTokenizerFast
| transformers/docs/source/ja/model_doc/barthez.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/barthez.md",
"repo_id": "transformers",
"token_count": 1461
} |
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# BORT
<Tip warning={true}>
このモデルはメンテナンス モードのみであり、コードを変更する新しい PR は受け付けられません。
このモデルの実行中に問題が発生した場合は、このモデルをサポートしていた最後のバージョン (v4.30.0) を再インストールしてください。
これを行うには、コマンド `pip install -U Transformers==4.30.0` を実行します。
</Tip>
## Overview
BORT モデルは、[Optimal Subarchitecture Extraction for BERT](https://arxiv.org/abs/2010.10499) で提案されました。
Adrian de Wynter and Daniel J. Perry.これは、BERT のアーキテクチャ パラメータの最適なサブセットです。
著者は「ボルト」と呼んでいます。
論文の要約は次のとおりです。
*Devlin らから BERT アーキテクチャのアーキテクチャ パラメータの最適なサブセットを抽出します。 (2018)
ニューラル アーキテクチャ検索のアルゴリズムにおける最近の画期的な技術を適用します。この最適なサブセットを次のように呼びます。
"Bort" は明らかに小さく、有効 (つまり、埋め込み層を考慮しない) サイズは 5.5% です。
オリジナルの BERT 大規模アーキテクチャ、およびネット サイズの 16%。 Bort は 288 GPU 時間で事前トレーニングすることもできます。
最高パフォーマンスの BERT パラメトリック アーキテクチャ バリアントである RoBERTa-large の事前トレーニングに必要な時間の 1.2%
(Liu et al., 2019)、同じマシンで BERT-large をトレーニングするのに必要な GPU 時間の世界記録の約 33%
ハードウェア。また、CPU 上で 7.9 倍高速であるだけでなく、他の圧縮バージョンよりもパフォーマンスが優れています。
アーキテクチャ、および一部の非圧縮バリアント: 0.3% ～ 31% のパフォーマンス向上が得られます。
BERT-large に関して、複数の公開自然言語理解 (NLU) ベンチマークにおける絶対的な評価。*
このモデルは [stefan-it](https://huggingface.co/stefan-it) によって提供されました。元のコードは[ここ](https://github.com/alexa/bort/)にあります。
## Usage tips
- BORT のモデル アーキテクチャは BERT に基づいています。詳細については、[BERT のドキュメント ページ](bert) を参照してください。
モデルの API リファレンスと使用例。
- BORT は BERT トークナイザーの代わりに RoBERTa トークナイザーを使用します。トークナイザーの API リファレンスと使用例については、[RoBERTa のドキュメント ページ](roberta) を参照してください。
- BORT には、 [Agora](https://adewynter.github.io/notes/bort_algorithms_and_applications.html#fine-tuning-with-algebraic-topology) と呼ばれる特定の微調整アルゴリズムが必要です。
残念ながらまだオープンソース化されていません。誰かが実装しようとすると、コミュニティにとって非常に役立ちます。
BORT の微調整を機能させるためのアルゴリズム。 | transformers/docs/source/ja/model_doc/bort.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/bort.md",
"repo_id": "transformers",
"token_count": 1598
} |
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# ConvNeXt V2
## Overview
ConvNeXt V2 モデルは、Sanghyun Woo、Shobhik Debnath、Ronghang Hu、Xinlei Chen、Zhuang Liu, In So Kweon, Saining Xie. によって [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) で提案されました。
ConvNeXt V2 は、Vision Transformers の設計からインスピレーションを得た純粋な畳み込みモデル (ConvNet) であり、[ConvNeXT](convnext) の後継です。
論文の要約は次のとおりです。
*アーキテクチャの改善と表現学習フレームワークの改善により、視覚認識の分野は 2020 年代初頭に急速な近代化とパフォーマンスの向上を実現しました。たとえば、ConvNeXt に代表される最新の ConvNet は、さまざまなシナリオで強力なパフォーマンスを実証しています。これらのモデルはもともと ImageNet ラベルを使用した教師あり学習用に設計されましたが、マスク オートエンコーダー (MAE) などの自己教師あり学習手法からも潜在的に恩恵を受けることができます。ただし、これら 2 つのアプローチを単純に組み合わせると、パフォーマンスが標準以下になることがわかりました。この論文では、完全畳み込みマスク オートエンコーダ フレームワークと、チャネル間の機能競合を強化するために ConvNeXt アーキテクチャに追加できる新しい Global Response Normalization (GRN) 層を提案します。この自己教師あり学習手法とアーキテクチャの改善の共同設計により、ConvNeXt V2 と呼ばれる新しいモデル ファミリが誕生しました。これにより、ImageNet 分類、COCO 検出、ADE20K セグメンテーションなどのさまざまな認識ベンチマークにおける純粋な ConvNet のパフォーマンスが大幅に向上します。また、ImageNet でトップ 1 の精度 76.7% を誇る効率的な 370 万パラメータの Atto モデルから、最先端の 88.9% を達成する 650M Huge モデルまで、さまざまなサイズの事前トレーニング済み ConvNeXt V2 モデルも提供しています。公開トレーニング データのみを使用した精度*。
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnextv2_architecture.png"
alt="描画" width="600"/>
<small> ConvNeXt V2 アーキテクチャ。 <a href="https://arxiv.org/abs/2301.00808">元の論文</a>から抜粋。</small>
このモデルは [adirik](https://huggingface.co/adirik) によって提供されました。元のコードは [こちら](https://github.com/facebookresearch/ConvNeXt-V2) にあります。
## Resources
ConvNeXt V2 の使用を開始するのに役立つ公式 Hugging Face およびコミュニティ (🌎 で示される) リソースのリスト。
<PipelineTag pipeline="image-classification"/>
- [`ConvNextV2ForImageClassification`] は、この [サンプル スクリプト](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) および [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb)。
ここに含めるリソースの送信に興味がある場合は、お気軽にプル リクエストを開いてください。審査させていただきます。リソースは、既存のリソースを複製するのではなく、何か新しいものを示すことが理想的です。
## ConvNextV2Config
[[autodoc]] ConvNextV2Config
## ConvNextV2Model
[[autodoc]] ConvNextV2Model
- forward
## ConvNextV2ForImageClassification
[[autodoc]] ConvNextV2ForImageClassification
- forward
## TFConvNextV2Model
[[autodoc]] TFConvNextV2Model
- call
## TFConvNextV2ForImageClassification
[[autodoc]] TFConvNextV2ForImageClassification
- call
| transformers/docs/source/ja/model_doc/convnextv2.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/convnextv2.md",
"repo_id": "transformers",
"token_count": 1916
} |
<!---
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
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
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# Model training anatomy
モデルトレーニングの効率を向上させるために適用できるパフォーマンス最適化テクニックを理解するには、トレーニング中にGPUがどのように利用されるか、および実行される操作に応じて計算強度がどのように変化するかを理解することが役立ちます。
まずは、GPUの利用例とモデルのトレーニング実行に関する示唆に富む例を探求することから始めましょう。デモンストレーションのために、いくつかのライブラリをインストールする必要があります:
```bash
pip install transformers datasets accelerate nvidia-ml-py3
```
`nvidia-ml-py3` ライブラリは、Python内からモデルのメモリ使用状況をモニターすることを可能にします。おそらく、ターミナルでの `nvidia-smi` コマンドについてはお聞きかもしれませんが、このライブラリを使用すると、Pythonから同じ情報にアクセスできます。
それから、いくつかのダミーデータを作成します。100から30000の間のランダムなトークンIDと、分類器のためのバイナリラベルです。合計で、512のシーケンスがあり、それぞれの長さは512で、PyTorchフォーマットの [`~datasets.Dataset`] に格納されます。
```py
>>> import numpy as np
>>> from datasets import Dataset
>>> seq_len, dataset_size = 512, 512
>>> dummy_data = {
... "input_ids": np.random.randint(100, 30000, (dataset_size, seq_len)),
... "labels": np.random.randint(0, 1, (dataset_size)),
... }
>>> ds = Dataset.from_dict(dummy_data)
>>> ds.set_format("pt")
```
[`Trainer`]を使用してGPU利用率とトレーニング実行の要約統計情報を表示するために、2つのヘルパー関数を定義します。
```py
>>> from pynvml import *
>>> def print_gpu_utilization():
... nvmlInit()
... handle = nvmlDeviceGetHandleByIndex(0)
... info = nvmlDeviceGetMemoryInfo(handle)
... print(f"GPU memory occupied: {info.used//1024**2} MB.")
>>> def print_summary(result):
... print(f"Time: {result.metrics['train_runtime']:.2f}")
... print(f"Samples/second: {result.metrics['train_samples_per_second']:.2f}")
... print_gpu_utilization()
```
以下は、無料のGPUメモリから開始していることを確認しましょう：
```py
>>> print_gpu_utilization()
GPU memory occupied: 0 MB.
```
GPUメモリがモデルを読み込む前のように占有されていないように見えます。これがお使いのマシンでの状況でない場合は、GPUメモリを使用しているすべてのプロセスを停止してください。ただし、すべての空きGPUメモリをユーザーが使用できるわけではありません。モデルがGPUに読み込まれると、カーネルも読み込まれ、1〜2GBのメモリを使用することがあります。それがどれくらいかを確認するために、GPUに小さなテンソルを読み込むと、カーネルも読み込まれます。
```py
>>> import torch
>>> torch.ones((1, 1)).to("cuda")
>>> print_gpu_utilization()
GPU memory occupied: 1343 MB.
```
カーネルだけで1.3GBのGPUメモリを使用していることがわかります。次に、モデルがどれだけのスペースを使用しているかを見てみましょう。
## Load Model
まず、`google-bert/bert-large-uncased` モデルを読み込みます。モデルの重みを直接GPUに読み込むことで、重みだけがどれだけのスペースを使用しているかを確認できます。
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-large-uncased").to("cuda")
>>> print_gpu_utilization()
GPU memory occupied: 2631 MB.
```
モデルの重みだけで、GPUメモリを1.3 GB使用していることがわかります。正確な数値は、使用している具体的なGPUに依存します。新しいGPUでは、モデルの重みが最適化された方法で読み込まれるため、モデルの使用を高速化することがあるため、モデルがより多くのスペースを占有することがあります。さて、`nvidia-smi` CLIと同じ結果が得られるかを簡単に確認することもできます。
```bash
nvidia-smi
```
```bash
Tue Jan 11 08:58:05 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.91.03 Driver Version: 460.91.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla V100-SXM2... On | 00000000:00:04.0 Off | 0 |
| N/A 37C P0 39W / 300W | 2631MiB / 16160MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 3721 C ...nvs/codeparrot/bin/python 2629MiB |
+-----------------------------------------------------------------------------+
```
前回と同じ数値を取得し、16GBのメモリを搭載したV100 GPUを使用していることがわかります。さて、モデルのトレーニングを開始し、GPUメモリの消費がどのように変化するかを確認してみましょう。まず、いくつかの標準的なトレーニング引数を設定します:
```py
default_args = {
"output_dir": "tmp",
"eval_strategy": "steps",
"num_train_epochs": 1,
"log_level": "error",
"report_to": "none",
}
```
<Tip>
複数の実験を実行する予定がある場合、実験間でメモリを適切にクリアするために、実験の間に Python カーネルを再起動してください。
</Tip>
## Memory utilization at vanilla training
[`Trainer`] を使用して、GPU パフォーマンスの最適化テクニックを使用せずにバッチサイズ 4 でモデルをトレーニングしましょう：
```py
>>> from transformers import TrainingArguments, Trainer, logging
>>> logging.set_verbosity_error()
>>> training_args = TrainingArguments(per_device_train_batch_size=4, **default_args)
>>> trainer = Trainer(model=model, args=training_args, train_dataset=ds)
>>> result = trainer.train()
>>> print_summary(result)
```
```
Time: 57.82
Samples/second: 8.86
GPU memory occupied: 14949 MB.
```
既に、比較的小さいバッチサイズでも、GPUのほとんどのメモリがすでに使用されていることがわかります。しかし、より大きなバッチサイズを使用することは、しばしばモデルの収束が速くなったり、最終的な性能が向上したりすることがあります。したがって、理想的には、バッチサイズをモデルの要件に合わせて調整したいのですが、GPUの制限に合わせて調整する必要はありません。興味深いことに、モデルのサイズよりもはるかに多くのメモリを使用しています。なぜそうなるのかを少し理解するために、モデルの操作とメモリの必要性を見てみましょう。
## Anatomy of Model's Operations
Transformerアーキテクチャには、計算強度によって以下の3つの主要な操作グループが含まれています。
1. **テンソルの収縮**
線形層とMulti-Head Attentionのコンポーネントは、すべてバッチ処理された **行列-行列の乗算** を行います。これらの操作は、Transformerのトレーニングにおいて最も計算集約的な部分です。
2. **統計的正規化**
Softmaxと層正規化は、テンソルの収縮よりも計算負荷が少なく、1つまたは複数の **縮約操作** を含み、その結果がマップを介して適用されます。
3. **要素ごとの演算子**
これらは残りの演算子です：**バイアス、ドロップアウト、活性化、および残差接続** です。これらは最も計算集約的な操作ではありません。
パフォーマンスのボトルネックを分析する際に、この知識は役立つことがあります。
この要約は、[Data Movement Is All You Need: Optimizing Transformers 2020に関するケーススタディ](https://arxiv.org/abs/2007.00072)から派生しています。
## Anatomy of Model's Memory
モデルのトレーニングがGPUに配置されたモデルよりもはるかに多くのメモリを使用することを見てきました。これは、トレーニング中にGPUメモリを使用する多くのコンポーネントが存在するためです。GPUメモリ上のコンポーネントは以下の通りです：
1. モデルの重み
2. オプティマイザの状態
3. 勾配
4. 勾配計算のために保存された前向き活性化
5. 一時バッファ
6. 機能固有のメモリ
通常、AdamWを使用して混合精度でトレーニングされたモデルは、モデルパラメータごとに18バイトとアクティベーションメモリが必要です。推論ではオプティマイザの状態と勾配は不要ですので、これらを差し引くことができます。したがって、混合精度の推論においては、モデルパラメータごとに6バイトとアクティベーションメモリが必要です。
詳細を見てみましょう。
**モデルの重み:**
- fp32トレーニングのパラメーター数 * 4バイト
- ミックスプレシジョントレーニングのパラメーター数 * 6バイト（メモリ内にfp32とfp16のモデルを維持）
**オプティマイザの状態:**
- 通常のAdamWのパラメーター数 * 8バイト（2つの状態を維持）
- 8-bit AdamWオプティマイザのパラメーター数 * 2バイト（[bitsandbytes](https://github.com/TimDettmers/bitsandbytes)のようなオプティマイザ）
- モーメンタムを持つSGDのようなオプティマイザのパラメーター数 * 4バイト（1つの状態を維持）
**勾配**
- fp32またはミックスプレシジョントレーニングのパラメーター数 * 4バイト（勾配は常にfp32で保持）
**フォワードアクティベーション**
- サイズは多くの要因に依存し、主要な要因はシーケンスの長さ、隠れ層のサイズ、およびバッチサイズです。
フォワードとバックワードの関数によって渡され、返される入力と出力、および勾配計算のために保存されるフォワードアクティベーションがあります。
**一時的なメモリ**
さらに、計算が完了した後に解放されるさまざまな一時変数がありますが、これらは一時的に追加のメモリを必要とし、OOMに達する可能性があります。したがって、コーディング時にはこのような一時変数に戦略的に考え、必要なくなったら明示的に解放することが非常に重要です。
**機能固有のメモリ**
次に、ソフトウェアには特別なメモリ要件がある場合があります。たとえば、ビームサーチを使用してテキストを生成する場合、ソフトウェアは複数の入力と出力のコピーを維持する必要があります。
**`forward`と`backward`の実行速度**
畳み込み層と線形層では、バックワードにフォワードと比べて2倍のFLOPSがあり、一般的には約2倍遅くなります（バックワードのサイズが不便であることがあるため、それ以上になることがあります）。 アクティベーションは通常、バンド幅制限されており、バックワードでアクティベーションがフォワードよりも多くのデータを読むことが一般的です（たとえば、アクティベーションフォワードは1回読み取り、1回書き込み、アクティベーションバックワードはフォワードのgradOutputおよび出力を2回読み取り、1回書き込みます）。
ご覧の通り、GPUメモリを節約したり操作を高速化できる可能性のあるいくつかの場所があります。 GPUの利用と計算速度に影響を与える要因を理解したので、パフォーマンス最適化の技術については、[単一GPUでの効率的なトレーニングのための方法とツール](perf_train_gpu_one)のドキュメンテーションページを参照してください。
詳細を見てみましょう。
| transformers/docs/source/ja/model_memory_anatomy.md/0 | {
"file_path": "transformers/docs/source/ja/model_memory_anatomy.md",
"repo_id": "transformers",
"token_count": 5987
} |
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# Text to speech
[[open-in-colab]]
テキスト読み上げ (TTS) は、テキストから自然な音声を作成するタスクです。音声は複数の形式で生成できます。
言語と複数の話者向け。現在、いくつかのテキスト読み上げモデルが 🤗 Transformers で利用可能です。
[Bark](../model_doc/bark)、[MMS](../model_doc/mms)、[VITS](../model_doc/vits)、および [SpeechT5](../model_doc/speecht5)。
`text-to-audio`パイプライン (またはその別名 - `text-to-speech`) を使用して、音声を簡単に生成できます。 Bark などの一部のモデルは、
笑い、ため息、泣きなどの非言語コミュニケーションを生成したり、音楽を追加したりするように条件付けすることもできます。
Bark で`text-to-speech`パイプラインを使用する方法の例を次に示します。
```py
>>> from transformers import pipeline
>>> pipe = pipeline("text-to-speech", model="suno/bark-small")
>>> text = "[clears throat] This is a test ... and I just took a long pause."
>>> output = pipe(text)
```
ノートブックで結果の音声を聞くために使用できるコード スニペットを次に示します。
```python
>>> from IPython.display import Audio
>>> Audio(output["audio"], rate=output["sampling_rate"])
```
Bark およびその他の事前トレーニングされた TTS モデルができることの詳細な例については、次のドキュメントを参照してください。
[音声コース](https://huggingface.co/learn/audio-course/chapter6/pre-trained_models)。
TTS モデルを微調整する場合、現在微調整できるのは SpeechT5 のみです。 SpeechT5 は、次の組み合わせで事前トレーニングされています。
音声からテキストへのデータとテキストから音声へのデータ。両方のテキストに共有される隠された表現の統一された空間を学習できるようにします。
そしてスピーチ。これは、同じ事前トレーニング済みモデルをさまざまなタスクに合わせて微調整できることを意味します。さらに、SpeechT5
X ベクトル スピーカーの埋め込みを通じて複数のスピーカーをサポートします。
このガイドの残りの部分では、次の方法を説明します。
1. [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) のオランダ語 (`nl`) 言語サブセット上の英語音声で元々トレーニングされた [SpeechT5](../model_doc/speecht5) を微調整します。 データセット。
2. パイプラインを使用するか直接使用するかの 2 つの方法のいずれかで、洗練されたモデルを推論に使用します。
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 datasets soundfile speechbrain accelerate
```
SpeechT5 のすべての機能がまだ正式リリースにマージされていないため、ソースから 🤗Transformers をインストールします。
```bash
pip install git+https://github.com/huggingface/transformers.git
```
<Tip>
このガイドに従うには、GPU が必要です。ノートブックで作業している場合は、次の行を実行して GPU が利用可能かどうかを確認します。
```bash
!nvidia-smi
```
</Tip>
Hugging Face アカウントにログインして、モデルをアップロードしてコミュニティと共有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 the dataset
[VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) は、以下で構成される大規模な多言語音声コーパスです。
データは 2009 年から 2020 年の欧州議会のイベント記録をソースとしています。 15 件分のラベル付き音声文字起こしデータが含まれています。
ヨーロッパの言語。このガイドではオランダ語のサブセットを使用していますが、自由に別のサブセットを選択してください。
VoxPopuli またはその他の自動音声認識 (ASR) データセットは最適ではない可能性があることに注意してください。
TTS モデルをトレーニングするためのオプション。過剰なバックグラウンドノイズなど、ASR にとって有益となる機能は次のとおりです。
通常、TTS では望ましくありません。ただし、最高品質、多言語、マルチスピーカーの TTS データセットを見つけるのは非常に困難な場合があります。
挑戦的。
データをロードしましょう:
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("facebook/voxpopuli", "nl", split="train")
>>> len(dataset)
20968
```
微調整には 20968 個の例で十分です。 SpeechT5 はオーディオ データのサンプリング レートが 16 kHz であることを想定しているため、
データセット内の例がこの要件を満たしていることを確認してください。
```py
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
```
## Preprocess the data
使用するモデル チェックポイントを定義し、適切なプロセッサをロードすることから始めましょう。
```py
>>> from transformers import SpeechT5Processor
>>> checkpoint = "microsoft/speecht5_tts"
>>> processor = SpeechT5Processor.from_pretrained(checkpoint)
```
### Text cleanup for SpeechT5 tokenization
まずはテキストデータをクリーンアップすることから始めます。テキストを処理するには、プロセッサのトークナイザー部分が必要です。
```py
>>> tokenizer = processor.tokenizer
```
データセットの例には、`raw_text`機能と `normalized_text`機能が含まれています。テキスト入力としてどの機能を使用するかを決めるときは、
SpeechT5 トークナイザーには数値のトークンがないことを考慮してください。 `normalized_text`には数字が書かれています
テキストとして出力します。したがって、これはより適切であり、入力テキストとして `normalized_text` を使用することをお勧めします。
SpeechT5 は英語でトレーニングされているため、オランダ語のデータセット内の特定の文字を認識しない可能性があります。もし
残っているように、これらの文字は `<unk>`トークンに変換されます。ただし、オランダ語では、`à`などの特定の文字は
音節を強調することに慣れています。テキストの意味を保持するために、この文字を通常の`a`に置き換えることができます。
サポートされていないトークンを識別するには、`SpeechT5Tokenizer`を使用してデータセット内のすべての一意の文字を抽出します。
文字をトークンとして扱います。これを行うには、以下を連結する `extract_all_chars` マッピング関数を作成します。
すべての例からの転写を 1 つの文字列にまとめ、それを文字セットに変換します。
すべての文字起こしが一度に利用できるように、`dataset.map()`で`batched=True`と`batch_size=-1`を必ず設定してください。
マッピング機能。
```py
>>> def extract_all_chars(batch):
... all_text = " ".join(batch["normalized_text"])
... vocab = list(set(all_text))
... return {"vocab": [vocab], "all_text": [all_text]}
>>> vocabs = dataset.map(
... extract_all_chars,
... batched=True,
... batch_size=-1,
... keep_in_memory=True,
... remove_columns=dataset.column_names,
... )
>>> dataset_vocab = set(vocabs["vocab"][0])
>>> tokenizer_vocab = {k for k, _ in tokenizer.get_vocab().items()}
```
これで、2 つの文字セットができました。1 つはデータセットの語彙を持ち、もう 1 つはトークナイザーの語彙を持ちます。
データセット内でサポートされていない文字を特定するには、これら 2 つのセットの差分を取ることができます。結果として
set には、データセットにはあるがトークナイザーには含まれていない文字が含まれます。
```py
>>> dataset_vocab - tokenizer_vocab
{' ', 'à', 'ç', 'è', 'ë', 'í', 'ï', 'ö', 'ü'}
```
前の手順で特定されたサポートされていない文字を処理するには、これらの文字を
有効なトークン。スペースはトークナイザーですでに `▁` に置き換えられているため、個別に処理する必要がないことに注意してください。
```py
>>> replacements = [
... ("à", "a"),
... ("ç", "c"),
... ("è", "e"),
... ("ë", "e"),
... ("í", "i"),
... ("ï", "i"),
... ("ö", "o"),
... ("ü", "u"),
... ]
>>> def cleanup_text(inputs):
... for src, dst in replacements:
... inputs["normalized_text"] = inputs["normalized_text"].replace(src, dst)
... return inputs
>>> dataset = dataset.map(cleanup_text)
```
テキスト内の特殊文字を扱ったので、今度は音声データに焦点を移します。
### Speakers
VoxPopuli データセットには複数の話者の音声が含まれていますが、データセットには何人の話者が含まれているのでしょうか?に
これを決定すると、一意の話者の数と、各話者がデータセットに寄与する例の数を数えることができます。
データセットには合計 20,968 個の例が含まれており、この情報により、分布をより深く理解できるようになります。
講演者とデータ内の例。
```py
>>> from collections import defaultdict
>>> speaker_counts = defaultdict(int)
>>> for speaker_id in dataset["speaker_id"]:
... speaker_counts[speaker_id] += 1
```
ヒストグラムをプロットすると、各話者にどれだけのデータがあるかを把握できます。
```py
>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.hist(speaker_counts.values(), bins=20)
>>> plt.ylabel("Speakers")
>>> plt.xlabel("Examples")
>>> plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/tts_speakers_histogram.png" alt="Speakers histogram"/>
</div>
ヒストグラムから、データセット内の話者の約 3 分の 1 の例が 100 未満であることがわかります。
約 10 人の講演者が 500 以上の例を持っています。トレーニング効率を向上させ、データセットのバランスをとるために、次のことを制限できます。
100 ～ 400 個の例を含むデータを講演者に提供します。
```py
>>> def select_speaker(speaker_id):
... return 100 <= speaker_counts[speaker_id] <= 400
>>> dataset = dataset.filter(select_speaker, input_columns=["speaker_id"])
```
残りのスピーカーの数を確認してみましょう。
```py
>>> len(set(dataset["speaker_id"]))
42
```
残りの例がいくつあるか見てみましょう。
```py
>>> len(dataset)
9973
```
約 40 人のユニークな講演者からの 10,000 弱の例が残りますが、これで十分です。
例が少ないスピーカーの中には、例が長い場合、実際にはより多くの音声が利用できる場合があることに注意してください。しかし、
各話者の音声の合計量を決定するには、データセット全体をスキャンする必要があります。
各オーディオ ファイルのロードとデコードを伴う時間のかかるプロセス。そのため、ここではこのステップをスキップすることにしました。
### Speaker embeddings
TTS モデルが複数のスピーカーを区別できるようにするには、サンプルごとにスピーカーの埋め込みを作成する必要があります。
スピーカーの埋め込みは、特定のスピーカーの音声特性をキャプチャするモデルへの追加入力です。
これらのスピーカー埋め込みを生成するには、事前トレーニングされた [spkrec-xvect-voxceleb](https://huggingface.co/speechbrain/spkrec-xvect-voxceleb) を使用します。
SpeechBrain のモデル。
入力オーディオ波形を受け取り、512 要素のベクトルを出力する関数 `create_speaker_embedding()` を作成します。
対応するスピーカー埋め込みが含まれます。
```py
>>> import os
>>> import torch
>>> from speechbrain.pretrained import EncoderClassifier
>>> spk_model_name = "speechbrain/spkrec-xvect-voxceleb"
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> speaker_model = EncoderClassifier.from_hparams(
... source=spk_model_name,
... run_opts={"device": device},
... savedir=os.path.join("/tmp", spk_model_name),
... )
>>> def create_speaker_embedding(waveform):
... with torch.no_grad():
... speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
... speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
... speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
... return speaker_embeddings
```
`speechbrain/spkrec-xvect-voxceleb`モデルは、VoxCeleb からの英語音声でトレーニングされたことに注意することが重要です。
データセットですが、このガイドのトレーニング例はオランダ語です。このモデルは今後も生成されると信じていますが、
オランダ語のデータセットに適切な話者埋め込みを行っても、この仮定はすべての場合に当てはまらない可能性があります。
最適な結果を得るには、最初にターゲット音声で X ベクトル モデルをトレーニングすることをお勧めします。これにより、モデルが確実に
オランダ語に存在する独特の音声特徴をよりよく捉えることができます。
### Processing the dataset
最後に、モデルが期待する形式にデータを処理しましょう。を取り込む `prepare_dataset` 関数を作成します。
これは 1 つの例であり、`SpeechT5Processor` オブジェクトを使用して入力テキストをトークン化し、ターゲット オーディオをログメル スペクトログラムにロードします。
また、追加の入力としてスピーカーの埋め込みも追加する必要があります。
```py
>>> def prepare_dataset(example):
... audio = example["audio"]
... example = processor(
... text=example["normalized_text"],
... audio_target=audio["array"],
... sampling_rate=audio["sampling_rate"],
... return_attention_mask=False,
... )
... # strip off the batch dimension
... example["labels"] = example["labels"][0]
... # use SpeechBrain to obtain x-vector
... example["speaker_embeddings"] = create_speaker_embedding(audio["array"])
... return example
```
単一の例を見て、処理が正しいことを確認します。
```py
>>> processed_example = prepare_dataset(dataset[0])
>>> list(processed_example.keys())
['input_ids', 'labels', 'stop_labels', 'speaker_embeddings']
```
スピーカーのエンベディングは 512 要素のベクトルである必要があります。
```py
>>> processed_example["speaker_embeddings"].shape
(512,)
```
ラベルは、80 メル ビンを含むログメル スペクトログラムである必要があります。
```py
>>> import matplotlib.pyplot as plt
>>> plt.figure()
>>> plt.imshow(processed_example["labels"].T)
>>> plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/tts_logmelspectrogram_1.png" alt="Log-mel spectrogram with 80 mel bins"/>
</div>
補足: このスペクトログラムがわかりにくいと感じる場合は、低周波を配置する規則に慣れていることが原因である可能性があります。
プロットの下部に高周波、上部に高周波が表示されます。ただし、matplotlib ライブラリを使用してスペクトログラムを画像としてプロットする場合、
Y 軸が反転され、スペクトログラムが上下逆に表示されます。
次に、処理関数をデータセット全体に適用します。これには 5 ～ 10 分かかります。
```py
>>> dataset = dataset.map(prepare_dataset, remove_columns=dataset.column_names)
```
データセット内の一部の例が、モデルが処理できる最大入力長 (600 トークン) を超えていることを示す警告が表示されます。
それらの例をデータセットから削除します。ここではさらに進んで、より大きなバッチ サイズを可能にするために、200 トークンを超えるものはすべて削除します。
```py
>>> def is_not_too_long(input_ids):
... input_length = len(input_ids)
... return input_length < 200
>>> dataset = dataset.filter(is_not_too_long, input_columns=["input_ids"])
>>> len(dataset)
8259
```
次に、基本的なトレーニング/テスト分割を作成します。
```py
>>> dataset = dataset.train_test_split(test_size=0.1)
```
### Data collator
複数の例を 1 つのバッチに結合するには、カスタム データ照合器を定義する必要があります。このコレーターは、短いシーケンスをパディングで埋め込みます。
トークンを使用して、すべての例が同じ長さになるようにします。スペクトログラム ラベルの場合、埋め込まれた部分は特別な値 `-100` に置き換えられます。この特別な価値は
スペクトログラム損失を計算するときに、スペクトログラムのその部分を無視するようにモデルに指示します。
```py
>>> from dataclasses import dataclass
>>> from typing import Any, Dict, List, Union
>>> @dataclass
... class TTSDataCollatorWithPadding:
... processor: Any
... def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
... input_ids = [{"input_ids": feature["input_ids"]} for feature in features]
... label_features = [{"input_values": feature["labels"]} for feature in features]
... speaker_features = [feature["speaker_embeddings"] for feature in features]
... # collate the inputs and targets into a batch
... batch = processor.pad(input_ids=input_ids, labels=label_features, return_tensors="pt")
... # replace padding with -100 to ignore loss correctly
... batch["labels"] = batch["labels"].masked_fill(batch.decoder_attention_mask.unsqueeze(-1).ne(1), -100)
... # not used during fine-tuning
... del batch["decoder_attention_mask"]
... # round down target lengths to multiple of reduction factor
... if model.config.reduction_factor > 1:
... target_lengths = torch.tensor([len(feature["input_values"]) for feature in label_features])
... target_lengths = target_lengths.new(
... [length - length % model.config.reduction_factor for length in target_lengths]
... )
... max_length = max(target_lengths)
... batch["labels"] = batch["labels"][:, :max_length]
... # also add in the speaker embeddings
... batch["speaker_embeddings"] = torch.tensor(speaker_features)
... return batch
```
SpeechT5 では、モデルのデコーダ部分への入力が 2 分の 1 に削減されます。つまり、すべてのデータが破棄されます。
ターゲット シーケンスからの他のタイムステップ。次に、デコーダは 2 倍の長さのシーケンスを予測します。オリジナル以来
ターゲット シーケンスの長さが奇数である可能性がある場合、データ照合機能はバッチの最大長を切り捨てて、
2の倍数。
```py
>>> data_collator = TTSDataCollatorWithPadding(processor=processor)
```
## Train the model
プロセッサのロードに使用したのと同じチェックポイントから事前トレーニングされたモデルをロードします。
```py
>>> from transformers import SpeechT5ForTextToSpeech
>>> model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
```
`use_cache=True`オプションは、勾配チェックポイントと互換性がありません。トレーニングのために無効にします。
```py
>>> model.config.use_cache = False
```
トレーニング引数を定義します。ここでは、トレーニング プロセス中に評価メトリクスを計算していません。代わりに、
損失だけを見てください。
```python
>>> from transformers import Seq2SeqTrainingArguments
>>> training_args = Seq2SeqTrainingArguments(
... output_dir="speecht5_finetuned_voxpopuli_nl", # change to a repo name of your choice
... per_device_train_batch_size=4,
... gradient_accumulation_steps=8,
... learning_rate=1e-5,
... warmup_steps=500,
... max_steps=4000,
... gradient_checkpointing=True,
... fp16=True,
... eval_strategy="steps",
... per_device_eval_batch_size=2,
... save_steps=1000,
... eval_steps=1000,
... logging_steps=25,
... report_to=["tensorboard"],
... load_best_model_at_end=True,
... greater_is_better=False,
... label_names=["labels"],
... push_to_hub=True,
... )
```
`Trainer`オブジェクトをインスタンス化し、モデル、データセット、データ照合器をそれに渡します。
```py
>>> from transformers import Seq2SeqTrainer
>>> trainer = Seq2SeqTrainer(
... args=training_args,
... model=model,
... train_dataset=dataset["train"],
... eval_dataset=dataset["test"],
... data_collator=data_collator,
... processing_class=processor,
... )
```
これで、トレーニングを開始する準備が整いました。トレーニングには数時間かかります。 GPU に応じて、
トレーニングを開始するときに、CUDA の「メモリ不足」エラーが発生する可能性があります。この場合、減らすことができます
`per_device_train_batch_size`を 2 倍に増分し、`gradient_accumulation_steps`を 2 倍に増やして補正します。
```py
>>> trainer.train()
```
パイプラインでチェックポイントを使用できるようにするには、必ずプロセッサをチェックポイントとともに保存してください。
```py
>>> processor.save_pretrained("YOUR_ACCOUNT_NAME/speecht5_finetuned_voxpopuli_nl")
```
最終モデルを 🤗 ハブにプッシュします。
```py
>>> trainer.push_to_hub()
```
## Inference
### Inference with a pipeline
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。
まず、対応するパイプラインでそれを使用する方法を見てみましょう。 `"text-to-speech"` パイプラインを作成しましょう
チェックポイント:
```py
>>> from transformers import pipeline
>>> pipe = pipeline("text-to-speech", model="YOUR_ACCOUNT_NAME/speecht5_finetuned_voxpopuli_nl")
```
ナレーションを希望するオランダ語のテキストを選択してください。例:
```py
>>> text = "hallo allemaal, ik praat nederlands. groetjes aan iedereen!"
```
パイプラインで SpeechT5 を使用するには、スピーカーの埋め込みが必要です。テスト データセットの例から取得してみましょう。
```py
>>> example = dataset["test"][304]
>>> speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0)
```
これで、テキストとスピーカーの埋め込みをパイプラインに渡すことができ、残りはパイプラインが処理します。
```py
>>> forward_params = {"speaker_embeddings": speaker_embeddings}
>>> output = pipe(text, forward_params=forward_params)
>>> output
{'audio': array([-6.82714235e-05, -4.26525949e-04, 1.06134125e-04, ...,
-1.22392643e-03, -7.76011671e-04, 3.29112721e-04], dtype=float32),
'sampling_rate': 16000}
```
その後、結果を聞くことができます。
```py
>>> from IPython.display import Audio
>>> Audio(output['audio'], rate=output['sampling_rate'])
```
### Run inference manually
パイプラインを使用しなくても同じ推論結果を得ることができますが、より多くの手順が必要になります。
🤗 ハブからモデルをロードします。
```py
>>> model = SpeechT5ForTextToSpeech.from_pretrained("YOUR_ACCOUNT/speecht5_finetuned_voxpopuli_nl")
```
テスト データセットから例を選択して、スピーカーの埋め込みを取得します。
```py
>>> example = dataset["test"][304]
>>> speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0)
```
入力テキストを定義し、トークン化します。
```py
>>> text = "hallo allemaal, ik praat nederlands. groetjes aan iedereen!"
>>> inputs = processor(text=text, return_tensors="pt")
```
モデルを使用してスペクトログラムを作成します。
```py
>>> spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
```
次のことを行う場合は、スペクトログラムを視覚化します。
```py
>>> plt.figure()
>>> plt.imshow(spectrogram.T)
>>> plt.show()
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/tts_logmelspectrogram_2.png" alt="Generated log-mel spectrogram"/>
</div>
最後に、ボコーダーを使用してスペクトログラムをサウンドに変換します。
```py
>>> with torch.no_grad():
... speech = vocoder(spectrogram)
>>> from IPython.display import Audio
>>> Audio(speech.numpy(), rate=16000)
```
私たちの経験では、このモデルから満足のいく結果を得るのは難しい場合があります。スピーカーの品質
埋め込みは重要な要素であるようです。 SpeechT5 は英語の x ベクトルで事前トレーニングされているため、最高のパフォーマンスを発揮します
英語スピーカーの埋め込みを使用する場合。合成音声の音質が悪い場合は、別のスピーカー埋め込みを使用してみてください。
トレーニング期間を長くすると、結果の質も向上する可能性があります。それでも、そのスピーチは明らかに英語ではなくオランダ語です。
話者の音声特性をキャプチャします (例の元の音声と比較)。
もう 1 つ実験すべきことは、モデルの構成です。たとえば、`config.reduction_factor = 1`を使用してみてください。
これにより結果が改善されるかどうかを確認してください。
最後に、倫理的配慮を考慮することが不可欠です。 TTS テクノロジーには数多くの有用な用途がありますが、
また、知らないうちに誰かの声を偽装するなど、悪意のある目的に使用される可能性もあります。お願いします
TTS は賢明かつ責任を持って使用してください。
| transformers/docs/source/ja/tasks/text-to-speech.md/0 | {
"file_path": "transformers/docs/source/ja/tasks/text-to-speech.md",
"repo_id": "transformers",
"token_count": 12015
} |
# docstyle-ignore
INSTALL_CONTENT = """
# Transformers 설치 방법
! pip install transformers datasets evaluate accelerate
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}]
black_avoid_patterns = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| transformers/docs/source/ko/_config.py/0 | {
"file_path": "transformers/docs/source/ko/_config.py",
"repo_id": "transformers",
"token_count": 259
} |
<!--Copyright 2024 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.
-->
# DeepSpeed[[deepspeed]]
[DeepSpeed](https://www.deepspeed.ai/)는 분산 학습 메모리를 효율적이고 빠르게 만드는 PyTorch 최적화 라이브러리입니다. 그 핵심은 대규모 모델을 규모에 맞게 훈련할 수 있는 [Zero Redundancy Optimizer(ZeRO)](https://hf.co/papers/1910.02054)입니다. ZeRO는 여러 단계로 작동합니다:
* ZeRO-1, GPU 간 최적화 상태 분할
* ZeRO-2, GPU 간 그레이디언트 분할
* ZeRO-3, GPU 간 매개변수 분할
GPU가 제한된 환경에서 ZeRO는 최적화 메모리와 계산을 GPU에서 CPU로 오프로드하여 단일 GPU에 대규모 모델을 장착하고 훈련할 수 있습니다. DeepSpeed는 모든 ZeRO 단계 및 오프로딩을 위해 Transformers [`Trainer`] 클래스와 통합되어 있습니다. 구성 파일을 제공하거나 제공된 템플릿을 사용하기만 하면 됩니다. 추론의 경우, Transformers는 대용량 모델을 가져올 수 있으므로 ZeRO-3 및 오프로딩을 지원합니다.
이 가이드에서는 DeepSpeed 트레이닝을 배포하는 방법, 활성화할 수 있는 기능, 다양한 ZeRO 단계에 대한 구성 파일 설정 방법, 오프로딩, 추론 및 [`Trainer`] 없이 DeepSpeed를 사용하는 방법을 안내해 드립니다.
## 설치[[installation]]
DeepSpeed는 PyPI 또는 Transformers에서 설치할 수 있습니다(자세한 설치 옵션은 DeepSpeed [설치 상세사항](https://www.deepspeed.ai/tutorials/advanced-install/) 또는 GitHub [README](https://github.com/deepspeedai/DeepSpeed#installation)를 참조하세요).
<Tip>
DeepSpeed를 설치하는 데 문제가 있는 경우 [DeepSpeed CUDA 설치](../debugging#deepspeed-cuda-installation) 가이드를 확인하세요. DeepSpeed에는 pip 설치 가능한 PyPI 패키지로 설치할 수 있지만, 하드웨어에 가장 잘 맞고 PyPI 배포판에서는 제공되지 않는 1비트 Adam과 같은 특정 기능을 지원하려면 [소스에서 설치하기](https://www.deepspeed.ai/tutorials/advanced-install/#install-deepspeed-from-source)를 적극 권장합니다.
</Tip>
<hfoptions id="install">
<hfoption id="PyPI">
```bash
pip install deepspeed
```
</hfoption>
<hfoption id="Transformers">
```bash
pip install transformers[deepspeed]
```
</hfoption>
</hfoptions>
## 메모리 요구량[[memory-requirements]]
시작하기 전에 모델에 맞는 충분한 GPU 및 CPU 메모리가 있는지 확인하는 것이 좋습니다. DeepSpeed는 필요한 CPU/GPU 메모리를 추정할 수 있는 도구를 제공합니다. 예를 들어, 단일 GPU에서 [bigscience/T0_3B](bigscience/T0_3B) 모델의 메모리 요구 사항을 추정할 수 있습니다:
```bash
$ python -c 'from transformers import AutoModel; \
from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \
model = AutoModel.from_pretrained("bigscience/T0_3B"); \
estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=1, num_nodes=1)'
[...]
Estimated memory needed for params, optim states and gradients for a:
HW: Setup with 1 node, 1 GPU per node.
SW: Model with 2783M total params, 65M largest layer params.
per CPU | per GPU | Options
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0
62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=1
62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=0
0.37GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=1
15.56GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=0
```
즉, CPU 오프로드가 없는 단일 80GB GPU 또는 오프로드 할 8GB GPU와 최대 60GB CPU가 필요합니다 (이는 매개변수, 최적화 상태 및 그레이디언트에 대한 메모리 요구 사항일 뿐이며 CUDA 커널 및 활성화에는 조금 더 필요합니다). 또한 더 작은 GPU를 대여하거나 구입하는 것이 더 저렴하지만 모델을 훈련하는 데 시간이 더 오래 걸리므로 비용과 속도 간의 균형을 고려해야 합니다.
GPU 메모리가 충분하다면 CPU/NVMe 오프로드를 비활성화하여 모든 작업을 더 빠르게 처리하세요.
## ZeRO 단계 설정하기[[select-a-zero-stage]]
DeepSpeed를 설치하고 메모리 요구 사항을 더 잘 파악했다면 다음 단계는 사용할 ZeRO 스테이지를 선택하는 것입니다. 가장 빠르고 메모리 효율이 높은 순서대로 정렬하면 다음과 같습니다:
| 속도 | 메모리 효율 |
|------------------|------------------|
| ZeRO-1 | ZeRO-3 + offload |
| ZeRO-2 | ZeRO-3 |
| ZeRO-2 + offload | ZeRO-2 + offload |
| ZeRO-3 | ZeRO-2 |
| ZeRO-3 + offload | ZeRO-1 |
자신에게 가장 적합한 방법을 찾으려면 가장 빠른 방법부터 시작하고 메모리가 부족하면 더 느리지만 메모리 효율이 높은 다음 단계를 시도하세요. 속도와 메모리 사용량 사이의 적절한 균형을 찾기 위해 (가장 메모리 효율적이거나 가장 빠른 것부터 시작하여) 원하는 방향으로 자유롭게 작업하세요.
일반적으로 사용할 수 있는 프로세스는 다음과 같습니다(배치 크기 1로 시작):
1. 그레이디언트 체크포인팅 활성화
2. ZeRO-2 시도
3. ZeRO-2와 매개변수 오프로드 시도
4. ZeRO-3 시도
5. ZeRO-3과 매개변수 CPU 오프로드 시도
6. ZeRO-3, 매개변수와 옵티마이저 CPU 오프로드 시도
7. [`~GenerationMixin.generate`] 메소드를 사용하는 경우 더 좁은 빔 서치 검색 범위와 같은 다양한 기본값을 낮춰보기
8. 전체 정밀도 가중치보다 반정밀도(구형 GPU 구조의 경우 fp16, 암페어 이후 GPU의 경우 bf16)를 혼합해보기
9. 가능하면 하드웨어를 더 추가하거나 Infinity가 매개변수와 옵티마이저를 NVMe로 오프로드하도록 활성화
10. 메모리가 부족하지 않으면 유효 처리량을 측정한 다음 배치 크기를 최대한 크게 늘려 GPU 효율성을 극대화
11. 마지막으로 일부 오프로드 기능을 비활성화하거나 더 빠른 ZeRO 스테이지를 사용하고 배치 크기를 늘리거나 줄여 속도와 메모리 사용량 간의 최적의 균형을 찾아 트레이닝 설정을 최적화
## DeepSpeed 구성 파일[[deepspeed-configuration-file]]
DeepSpeed는 트레이닝 실행 방법을 구성하는 모든 매개변수가 포함된 구성 파일을 통해 [`Trainer`] 클래스와 함께 작동합니다. 트레이닝 스크립트를 실행하면 DeepSpeed는 [`Trainer`]로부터 받은 구성을 콘솔에 기록하므로 어떤 구성이 사용되었는지 정확히 확인할 수 있습니다.
<Tip>
DeepSpeed 구성 옵션의 전체 목록은 [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/)에서 확인할 수 있습니다. 또한 [DeepSpeedExamples](https://github.com/deepspeedai/DeepSpeedExamples) 리포지토리 또는 기본 [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) 리포지토리에서 다양한 DeepSpeed 구성 예제에 대한 보다 실용적인 예제를 찾을 수 있습니다. 구체적인 예제를 빠르게 찾으려면 다음과 같이 하세요:
```bash
git clone https://github.com/deepspeedai/DeepSpeedExamples
cd DeepSpeedExamples
find . -name '*json'
# Lamb 옵티마이저 샘플 찾기
grep -i Lamb $(find . -name '*json')
```
</Tip>
명령줄 인터페이스에서 트레이닝하는 경우 DeepSpeed 구성 파일은 JSON 파일의 경로로 전달되거나 노트북 설정에서 [`Trainer`]를 사용하는 경우 중첩된 `dict` 객체로 전달됩니다.
<hfoptions id="pass-config">
<hfoption id="path to file">
```py
TrainingArguments(..., deepspeed="path/to/deepspeed_config.json")
```
</hfoption>
<hfoption id="nested dict">
```py
ds_config_dict = dict(scheduler=scheduler_params, optimizer=optimizer_params)
args = TrainingArguments(..., deepspeed=ds_config_dict)
trainer = Trainer(model, args, ...)
```
</hfoption>
</hfoptions>
### DeepSpeed와 Trainer 매개변수[[deepspeed-and-trainer-parameters]]
구성 매개변수에는 세 가지 유형이 있습니다:
1. 일부 구성 매개변수는 [`Trainer`]와 DeepSpeed가 공유하며, 정의가 충돌하는 경우 오류를 식별하기 어려울 수 있습니다. 이러한 공유 구성 매개변수는 [`Trainer`] 명령줄 인수에서 쉽게 설정할 수 있습니다.
2. 모델 설정에서 자동으로 도출되는 일부 설정 매개변수는 수동으로 값을 조정할 필요가 없습니다. [`Trainer`]는 구성 값 `auto`를 사용하여 가장 정확하거나 효율적인 값을 설정합니다. 직접 구성 매개변수를 명시적으로 설정할 수도 있지만, [`Trainer`] 인수와 DeepSpeed 설정 매개변수가 일치하도록 주의해야 합니다. 일치하지 않으면 감지하기 매우 어려운 방식으로 훈련이 실패할 수 있습니다!
3. 교육 요구 사항에 따라 수동으로 설정해야 하는 일부 설정 매개변수는 DeepSpeed에만 해당됩니다.
DeepSpeed 구성을 수정하고 [`TrainingArguments`]를 편집할 수도 있습니다:
1. 기본 구성으로 사용할 DeepSpeed 구성 파일을 생성하거나 로드합니다.
2. 다음 DeepSpeed 구성을 기반으로 [`TrainingArguments`] 객체를 생성합니다.
`scheduler.params.total_num_steps`와 같은 일부 값은 트레이닝 중 [`Trainer`]에 의해 계산됩니다.
### ZeRO 구성[[zero-configuration]]
세 가지 구성이 있으며, 각 구성은 서로 다른 ZeRO 단계에 해당합니다. 1단계는 확장성 측면에서 그다지 눈여겨볼만하지 않으므로 이 가이드에서는 2단계와 3단계에 중점을 둡니다. `zero_optimization` 구성에는 활성화할 항목과 구성 방법에 대한 모든 옵션이 포함되어 있습니다. 각 매개변수에 대한 자세한 설명은 [DeepSpeed 구성 JSON](https://www.deepspeed.ai/docs/config-json/) 참조를 참조하세요.
<Tip warning={true}>
DeepSpeed는 매개변수 이름의 유효성을 검사하지 않으며 오타가 있으면 매개변수의 기본 설정으로 대체합니다. DeepSpeed 엔진 시작 로그 메시지를 보고 어떤 값을 사용할지 확인할 수 있습니다.
</Tip>
[`Trainer`]는 동등한 명령줄 인수를 제공하지 않으므로 다음 구성은 DeepSpeed로 설정해야 합니다.
<hfoptions id="zero-config">
<hfoption id="ZeRO-1">
ZeRO-1은 옵티마이저 상태를 GPU에 분할하여 약간의 속도 향상을 기대할 수 있습니다. ZeRO-1 구성은 다음과 같이 설정할 수 있습니다:
```yml
{
"zero_optimization": {
"stage": 1
}
}
```
</hfoption>
<hfoption id="ZeRO-2">
ZeRO-2는 GPU에서 옵티마이저와 그레이디언트를 분할합니다. 이 단계는 추론과 관련이 없는 기능이기 때문에 주로 훈련에 사용됩니다. 더 나은 성능을 위해 구성해야 할 몇 가지 중요한 매개변수는 다음과 같습니다:
* GPU 메모리 사용량을 줄이려면 `offload_optimizer`를 활성화해야 합니다.
* `true`로 설정된 경우 `overlap_comm`은 GPU 메모리 사용량 증가를 상쇄하여 지연 시간을 줄입니다. 이 기능은 4.5배의 `allgather_bucket_size` 및 `reduce_bucket_size`값을 사용합니다. 이 예에서는 `5e8`로 설정되어 있으므로 9GB의 GPU 메모리가 필요합니다. GPU 메모리가 8GB 이하인 경우, 메모리 요구량을 낮추고 메모리 부족(OOM) 오류를 방지하기 위해 `overlap_comm`을 줄여야 합니다.
* `allgather_bucket_size`와 `reduce_bucket_size`는 사용 가능한 GPU 메모리와 통신 속도를 절충합니다. 값이 작을수록 통신 속도가 느려지고 더 많은 GPU 메모리를 사용할 수 있습니다. 예를 들어, 배치 크기가 큰 것이 약간 느린 훈련 시간보다 더 중요한지 균형을 맞출 수 있습니다.
* DeepSpeed 0.4.4에서는 CPU 오프로딩을 위해 `round_robin_gradients`를 사용할 수 있습니다. 이 기능은 세분화된 그레이디언트 파티셔닝을 통해 등급 간 그레이디언트 복사를 CPU 메모리로 병렬화합니다. 성능 이점은 그레이디언트 누적 단계(최적화 단계 간 복사 횟수 증가) 또는 GPU 수(병렬 처리 증가)에 따라 증가합니다.
```yml
{
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"contiguous_gradients": true
"round_robin_gradients": true
}
}
```
</hfoption>
<hfoption id="ZeRO-3">
ZeRO-3는 옵티마이저, 그래디언트, 매개변수를 여러 GPU에 걸쳐 분할합니다. ZeRO-2와 달리 ZeRO-3는 여러 GPU에 대규모 모델을 가져올 수 있기 때문에 훈련 외에도 추론에도 사용할 수 있습니다. 구성해야 할 몇 가지 중요한 매개변수는 다음과 같습니다:
* `device: "cpu"` 는 GPU 메모리가 부족하고 사용 가능한 CPU 메모리가 있는 경우 도움이 될 수 있습니다. 이를 통해 모델 매개변수를 CPU로 오프로드할 수 있습니다.
* `pin_memory: true` 는 처리량을 향상시킬 수 있지만, 핀 메모리는 메모리를 요청한 특정 프로세스를 위해 예약되어 있고 일반적으로 일반 CPU 메모리보다 훨씬 빠르게 액세스되기 때문에 다른 프로세스에서 사용할 수 있는 메모리가 줄어듭니다.
* `stage3_max_live_parameters` 는 특정 시간에 GPU에 유지하려는 전체 매개변수의 상한값입니다. OOM 오류가 발생하면 이 값을 줄이세요.
* `stage3_max_reuse_distance` 는 향후 매개변수를 다시 사용할 시기를 결정하는 값으로, 매개변수를 버릴지 유지할지 결정하는 데 도움이 됩니다. 매개변수를 재사용할 경우(`stage3_max_reuse_distance`보다 작은 값인 경우) 통신 오버헤드를 줄이기 위해 매개변수를 유지합니다. 이 기능은 활성화 체크포인팅이 활성화되어 있고 역전파 계산시까지 순전파 시점의 매개변수를 유지하려는 경우에 매우 유용합니다. 그러나 OOM 오류가 발생하면 이 값을 줄이세요.
* 모델 저장 시 `stage3_gather_16bit_weights_on_model_save`는 fp16 가중치를 통합합니다. 대규모 모델을 학습하거나 여러 GPU를 사용할 경우 메모리와 속도 측면에서 비용이 많이 듭니다. 훈련을 재개할 계획이라면 이 옵션을 활성화해야 합니다.
* `sub_group_size` 는 최적화 단계에서 업데이트되는 매개변수를 제어합니다. 매개변수는 `sub_group_size`의 버킷으로 그룹화되며 각 버킷은 한 번에 하나씩 업데이트됩니다. NVMe 오프로드와 함께 사용하는 경우 `sub_group_size`는 최적화 단계 중 모델 상태가 CPU 메모리로 이동하는 시점을 결정합니다. 이렇게 하면 매우 큰 모델의 CPU 메모리 부족을 방지할 수 있습니다. NVMe 오프로드를 사용하지 않는 경우 `sub_group_size`를 기본값으로 둘 수 있지만, 사용하는 경우 변경하는 것이 좋습니다:
1. 옵티마이저 단계에서 OOM 오류가 발생합니다. 이 경우, 임시 버퍼의 메모리 사용량을 줄이려면 `sub_group_size`를 줄이세요.
2. 옵티마이저 단계에서 시간이 너무 오래 걸립니다. 이 경우 데이터 버퍼 증가로 인한 대역폭 사용률을 개선하기 위해 `sub_group_size`를 늘리세요.
* `reduce_bucket_size`, `stage3_prefetch_bucket_size`, `stage3_param_persistence_threshold`는 모델의 숨겨진 크기에 따라 달라집니다. 이 값들을 `auto`으로 설정하고 [`Trainer`]가 자동으로 값을 할당하도록 허용하는 것이 좋습니다.
```yml
{
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
}
}
```
[`deepspeed.zero.Init`](https://deepspeed.readthedocs.io/en/latest/zero3.html#deepspeed.zero.Init) 컨텍스트 매니저를 사용하면 모델을 더 빠르게 초기화할 수 있습니다:
```py
from transformers import T5ForConditionalGeneration, T5Config
import deepspeed
with deepspeed.zero.Init():
config = T5Config.from_pretrained("google-t5/t5-small")
model = T5ForConditionalGeneration(config)
```
사전 학습된 모델의 경우, 딥스피드 구성 파일에 `is_deepspeed_zero3_enabled: true`가 [`TrainingArguments`]에 설정되어 있어야 하며, ZeRO 구성이 활성화되어 있어야 합니다. 훈련된 모델 [`~PreTrainedModel.from_pretrained`]을 호출하기 **전에** [`TrainingArguments`] 객체를 생성해야 합니다.
```py
from transformers import AutoModel, Trainer, TrainingArguments
training_args = TrainingArguments(..., deepspeed=ds_config)
model = AutoModel.from_pretrained("google-t5/t5-small")
trainer = Trainer(model=model, args=training_args, ...)
```
fp16 가중치가 단일 GPU에 맞지 않는 경우 ZeRO-3이 필요합니다. fp16 가중치를 로드할 수 있는 경우, [`~PreTrainedModel.from_pretrained`]에 `torch_dtype=torch.float16`을 지정해야 합니다.
ZeRO-3의 또 다른 고려 사항은 여러 개의 GPU를 사용하는 경우 현재 실행 중인 레이어의 매개변수가 아닌 한 단일 GPU에 모든 매개변수가 없다는 것입니다. 사전 훈련된 모델 가중치를 [`~PreTrainedModel.from_pretrained`]에 로드하는 등 모든 레이어의 모든 매개변수에 한 번에 액세스하려면 한 번에 하나의 레이어를 로드하고 즉시 모든 GPU에 파티셔닝합니다. 이는 매우 큰 모델의 경우 메모리 제한으로 인해 하나의 GPU에 가중치를 로드한 다음 다른 GPU에 분산할 수 없기 때문입니다.
다음과 같이 보이는 모델 매개변수 가중치(여기서 `tensor([1.])`) 또는 매개변수 크기가 더 큰 다차원 형태 대신 1인 경우, 이는 매개변수가 분할되어 있으며 이것이 ZeRO-3 플레이스홀더인 것을 의미합니다.
```py
tensor([1.0], device="cuda:0", dtype=torch.float16, requires_grad=True)
```
<Tip>
ZeRO-3로 대규모 모델을 초기화하고 매개변수에 액세스하는 방법에 대한 자세한 내용은 [Constructing Massive Models](https://deepspeed.readthedocs.io/en/latest/zero3.html#constructing-massive-models) 및 [Gathering Parameters](https://deepspeed.readthedocs.io/en/latest/zero3.html#gathering-parameters) 가이드를 참조하세요.
</Tip>
</hfoption>
</hfoptions>
### NVMe 설정[[nvme-configuration]]
[ZeRO-Infinity](https://hf.co/papers/2104.07857)를 사용하면 모델 상태를 CPU 및/또는 NVMe로 오프로드하여 더 많은 메모리를 절약할 수 있습니다. 스마트 파티셔닝 및 타일링 알고리즘을 통해 각 GPU는 오프로딩 중에 매우 적은 양의 데이터를 주고받을 수 있으므로 최신 NVMe는 훈련 프로세스에 사용할 수 있는 것보다 훨씬 더 큰 총 메모리 풀에 맞출 수 있습니다. ZeRO-Infinity에는 ZeRO-3가 필요합니다.
사용 가능한 CPU 및/또는 NVMe 메모리에 따라 [옵티마이저](https://www.deepspeed.ai/docs/config-json/#optimizer-offloading)와 [매개변수](https://www.deepspeed.ai/docs/config-json/#parameter-offloading) 중 하나만 오프로드하거나 아무것도 오프로드하지 않을 수 있습니다. 또한 일반 하드 드라이브나 솔리드 스테이트 드라이브에서도 작동하지만 속도가 현저히 느려지므로 `nvme_path`가 NVMe 장치를 가리키고 있는지 확인해야 합니다. 최신 NVMe를 사용하면 읽기 작업의 경우 최대 3.5GB/s, 쓰기 작업의 경우 최대 3GB/s의 전송 속도를 기대할 수 있습니다. 마지막으로, 트레이닝 설정에서 [벤치마크 실행하기](https://github.com/deepspeedai/DeepSpeed/issues/998)을 통해 최적의 'aio' 구성을 결정합니다.
아래 예제 ZeRO-3/Infinity 구성 파일은 대부분의 매개변수 값을 `auto`으로 설정하고 있지만, 수동으로 값을 추가할 수도 있습니다.
```yml
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "nvme",
"nvme_path": "/local_nvme",
"pin_memory": true,
"buffer_count": 4,
"fast_init": false
},
"offload_param": {
"device": "nvme",
"nvme_path": "/local_nvme",
"pin_memory": true,
"buffer_count": 5,
"buffer_size": 1e8,
"max_in_cpu": 1e9
},
"aio": {
"block_size": 262144,
"queue_depth": 32,
"thread_count": 1,
"single_submit": false,
"overlap_events": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
```
## DeepSpeed 구성[[deepspeed-features]]
이 섹션에서 간략하게 설명하는 몇 가지 중요한 매개변수를 DeepSpeed 구성 파일에 지정할 수 있습니다.
### 활성화/그레이디언트 체크포인팅[[activationgradient-checkpointing]]
활성화 및 그레이디언트 체크포인팅은 속도를 더 많은 GPU 메모리와 교환하여 GPU 메모리가 부족한 상황을 극복하거나 배치 크기를 늘려 성능을 향상시킬 수 있습니다. 이 기능을 활성화하려면 다음과 같이 하세요:
1. 허깅 페이스 모델의 경우, [`Trainer`]에서 `model.gradient_checkpointing_enable()` 또는 `--gradient_checkpointing`을 설정합니다.
2. 허깅 페이스가 아닌 모델의 경우, 딥스피드 [Activation Checkpointing API](https://deepspeed.readthedocs.io/en/latest/activation-checkpointing.html)를 사용합니다. 트랜스포머 모델링 코드를 대체하고 `torch.utils.checkpoint`를 DeepSpeed API로 대체할 수도 있습니다. 이 접근 방식은 순방향 활성화를 다시 계산하는 대신 CPU 메모리로 오프로드할 수 있으므로 더 유연합니다.
### 옵티마이저와 스케줄러[[optimizer-and-scheduler]]
`offload_optimizer`를 활성화하지 않는 한 DeepSpeed와 트랜스포머 옵티마이저 및 스케줄러를 혼합하여 사용할 수 있습니다. `offload_optimizer`를 활성화하면 CPU와 GPU 구현이 모두 있는 경우 DeepSpeed가 아닌 최적화기(LAMB 제외)를 사용할 수 있습니다.
<Tip warning={true}>
구성 파일의 최적화 프로그램 및 스케줄러 매개변수는 명령줄에서 설정할 수 있으므로 오류를 찾기 어렵지 않습니다. 예를 들어 학습 속도가 다른 곳에서 다른 값으로 설정된 경우 명령줄에서 이를 재정의할 수 있습니다. 최적화 프로그램 및 스케줄러 매개변수 외에도 [`Trainer`] 명령줄 인수가 DeepSpeed 구성과 일치하는지 확인해야 합니다.
</Tip>
<hfoptions id="opt-sched">
<hfoption id="optimizer">
DeepSpeed는 여러 [옵티마이저](https://www.deepspeed.ai/docs/config-json/#optimizer-parameters)를 제공하지만(Adam, AdamW, OneBitAdam 및 LAMB) PyTorch에서 다른 옵티마이저를 가져올 수도 있습니다. 설정에서 옵티마이저를 구성하지 않으면 [`Trainer`]가 자동으로 AdamW를 선택하고 명령줄에서 제공된 값 또는 기본값을 사용합니다: `lr`, `adam_beta1`, `adam_beta2`, `adam_epsilon`, `weight_decay`.
매개변수를 `"auto"`으로 설정하거나 원하는 값을 직접 수동으로 입력할 수 있습니다.
```yaml
{
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
}
}
```
최상위 구성에 다음을 추가하여 지원되지 않는 옵티마이저를 사용할 수도 있습니다.
```yaml
{
"zero_allow_untested_optimizer": true
}
```
DeepSpeed==0.8.3부터 오프로드를 사용하려면 오프로드가 DeepSpeed의 CPU Adam 옵티마이저에서 가장 잘 작동하므로 최상위 수준 구성에 다음 사항을 추가해야 합니다.
```yaml
{
"zero_force_ds_cpu_optimizer": false
}
```
</hfoption>
<hfoption id="scheduler">
DeepSpeed는 LRRangeTest, OneCycle, WarmupLR 및 WarmupDecayLR learning rate[schedulers](https://www.deepspeed.ai/docs/config-json/#scheduler-parameters)를 지원합니다.
트랜스포머와 DeepSpeed는 동일한 두 가지 스케줄러를 제공합니다:
* WarmupLR은 Transformers의 `--lr_scheduler_type constant_warmup`과 동일합니다.
* WarmupDecayLR은 Transformers의 `--lr_scheduler_type linear`와 동일합니다(Transformers에서 사용되는 기본 스케줄러입니다).
설정에서 스케줄러를 구성하지 않으면[`Trainer`]는 자동으로 WarmupDecayLR을 선택하고 명령줄에서 제공된 값 또는 기본값을 사용합니다: `warmup_min_lr`, `warmup_max_lr`, `warmup_num_steps`, `total_num_steps` (`max_steps`가 제공되지 않으면 런타임 중에 자동으로 계산됨).
매개변수를 `"auto"`으로 설정하거나 원하는 값을 직접 수동으로 입력할 수 있습니다.
```yaml
{
"scheduler": {
"type": "WarmupDecayLR",
"params": {
"total_num_steps": "auto",
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
}
}
```
</hfoption>
</hfoptions>
### 정밀도[[precision]]
DeepSpeed는 fp32, fp16 및 bf16 혼합 정밀도를 지원합니다.
<hfoptions id="precision">
<hfoption id="fp32">
모델이 혼합 정밀도로 사전 학습되지 않은 경우와 같이 혼합 정밀도로 잘 작동하지 않는 경우 NaN 손실을 유발할 수 있는 오버플로 또는 언더플로 문제가 발생할 수 있습니다. 이러한 경우에는 기본 fp16 모드를 명시적으로 비활성화하여 전체 fp32 정밀도를 사용해야 합니다.
```yaml
{
"fp16": {
"enabled": false
}
}
```
Ampere GPU 및 PyTorch 1.7 이상의 경우 일부 연산에 대해 더 효율적인 [tf32](https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) 형식으로 자동 전환되지만 결과는 여전히 fp32로 표시됩니다. [`Trainer`]에서 `--tf32`를 설정하여 활성화하고 `--tf32 0` 또는 `--no_tf32`를 비활성화하면 제어할 수 있습니다.
</hfoption>
<hfoption id="fp16">
PyTorch AMP와 같은 fp16 혼합 정밀도를 구성하면 메모리 사용량이 줄어들고 훈련 속도가 빨라집니다.[`Trainer`]는 `args.fp16_backend` 값에 따라 fp16을 자동으로 활성화 또는 비활성화하며, 나머지 구성은 사용자가 설정할 수 있습니다. 명령줄에서 다음 인수를 전달하면 fp16이 활성화됩니다: `fp16`, `--fp16_backend amp` 또는 `--fp16_full_eval`.
```yaml
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
}
}
```
추가 딥스피드 fp16 훈련 옵션은 [fp16 훈련 옵션](https://www.deepspeed.ai/docs/config-json/#fp16-training-options) 참조를 참조하세요.
Apex와 같은 fp16 혼합 정밀도를 구성하려면 아래 그림과 같이 `"auto"` 또는 직접 값을 설정합니다.[`Trainer`]는 `args.fp16_backend` 및 `args.fp16_opt_level`의 값에 따라 `amp`를 자동으로 구성합니다. 다음 인수를 전달하면 명령줄에서 활성화할 수도 있습니다: `fp16`, `--fp16_backend apex` 또는 `--fp16_opt_level 01`.
```yaml
{
"amp": {
"enabled": "auto",
"opt_level": "auto"
}
}
```
</hfoption>
<hfoption id="bf16">
bf16을 사용하려면 DeepSpeed==0.6.0 이상이 필요합니다. bf16은 fp32와 동적 범위가 동일하며 손실 스케일링이 필요하지 않습니다. 그러나 [gradient accumulation](#gradient-accumulation)을 bf16과 함께 사용하면 이 형식의 낮은 정밀도로 인해 손실이 발생할 수 있으므로 원하지 않는 그레이디언트가 bf16에 누적될 수 있습니다.
bf16은 설정 파일에서 설정하거나 다음 인수를 전달하면 명령줄에서 활성화할 수 있습니다: `--bf16` 또는 `--bf16_full_eval`.
```yaml
{
"bf16": {
"enabled": "auto"
}
}
```
</hfoption>
</hfoptions>
### 배치 크기[[batch-size]]
배치 크기는 자동으로 구성하거나 명시적으로 설정할 수 있습니다. `"auto"` 옵션을 사용하도록 선택하면 [`Trainer`]는 `train_micro_batch_size_per_gpu`를 args.`per_device_train_batch_size`의 값으로, `train_batch_size`를 `args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps`로 설정합니다.
```yaml
{
"train_micro_batch_size_per_gpu": "auto",
"train_batch_size": "auto"
}
```
### 그레이디언트 누적[[gradient-accumulation]]
그레이디언트 누적을 자동으로 구성하거나 명시적으로 설정할 수 있습니다. `"auto"` 옵션을 사용하도록 선택하면 [`Trainer`]가 `args.gradient_accumulation_steps`의 값으로 설정합니다.
```yaml
{
"gradient_accumulation_steps": "auto"
}
```
### 그레이디언트 클리핑[[gradient-clipping]]
그레이디언트 클리핑은 자동으로 구성하거나 명시적으로 설정할 수 있습니다. `"auto"` 옵션을 사용하도록 선택하면 [`Trainer`]가 `args.max_grad_norm`의 값으로 설정합니다.
```yaml
{
"gradient_clipping": "auto"
}
```
### 통신 데이터 유형(Communication data type)[[communication-data-type]]
축소, 수집 및 분산 작업과 같은 통신 집합체의 경우 별도의 데이터 유형이 사용됩니다.
모든 수집 및 분산 작업은 데이터와 동일한 데이터 유형으로 수행됩니다. 예를 들어 bf16으로 훈련하는 경우, 수집은 비손실 연산이므로 데이터도 bf16으로 수집됩니다.
예를 들어 그레이디언트가 여러 GPU에 걸쳐 평균화되는 경우와 같이 감소 연산은 손실이 발생합니다. 통신이 fp16 또는 bf16으로 수행되는 경우, 낮은 정밀도로 여러 숫자를 더하면 정확하지 않기 때문에 손실이 발생할 가능성이 더 높습니다. 특히 fp16보다 정밀도가 낮은 bf16의 경우 더욱 그렇습니다. 이러한 이유로 기울기를 평균화할 때 손실이 최소화되므로 감소 연산에는 fp16이 기본값으로 사용됩니다.
통신 데이터 유형은 설정 파일에서 `communication_data_type` 매개변수를 설정하여 선택할 수 있습니다. 예를 들어, fp32를 선택하면 약간의 오버헤드가 추가되지만 감소 연산이 fp32에 누적되고 준비가 되면 훈련 중인 반정밀 dtype으로 다운캐스트됩니다.
```yaml
{
"communication_data_type": "fp32"
}
```
## 모델 배포[[deployment]]
[torchrun](https://pytorch.org/docs/stable/elastic/run.html), `deepspeed` 런처 또는 [Accelerate](https://huggingface.co/docs/accelerate/basic_tutorials/launch#using-accelerate-launch) 등 다양한 런처를 통해 DeepSpeed를 배포할 수 있습니다. 배포하려면 [`Trainer`] 명령줄에 `--deepspeed ds_config.json`을 추가합니다. 필요한 명령줄 인수를 코드에 추가하려면 DeepSpeed의 [`add_config_arguments`](https://deepspeed.readthedocs.io/en/latest/initialize.html#argument-parsing) 유틸리티를 사용하는 것이 좋습니다.
이 가이드에서는 다양한 트레이닝 설정에 대해 `deepspeed` 런처로 DeepSpeed를 배포하는 방법을 보여드립니다. 보다 실용적인 사용 예제는 이 [post](https://github.com/huggingface/transformers/issues/8771#issuecomment-759248400)에서 확인할 수 있습니다.
<hfoptions id="deploy">
<hfoption id="multi-GPU">
여러 GPU에 DeepSpeed를 배포하려면 `--num_gpus` 매개변수를 추가하세요. 사용 가능한 모든 GPU를 사용하려는 경우 `--num_gpus`를 추가할 필요가 없습니다. 아래 예제에서는 2개의 GPU를 사용합니다.
```bash
deepspeed --num_gpus=2 examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero3.json \
--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config "ro-en" \
--source_lang en --target_lang ro
```
</hfoption>
<hfoption id="single-GPU">
단일 GPU에 DeepSpeed를 배포하려면 `--num_gpus` 매개변수를 추가하세요. GPU가 1개만 있는 경우 이 값을 명시적으로 설정할 필요는 없습니다. DeepSpeed는 지정된 노드에서 볼 수 있는 모든 GPU를 배포하므로 이 값을 명시적으로 설정할 필요는 없습니다.
```bash
deepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero2.json \
--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config "ro-en" \
--source_lang en --target_lang ro
```
DeepSpeed는 단 하나의 GPU로도 여전히 유용합니다:
1. 일부 계산과 메모리를 CPU로 오프로드하여 더 큰 배치 크기를 사용하거나 일반적으로 맞지 않는 매우 큰 모델을 맞추기 위해 모델에 더 많은 GPU 리소스를 사용할 수 있도록 합니다.
2. 스마트 GPU 메모리 관리 시스템으로 메모리 조각화를 최소화하여 더 큰 모델과 데이터 배치에 맞출 수 있습니다.
<Tip>
단일 GPU에서 더 나은 성능을 얻으려면 [ZeRO-2](#zero-configuration) 구성 파일에서 `allgather_bucket_size` 및 `reduce_bucket_size` 값을 2e8로 설정하세요.
</Tip>
</hfoption>
</hfoptions>
### 다중 노드 환경에서의 모델 배포[[multi-node-deployment]]
노드는 워크로드를 실행하기 위한 하나 이상의 GPU입니다. 더 강력한 설정은 멀티 노드 설정으로, `deepspeed` 런처로 실행할 수 있습니다. 이 가이드에서는 각각 8개의 GPU가 있는 두 개의 노드가 있다고 가정해 보겠습니다. 첫 번째 노드는 `ssh hostname1`로, 두 번째 노드는 `ssh hostname2`로 접속할 수 있습니다. 두 노드 모두 비밀번호 없이 ssh를 통해 로컬로 서로 통신할 수 있어야 합니다.
기본적으로 DeepSpeed는 멀티노드 환경에서 공유 저장소를 사용할 것으로 예상합니다. 그렇지 않고 각 노드가 로컬 파일 시스템만 볼 수 있는 경우, 공유 파일 시스템에 대한 액세스 없이 로딩할 수 있도록 [`checkpoint`](https://www.deepspeed.ai/docs/config-json/#checkpoint-options)를 포함하도록 구성 파일을 조정해야 합니다:
```yaml
{
"checkpoint": {
"use_node_local_storage": true
}
}
```
[`Trainer`]의 ``--save_on_each_node` 인수를 사용하여 위의 `checkpoint`를 구성에 자동으로 추가할 수도 있습니다.
<hfoptions id="multinode">
<hfoption id="torchrun">
[torchrun](https://pytorch.org/docs/stable/elastic/run.html)의 경우, 각 노드에 ssh로 접속한 후 두 노드 모두에서 다음 명령을 실행해야 합니다. 런처는 두 노드가 동기화될 때까지 기다렸다가 트레이닝을 시작합니다.
```bash
torchrun --nproc_per_node=8 --nnode=2 --node_rank=0 --master_addr=hostname1 \
--master_port=9901 your_program.py <normal cl args> --deepspeed ds_config.json
```
</hfoption>
<hfoption id="deepspeed">
`deepspeed` 런처의 경우, 먼저 `hostfile`을 생성합니다.
```bash
hostname1 slots=8
hostname2 slots=8
```
그런 다음 다음 명령어로 트레이닝을 시작할 수 있습니다. `deepspeed` 런처는 두 노드에서 동시에 명령을 자동으로 실행합니다.
```bash
deepspeed --num_gpus 8 --num_nodes 2 --hostfile hostfile --master_addr hostname1 --master_port=9901 \
your_program.py <normal cl args> --deepspeed ds_config.json
```
다중 노드 컴퓨팅 리소스 구성에 대한 자세한 내용은 [Resource Configuration (multi-node)](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node) 가이드를 참조하세요.
</hfoption>
</hfoptions>
### SLURM[[slurm]]
SLURM 환경에서는 특정 SLURM 환경에 맞게 SLURM 스크립트를 조정해야 합니다.SLURM 스크립트 예시는 다음과 같습니다:
```bash
#SBATCH --job-name=test-nodes # 작업 이름
#SBATCH --nodes=2 # 노드 수
#SBATCH --ntasks-per-node=1 # 중요 - 노드당 분산 작업 1개!
#SBATCH --cpus-per-task=10 # 작업당 CPU 코어 수
#SBATCH --gres=gpu:8 # gpu 수
#SBATCH --time 20:00:00 # 최대 실행 시간 (HH:MM:SS)
#SBATCH --output=%x-%j.out # 출력 파일 이름
export GPUS_PER_NODE=8
export MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
export MASTER_PORT=9901
srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
--nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
--master_addr $MASTER_ADDR --master_port $MASTER_PORT \
your_program.py <normal cl args> --deepspeed ds_config.json'
```
그런 다음 모든 노드에서 동시에 학습을 시작하는 다음 명령을 사용하여 다중 노드 배포를 예약할 수 있습니다.
```bash
sbatch launch.slurm
```
### 노트북[[notebook]]
`deepspeed` 런처는 노트북에서의 배포를 지원하지 않으므로 분산 환경을 에뮬레이션해야 합니다. 하지만 이는 1개의 GPU에서만 작동합니다. 1개 이상의 GPU를 사용하려면 딥스피드가 작동할 수 있는 다중 프로세스 환경을 사용해야 합니다. 즉, 여기에 표시된 것처럼 에뮬레이션할 수 없는 `deepspeed` 런처를 사용해야 합니다.
```py
# DeepSpeed는 단일 프로세스만 사용하더라도 분산 환경을 필요로 합니다.
# 이 코드로 분산 환경을 모방합니다.
import os
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "9994" # RuntimeError: Address already in use 오류 발생 시 수정
os.environ["RANK"] = "0"
os.environ["LOCAL_RANK"] = "0"
os.environ["WORLD_SIZE"] = "1"
# 이제 평소와 같이 진행하되, DeepSpeed 설정 파일을 전달합니다.
training_args = TrainingArguments(..., deepspeed="ds_config_zero3.json")
trainer = Trainer(...)
trainer.train()
```
현재 디렉터리의 노트북에 구성 파일을 즉석에서 만들고 싶다면 전용 셀을 만들 수 있습니다.
```py
%%bash
cat <<'EOT' > ds_config_zero3.json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}
EOT
```
트레이닝 스크립트가 노트북 셀이 아닌 파일에 있는 경우, 노트북 셀의 셸에서 `deepspeed`를 정상적으로 실행할 수 있습니다. 예를 들어 `run_translation.py`를 시작하려면 다음과 같이 하세요.:
```py
!git clone https://github.com/huggingface/transformers
!cd transformers; deepspeed examples/pytorch/translation/run_translation.py ...
```
또한 `%%bash` 매직을 사용하여 여러 줄의 코드를 작성하여 셸 프로그램을 실행할 수도 있지만 교육이 완료될 때까지 로그를 볼 수 없습니다. `%%bash` 매직으로 분산 환경을 에뮬레이션할 필요는 없습니다.
```py
%%bash
git clone https://github.com/huggingface/transformers
cd transformers
deepspeed examples/pytorch/translation/run_translation.py ...
```
## 모델 가중치 저장하기[[save-model-weights]]
딥스피드는 기본 고정밀 fp32 가중치를 사용자 지정 체크포인트 최적화 파일(glob 패턴은 `global_step*/*optim_states.pt`처럼 보입니다)에 저장하고 일반 체크포인트 아래에 저장합니다.
<hfoptions id="save">
<hfoption id="fp16">
ZeRO-2로 훈련된 모델은 pytorch_model.bin 가중치를 fp16에 저장합니다. ZeRO-3으로 훈련된 모델의 모델 가중치를 fp16에 저장하려면 모델 가중치가 여러 GPU에 분할되어 있으므로 `“stage3_gather_16bit_weights_on_model_save”: true`를 설정해야 합니다. 그렇지 않으면 [`Trainer`]가 가중치를 fp16에 저장하지 않고 pytorch_model.bin 파일을 생성하지 않습니다. 이는 DeepSpeed의 state_dict에 실제 가중치 대신 플레이스홀더가 포함되어 있어 이를 로드할 수 없기 때문입니다.
```yaml
{
"zero_optimization": {
"stage3_gather_16bit_weights_on_model_save": true
}
}
```
</hfoption>
<hfoption id="fp32">
전체 정밀 가중치는 많은 메모리가 필요할 수 있으므로 트레이닝 중에 저장해서는 안 됩니다. 일반적으로 훈련이 완료된 후 오프라인으로 fp32 가중치를 저장하는 것이 가장 좋습니다. 그러나 여유 CPU 메모리가 많은 경우 훈련 중에 fp32 가중치를 저장할 수 있습니다. 이 섹션에서는 온라인과 오프라인 방식을 모두 다룹니다.
### 온라인 환경[[online]]
다음과 같이 최신 체크포인트를 로드하려면 체크포인트를 하나 이상 저장해야 합니다:
```py
from transformers.trainer_utils import get_last_checkpoint
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
checkpoint_dir = get_last_checkpoint(trainer.args.output_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
```
`--load_best_model_at_end` 매개변수를 활성화하여 [`TrainingArguments`]에서 최적의 체크포인트를 추적하는 경우, 먼저 학습을 완료하고 최종 모델을 명시적으로 저장할 수 있습니다. 그런 다음 아래와 같이 다시 로드할 수 있습니다:
```py
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
checkpoint_dir = os.path.join(trainer.args.output_dir, "checkpoint-final")
trainer.deepspeed.save_checkpoint(checkpoint_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
```
<Tip>
`load_state_dict_from_zero_checkpoint`가 실행되면 동일한 애플리케이션의 컨텍스트에서 모델을 더 이상 DeepSpeed에서 사용할 수 없습니다. `model.load_state_dict(state_dict)`는 모든 딥스피드 마법을 제거하므로 딥스피드 엔진을 다시 초기화해야 합니다. 이 기능은 훈련이 끝날 때만 사용하세요.
</Tip>
fp32 가중치의 state_dict를 추출하여 로드할 수도 있습니다:
```py
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # cpu에 이미 존재함
model = model.cpu()
model.load_state_dict(state_dict)
```
### 오프라인 환경[[offline]]
DeepSpeed는 언제든지 가중치를 추출할 수 있도록 체크포인트 폴더의 최상위 레벨에 zero_to_fp32.py 스크립트를 제공합니다. 이 스크립트는 독립형 스크립트로 구성 파일이나 [`Trainer`]가 필요하지 않습니다.
예를 들어 체크포인트 폴더가 다음과 같은 경우입니다:
```bash
$ ls -l output_dir/checkpoint-1/
-rw-rw-r-- 1 stas stas 1.4K Mar 27 20:42 config.json
drwxrwxr-x 2 stas stas 4.0K Mar 25 19:52 global_step1/
-rw-rw-r-- 1 stas stas 12 Mar 27 13:16 latest
-rw-rw-r-- 1 stas stas 827K Mar 27 20:42 optimizer.pt
-rw-rw-r-- 1 stas stas 231M Mar 27 20:42 pytorch_model.bin
-rw-rw-r-- 1 stas stas 623 Mar 27 20:42 scheduler.pt
-rw-rw-r-- 1 stas stas 1.8K Mar 27 20:42 special_tokens_map.json
-rw-rw-r-- 1 stas stas 774K Mar 27 20:42 spiece.model
-rw-rw-r-- 1 stas stas 1.9K Mar 27 20:42 tokenizer_config.json
-rw-rw-r-- 1 stas stas 339 Mar 27 20:42 trainer_state.json
-rw-rw-r-- 1 stas stas 2.3K Mar 27 20:42 training_args.bin
-rwxrw-r-- 1 stas stas 5.5K Mar 27 13:16 zero_to_fp32.py*
```
딥스피드 체크포인트(ZeRO-2 또는 ZeRO-3) 하위 폴더 `global_step1`에서 fp32 가중치를 재구성하려면 다음 명령을 실행하여 여러 GPU의 전체 fp32 가중치를 단일 pytorch_model.bin 파일로 생성하고 통합합니다. 스크립트는 자동으로 체크포인트가 포함된 하위 폴더를 찾습니다.
```py
python zero_to_fp32.py . pytorch_model.bin
```
<Tip>
자세한 사용법은 `python zero_to_fp32.py -h`를 실행하세요. 이 스크립트에는 최종 fp32 가중치의 2배의 일반 RAM이 필요합니다.
</Tip>
</hfoption>
</hfoptions>
## ZeRO Inference[[zero-inference]]
[ZeRO Inference](https://www.deepspeed.ai/2022/09/09/zero-inference.html)는 모델 가중치를 CPU 또는 NVMe 메모리에 배치하여 GPU에 부담을 주지 않으므로 GPU에서 대규모 모델을 사용하여 추론을 실행할 수 있습니다. 추론은 최적화 상태 및 그레이디언트에 많은 양의 메모리를 추가로 필요로 하지 않으므로 동일한 하드웨어에 훨씬 더 큰 배치 및/또는 시퀀스 길이를 맞출 수 있습니다.
ZeRO Inference는 [ZeRO-3](#zero-configuration)와 동일한 구성 파일을 공유하며, ZeRO-2 및 ZeRO-1 구성은 추론에 아무런 이점을 제공하지 않으므로 작동하지 않습니다.
ZeRO Inference를 실행하려면 일반적인 훈련 인수를 [`TrainingArguments`] 클래스에 전달하고 `--do_eval` 인수를 추가합니다.
```bash
deepspeed --num_gpus=2 your_program.py <normal cl args> --do_eval --deepspeed ds_config.json
```
## Trainer 없이 DeepSpeed 사용하기[[non-trainer-deepspeed-integration]]
DeepSpeed는 [`Trainer`] 클래스가 없는 트랜스포머에서도 작동합니다. 이는 [`~PreTrainedModel.from_pretrained`]를 호출할 때 ZeRO-3 매개변수를 수집하고 모델을 여러 GPU에 분할하는 작업만 처리하는 [`HfDeepSpeedConfig`]가 처리합니다.
<Tip>
모든 것이 자동으로 처리되기를 원한다면, [`Trainer`]와 함께 DeepSpeed를 사용해 보세요! [DeepSpeed 문서](https://www.deepspeed.ai/)를 참조하여 설정 파일에서 매개변수 값을 수동으로 구성해야 합니다(`"auto"` 값은 사용할 수 없음).
</Tip>
ZeRO-3를 효율적으로 배포하려면 모델 앞에 [`HfDeepSpeedConfig`] 객체를 인스턴스화하고 해당 객체를 유지해야 합니다:
<hfoptions id="models">
<hfoption id="pretrained model">
```py
from transformers.integrations import HfDeepSpeedConfig
from transformers import AutoModel
import deepspeed
ds_config = {...} # deepspeed 설정 객체 또는 파일 경로
# Zero 3를 감지하기 위해 모델을 인스턴스화하기 전에 반드시 실행해야 합니다
dschf = HfDeepSpeedConfig(ds_config) # 이 객체를 유지하세요.
model = AutoModel.from_pretrained("openai-community/gpt2")
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
```
</hfoption>
<hfoption id="non-pretrained model">
[`HfDeepSpeedConfig`] is not required for ZeRO-1 or ZeRO-2.
```py
from transformers.integrations import HfDeepSpeedConfig
from transformers import AutoModel, AutoConfig
import deepspeed
ds_config = {...} # deepspeed 설정 객체 또는 파일 경로
# Zero 3를 감지하기 위해 모델을 인스턴스화하기 전에 반드시 실행해야 합니다
dschf = HfDeepSpeedConfig(ds_config) # 이 객체를 유지하세요.
config = AutoConfig.from_pretrained("openai-community/gpt2")
model = AutoModel.from_config(config)
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
```
</hfoption>
</hfoptions>
### Trainer 없이 ZeRO Inference 사용하기[[non-trainer-zero-inference]]
단일 GPU에 모델을 맞출 수 없는 경우 [`Trainer`]없이 ZeRO 추론을 실행하려면 추가 GPU를 사용하거나 CPU 메모리로 오프로드를 시도하세요. 여기서 이해해야 할 중요한 뉘앙스는 ZeRO가 설계된 방식에 따라 서로 다른 GPU에서 서로 다른 입력을 병렬로 처리할 수 있다는 것입니다.
반드시 확인하세요:
* GPU 메모리가 충분한 경우 CPU 오프로드를 비활성화합니다(속도가 느려지므로).
* Ampere 이상의 GPU를 사용하는 경우 bf16을 활성화하면 속도가 빨라집니다. 이러한 GPU가 없는 경우 오버플로 오류가 발생할 수 있으므로 bf16으로 사전 학습된 모델(T5 모델)을 사용하지 않는 한 fp16을 활성화할 수 있습니다.
단일 GPU에 맞지 않는 모델에서 [`Trainer`] 없이 ZeRO 추론을 실행하는 방법에 대한 더 나은 아이디어를 얻으려면 다음 스크립트를 살펴보시기 바랍니다.
```py
#!/usr/bin/env python
# 이 스크립트는 단일 GPU에 모델을 맞출 수 없을 때 추론 모드에서 Deepspeed ZeRO를 사용하는 방법을 보여줍니다.
#
# 1. CPU 오프로드와 함께 1개의 GPU 사용
# 2. 또는 여러 GPU 사용
#
# 먼저 deepspeed를 설치해야 합니다: pip install deepspeed
#
# 여기서는 약 15GB의 GPU RAM이 필요한 3B "bigscience/T0_3B" 모델을 사용합니다 - 따라서 1개의 큰 GPU나 2개의
# 작은 GPU로 처리할 수 있습니다. 또는 1개의 작은 GPU와 많은 CPU 메모리로도 가능합니다.
#
# 약 50GB가 필요한 "bigscience/T0"와 같은 더 큰 모델을 사용하려면, 80GB GPU가 없는 한
# 2-4개의 GPU가 필요할 것입니다. 그리고 여러 입력을 한 번에 처리하고 싶다면
# 스크립트를 수정하여 더 많은 GPU를 처리할 수 있습니다.
#
# 제공된 deepspeed 설정은 CPU 메모리 오프로딩도 활성화하므로, 사용 가능한 CPU 메모리가 많고
# 속도 저하를 감수할 수 있다면 일반적으로 단일 GPU에 맞지 않는 모델을 로드할 수 있을 것입니다.
# GPU 메모리가 충분하다면 CPU로의 오프로드를 원하지 않을 때 프로그램이 더 빠르게 실행될 것입니다 - 그럴 때는 해당 섹션을 비활성화하세요.
#
# 1개의 GPU에 배포하려면:
#
# deepspeed --num_gpus 1 t0.py
# 또는:
# python -m torch.distributed.run --nproc_per_node=1 t0.py
#
# 2개의 GPU에 배포하려면:
#
# deepspeed --num_gpus 2 t0.py
# 또는:
# python -m torch.distributed.run --nproc_per_node=2 t0.py
from transformers import AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM
from transformers.integrations import HfDeepSpeedConfig
import deepspeed
import os
import torch
os.environ["TOKENIZERS_PARALLELISM"] = "false" # 토크나이저의 병렬 처리에 관한 경고를 피하기 위함입니다.
# 분산 환경 설정
local_rank = int(os.getenv("LOCAL_RANK", "0"))
world_size = int(os.getenv("WORLD_SIZE", "1"))
torch.cuda.set_device(local_rank)
deepspeed.init_distributed()
model_name = "bigscience/T0_3B"
config = AutoConfig.from_pretrained(model_name)
model_hidden_size = config.d_model
# 배치 크기는 world_size로 나누어 떨어져야 하지만, world_size보다 클 수 있습니다
train_batch_size = 1 * world_size
# ds_config 참고사항
#
# - Ampere 이상의 GPU를 사용하는 경우 bf16을 활성화하세요 - 이는 혼합 정밀도로 실행되어
# 더 빠를 것입니다.
#
# - 오래된 GPU의 경우 fp16을 활성화할 수 있지만, bf16으로 사전 훈련되지 않은 모델에서만 작동합니다 - 예를 들어
# 모든 공식 t5 모델은 bf16으로 사전 훈련되었습니다
#
# - CPU 오프로드를 원하지 않는다면 offload_param.device를 "none"으로 설정하거나 `offload_param` 섹션을
# 완전히 제거하세요
#
# - `offload_param`을 사용하는 경우, stage3_param_persistence_threshold를 수동으로 미세 조정하여
# 어떤 매개변수가 GPU에 남아있어야 하는지 제어할 수 있습니다 - 값이 클수록 오프로드 크기가 작아집니다
#
# Deepspeed 설정에 대한 자세한 정보는 다음을 참조하세요
# https://huggingface.co/docs/transformers/main/main_classes/deepspeed
# 일관성을 위해 json과 동일한 형식을 유지하되, true/false에는 소문자를 사용합니다
# fmt: off
ds_config = {
"fp16": {
"enabled": False
},
"bf16": {
"enabled": False
},
"zero_optimization": {
"stage": 3,
"offload_param": {
"device": "cpu",
"pin_memory": True
},
"overlap_comm": True,
"contiguous_gradients": True,
"reduce_bucket_size": model_hidden_size * model_hidden_size,
"stage3_prefetch_bucket_size": 0.9 * model_hidden_size * model_hidden_size,
"stage3_param_persistence_threshold": 10 * model_hidden_size
},
"steps_per_print": 2000,
"train_batch_size": train_batch_size,
"train_micro_batch_size_per_gpu": 1,
"wall_clock_breakdown": False
}
# fmt: on
# 다음 줄은 모델의 `from_pretrained` 메소드가 호출될 때
# deepspeed.zero.Init를 사용하여 모델을 여러 GPU에 직접 분할하도록 transformers에 지시합니다.
#
# **이는 AutoModelForSeq2SeqLM.from_pretrained(model_name)로 모델을 로드하기 전에 실행되어야 합니다**
#
# 그렇지 않으면 모델이 먼저 정상적으로 로드된 후 포워드 시에만 분할되는데, 이는
# 덜 효율적이며 CPU RAM이 부족할 경우 실패할 수 있습니다
dschf = HfDeepSpeedConfig(ds_config) # 이 객체를 유지하세요
# 이제 모델을 로드할 수 있습니다.
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Deepspeed ZeRO를 초기화하고 엔진 객체만 저장
ds_engine = deepspeed.initialize(model=model, config_params=ds_config)[0]
ds_engine.module.eval() # inference
# Deepspeed ZeRO는 각 GPU에서 서로 관련 없는 입력을 처리할 수 있습니다. 따라서 2개의 GPU를 사용하면 한 번에 2개의 입력을 처리할 수 있습니다.
# GPU를 더 많이 사용하는 경우 그에 맞게 조정하세요.
# 물론 처리할 입력이 하나뿐이라면 두 GPU에 동일한 문자열을 전달해야 합니다.
# GPU를 하나만 사용하는 경우에는 rank 0만 갖게 됩니다.
rank = torch.distributed.get_rank()
if rank == 0:
text_in = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"
elif rank == 1:
text_in = "Is this review positive or negative? Review: this is the worst restaurant ever"
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer.encode(text_in, return_tensors="pt").to(device=local_rank)
with torch.no_grad():
outputs = ds_engine.module.generate(inputs, synced_gpus=True)
text_out = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"rank{rank}:\n in={text_in}\n out={text_out}")
```
스크립트를 t0.py로 저장하고 실행합니다:
```bash
$ deepspeed --num_gpus 2 t0.py
rank0:
in=Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy
out=Positive
rank1:
in=Is this review positive or negative? Review: this is the worst restaurant ever
out=negative
```
이것은 매우 기본적인 예시이므로 사용 사례에 맞게 조정할 수 있습니다.
### 생성[[generate]]
생성에 ZeRO-3와 함께 여러 개의 GPU를 사용하려면 [`~GenerationMixin.generate`] 메서드에서 `synced_gpus=True`를 설정하여 GPU를 동기화해야 합니다. 그렇지 않으면 한 GPU가 다른 GPU보다 먼저 생성을 완료하면 나머지 GPU가 먼저 완료한 GPU로부터 가중치 샤드를 받지 못하여 전체 시스템이 중단됩니다.
트랜스포머>=4.28의 경우, 생성 중에 여러 개의 GPU가 감지되면 `synced_gpus`가 자동으로 `True`로 설정됩니다.
## 트러블슈팅[[troubleshoot]]
문제가 발생하면 DeepSpeed가 문제의 원인이 아닌 경우가 많으므로(아주 명백하고 예외적으로 DeepSpeed 모듈을 볼 수 있는 경우가 아니라면) DeepSpeed가 문제의 원인인지 고려해야 합니다! 첫 번째 단계는 DeepSpeed 없이 설정을 다시 시도하고 문제가 지속되면 문제를 신고하는 것입니다. 문제가 핵심적인 DeepSpeed 문제이고 transformers와 관련이 없는 경우, [DeepSpeed 리포지토리](https://github.com/deepspeedai/DeepSpeed)에서 이슈를 개설하세요.
transformers와 관련된 이슈를 개설할 때에는 다음 정보를 제공해 주세요:
* 전체 DeepSpeed 구성 파일
*[`Trainer`]의 명령줄 인수, 또는[`Trainer`] 설정을 직접 작성하는 경우[`TrainingArguments`] 인수(관련 없는 항목이 수십 개 있는 [`TrainingArguments`]는 덤프하지 마세요).
* 다음 코드의 출력 결과:
```bash
python -c 'import torch; print(f"torch: {torch.__version__}")'
python -c 'import transformers; print(f"transformers: {transformers.__version__}")'
python -c 'import deepspeed; print(f"deepspeed: {deepspeed.__version__}")'
```
* 문제를 재현할 수 있는 Google Colab 노트북 링크
* 불가능할 경우 기존 예제를 사용하여 문제를 재현할 수 있는 표준 및 사용자 지정이 아닌 데이터 집합을 사용할 수 있습니다.
다음 섹션에서는 가장 일반적인 두 가지 문제를 해결하기 위한 가이드를 제공합니다.
### DeepSpeed 프로세스가 시작 단계에서 종료되었을 경우[[deepspeed-process-killed-at-startup]]
실행 중에 트레이스백 없이 DeepSpeed 프로세스가 종료되면 일반적으로 프로그램이 시스템보다 많은 CPU 메모리를 할당하려고 시도했거나 프로세스가 허용된 것보다 많은 CPU 메모리를 할당하려고 시도하여 OS 커널이 프로세스를 종료했음을 의미합니다. 이 경우 구성 파일에 `offload_optimizer`, `offload_param` 또는 둘 다 CPU로 오프로드하도록 구성되어 있는지 확인하세요.
NVMe 및 ZeRO-3를 설정한 경우 NVMe로 오프로드를 실험해 보세요(모델의 메모리 요구 사항을 [확인](https://deepspeed.readthedocs.io/en/latest/memory.html)하세요).
### NaN 손실[[nan-loss]]
모델을 bf16으로 사전 훈련한 다음 fp16으로 사용하려고 할 때 NaN 손실이 발생하는 경우가 많습니다(특히 TPU 훈련 모델에 해당). 이 문제를 해결하려면 하드웨어가 이를 지원하는 경우(TPU, Ampere GPU 이상) fp32 또는 bf16을 사용하세요.
다른 문제는 fp16 사용과 관련이 있을 수 있습니다. 예를 들어 이것이 fp16 구성인 경우입니다:
```yaml
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
}
}
```
로그에 다음과 같은 `OVERFLOW!` 메시지가 표시될 수 있습니다:
```bash
0%| | 0/189 [00:00<?, ?it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 262144
1%|▌ | 1/189 [00:00<01:26, 2.17it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 262144, reducing to 131072.0
1%|█▏
[...]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
14%|████████████████▌ | 27/189 [00:14<01:13, 2.21it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
15%|█████████████████▏ | 28/189 [00:14<01:13, 2.18it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
15%|█████████████████▊ | 29/189 [00:15<01:13, 2.18it/s]
[deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1, reducing to 1
[...]
```
이는 DeepSpeed 손실 스케일러가 손실 오버플로를 극복할 수 있는 스케일링 계수를 찾을 수 없음을 의미합니다. 이 문제를 해결하려면 `initial_scale_power` 값을 더 높게 설정하세요(일반적으로 32가 적절합니다).
## 리소스[[resources]]
DeepSpeed ZeRO는 제한된 GPU 리소스로 추론을 위해 매우 큰 모델을 훈련하고 로드하는 강력한 기술로, 누구나 쉽게 사용할 수 있습니다. DeepSpeed에 대해 자세히 알아보려면 [블로그 포스트](https://www.microsoft.com/en-us/research/search/?q=deepspeed), [공식 문서](https://www.deepspeed.ai/getting-started/), [깃허브 리포지토리](https://github.com/deepspeedai/DeepSpeed)를 참조하세요.
다음 문서도 ZeRO에 대해 자세히 알아볼 수 있는 훌륭한 자료입니다:
* [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://hf.co/papers/1910.02054)
* [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://hf.co/papers/2101.06840)
* [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://hf.co/papers/2104.07857)
| transformers/docs/source/ko/deepspeed.md/0 | {
"file_path": "transformers/docs/source/ko/deepspeed.md",
"repo_id": "transformers",
"token_count": 43919
} |
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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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# 양자화[[quantization]]
양자화 기법은 가중치와 활성화를 8비트 정수(int8)와 같은 더 낮은 정밀도의 데이터 타입으로 표현함으로써 메모리와 계산 비용을 줄입니다. 이를 통해 일반적으로는 메모리에 올릴 수 없는 더 큰 모델을 로드할 수 있고, 추론 속도를 높일 수 있습니다. Transformers는 AWQ와 GPTQ 양자화 알고리즘을 지원하며, bitsandbytes를 통해 8비트와 4비트 양자화를 지원합니다.
Transformers에서 지원되지 않는 양자화 기법들은 [`HfQuantizer`] 클래스를 통해 추가될 수 있습니다.
<Tip>
모델을 양자화하는 방법은 이 [양자화](../quantization) 가이드를 통해 배울 수 있습니다.
</Tip>
## QuantoConfig[[transformers.QuantoConfig]]
[[autodoc]] QuantoConfig
## AqlmConfig[[transformers.AqlmConfig]]
[[autodoc]] AqlmConfig
## VptqConfig[[transformers.VptqConfig]]
[[autodoc]] VptqConfig
## AwqConfig[[transformers.AwqConfig]]
[[autodoc]] AwqConfig
## EetqConfig[[transformers.EetqConfig]]
[[autodoc]] EetqConfig
## GPTQConfig[[transformers.GPTQConfig]]
[[autodoc]] GPTQConfig
## BitsAndBytesConfig[[#transformers.BitsAndBytesConfig]]
[[autodoc]] BitsAndBytesConfig
## HfQuantizer[[transformers.quantizers.HfQuantizer]]
[[autodoc]] quantizers.base.HfQuantizer
## HqqConfig[[transformers.HqqConfig]]
[[autodoc]] HqqConfig
## FbgemmFp8Config[[transformers.FbgemmFp8Config]]
[[autodoc]] FbgemmFp8Config
## CompressedTensorsConfig[[transformers.CompressedTensorsConfig]]
[[autodoc]] CompressedTensorsConfig
## TorchAoConfig[[transformers.TorchAoConfig]]
[[autodoc]] TorchAoConfig
| transformers/docs/source/ko/main_classes/quantization.md/0 | {
"file_path": "transformers/docs/source/ko/main_classes/quantization.md",
"repo_id": "transformers",
"token_count": 1133
} |
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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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# CLIP[[clip]]
## 개요[[overview]]
CLIP 모델은 Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh,
Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever가 제안한 [자연어 지도(supervision)를 통한 전이 가능한 시각 모델 학습](https://arxiv.org/abs/2103.00020)라는 논문에서 소개되었습니다. CLIP(Contrastive Language-Image Pre-Training)은 다양한 이미지와 텍스트 쌍으로 훈련된 신경망 입니다. GPT-2와 3의 제로샷 능력과 유사하게, 해당 작업에 직접적으로 최적화하지 않고도 주어진 이미지에 대해 가장 관련성 있는 텍스트 스니펫을 예측하도록 자연어로 지시할 수 있습니다.
해당 논문의 초록입니다.
*최신 컴퓨터 비전 시스템은 미리 정해진 고정된 객체 카테고리 집합을 예측하도록 훈련됩니다. 이러한 제한된 형태의 지도는 다른 시각적 개념을 지정하기 위해 추가적인 라벨링된 데이터가 필요하므로 그 일반성과 사용성을 제한합니다. 이미지 원시 텍스트에서 직접 학습하는 것은 훨씬 더 광범위한 지도 소스를 활용하는 아주 좋은 대안입니다. 이미지와 캡션을 맞추는 간단한 사전 학습 작업이, 인터넷에서 수집한 4억 쌍의 이미지-텍스트 데이터셋에서 SOTA 수준의 이미지 표현을 처음부터 효율적이고 확장 가능하게 학습하는 방법임을 확인할 수 있습니다. 사전 훈련 후, 자연어는 학습된 시각적 개념을 참조하거나 새로운 개념을 설명하는 데 사용되어 모델의 하위 작업으로의 제로샷 전이를 가능하게 합니다. 해당 논문에서는 OCR, 비디오 내 행동 인식, 지리적 위치 파악, 그리고 많은 종류의 세밀한 객체 분류 등 30개 이상의 다양한 기존 컴퓨터 비전 데이터셋에 대한 벤치마킹을 통해 이 접근 방식의 성능을 연구합니다. 이 모델은 대부분의 작업에 대해 의미 있게 전이되며, 종종 데이터셋별 훈련 없이도 완전 지도 학습 기준선과 경쟁력 있는 성능을 보입니다. 예를 들어, ImageNet에서 원래 ResNet-50의 정확도를 제로샷으로 일치시키는데, 이는 ResNet-50이 훈련된 128만 개의 훈련 예제를 전혀 사용할 필요가 없었습니다. 코드 및 사전 훈련된 모델 가중치는 이 https URL에서 공개합니다.*
이 모델은 [valhalla](https://huggingface.co/valhalla)에 의해 기여되었습니다.
원본 코드는 [이곳](https://github.com/openai/CLIP)에서 확인할 수 있습니다.
## 사용 팁과 예시[[usage-tips-and-example]]
CLIP은 멀티모달 비전 밒 언어 모델입니다. 이미지-텍스트 유사도 계산과 제로샷 이미지 분류에 사용될 수 있습니다. CLIP은 ViT와 유사한 트랜스포머를 사용하여 시각적 특징을 추출하고, 인과적 언어 모델을 사용하여 텍스트 특징을 추출합니다. 그 후 텍스트와 시각적 특징 모두 동일한 차원의 잠재(latent) 공간으로 투영됩니다. 투영된 이미지와 텍스트 특징 사이의 내적이 유사도 점수로 사용됩니다.
트랜스포머 인코더에 이미지를 입력하기 위해, 각 이미지는 고정 크기의 겹치지 않는 패치들의 시퀀스로 분할되고, 이후 선형 임베딩됩니다. [CLS]토큰이 전체 이미지의 표현으로 추가됩니다. 저자들은 또한 절대 위치 임베딩을 추가하고, 결과로 나온 벡터 시퀀스를 표준 트랜스포머 인토더에 입력합니다. [`CLIPImageProcessor`]는 모델을 위해 이미지를 리사이즈(또는 재스캐일링)하고 정규화하는데 사용될 수 있습니다.
[`CLIPTokenizer`]는 텍스트를 인코딩하는데 사용됩니다. [`CLIPProcessor`]는 [`CLIPImageProcessor`]와 [`CLIPTokenizer`]를 하나의 인스턴스로 감싸서 텍스트를 인코딩하고 이미지를 준비하는데 모두 사용됩니다.
다음 예시는 [`CLIPProcessor`]와 [`CLIPModel`]을 사용하여 이미지-텍스트 유사도 점수를 얻는 방법을 보여줍니다.
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import CLIPProcessor, CLIPModel
>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> 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 # 이미지-텍스트 유사성 점수
>>> probs = logits_per_image.softmax(dim=1) # 확률을 레이블링 하기위해서 소프트맥스를 취합니다.
```
### CLIP과 플래시 어텐션2 결합[[combining-clip-and-flash-attention-2]]
먼저 최신버전의 플래시 어텐션2를 설치합니다.
```bash
pip install -U flash-attn --no-build-isolation
```
플래시 어텐션2와 호환되는 하드웨어를 가지고 있는지 확인하세요. 이에 대한 자세한 내용은 flash-attn 리포지토리의 공식문서에서 확인할 수 있습니다. 또한 모델을 반정밀도(`torch.float16`)로 로드하는 것을 잊지 마세요.
<Tip warning={true}>
작은 배치 크기를 사용할 때, 플래시 어텐션을 사용하면 모델이 느려지는 것을 느낄 수 있습니다.아래의 [플래시 어텐션과 SDPA를 사용한 예상 속도 향상](#Expected-speedups-with-Flash-Attention-and-SDPA) 섹션을 참조하여 적절한 어텐션 구현을 선택하세요.
</Tip>
플래시 어텐션2를 사용해서 모델을 로드하고 구동하기 위해서 다음 스니펫을 참고하세요:
```python
>>> import torch
>>> import requests
>>> from PIL import Image
>>> from transformers import CLIPProcessor, CLIPModel
>>> device = "cuda"
>>> torch_dtype = torch.float16
>>> model = CLIPModel.from_pretrained(
... "openai/clip-vit-base-patch32",
... attn_implementation="flash_attention_2",
... device_map=device,
... torch_dtype=torch_dtype,
... )
>>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> 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)
>>> inputs.to(device)
>>> with torch.no_grad():
... with torch.autocast(device):
... outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # 이미지-텍스트 유사성 점수
>>> probs = logits_per_image.softmax(dim=1) # 확률을 레이블링 하기위해서 소프트맥스를 취합니다.
>>> print(probs)
tensor([[0.9946, 0.0052]], device='cuda:0', dtype=torch.float16)
```
### 스케일된 내적 어텐션 (Scaled dot-product Attention(SDPA)) 사용하기[[using-scaled-dot-product-attention-sdpa]]
파이토치는 `torch.nn.functional`의 일부로 네이티브 스케일된 내적 어텐션(SPDA) 연산자를 포함하고 있습니다. 이 함수는 입력과 사용 중인 하드웨어에 따라 적용될 수 있는 여러 구현을 포함합니다. 자세한 정보는 [공식문서](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)나 [GPU 추론](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention) 페이지를 참조하세요.
`torch>=2.1.1`에서는 구현이 가능할 때 SDPA가 기본적으로 사용되지만, `from_pretrained()` 함수에서 `attn_implementation="sdpa"`를 설정하여 SDPA를 명시적으로 사용하도록 요청할 수도 있습니다.
```python
from transformers import CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32", torch_dtype=torch.float16, attn_implementation="sdpa")
```
최고의 속도향상을 위해서, 반정밀도로 모델을 로드하는 것을 추천합니다. (예를들면 `torch.float16` 또는 `torch.bfloat16`).
### 플래시 어텐션과 스케일된 내적 어텐션(SDPA)으로 인해 예상되는 속도향상[[expected-speedups-with-flash-attention-and-sdpa]]
로컬 벤치마크(NVIDIA A10G, PyTorch 2.3.1+cu121)에서 `float16`을 사용하여 `"openai/clip-vit-large-patch14"` 체크포인트로 추론을 수행했을 때, 다음과 같은 속도 향상을 확인 했습니다.
[코드](https://gist.github.com/qubvel/ac691a54e54f9fae8144275f866a7ff8):
#### CLIPTextModel[[cliptextmodel]]
| Num text labels | Eager (s/iter) | FA2 (s/iter) | FA2 speedup | SDPA (s/iter) | SDPA speedup |
|------------------:|-----------------:|---------------:|--------------:|----------------:|---------------:|
| 4 | 0.009 | 0.012 | 0.737 | 0.007 | 1.269 |
| 16 | 0.009 | 0.014 | 0.659 | 0.008 | 1.187 |
| 32 | 0.018 | 0.021 | 0.862 | 0.016 | 1.142 |
| 64 | 0.034 | 0.034 | 1.001 | 0.03 | 1.163 |
| 128 | 0.063 | 0.058 | 1.09 | 0.054 | 1.174 |
#### CLIPVisionModel[[clipvisionmodel]]
| Image batch size | Eager (s/iter) | FA2 (s/iter) | FA2 speedup | SDPA (s/iter) | SDPA speedup |
|-------------------:|-----------------:|---------------:|--------------:|----------------:|---------------:|
| 1 | 0.016 | 0.013 | 1.247 | 0.012 | 1.318 |
| 4 | 0.025 | 0.021 | 1.198 | 0.021 | 1.202 |
| 16 | 0.093 | 0.075 | 1.234 | 0.075 | 1.24 |
| 32 | 0.181 | 0.147 | 1.237 | 0.146 | 1.241 |
#### CLIPModel[[clipmodel]]
| Image batch size | Num text labels | Eager (s/iter) | FA2 (s/iter) | FA2 speedup | SDPA (s/iter) | SDPA speedup |
|-------------------:|------------------:|-----------------:|---------------:|--------------:|----------------:|---------------:|
| 1 | 4 | 0.025 | 0.026 | 0.954 | 0.02 | 1.217 |
| 1 | 16 | 0.026 | 0.028 | 0.918 | 0.02 | 1.287 |
| 1 | 64 | 0.042 | 0.046 | 0.906 | 0.036 | 1.167 |
| 4 | 4 | 0.028 | 0.033 | 0.849 | 0.024 | 1.189 |
| 4 | 16 | 0.034 | 0.035 | 0.955 | 0.029 | 1.169 |
| 4 | 64 | 0.059 | 0.055 | 1.072 | 0.05 | 1.179 |
| 16 | 4 | 0.096 | 0.088 | 1.091 | 0.078 | 1.234 |
| 16 | 16 | 0.102 | 0.09 | 1.129 | 0.083 | 1.224 |
| 16 | 64 | 0.127 | 0.11 | 1.157 | 0.105 | 1.218 |
| 32 | 4 | 0.185 | 0.159 | 1.157 | 0.149 | 1.238 |
| 32 | 16 | 0.19 | 0.162 | 1.177 | 0.154 | 1.233 |
| 32 | 64 | 0.216 | 0.181 | 1.19 | 0.176 | 1.228 |
## 자료[[resources]]
CLIP을 시작하는 데 도움이 되는 Hugging Face와 community 자료 목록(🌎로 표시됨) 입니다.
- [원격 센싱 (인공위성) 이미지와 캡션을 가지고 CLIP 미세조정하기](https://huggingface.co/blog/fine-tune-clip-rsicd):
[RSICD dataset](https://github.com/201528014227051/RSICD_optimal)을 가지고 CLIP을 미세조정 하는 방법과 데이터 증강에 대한 성능 비교에 대한 블로그 포스트
- 이 [예시 스크립트](https://github.com/huggingface/transformers/tree/main/examples/pytorch/contrastive-image-text)는 [COCO dataset](https://cocodataset.org/#home)를 이용한 사전학습된 비전과 텍스트와 인코더를 사용해서 CLIP같은 비전-텍스트 듀얼 모델을 어떻게 학습시키는지 보여줍니다.
<PipelineTag pipeline="image-to-text"/>
- 사전학습된 CLIP모델을 이미지 캡셔닝을 위한 빔서치 추론에 어떻게 활용하는지에 관한 [노트북](https://colab.research.google.com/drive/1tuoAC5F4sC7qid56Z0ap-stR3rwdk0ZV?usp=sharing)
**이미지 검색**
- 사전학습된 CLIP모델과 MRR(Mean Reciprocal Rank) 점수 연산을 사용한 이미지 검색에 대한 [노트북](https://colab.research.google.com/drive/1bLVwVKpAndpEDHqjzxVPr_9nGrSbuOQd?usp=sharing). 🌎
- 이미지 검색과 유사성 점수에 대해 보여주는 [노트북](https://colab.research.google.com/github/deep-diver/image_search_with_natural_language/blob/main/notebooks/Image_Search_CLIP.ipynb). 🌎
- Multilingual CLIP를 사용해서 이미지와 텍스트를 어떻게 같은 벡터 공간에 매핑 시키는지에 대한 [노트북](https://colab.research.google.com/drive/1xO-wC_m_GNzgjIBQ4a4znvQkvDoZJvH4?usp=sharing). 🌎
- [Unsplash](https://unsplash.com)와 [TMDB](https://www.themoviedb.org/) 데이터셋을 활용한 의미론적(semantic) 이미지 검색에서 CLIP을 구동하는 방법에 대한 [노트북](https://colab.research.google.com/github/vivien000/clip-demo/blob/master/clip.ipynb#scrollTo=uzdFhRGqiWkR). 🌎
**설명 가능성**
- 입력 토큰과 이미지 조각(segment) 사이의 유사성을 시각화 시키는 방법에 대한 [노트북](https://colab.research.google.com/github/hila-chefer/Transformer-MM-Explainability/blob/main/CLIP_explainability.ipynb). 🌎
여기에 포함될 자료를 제출하고 싶으시다면 PR(Pull Request)를 열어주세요. 리뷰 해드리겠습니다! 자료는 기존 자료를 복제하는 대신 새로운 내용을 담고 있어야 합니다.
## CLIPConfig[[transformers.CLIPConfig]]
[[autodoc]] CLIPConfig
- from_text_vision_configs
## CLIPTextConfig[[transformers.CLIPTextConfig]]
[[autodoc]] CLIPTextConfig
## CLIPVisionConfig[[transformers.CLIPVisionConfig]]
[[autodoc]] CLIPVisionConfig
## CLIPTokenizer[[transformers.CLIPTokenizer]]
[[autodoc]] CLIPTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## CLIPTokenizerFast[[transformers.CLIPTokenizerFast]]
[[autodoc]] CLIPTokenizerFast
## CLIPImageProcessor[[transformers.CLIPImageProcessor]]
[[autodoc]] CLIPImageProcessor
- preprocess
## CLIPFeatureExtractor[[transformers.CLIPFeatureExtractor]]
[[autodoc]] CLIPFeatureExtractor
## CLIPProcessor[[transformers.CLIPProcessor]]
[[autodoc]] CLIPProcessor
<frameworkcontent>
<pt>
## CLIPModel[[transformers.CLIPModel]]
[[autodoc]] CLIPModel
- forward
- get_text_features
- get_image_features
## CLIPTextModel[[transformers.CLIPTextModel]]
[[autodoc]] CLIPTextModel
- forward
## CLIPTextModelWithProjection[[transformers.CLIPTextModelWithProjection]]
[[autodoc]] CLIPTextModelWithProjection
- forward
## CLIPVisionModelWithProjection[[transformers.CLIPVisionModelWithProjection]]
[[autodoc]] CLIPVisionModelWithProjection
- forward
## CLIPVisionModel[[transformers.CLIPVisionModel]]
[[autodoc]] CLIPVisionModel
- forward
## CLIPForImageClassification[[transformers.CLIPForImageClassification]]
[[autodoc]] CLIPForImageClassification
- forward
</pt>
<tf>
## TFCLIPModel[[transformers.TFCLIPModel]]
[[autodoc]] TFCLIPModel
- call
- get_text_features
- get_image_features
## TFCLIPTextModel[[transformers.TFCLIPTextModel]]
[[autodoc]] TFCLIPTextModel
- call
## TFCLIPVisionModel[[transformers.TFCLIPVisionModel]]
[[autodoc]] TFCLIPVisionModel
- call
</tf>
<jax>
## FlaxCLIPModel[[transformers.FlaxCLIPModel]]
[[autodoc]] FlaxCLIPModel
- __call__
- get_text_features
- get_image_features
## FlaxCLIPTextModel[[transformers.FlaxCLIPTextModel]]
[[autodoc]] FlaxCLIPTextModel
- __call__
## FlaxCLIPTextModelWithProjection[[transformers.FlaxCLIPTextModelWithProjection]]
[[autodoc]] FlaxCLIPTextModelWithProjection
- __call__
## FlaxCLIPVisionModel[[transformers.FlaxCLIPVisionModel]]
[[autodoc]] FlaxCLIPVisionModel
- __call__
</jax>
</frameworkcontent>
| transformers/docs/source/ko/model_doc/clip.md/0 | {
"file_path": "transformers/docs/source/ko/model_doc/clip.md",
"repo_id": "transformers",
"token_count": 11235
} |
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# 맘바[[mamba]]
## 개요[[overview]]
맘바(Mamba) 모델은 Albert Gu, Tri Dao가 제안한 [맘바: 선택적 상태 공간을 이용한 선형 시간 시퀀스 모델링](https://arxiv.org/abs/2312.00752)라는 논문에서 소개 되었습니다.
이 모델은 `state-space-models`을 기반으로 한 새로운 패러다임 아키텍처입니다. 직관적인 이해를 얻고 싶다면 [이곳](https://srush.github.io/annotated-s4/)을 참고 하세요.
해당 논문의 초록입니다:
*현재 딥러닝에서 흥미로운 응용 프로그램을 구동하는 대부분의 기초 모델들은 거의 보편적으로 트랜스포머 아키텍처와 그 핵심 어텐션 모듈을 기반으로 합니다. 선형 어텐션, 게이트된 컨볼루션과 순환 모델, 구조화된 상태 공간 모델(SSM) 등 많은 준이차시간(subquadratic-time) 아키텍처가 긴 시퀀스에 대한 트랜스포머의 계산 비효율성을 해결하기 위해 개발되었지만, 언어와 같은 중요한 양식에서는 어텐션만큼 성능을 내지 못했습니다. 우리는 이러한 모델의 주요 약점이 내용 기반 추론을 수행하지 못한다는 점임을 알고 몇 가지를 개선했습니다. 첫째, SSM 매개변수를 입력의 함수로 만드는 것만으로도 이산 모달리티(discrete modalities)의 약점을 해결할 수 있어, 현재 토큰에 따라 시퀀스 길이 차원을 따라 정보를 선택적으로 전파하거나 잊을 수 있게 합니다. 둘째, 이러한 변경으로 효율적인 컨볼루션을 사용할 수 없게 되었지만, 우리는 순환 모드에서 하드웨어를 인식하는 병렬 알고리즘을 설계했습니다. 우리는 이러한 선택적 SSM을 어텐션이나 MLP 블록도 없는 단순화된 종단간 신경망 아키텍처인 맘바에 통합시켰습니다. 맘바는 빠른 추론(트랜스포머보다 5배 높은 처리량)과 시퀀스 길이에 대한 선형 확장성을 누리며, 백만 길이 시퀀스까지 실제 데이터에서 성능이 향상됩니다. 일반적인 시퀀스 모델 백본으로서 맘바는 언어, 오디오, 유전체학과 같은 여러 양식에서 최첨단 성능을 달성합니다. 언어 모델링에서 우리의 맘바-3B 모델은 같은 크기의 트랜스포머를 능가하고 두 배 크기의 트랜스포머와 맞먹는 성능을 보이며, 사전 훈련과 다운스트림 평가 모두에서 성능을 나타납니다.*
팁:
- 맘바는 고전적인 트랜스포머와 견줄 만한 새로운 `상태 공간 모델` 아키텍처입니다. 이는 구조화된 상태 공간 모델의 발전 선상에 있으며, [플래시어텐션](https://github.com/Dao-AILab/flash-attention)의 정신을 따르는 효율적인 하드웨어 인식 설계와 구현을 특징으로 합니다.
- 맘바는 `어텐션` 레이어와 동등한 `믹서(mixer)` 레이어를 쌓습니다. `맘바`의 핵심 로직은 `MambaMixer` 클래스에 있습니다.
- 두 가지 구현이 공존합니다: 하나는 최적화되어 빠른 cuda커널을 사용하고, 다른 하나는 단순하지만 모든 장치에서 실행할 수 있습니다!
- 현재 구현은 원본 cuda커널을 활용합니다: 맘바를 위한 플래시 어텐션의 역할을 하는 것은 [`mamba-ssm`](https://github.com/state-spaces/mamba)와 [`causal_conv1d`](https://github.com/Dao-AILab/causal-conv1d) 저장소에 호스팅되어 있습니다. 하드웨어가 지원한다면 반드시 설치하세요!
- cuda 커널을 최적화하는 방향 보다는, 단순하지만 모든 장치에서 실행가능하도록하는 방향인 '단순구현'의 성능을 빠르게 향상시키는 기여를 더 환영하고 있습니다. 🤗
이 모델은 [ArthurZ](https://huggingface.co/ArthurZ)에 의해 기여되었습니다.
원본 코드는 [이곳](https://github.com/state-spaces/mamba)에서 확인할 수 있습니다.
# 사용
### 간단한 생성 예제
```python
from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf")
input_ids = tokenizer("Hey how are you doing?", return_tensors= "pt")["input_ids"]
out = model.generate(input_ids, max_new_tokens=10)
print(tokenizer.batch_decode(out))
```
### Peft 파인튜닝
느린 버전은 학습에서 아주 안정적이진 않습니다. 빠른 버전은 `float32`가 필요합니다!
```python
from datasets import load_dataset
from trl import SFTTrainer
from peft import LoraConfig
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
model_id = "state-spaces/mamba-130m-hf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
dataset = load_dataset("Abirate/english_quotes", split="train")
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=4,
logging_dir='./logs',
logging_steps=10,
learning_rate=2e-3
)
lora_config = LoraConfig(
r=8,
target_modules=["x_proj", "embeddings", "in_proj", "out_proj"],
task_type="CAUSAL_LM",
bias="none"
)
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
args=training_args,
peft_config=lora_config,
train_dataset=dataset,
dataset_text_field="quote",
)
trainer.train()
```
## MambaConfig
[[autodoc]] MambaConfig
## MambaModel
[[autodoc]] MambaModel
- forward
## MambaLMHeadModel
[[autodoc]] MambaForCausalLM
- forward
| transformers/docs/source/ko/model_doc/mamba.md/0 | {
"file_path": "transformers/docs/source/ko/model_doc/mamba.md",
"repo_id": "transformers",
"token_count": 4168
} |
<!--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
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.
-->
# Video Vision Transformer (ViViT) [[video-vision-transformer-vivit]]
## 개요 [[overview]]
Vivit 모델은 Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid가 제안한 논문 [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691)에서 소개되었습니다. 이 논문은 비디오 이해를 위한 pure-transformer 기반의 모델 집합 중에서 최초로 성공한 모델 중 하나를 소개합니다.
논문의 초록은 다음과 같습니다:
*우리는 이미지 분류에서 최근 성공을 거둔 순수 트랜스포머 기반 모델을 바탕으로 비디오 분류를 위한 모델을 제안합니다. 본 모델은 입력 비디오로부터 시공간 토큰을 추출한 후, 이를 일련의 트랜스포머 레이어로 인코딩합니다. 비디오에서 발생하는 긴 토큰 시퀀스를 처리하기 위해, 입력의 공간 및 시간 차원을 분리하는 여러 효율적인 모델 변형을 제안합니다. 트랜스포머 기반 모델은 대규모 학습 데이터셋에서만 효과적이라는 것이 일반적이지만, 우리는 학습 중 모델을 효과적으로 정규화하고, 사전 학습된 이미지 모델을 활용함으로써 상대적으로 작은 데이터셋에서도 학습할 수 있는 방법을 보여줍니다. 또한, 철저한 소거(ablation) 연구를 수행하고 Kinetics 400 및 600, Epic Kitchens, Something-Something v2, Moments in Time을 포함한 여러 비디오 분류 벤치마크에서 최첨단 성과를 달성하여, 기존의 3D 합성곱 신경망 기반 방법들을 능가합니다.*
이 모델은 [jegormeister](https://huggingface.co/jegormeister)가 기여하였습니다. 원본 코드(JAX로 작성됨)는 [여기](https://github.com/google-research/scenic/tree/main/scenic/projects/vivit)에서 확인할 수 있습니다.
## VivitConfig [[transformers.VivitConfig]]
[[autodoc]] VivitConfig
## VivitImageProcessor [[transformers.VivitImageProcessor]]
[[autodoc]] VivitImageProcessor
- preprocess
## VivitModel [[transformers.VivitModel]]
[[autodoc]] VivitModel
- forward
## VivitForVideoClassification [[transformers.VivitForVideoClassification]]
[[autodoc]] transformers.VivitForVideoClassification
- forward
| transformers/docs/source/ko/model_doc/vivit.md/0 | {
"file_path": "transformers/docs/source/ko/model_doc/vivit.md",
"repo_id": "transformers",
"token_count": 1653
} |
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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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rendered properly in your Markdown viewer.
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# TensorFlow로 TPU에서 훈련하기[[training-on-tpu-with-tensorflow]]
<Tip>
자세한 설명이 필요하지 않고 바로 TPU 샘플 코드를 시작하고 싶다면 [우리의 TPU 예제 노트북!](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb)을 확인하세요.
</Tip>
### TPU가 무엇인가요?[[what-is-a-tpu]]
TPU는 **텐서 처리 장치**입니다. Google에서 설계한 하드웨어로, GPU처럼 신경망 내에서 텐서 연산을 더욱 빠르게 처리하기 위해 사용됩니다. 네트워크 훈련과 추론 모두에 사용할 수 있습니다. 일반적으로 Google의 클라우드 서비스를 통해 이용할 수 있지만, Google Colab과 Kaggle Kernel을 통해 소규모 TPU를 무료로 직접 이용할 수도 있습니다.
[🤗 Transformers의 모든 Tensorflow 모델은 Keras 모델](https://huggingface.co/blog/tensorflow-philosophy)이기 때문에, 이 문서에서 다루는 대부분의 메소드는 대체로 모든 Keras 모델을 위한 TPU 훈련에 적용할 수 있습니다! 하지만 Transformer와 데이터 세트의 HuggingFace 생태계(hug-o-system?)에 특화된 몇 가지 사항이 있으며, 해당 사항에 대해 설명할 때 반드시 언급하도록 하겠습니다.
### 어떤 종류의 TPU가 있나요?[[what-kinds-of-tpu-are-available]]
신규 사용자는 TPU의 범위와 다양한 이용 방법에 대해 매우 혼란스러워하는 경우가 많습니다. **TPU 노드**와 **TPU VM**의 차이점은 가장 먼저 이해해야 할 핵심적인 구분 사항입니다.
**TPU 노드**를 사용한다면, 실제로는 원격 TPU를 간접적으로 이용하는 것입니다. 네트워크와 데이터 파이프라인을 초기화한 다음, 이를 원격 노드로 전달할 별도의 VM이 필요합니다. Google Colab에서 TPU를 사용하는 경우, **TPU 노드** 방식으로 이용하게 됩니다.
TPU 노드를 사용하는 것은 이를 사용하지 않는 사용자에게 예기치 않은 현상이 발생하기도 합니다! 특히, TPU는 파이썬 코드를 실행하는 기기(machine)와 물리적으로 다른 시스템에 있기 때문에 로컬 기기에 데이터를 저장할 수 없습니다. 즉, 컴퓨터의 내부 저장소에서 가져오는 데이터 파이프라인은 절대 작동하지 않습니다! 로컬 기기에 데이터를 저장하는 대신에, 데이터 파이프라인이 원격 TPU 노드에서 실행 중일 때에도 데이터 파이프라인이 계속 이용할 수 있는 Google Cloud Storage에 데이터를 저장해야 합니다.
<Tip>
메모리에 있는 모든 데이터를 `np.ndarray` 또는 `tf.Tensor`로 맞출 수 있다면, Google Cloud Storage에 업로드할 필요 없이, Colab 또는 TPU 노드를 사용해서 해당 데이터에 `fit()` 할 수 있습니다.
</Tip>
<Tip>
**🤗특수한 Hugging Face 팁🤗:** TF 코드 예제에서 볼 수 있는 `Dataset.to_tf_dataset()` 메소드와 그 상위 래퍼(wrapper)인 `model.prepare_tf_dataset()`는 모두 TPU 노드에서 작동하지 않습니다. 그 이유는 `tf.data.Dataset`을 생성하더라도 “순수한” `tf.data` 파이프라인이 아니며 `tf.numpy_function` 또는 `Dataset.from_generator()`를 사용하여 기본 HuggingFace `Dataset`에서 데이터를 전송하기 때문입니다. 이 HuggingFace `Dataset`는 로컬 디스크에 있는 데이터로 지원되며 원격 TPU 노드가 읽을 수 없습니다.
</Tip>
TPU를 이용하는 두 번째 방법은 **TPU VM**을 사용하는 것입니다. TPU VM을 사용할 때, GPU VM에서 훈련하는 것과 같이 TPU가 장착된 기기에 직접 연결합니다. 특히 데이터 파이프라인과 관련하여, TPU VM은 대체로 작업하기 더 쉽습니다. 위의 모든 경고는 TPU VM에는 해당되지 않습니다!
이 문서는 의견이 포함된 문서이며, 저희의 의견이 여기에 있습니다: **가능하면 TPU 노드를 사용하지 마세요.** TPU 노드는 TPU VM보다 더 복잡하고 디버깅하기가 더 어렵습니다. 또한 향후에는 지원되지 않을 가능성이 높습니다. Google의 최신 TPU인 TPUv4는 TPU VM으로만 이용할 수 있으므로, TPU 노드는 점점 더 "구식" 이용 방법이 될 것으로 전망됩니다. 그러나 TPU 노드를 사용하는 Colab과 Kaggle Kernel에서만 무료 TPU 이용이 가능한 것으로 확인되어, 필요한 경우 이를 다루는 방법을 설명해 드리겠습니다! 이에 대한 자세한 설명이 담긴 코드 샘플은 [TPU 예제 노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb)에서 확인하시기 바랍니다.
### 어떤 크기의 TPU를 사용할 수 있나요?[[what-sizes-of-tpu-are-available]]
단일 TPU(v2-8/v3-8/v4-8)는 8개의 복제본(replicas)을 실행합니다. TPU는 수백 또는 수천 개의 복제본을 동시에 실행할 수 있는 **pod**로 존재합니다. 단일 TPU를 하나 이상 사용하지만 전체 Pod보다 적게 사용하는 경우(예를 들면, v3-32), TPU 구성을 **pod 슬라이스**라고 합니다.
Colab을 통해 무료 TPU에 이용하는 경우, 기본적으로 단일 v2-8 TPU를 제공받습니다.
### XLA에 대해 들어본 적이 있습니다. XLA란 무엇이고 TPU와 어떤 관련이 있나요?[[i-keep-hearing-about-this-xla-thing-whats-xla-and-how-does-it-relate-to-tpus]]
XLA는 최적화 컴파일러로, TensorFlow와 JAX에서 모두 사용됩니다. JAX에서는 유일한 컴파일러이지만, TensorFlow에서는 선택 사항입니다(하지만 TPU에서는 필수입니다!). Keras 모델을 훈련할 때 이를 활성화하는 가장 쉬운 방법은 `jit_compile=True` 인수를 `model.compile()`에 전달하는 것입니다. 오류가 없고 성능이 양호하다면, TPU로 전환할 준비가 되었다는 좋은 신호입니다!
TPU에서 디버깅하는 것은 대개 CPU/GPU보다 조금 더 어렵기 때문에, TPU에서 시도하기 전에 먼저 XLA로 CPU/GPU에서 코드를 실행하는 것을 권장합니다. 물론 오래 학습할 필요는 없습니다. 즉, 모델과 데이터 파이프라인이 예상대로 작동하는지 확인하기 위해 몇 단계만 거치면 됩니다.
<Tip>
XLA로 컴파일된 코드는 대체로 더 빠릅니다. 따라서 TPU에서 실행할 계획이 없더라도, `jit_compile=True`를 추가하면 성능이 향상될 수 있습니다. 하지만 XLA 호환성에 대한 아래 주의 사항을 반드시 확인하세요!
</Tip>
<Tip warning={true}>
**뼈아픈 경험에서 얻은 팁:** `jit_compile=True`를 사용하면 속도를 높이고 CPU/GPU 코드가 XLA와 호환되는지 검증할 수 있는 좋은 방법이지만, 실제 TPU에서 훈련할 때 그대로 남겨두면 많은 문제를 초래할 수 있습니다. XLA 컴파일은 TPU에서 암시적으로 이뤄지므로, 실제 TPU에서 코드를 실행하기 전에 해당 줄을 제거하는 것을 잊지 마세요!
</Tip>
### 제 XLA 모델과 호환하려면 어떻게 해야 하나요?[[how-do-i-make-my-model-xla-compatible]]
대부분의 경우, 여러분의 코드는 이미 XLA와 호환될 것입니다! 그러나 표준 TensorFlow에서 작동하지만, XLA에서는 작동하지 않는 몇 가지 사항이 있습니다. 이를 아래 세 가지 핵심 규칙으로 간추렸습니다:
<Tip>
**특수한 HuggingFace 팁🤗:** 저희는 TensorFlow 모델과 손실 함수를 XLA와 호환되도록 재작성하는 데 많은 노력을 기울였습니다. 저희의 모델과 손실 함수는 대개 기본적으로 규칙 #1과 #2를 따르므로 `transformers` 모델을 사용하는 경우, 이를 건너뛸 수 있습니다. 하지만 자체 모델과 손실 함수를 작성할 때는 이러한 규칙을 잊지 마세요!
</Tip>
#### XLA 규칙 #1: 코드에서 “데이터 종속 조건문”을 사용할 수 없습니다[[xla-rule-1-your-code-cannot-have-datadependent-conditionals]]
어떤 `if`문도 `tf.Tensor` 내부의 값에 종속될 수 없다는 것을 의미합니다. 예를 들어, 이 코드 블록은 XLA로 컴파일할 수 없습니다!
```python
if tf.reduce_sum(tensor) > 10:
tensor = tensor / 2.0
```
처음에는 매우 제한적으로 보일 수 있지만, 대부분의 신경망 코드에서는 이를 수행할 필요가 없습니다. `tf.cond`를 사용하거나([여기](https://www.tensorflow.org/api_docs/python/tf/cond) 문서를 참조), 다음과 같이 조건문을 제거하고 대신 지표 변수를 사용하는 영리한 수학 트릭을 찾아내어 이 제한을 우회할 수 있습니다:
```python
sum_over_10 = tf.cast(tf.reduce_sum(tensor) > 10, tf.float32)
tensor = tensor / (1.0 + sum_over_10)
```
이 코드는 위의 코드와 정확히 동일한 효과를 구현하지만, 조건문을 제거하여 문제 없이 XLA로 컴파일되도록 합니다!
#### XLA 규칙 #2: 코드에서 "데이터 종속 크기"를 가질 수 없습니다[[xla-rule-2-your-code-cannot-have-datadependent-shapes]]
코드에서 모든 `tf.Tensor` 객체의 크기가 해당 값에 종속될 수 없다는 것을 의미합니다. 예를 들어, `tf.unique` 함수는 입력에서 각 고유 값의 인스턴스 하나를 포함하는 `tensor`를 반환하기 때문에 XLA로 컴파일할 수 없습니다. 이 출력의 크기는 입력 `Tensor`가 얼마나 반복적인지에 따라 분명히 달라질 것이므로, XLA는 이를 처리하지 못합니다!
일반적으로, 대부분의 신경망 코드는 기본값으로 규칙 2를 따릅니다. 그러나 문제가 되는 몇 가지 대표적인 사례가 있습니다. 가장 흔한 사례 중 하나는 **레이블 마스킹**을 사용하여 손실(loss)을 계산할 때, 해당 위치를 무시하도록 나타내기 위해 레이블을 음수 값으로 설정하는 경우입니다. 레이블 마스킹을 지원하는 NumPy나 PyTorch 손실 함수를 보면 [불 인덱싱](https://numpy.org/doc/stable/user/basics.indexing.html#boolean-array-indexing)을 사용하는 다음과 같은 코드를 자주 접할 수 있습니다:
```python
label_mask = labels >= 0
masked_outputs = outputs[label_mask]
masked_labels = labels[label_mask]
loss = compute_loss(masked_outputs, masked_labels)
mean_loss = torch.mean(loss)
```
이 코드는 NumPy나 PyTorch에서는 문제 없이 작동하지만, XLA에서는 손상됩니다! 왜 그럴까요? 얼마나 많은 위치가 마스킹되는지에 따라 `masked_outputs`와 `masked_labels`의 크기가 달라져서, **데이터 종속 크기**가 되기 때문입니다. 그러나 규칙 #1과 마찬가지로, 이 코드를 다시 작성하면 데이터 종속적 모양 크기가 정확히 동일한 출력을 산출할 수 있습니다.
```python
label_mask = tf.cast(labels >= 0, tf.float32)
loss = compute_loss(outputs, labels)
loss = loss * label_mask # Set negative label positions to 0
mean_loss = tf.reduce_sum(loss) / tf.reduce_sum(label_mask)
```
여기서, 모든 위치에 대한 손실을 계산하지만, 평균을 계산할 때 분자와 분모 모두에서 마스크된 위치를 0으로 처리합니다. 이는 데이터 종속 크기를 방지하고 XLA 호환성을 유지하면서 첫 번째 블록과 정확히 동일한 결과를 산출합니다. 규칙 #1에서와 동일한 트릭을 사용하여 `tf.bool`을 `tf.float32`로 변환하고 이를 지표 변수로 사용합니다. 해당 트릭은 매우 유용하며, 자체 코드를 XLA로 변환해야 할 경우 기억해 두세요!
#### XLA 규칙 #3: XLA는 각기 다른 입력 크기가 나타날 때마다 모델을 다시 컴파일해야 합니다[[xla-rule-3-xla-will-need-to-recompile-your-model-for-every-different-input-shape-it-sees]]
이것은 가장 큰 문제입니다. 입력 크기가 매우 가변적인 경우, XLA는 모델을 반복해서 다시 컴파일해야 하므로 성능에 큰 문제가 발생할 수 있습니다. 이 문제는 토큰화 후 입력 텍스트의 길이가 가변적인 NLP 모델에서 주로 발생합니다. 다른 모달리티에서는 정적 크기가 더 흔하며, 해당 규칙이 훨씬 덜 문제시 됩니다.
규칙 #3을 어떻게 우회할 수 있을까요? 핵심은 **패딩**입니다. 모든 입력을 동일한 길이로 패딩한 다음, `attention_mask`를 사용하면 어떤 XLA 문제도 없이 가변 크기에서 가져온 것과 동일한 결과를 가져올 수 있습니다. 그러나 과도한 패딩은 심각한 속도 저하를 야기할 수도 있습니다. 모든 샘플을 전체 데이터 세트의 최대 길이로 패딩하면, 무한한 패딩 토큰으로 구성된 배치가 생성되어 많은 연산과 메모리가 낭비될 수 있습니다!
이 문제에 대한 완벽한 해결책은 없습니다. 하지만, 몇 가지 트릭을 시도해볼 수 있습니다. 한 가지 유용한 트릭은 **샘플 배치를 32 또는 64 토큰과 같은 숫자의 배수까지 패딩하는 것입니다.** 이는 토큰 수가 소폭 증가하지만, 모든 입력 크기가 32 또는 64의 배수여야 하기 때문에 고유한 입력 크기의 수가 대폭 줄어듭니다. 고유한 입력 크기가 적다는 것은 XLA 컴파일 횟수가 적어진다는 것을 의미합니다!
<Tip>
**🤗특수한 HuggingFace 팁🤗:** 토크나이저와 데이터 콜레이터에 도움이 될 수 있는 메소드가 있습니다. 토크나이저를 불러올 때 `padding="max_length"` 또는 `padding="longest"`를 사용하여 패딩된 데이터를 출력하도록 할 수 있습니다. 토크나이저와 데이터 콜레이터는 나타나는 고유한 입력 크기의 수를 줄이기 위해 사용할 수 있는 `pad_to_multiple_of` 인수도 있습니다!
</Tip>
### 실제 TPU로 모델을 훈련하려면 어떻게 해야 하나요?[[how-do-i-actually-train-my-model-on-tpu]]
훈련이 XLA와 호환되고 (TPU 노드/Colab을 사용하는 경우) 데이터 세트가 적절하게 준비되었다면, TPU에서 실행하는 것은 놀랍도록 쉽습니다! 코드에서 몇 줄만 추가하여, TPU를 초기화하고 모델과 데이터 세트가 `TPUStrategy` 범위 내에 생성되도록 변경하면 됩니다. [우리의 TPU 예제 노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb)을 참조하여 실제로 작동하는 모습을 확인해 보세요!
### 요약[[summary]]
여기에 많은 내용이 포함되어 있으므로, TPU 훈련을 위한 모델을 준비할 때 따를 수 있는 간략한 체크리스트로 요약해 보겠습니다:
- 코드가 XLA의 세 가지 규칙을 따르는지 확인합니다.
- CPU/GPU에서 `jit_compile=True`로 모델을 컴파일하고 XLA로 훈련할 수 있는지 확인합니다.
- 데이터 세트를 메모리에 가져오거나 TPU 호환 데이터 세트를 가져오는 방식을 사용합니다([노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) 참조)
- 코드를 Colab(accelerator가 “TPU”로 설정됨) 또는 Google Cloud의 TPU VM으로 마이그레이션합니다.
- TPU 초기화 코드를 추가합니다([노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) 참조)
- `TPUStrategy`를 생성하고 데이터 세트를 가져오는 것과 모델 생성이 `strategy.scope()` 내에 있는지 확인합니다([노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) 참조)
- TPU로 이동할 때 `jit_compile=True`를 다시 설정하는 것을 잊지 마세요!
- 🙏🙏🙏🥺🥺🥺
- model.fit()을 불러옵니다.
- 여러분이 해냈습니다! | transformers/docs/source/ko/perf_train_tpu_tf.md/0 | {
"file_path": "transformers/docs/source/ko/perf_train_tpu_tf.md",
"repo_id": "transformers",
"token_count": 12239
} |
<!--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
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http://www.apache.org/licenses/LICENSE-2.0
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# ONNX로 내보내기 [[export-to-onnx]]
🤗 Transformers 모델을 제품 환경에서 배포하기 위해서는 모델을 직렬화된 형식으로 내보내고 특정 런타임과 하드웨어에서 로드하고 실행할 수 있으면 유용합니다.
🤗 Optimum은 Transformers의 확장으로, PyTorch 또는 TensorFlow에서 모델을 ONNX와 TFLite와 같은 직렬화된 형식으로 내보낼 수 있도록 하는 `exporters` 모듈을 통해 제공됩니다. 🤗 Optimum은 또한 성능 최적화 도구 세트를 제공하여 특정 하드웨어에서 모델을 훈련하고 실행할 때 최대 효율성을 달성할 수 있습니다.
이 안내서는 🤗 Optimum을 사용하여 🤗 Transformers 모델을 ONNX로 내보내는 방법을 보여줍니다. TFLite로 모델을 내보내는 안내서는 [TFLite로 내보내기 페이지](tflite)를 참조하세요.
## ONNX로 내보내기 [[export-to-onnx]]
[ONNX (Open Neural Network eXchange)](http://onnx.ai)는 PyTorch와 TensorFlow를 포함한 다양한 프레임워크에서 심층 학습 모델을 나타내는 데 사용되는 공통 연산자 세트와 공통 파일 형식을 정의하는 오픈 표준입니다. 모델이 ONNX 형식으로 내보내지면 이러한 연산자를 사용하여 신경망을 통해 데이터가 흐르는 흐름을 나타내는 계산 그래프(일반적으로 _중간 표현_이라고 함)가 구성됩니다.
표준화된 연산자와 데이터 유형을 가진 그래프를 노출함으로써, ONNX는 프레임워크 간에 쉽게 전환할 수 있습니다. 예를 들어, PyTorch에서 훈련된 모델을 ONNX 형식으로 내보내고 TensorFlow에서 가져올 수 있습니다(그 반대도 가능합니다).
ONNX 형식으로 내보낸 모델은 다음과 같이 사용할 수 있습니다:
- [그래프 최적화](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/optimization) 및 [양자화](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization)와 같은 기법을 사용하여 추론을 위해 최적화됩니다.
- ONNX Runtime을 통해 실행할 수 있습니다. [`ORTModelForXXX` 클래스들](https://huggingface.co/docs/optimum/onnxruntime/package_reference/modeling_ort)을 통해 동일한 `AutoModel` API를 따릅니다. 이 API는 🤗 Transformers에서 사용하는 것과 동일합니다.
- [최적화된 추론 파이프라인](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/pipelines)을 사용할 수 있습니다. 이는 🤗 Transformers의 [`pipeline`] 함수와 동일한 API를 가지고 있습니다.
🤗 Optimum은 구성 객체를 활용하여 ONNX 내보내기를 지원합니다. 이러한 구성 객체는 여러 모델 아키텍처에 대해 미리 준비되어 있으며 다른 아키텍처에 쉽게 확장할 수 있도록 설계되었습니다.
미리 준비된 구성 목록은 [🤗 Optimum 문서](https://huggingface.co/docs/optimum/exporters/onnx/overview)를 참조하세요.
🤗 Transformers 모델을 ONNX로 내보내는 두 가지 방법이 있습니다. 여기에서 두 가지 방법을 모두 보여줍니다:
- 🤗 Optimum을 사용하여 CLI로 내보내기
- `optimum.onnxruntime`을 사용하여 🤗 Optimum으로 ONNX로 내보내기
### CLI를 사용하여 🤗 Transformers 모델을 ONNX로 내보내기 [[exporting-a-transformers-model-to-onnx-with-cli]]
🤗 Transformers 모델을 ONNX로 내보내려면 먼저 추가 종속성을 설치하세요:
```bash
pip install optimum[exporters]
```
사용 가능한 모든 인수를 확인하려면 [🤗 Optimum 문서](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli)를 참조하거나 명령줄에서 도움말을 보세요.
```bash
optimum-cli export onnx --help
```
예를 들어, 🤗 Hub에서 `distilbert/distilbert-base-uncased-distilled-squad`와 같은 모델의 체크포인트를 내보내려면 다음 명령을 실행하세요:
```bash
optimum-cli export onnx --model distilbert/distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
```
위와 같이 진행 상황을 나타내는 로그가 표시되고 결과인 `model.onnx`가 저장된 위치가 표시됩니다.
```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
```
위의 예제는 🤗 Hub에서 체크포인트를 내보내는 것을 설명합니다. 로컬 모델을 내보낼 때에는 모델의 가중치와 토크나이저 파일을 동일한 디렉토리(`local_path`)에 저장했는지 확인하세요. CLI를 사용할 때에는 🤗 Hub의 체크포인트 이름 대신 `model` 인수에 `local_path`를 전달하고 `--task` 인수를 제공하세요. 지원되는 작업의 목록은 [🤗 Optimum 문서](https://huggingface.co/docs/optimum/exporters/task_manager)를 참조하세요. `task` 인수가 제공되지 않으면 작업에 특화된 헤드 없이 모델 아키텍처로 기본 설정됩니다.
```bash
optimum-cli export onnx --model local_path --task question-answering distilbert_base_uncased_squad_onnx/
```
그 결과로 생성된 `model.onnx` 파일은 ONNX 표준을 지원하는 많은 [가속기](https://onnx.ai/supported-tools.html#deployModel) 중 하나에서 실행할 수 있습니다. 예를 들어, [ONNX Runtime](https://onnxruntime.ai/)을 사용하여 모델을 로드하고 실행할 수 있습니다:
```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)
```
Hub의 TensorFlow 체크포인트에 대해서도 동일한 프로세스가 적용됩니다. 예를 들어, [Keras organization](https://huggingface.co/keras-io)에서 순수한 TensorFlow 체크포인트를 내보내는 방법은 다음과 같습니다:
```bash
optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_squad_onnx/
```
### `optimum.onnxruntime`을 사용하여 🤗 Transformers 모델을 ONNX로 내보내기 [[exporting-a-transformers-model-to-onnx-with-optimumonnxruntime]]
CLI 대신에 `optimum.onnxruntime`을 사용하여 프로그래밍 방식으로 🤗 Transformers 모델을 ONNX로 내보낼 수도 있습니다. 다음과 같이 진행하세요:
```python
>>> from optimum.onnxruntime import ORTModelForSequenceClassification
>>> from transformers import AutoTokenizer
>>> model_checkpoint = "distilbert_base_uncased_squad"
>>> save_directory = "onnx/"
>>> # Load a model from transformers and export it to ONNX
>>> ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True)
>>> tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
>>> # Save the onnx model and tokenizer
>>> ort_model.save_pretrained(save_directory)
>>> tokenizer.save_pretrained(save_directory)
```
### 지원되지 않는 아키텍처의 모델 내보내기 [[exporting-a-model-for-an-unsupported-architecture]]
현재 내보낼 수 없는 모델을 지원하기 위해 기여하려면, 먼저 [`optimum.exporters.onnx`](https://huggingface.co/docs/optimum/exporters/onnx/overview)에서 지원되는지 확인한 후 지원되지 않는 경우에는 [🤗 Optimum에 기여](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/contribute)하세요.
### `transformers.onnx`를 사용하여 모델 내보내기 [[exporting-a-model-with-transformersonnx]]
<Tip warning={true}>
`tranformers.onnx`는 더 이상 유지되지 않습니다. 위에서 설명한 대로 🤗 Optimum을 사용하여 모델을 내보내세요. 이 섹션은 향후 버전에서 제거될 예정입니다.
</Tip>
🤗 Transformers 모델을 ONNX로 내보내려면 추가 종속성을 설치하세요:
```bash
pip install transformers[onnx]
```
`transformers.onnx` 패키지를 Python 모듈로 사용하여 준비된 구성을 사용하여 체크포인트를 내보냅니다:
```bash
python -m transformers.onnx --model=distilbert/distilbert-base-uncased onnx/
```
이렇게 하면 `--model` 인수에 정의된 체크포인트의 ONNX 그래프가 내보내집니다. 🤗 Hub에서 제공하는 체크포인트나 로컬에 저장된 체크포인트를 전달할 수 있습니다. 결과로 생성된 `model.onnx` 파일은 ONNX 표준을 지원하는 많은 가속기 중 하나에서 실행할 수 있습니다. 예를 들어, 다음과 같이 ONNX Runtime을 사용하여 모델을 로드하고 실행할 수 있습니다:
```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))
```
필요한 출력 이름(예: `["last_hidden_state"]`)은 각 모델의 ONNX 구성을 확인하여 얻을 수 있습니다. 예를 들어, DistilBERT의 경우 다음과 같습니다:
```python
>>> from transformers.models.distilbert import DistilBertConfig, DistilBertOnnxConfig
>>> config = DistilBertConfig()
>>> onnx_config = DistilBertOnnxConfig(config)
>>> print(list(onnx_config.outputs.keys()))
["last_hidden_state"]
```
Hub의 TensorFlow 체크포인트에 대해서도 동일한 프로세스가 적용됩니다. 예를 들어, 다음과 같이 순수한 TensorFlow 체크포인트를 내보냅니다:
```bash
python -m transformers.onnx --model=keras-io/transformers-qa onnx/
```
로컬에 저장된 모델을 내보내려면 모델의 가중치 파일과 토크나이저 파일을 동일한 디렉토리에 저장한 다음, transformers.onnx 패키지의 --model 인수를 원하는 디렉토리로 지정하여 ONNX로 내보냅니다:
```bash
python -m transformers.onnx --model=local-pt-checkpoint onnx/
``` | transformers/docs/source/ko/serialization.md/0 | {
"file_path": "transformers/docs/source/ko/serialization.md",
"repo_id": "transformers",
"token_count": 6886
} |
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# 객체 탐지 [[object-detection]]
[[open-in-colab]]
객체 탐지는 이미지에서 인스턴스(예: 사람, 건물 또는 자동차)를 감지하는 컴퓨터 비전 작업입니다. 객체 탐지 모델은 이미지를 입력으로 받고 탐지된 바운딩 박스의 좌표와 관련된 레이블을 출력합니다.
하나의 이미지에는 여러 객체가 있을 수 있으며 각각은 자체적인 바운딩 박스와 레이블을 가질 수 있습니다(예: 차와 건물이 있는 이미지).
또한 각 객체는 이미지의 다른 부분에 존재할 수 있습니다(예: 이미지에 여러 대의 차가 있을 수 있음).
이 작업은 보행자, 도로 표지판, 신호등과 같은 것들을 감지하는 자율 주행에 일반적으로 사용됩니다.
다른 응용 분야로는 이미지 내 객체 수 계산 및 이미지 검색 등이 있습니다.
이 가이드에서 다음을 배울 것입니다:
1. 합성곱 백본(인풋 데이터의 특성을 추출하는 합성곱 네트워크)과 인코더-디코더 트랜스포머 모델을 결합한 [DETR](https://huggingface.co/docs/transformers/model_doc/detr) 모델을 [CPPE-5](https://huggingface.co/datasets/cppe-5) 데이터 세트에 대해 미세조정 하기
2. 미세조정 한 모델을 추론에 사용하기.
<Tip>
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/object-detection)를 확인하는 것이 좋습니다.
</Tip>
시작하기 전에 필요한 모든 라이브러리가 설치되어 있는지 확인하세요:
```bash
pip install -q datasets transformers evaluate timm albumentations
```
허깅페이스 허브에서 데이터 세트를 가져오기 위한 🤗 Datasets과 모델을 학습하기 위한 🤗 Transformers, 데이터를 증강하기 위한 `albumentations`를 사용합니다.
DETR 모델의 합성곱 백본을 가져오기 위해서는 현재 `timm`이 필요합니다.
커뮤니티에 모델을 업로드하고 공유할 수 있도록 Hugging Face 계정에 로그인하는 것을 권장합니다. 프롬프트가 나타나면 토큰을 입력하여 로그인하세요:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## CPPE-5 데이터 세트 가져오기 [[load-the-CPPE-5-dataset]]
[CPPE-5](https://huggingface.co/datasets/cppe-5) 데이터 세트는 COVID-19 대유행 상황에서 의료 전문인력 보호 장비(PPE)를 식별하는 어노테이션이 포함된 이미지를 담고 있습니다.
데이터 세트를 가져오세요:
```py
>>> from datasets import load_dataset
>>> cppe5 = load_dataset("cppe-5")
>>> cppe5
DatasetDict({
train: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 1000
})
test: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 29
})
})
```
이 데이터 세트는 학습 세트 이미지 1,000개와 테스트 세트 이미지 29개를 갖고 있습니다.
데이터에 익숙해지기 위해, 예시가 어떻게 구성되어 있는지 살펴보세요.
```py
>>> cppe5["train"][0]
{'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=943x663 at 0x7F9EC9E77C10>,
'width': 943,
'height': 663,
'objects': {'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]],
'category': [4, 4, 0, 0]}}
```
데이터 세트에 있는 예시는 다음의 영역을 가지고 있습니다:
- `image_id`: 예시 이미지 id
- `image`: 이미지를 포함하는 `PIL.Image.Image` 객체
- `width`: 이미지의 너비
- `height`: 이미지의 높이
- `objects`: 이미지 안의 객체들의 바운딩 박스 메타데이터를 포함하는 딕셔너리:
- `id`: 어노테이션 id
- `area`: 바운딩 박스의 면적
- `bbox`: 객체의 바운딩 박스 ([COCO 포맷](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco)으로)
- `category`: 객체의 카테고리, 가능한 값으로는 `Coverall (0)`, `Face_Shield (1)`, `Gloves (2)`, `Goggles (3)` 및 `Mask (4)` 가 포함됩니다.
`bbox` 필드가 DETR 모델이 요구하는 COCO 형식을 따른다는 것을 알 수 있습니다.
그러나 `objects` 내부의 필드 그룹은 DETR이 요구하는 어노테이션 형식과 다릅니다. 따라서 이 데이터를 학습에 사용하기 전에 전처리를 적용해야 합니다.
데이터를 더 잘 이해하기 위해서 데이터 세트에서 한 가지 예시를 시각화하세요.
```py
>>> import numpy as np
>>> import os
>>> from PIL import Image, ImageDraw
>>> image = cppe5["train"][0]["image"]
>>> annotations = cppe5["train"][0]["objects"]
>>> draw = ImageDraw.Draw(image)
>>> categories = cppe5["train"].features["objects"].feature["category"].names
>>> id2label = {index: x for index, x in enumerate(categories, start=0)}
>>> label2id = {v: k for k, v in id2label.items()}
>>> for i in range(len(annotations["id"])):
... box = annotations["bbox"][i - 1]
... class_idx = annotations["category"][i - 1]
... x, y, w, h = tuple(box)
... draw.rectangle((x, y, x + w, y + h), outline="red", width=1)
... draw.text((x, y), id2label[class_idx], fill="white")
>>> image
```
<div class="flex justify-center">
<img src="https://i.imgur.com/TdaqPJO.png" alt="CPPE-5 Image Example"/>
</div>
바운딩 박스와 연결된 레이블을 시각화하려면 데이터 세트의 메타 데이터, 특히 `category` 필드에서 레이블을 가져와야 합니다.
또한 레이블 ID를 레이블 클래스에 매핑하는 `id2label`과 반대로 매핑하는 `label2id` 딕셔너리를 만들어야 합니다.
모델을 설정할 때 이러한 매핑을 사용할 수 있습니다. 이러한 매핑은 허깅페이스 허브에서 모델을 공유했을 때 다른 사람들이 재사용할 수 있습니다.
데이터를 더 잘 이해하기 위한 최종 단계로, 잠재적인 문제를 찾아보세요.
객체 감지를 위한 데이터 세트에서 자주 발생하는 문제 중 하나는 바운딩 박스가 이미지의 가장자리를 넘어가는 것입니다.
이러한 바운딩 박스를 "넘어가는 것(run away)"은 훈련 중에 오류를 발생시킬 수 있기에 이 단계에서 처리해야 합니다.
이 데이터 세트에도 같은 문제가 있는 몇 가지 예가 있습니다. 이 가이드에서는 간단하게하기 위해 데이터에서 이러한 이미지를 제거합니다.
```py
>>> remove_idx = [590, 821, 822, 875, 876, 878, 879]
>>> keep = [i for i in range(len(cppe5["train"])) if i not in remove_idx]
>>> cppe5["train"] = cppe5["train"].select(keep)
```
## 데이터 전처리하기 [[preprocess-the-data]]
모델을 미세 조정 하려면, 미리 학습된 모델에서 사용한 전처리 방식과 정확하게 일치하도록 사용할 데이터를 전처리해야 합니다.
[`AutoImageProcessor`]는 이미지 데이터를 처리하여 DETR 모델이 학습에 사용할 수 있는 `pixel_values`, `pixel_mask`, 그리고 `labels`를 생성하는 작업을 담당합니다.
이 이미지 프로세서에는 걱정하지 않아도 되는 몇 가지 속성이 있습니다:
- `image_mean = [0.485, 0.456, 0.406 ]`
- `image_std = [0.229, 0.224, 0.225]`
이 값들은 모델 사전 훈련 중 이미지를 정규화하는 데 사용되는 평균과 표준 편차입니다.
이 값들은 추론 또는 사전 훈련된 이미지 모델을 세밀하게 조정할 때 복제해야 하는 중요한 값입니다.
사전 훈련된 모델과 동일한 체크포인트에서 이미지 프로세서를 인스턴스화합니다.
```py
>>> from transformers import AutoImageProcessor
>>> checkpoint = "facebook/detr-resnet-50"
>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
```
`image_processor`에 이미지를 전달하기 전에, 데이터 세트에 두 가지 전처리를 적용해야 합니다:
- 이미지 증강
- DETR 모델의 요구에 맞게 어노테이션을 다시 포맷팅
첫째로, 모델이 학습 데이터에 과적합 되지 않도록 데이터 증강 라이브러리 중 아무거나 사용하여 변환을 적용할 수 있습니다. 여기에서는 [Albumentations](https://albumentations.ai/docs/) 라이브러리를 사용합니다...
이 라이브러리는 변환을 이미지에 적용하고 바운딩 박스를 적절하게 업데이트하도록 보장합니다.
🤗 Datasets 라이브러리 문서에는 [객체 탐지를 위해 이미지를 보강하는 방법에 대한 자세한 가이드](https://huggingface.co/docs/datasets/object_detection)가 있으며,
이 예제와 정확히 동일한 데이터 세트를 사용합니다. 여기서는 각 이미지를 (480, 480) 크기로 조정하고, 좌우로 뒤집고, 밝기를 높이는 동일한 접근법을 적용합니다:
```py
>>> import albumentations
>>> import numpy as np
>>> import torch
>>> transform = albumentations.Compose(
... [
... albumentations.Resize(480, 480),
... albumentations.HorizontalFlip(p=1.0),
... albumentations.RandomBrightnessContrast(p=1.0),
... ],
... bbox_params=albumentations.BboxParams(format="coco", label_fields=["category"]),
... )
```
이미지 프로세서는 어노테이션이 다음과 같은 형식일 것으로 예상합니다: `{'image_id': int, 'annotations': List[Dict]}`, 여기서 각 딕셔너리는 COCO 객체 어노테이션입니다. 단일 예제에 대해 어노테이션의 형식을 다시 지정하는 함수를 추가해 보겠습니다:
```py
>>> def formatted_anns(image_id, category, area, bbox):
... annotations = []
... for i in range(0, len(category)):
... new_ann = {
... "image_id": image_id,
... "category_id": category[i],
... "isCrowd": 0,
... "area": area[i],
... "bbox": list(bbox[i]),
... }
... annotations.append(new_ann)
... return annotations
```
이제 이미지와 어노테이션 전처리 변환을 결합하여 예제 배치에 사용할 수 있습니다:
```py
>>> # transforming a batch
>>> def transform_aug_ann(examples):
... image_ids = examples["image_id"]
... images, bboxes, area, categories = [], [], [], []
... for image, objects in zip(examples["image"], examples["objects"]):
... image = np.array(image.convert("RGB"))[:, :, ::-1]
... out = transform(image=image, bboxes=objects["bbox"], category=objects["category"])
... area.append(objects["area"])
... images.append(out["image"])
... bboxes.append(out["bboxes"])
... categories.append(out["category"])
... targets = [
... {"image_id": id_, "annotations": formatted_anns(id_, cat_, ar_, box_)}
... for id_, cat_, ar_, box_ in zip(image_ids, categories, area, bboxes)
... ]
... return image_processor(images=images, annotations=targets, return_tensors="pt")
```
이전 단계에서 만든 전처리 함수를 🤗 Datasets의 [`~datasets.Dataset.with_transform`] 메소드를 사용하여 데이터 세트 전체에 적용합니다.
이 메소드는 데이터 세트의 요소를 가져올 때마다 전처리 함수를 적용합니다.
이 시점에서는 전처리 후 데이터 세트에서 예시 하나를 가져와서 변환 후 모양이 어떻게 되는지 확인해 볼 수 있습니다.
이때, `pixel_values` 텐서, `pixel_mask` 텐서, 그리고 `labels`로 구성된 텐서가 있어야 합니다.
```py
>>> cppe5["train"] = cppe5["train"].with_transform(transform_aug_ann)
>>> cppe5["train"][15]
{'pixel_values': tensor([[[ 0.9132, 0.9132, 0.9132, ..., -1.9809, -1.9809, -1.9809],
[ 0.9132, 0.9132, 0.9132, ..., -1.9809, -1.9809, -1.9809],
[ 0.9132, 0.9132, 0.9132, ..., -1.9638, -1.9638, -1.9638],
...,
[-1.5699, -1.5699, -1.5699, ..., -1.9980, -1.9980, -1.9980],
[-1.5528, -1.5528, -1.5528, ..., -1.9980, -1.9809, -1.9809],
[-1.5528, -1.5528, -1.5528, ..., -1.9980, -1.9809, -1.9809]],
[[ 1.3081, 1.3081, 1.3081, ..., -1.8431, -1.8431, -1.8431],
[ 1.3081, 1.3081, 1.3081, ..., -1.8431, -1.8431, -1.8431],
[ 1.3081, 1.3081, 1.3081, ..., -1.8256, -1.8256, -1.8256],
...,
[-1.3179, -1.3179, -1.3179, ..., -1.8606, -1.8606, -1.8606],
[-1.3004, -1.3004, -1.3004, ..., -1.8606, -1.8431, -1.8431],
[-1.3004, -1.3004, -1.3004, ..., -1.8606, -1.8431, -1.8431]],
[[ 1.4200, 1.4200, 1.4200, ..., -1.6476, -1.6476, -1.6476],
[ 1.4200, 1.4200, 1.4200, ..., -1.6476, -1.6476, -1.6476],
[ 1.4200, 1.4200, 1.4200, ..., -1.6302, -1.6302, -1.6302],
...,
[-1.0201, -1.0201, -1.0201, ..., -1.5604, -1.5604, -1.5604],
[-1.0027, -1.0027, -1.0027, ..., -1.5604, -1.5430, -1.5430],
[-1.0027, -1.0027, -1.0027, ..., -1.5604, -1.5430, -1.5430]]]),
'pixel_mask': tensor([[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],
[1, 1, 1, ..., 1, 1, 1]]),
'labels': {'size': tensor([800, 800]), 'image_id': tensor([756]), 'class_labels': tensor([4]), 'boxes': tensor([[0.7340, 0.6986, 0.3414, 0.5944]]), 'area': tensor([519544.4375]), 'iscrowd': tensor([0]), 'orig_size': tensor([480, 480])}}
```
각각의 이미지를 성공적으로 증강하고 이미지의 어노테이션을 준비했습니다.
그러나 전처리는 아직 끝나지 않았습니다. 마지막 단계로, 이미지를 배치로 만들 사용자 정의 `collate_fn`을 생성합니다.
해당 배치에서 가장 큰 이미지에 이미지(현재 `pixel_values` 인)를 패드하고, 실제 픽셀(1)과 패딩(0)을 나타내기 위해 그에 해당하는 새로운 `pixel_mask`를 생성해야 합니다.
```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
```
## DETR 모델 학습시키기 [[training-the-DETR-model]]
이전 섹션에서 대부분의 작업을 수행하여 이제 모델을 학습할 준비가 되었습니다!
이 데이터 세트의 이미지는 리사이즈 후에도 여전히 용량이 크기 때문에, 이 모델을 미세 조정 하려면 적어도 하나의 GPU가 필요합니다.
학습은 다음의 단계를 수행합니다:
1. [`AutoModelForObjectDetection`]을 사용하여 전처리와 동일한 체크포인트를 사용하여 모델을 가져옵니다.
2. [`TrainingArguments`]에서 학습 하이퍼파라미터를 정의합니다.
3. 모델, 데이터 세트, 이미지 프로세서 및 데이터 콜레이터와 함께 [`Trainer`]에 훈련 인수를 전달합니다.
4. [`~Trainer.train`]를 호출하여 모델을 미세 조정 합니다.
전처리에 사용한 체크포인트와 동일한 체크포인트에서 모델을 가져올 때, 데이터 세트의 메타데이터에서 만든 `label2id`와 `id2label` 매핑을 전달해야 합니다.
또한, `ignore_mismatched_sizes=True`를 지정하여 기존 분류 헤드(모델에서 분류에 사용되는 마지막 레이어)를 새 분류 헤드로 대체합니다.
```py
>>> from transformers import AutoModelForObjectDetection
>>> model = AutoModelForObjectDetection.from_pretrained(
... checkpoint,
... id2label=id2label,
... label2id=label2id,
... ignore_mismatched_sizes=True,
... )
```
[`TrainingArguments`]에서 `output_dir`을 사용하여 모델을 저장할 위치를 지정한 다음, 필요에 따라 하이퍼파라미터를 구성하세요.
사용하지 않는 열을 제거하지 않도록 주의해야 합니다. 만약 `remove_unused_columns`가 `True`일 경우 이미지 열이 삭제됩니다.
이미지 열이 없는 경우 `pixel_values`를 생성할 수 없기 때문에 `remove_unused_columns`를 `False`로 설정해야 합니다.
모델을 Hub에 업로드하여 공유하려면 `push_to_hub`를 `True`로 설정하십시오(허깅페이스에 로그인하여 모델을 업로드해야 합니다).
```py
>>> from transformers import TrainingArguments
>>> training_args = TrainingArguments(
... output_dir="detr-resnet-50_finetuned_cppe5",
... per_device_train_batch_size=8,
... num_train_epochs=10,
... fp16=True,
... save_steps=200,
... logging_steps=50,
... learning_rate=1e-5,
... weight_decay=1e-4,
... save_total_limit=2,
... remove_unused_columns=False,
... push_to_hub=True,
... )
```
마지막으로 `model`, `training_args`, `collate_fn`, `image_processor`와 데이터 세트(`cppe5`)를 모두 가져온 후, [`~transformers.Trainer.train`]를 호출합니다.
```py
>>> from transformers import Trainer
>>> trainer = Trainer(
... model=model,
... args=training_args,
... data_collator=collate_fn,
... train_dataset=cppe5["train"],
... processing_class=image_processor,
... )
>>> trainer.train()
```
`training_args`에서 `push_to_hub`를 `True`로 설정한 경우, 학습 체크포인트는 허깅페이스 허브에 업로드됩니다.
학습 완료 후, [`~transformers.Trainer.push_to_hub`] 메소드를 호출하여 최종 모델을 허깅페이스 허브에 업로드합니다.
```py
>>> trainer.push_to_hub()
```
## 평가하기 [[evaluate]]
객체 탐지 모델은 일반적으로 일련의 <a href="https://cocodataset.org/#detection-eval">COCO-스타일 지표</a>로 평가됩니다.
기존에 구현된 평가 지표 중 하나를 사용할 수도 있지만, 여기에서는 허깅페이스 허브에 푸시한 최종 모델을 평가하는 데 `torchvision`에서 제공하는 평가 지표를 사용합니다.
`torchvision` 평가자(evaluator)를 사용하려면 실측값인 COCO 데이터 세트를 준비해야 합니다.
COCO 데이터 세트를 빌드하는 API는 데이터를 특정 형식으로 저장해야 하므로, 먼저 이미지와 어노테이션을 디스크에 저장해야 합니다.
학습을 위해 데이터를 준비할 때와 마찬가지로, cppe5["test"]에서의 어노테이션은 포맷을 맞춰야 합니다. 그러나 이미지는 그대로 유지해야 합니다.
평가 단계는 약간의 작업이 필요하지만, 크게 세 가지 주요 단계로 나눌 수 있습니다.
먼저, `cppe5["test"]` 세트를 준비합니다: 어노테이션을 포맷에 맞게 만들고 데이터를 디스크에 저장합니다.
```py
>>> import json
>>> # format annotations the same as for training, no need for data augmentation
>>> def val_formatted_anns(image_id, objects):
... annotations = []
... for i in range(0, len(objects["id"])):
... new_ann = {
... "id": objects["id"][i],
... "category_id": objects["category"][i],
... "iscrowd": 0,
... "image_id": image_id,
... "area": objects["area"][i],
... "bbox": objects["bbox"][i],
... }
... annotations.append(new_ann)
... return annotations
>>> # Save images and annotations into the files torchvision.datasets.CocoDetection expects
>>> def save_cppe5_annotation_file_images(cppe5):
... output_json = {}
... path_output_cppe5 = f"{os.getcwd()}/cppe5/"
... if not os.path.exists(path_output_cppe5):
... os.makedirs(path_output_cppe5)
... path_anno = os.path.join(path_output_cppe5, "cppe5_ann.json")
... categories_json = [{"supercategory": "none", "id": id, "name": id2label[id]} for id in id2label]
... output_json["images"] = []
... output_json["annotations"] = []
... for example in cppe5:
... ann = val_formatted_anns(example["image_id"], example["objects"])
... output_json["images"].append(
... {
... "id": example["image_id"],
... "width": example["image"].width,
... "height": example["image"].height,
... "file_name": f"{example['image_id']}.png",
... }
... )
... output_json["annotations"].extend(ann)
... output_json["categories"] = categories_json
... with open(path_anno, "w") as file:
... json.dump(output_json, file, ensure_ascii=False, indent=4)
... for im, img_id in zip(cppe5["image"], cppe5["image_id"]):
... path_img = os.path.join(path_output_cppe5, f"{img_id}.png")
... im.save(path_img)
... return path_output_cppe5, path_anno
```
다음으로, `cocoevaluator`와 함께 사용할 수 있는 `CocoDetection` 클래스의 인스턴스를 준비합니다.
```py
>>> import torchvision
>>> class CocoDetection(torchvision.datasets.CocoDetection):
... def __init__(self, img_folder, image_processor, ann_file):
... super().__init__(img_folder, ann_file)
... self.image_processor = image_processor
... def __getitem__(self, idx):
... # read in PIL image and target in COCO format
... img, target = super(CocoDetection, self).__getitem__(idx)
... # preprocess image and target: converting target to DETR format,
... # resizing + normalization of both image and target)
... image_id = self.ids[idx]
... target = {"image_id": image_id, "annotations": target}
... encoding = self.image_processor(images=img, annotations=target, return_tensors="pt")
... pixel_values = encoding["pixel_values"].squeeze() # remove batch dimension
... target = encoding["labels"][0] # remove batch dimension
... return {"pixel_values": pixel_values, "labels": target}
>>> im_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"])
>>> test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno)
```
마지막으로, 평가 지표를 가져와서 평가를 실행합니다.
```py
>>> import evaluate
>>> from tqdm import tqdm
>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco)
>>> val_dataloader = torch.utils.data.DataLoader(
... test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn
... )
>>> with torch.no_grad():
... for idx, batch in enumerate(tqdm(val_dataloader)):
... pixel_values = batch["pixel_values"]
... pixel_mask = batch["pixel_mask"]
... labels = [
... {k: v for k, v in t.items()} for t in batch["labels"]
... ] # these are in DETR format, resized + normalized
... # forward pass
... outputs = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
... orig_target_sizes = torch.stack([target["orig_size"] for target in labels], dim=0)
... results = im_processor.post_process(outputs, orig_target_sizes) # convert outputs of model to Pascal VOC format (xmin, ymin, xmax, ymax)
... module.add(prediction=results, reference=labels)
... del batch
>>> results = module.compute()
>>> print(results)
Accumulating evaluation results...
DONE (t=0.08s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.681
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.208
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.274
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.484
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590
```
이러한 결과는 [`~transformers.TrainingArguments`]의 하이퍼파라미터를 조정하여 더욱 개선될 수 있습니다. 한번 시도해 보세요!
## 추론하기 [[inference]]
DETR 모델을 미세 조정 및 평가하고, 허깅페이스 허브에 업로드 했으므로 추론에 사용할 수 있습니다.
미세 조정된 모델을 추론에 사용하는 가장 간단한 방법은 [`pipeline`]에서 모델을 사용하는 것입니다.
모델과 함께 객체 탐지를 위한 파이프라인을 인스턴스화하고, 이미지를 전달하세요:
```py
>>> from transformers import pipeline
>>> import requests
>>> url = "https://i.imgur.com/2lnWoly.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> obj_detector = pipeline("object-detection", model="devonho/detr-resnet-50_finetuned_cppe5")
>>> obj_detector(image)
```
만약 원한다면 수동으로 `pipeline`의 결과를 재현할 수 있습니다:
```py
>>> image_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
>>> with torch.no_grad():
... inputs = image_processor(images=image, return_tensors="pt")
... outputs = model(**inputs)
... target_sizes = torch.tensor([image.size[::-1]])
... results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
... box = [round(i, 2) for i in box.tolist()]
... print(
... f"Detected {model.config.id2label[label.item()]} with confidence "
... f"{round(score.item(), 3)} at location {box}"
... )
Detected Coverall with confidence 0.566 at location [1215.32, 147.38, 4401.81, 3227.08]
Detected Mask with confidence 0.584 at location [2449.06, 823.19, 3256.43, 1413.9]
```
결과를 시각화하겠습니다:
```py
>>> draw = ImageDraw.Draw(image)
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
... box = [round(i, 2) for i in box.tolist()]
... x, y, x2, y2 = tuple(box)
... draw.rectangle((x, y, x2, y2), outline="red", width=1)
... draw.text((x, y), model.config.id2label[label.item()], fill="white")
>>> image
```
<div class="flex justify-center">
<img src="https://i.imgur.com/4QZnf9A.png" alt="Object detection result on a new image"/>
</div>
| transformers/docs/source/ko/tasks/object_detection.md/0 | {
"file_path": "transformers/docs/source/ko/tasks/object_detection.md",
"repo_id": "transformers",
"token_count": 16832
} |
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# 토크나이저 요약[[summary-of-the-tokenizers]]
[[open-in-colab]]
이 페이지에서는 토큰화에 대해 자세히 살펴보겠습니다.
<Youtube id="VFp38yj8h3A"/>
[데이터 전처리하기 튜토리얼](preprocessing)에서 살펴본 것처럼, 텍스트를 토큰화하는 것은 텍스트를 단어 또는 서브워드로 분할하고 룩업 테이블을 통해 id로 변환하는 과정입니다.
단어 또는 서브워드를 id로 변환하는 것은 간단하기 때문에 이번 문서에서는 텍스트를 단어 또는 서브워드로 쪼개는 것(즉, 텍스트를 토큰화하는 것)에 중점을 두겠습니다.
구체적으로, 🤗 Transformers에서 사용되는 세 가지 주요 토큰화 유형인 [Byte-Pair Encoding (BPE)](#byte-pair-encoding), [WordPiece](#wordpiece), [SentencePiece](#sentencepiece)를 살펴보고 어떤 모델에서 어떤 토큰화 유형을 사용하는지 예시를 보여드리겠습니다.
각 모델 페이지에 연결된 토크나이저의 문서를 보면 사전 훈련 모델에서 어떤 토크나이저를 사용했는지 알 수 있습니다.
예를 들어, [`BertTokenizer`]를 보면 이 모델이 [WordPiece](#wordpiece)를 사용하는 것을 알 수 있습니다.
## 개요[[introduction]]
텍스트를 작은 묶음(chunk)으로 쪼개는 것은 보기보다 어려운 작업이며, 여러 가지 방법이 있습니다.
예를 들어, `"Don't you love 🤗 Transformers? We sure do."` 라는 문장을 살펴보도록 하겠습니다.
<Youtube id="nhJxYji1aho"/>
위 문장을 토큰화하는 간단한 방법은 공백을 기준으로 쪼개는 것입니다.
토큰화된 결과는 다음과 같습니다:
```
["Don't", "you", "love", "🤗", "Transformers?", "We", "sure", "do."]
```
이는 첫 번째 결과로는 합리적이지만, `"Transformers?"`와 `"do."`토큰을 보면 각각 `"Transformer"`와 `"do"`에 구두점이 붙어있는 것을 확인할 수 있습니다.
구두점을 고려해야 모델이 단어의 다른 표현과 그 뒤에 올 수 있는 모든 가능한 구두점을 학습할 필요가 없습니다. 그렇지 않으면 모델이 학습해야 하는 표현의 수가 폭발적으로 증가하게 됩니다.
구두점을 고려한 토큰화 결과는 다음과 같습니다:
```
["Don", "'", "t", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
```
이전보다 나아졌습니다. 하지만, `"Don't"`의 토큰화 결과도 수정이 필요합니다.
`"Don't"`는 `"do not"`의 줄임말이기 때문에 `["Do", "n't"]`로 토큰화되는 것이 좋습니다.
여기서부터 복잡해지기 시작합니다. 그리고 이 점이 각 모델마다 고유한 토큰화 유형이 존재하는 이유 중 하나입니다.
텍스트를 토큰화하는 데 적용하는 규칙에 따라 동일한 텍스트에 대해 토큰화된 결과가 달라집니다.
사전 훈련된 모델은 훈련 데이터를 토큰화하는 데 사용된 것과 동일한 규칙으로 토큰화된 입력을 제공해야만 제대로 작동합니다.
[spaCy](https://spacy.io/)와 [Moses](http://www.statmt.org/moses/?n=Development.GetStarted)는 유명한 규칙 기반 토크나이저입니다. 예제에 *spaCy*와 *Moses* 를 적용한 결과는 다음과 같습니다:
```
["Do", "n't", "you", "love", "🤗", "Transformers", "?", "We", "sure", "do", "."]
```
보시다시피 공백 및 구두점 토큰화와 규칙 기반 토큰화가 사용됩니다.
공백 및 구두점, 규칙 기반 토큰화은 모두 단어 문장을 단어로 쪼개는 단어 토큰화에 해당합니다.
이 토큰화 방법은 텍스트를 더 작은 묶음(chunk)로 분할하는 가장 직관적인 방법이지만, 대규모 텍스트 말뭉치에 대해서는 문제가 발생할 수 있습니다.
이 경우 공백 및 구두점 토큰화는 일반적으로 매우 큰 어휘(사용된 모든 고유 단어와 토큰 집합)을 생성합니다.
*예를 들어*, [Transformer XL](model_doc/transformerxl)은 공백 및 구두점 토큰화를 사용해 어휘(vocabulary) 크기가 267,735입니다!
어휘 크기가 크면 모델에 입력 및 출력 레이어로 엄청난 임베딩 행렬이 필요하므로 메모리와 시간 복잡성이 모두 증가합니다.
일반적으로 트랜스포머 모델은 어휘 크기가 50,000개를 넘는 경우가 드물며, 특히 단일 언어에 대해서만 사전 훈련된 경우에는 더욱 그렇습니다.
단순한 공백과 구두점 토큰화가 만족스럽지 않다면 단순히 문자를 토큰화하면 어떨까요?
<Youtube id="ssLq_EK2jLE"/>
문자 토큰화는 아주 간단하고 메모리와 시간 복잡도를 크게 줄일 수 있지만, 모델이 의미 있는 입력 표현을 학습하기에는 훨씬 더 어렵습니다.
*예를 들어*, 문자 `"t"`에 대한 의미 있는 문맥 독립적 표현을 배우는 것 보다 단어 `"today"`에 대한 의미 있는 문맥 독립적 표현을 배우는 것이 훨씬 더 어렵습니다.
문자 토큰화는 종종 성능 저하를 동반하기 때문에 두 가지 장점을 모두 얻기 위해 트랜스포머 모델은 **서브워드** 토큰화라고 하는 단어 수준과 문자 수준 토큰화의 하이브리드를 사용합니다.
## 서브워드 토큰화[[subword-tokenization]]
<Youtube id="zHvTiHr506c"/>
서브워드 토큰화 알고리즘은 자주 사용되는 단어는 더 작은 하위 단어로 쪼개고, 드문 단어는 의미 있는 하위 단어로 분해되어야 한다는 원칙에 따라 작동합니다.
예를 들어 `"annoyingly"`는 드문 단어로 간주되어 `"annoying"`과 `"ly"`로 분해될 수 있습니다.
`"annoyingly"`가 `"annoying"`과 `"ly"`의 합성어인 반면, `"annoying"`과 `"ly"` 둘 다 독립적인 서브워드로 자주 등장합니다.
이는 터키어와 같은 응집성 언어에서 특히 유용하며, 서브워드를 묶어 임의로 긴 복합 단어를 만들 수 있습니다.
서브워드 토큰화를 사용하면 모델이 의미 있는 문맥 독립적 표현을 학습하면서 합리적인 어휘 크기를 가질 수 있습니다.
또한, 서브워드 토큰화를 통해 모델은 이전에 본 적이 없는 단어를 알려진 서브워드로 분해하여 처리할 수 있습니다.
예를 들어, [`~transformers.BertTokenizer`]는 `"I have a new GPU!"` 라는 문장을 아래와 같이 토큰화합니다:
```py
>>> from transformers import BertTokenizer
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> tokenizer.tokenize("I have a new GPU!")
["i", "have", "a", "new", "gp", "##u", "!"]
```
대소문자가 없는 모델을 사용해 문장의 시작이 소문자로 표기되었습니다.
단어 `["i", "have", "a", "new"]`는 토크나이저의 어휘에 속하지만, `"gpu"`는 속하지 않는 것을 확인할 수 있습니다.
결과적으로 토크나이저는 `"gpu"`를 알려진 두 개의 서브워드로 쪼갭니다: `["gp" and "##u"]`.
`"##"`은 토큰의 나머지 부분이 공백 없이 이전 토큰에 연결되어야(attach) 함을 의미합니다(토큰화 디코딩 또는 역전을 위해).
또 다른 예로, [`~transformers.XLNetTokenizer`]는 이전에 예시 문장을 다음과 같이 토큰화합니다:
```py
>>> from transformers import XLNetTokenizer
>>> tokenizer = XLNetTokenizer.from_pretrained("xlnet/xlnet-base-cased")
>>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
["▁Don", "'", "t", "▁you", "▁love", "▁", "🤗", "▁", "Transform", "ers", "?", "▁We", "▁sure", "▁do", "."]
```
`"▁"`가 가지는 의미는 [SentencePiece](#sentencepiece)에서 다시 살펴보도록 하겠습니다.
보다시피 `"Transformers"` 라는 드문 단어는 서브워드 `"Transform"`와 `"ers"`로 쪼개집니다.
이제 다양한 하위 단어 토큰화 알고리즘이 어떻게 작동하는지 살펴보겠습니다.
이러한 토큰화 알고리즘은 일반적으로 해당 모델이 학습되는 말뭉치에 대해 수행되는 어떤 형태의 학습에 의존한다는 점에 유의하세요.
<a id='byte-pair-encoding'></a>
### 바이트 페어 인코딩 (Byte-Pair Encoding, BPE)[[bytepair-encoding-bpe]]
바이트 페어 인코딩(BPE)은 [Neural Machine Translation of Rare Words with Subword Units (Sennrich et
al., 2015)](https://arxiv.org/abs/1508.07909) 에서 소개되었습니다.
BPE는 훈련 데이터를 단어로 분할하는 사전 토크나이저(pre-tokenizer)에 의존합니다.
사전 토큰화(Pretokenization)에는 [GPT-2](model_doc/gpt2), [Roberta](model_doc/roberta)와 같은 간단한 공백 토큰화가 있습니다.
복잡한 사전 토큰화에는 규칙 기반 토큰화가 해당하는데, 훈련 말뭉치에서 각 단어의 빈도를 계산하기 위해 사용합니다.
[XLM](model_doc/xlm), 대부분의 언어에서 Moses를 사용하는 [FlauBERT](model_doc/flaubert), Spacy와 ftfy를 사용하는 [GPT](model_doc/gpt)가 해당합니다.
사전 토큰화 이후에, 고유 단어 집합가 생성되고 훈련 데이터에서 각 단어가 등장하는 빈도가 결정됩니다.
다음으로, BPE는 고유 단어 집합에 나타나는 모든 기호로 구성된 기본 어휘를 생성하고 기본 어휘의 두 기호에서 새로운 기호를 형성하는 병합 규칙을 학습합니다.
어휘가 원하는 어휘 크기에 도달할 때까지 위의 과정을 반복합니다.
어휘 크기는 토크나이저를 훈련시키기 전에 정의해야 하는 하이퍼파라미터라는 점을 유의하세요.
예를 들어, 사전 토큰화 후 빈도를 포함한 다음과 같은 어휘 집합이 결정되었다고 가정해 보겠습니다:
```
("hug", 10), ("pug", 5), ("pun", 12), ("bun", 4), ("hugs", 5)
```
결과적으로 기본 어휘는 `["b", "g", "h", "n", "p", "s", "u"]` 이고, 각 단어를 기본 어휘에 속하는 기호로 쪼개면 아래와 같습니다:
```
("h" "u" "g", 10), ("p" "u" "g", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "u" "g" "s", 5)
```
그런 다음 BPE는 가능한 각 기호 쌍의 빈도를 계산하여 가장 자주 발생하는 기호 쌍을 선택합니다.
위의 예시에서 `"h"` 뒤에 오는 `"u"`는 _10 + 5 = 15_ 번 등장합니다. (`"hug"`에서 10번, `"hugs"`에서 5번 등장)
하지만, 가장 등장 빈도가 높은 기호 쌍은 `"u"` 뒤에 오는 `"g"`입니다. _10 + 5 + 5 = 20_ 으로 총 20번 등장합니다.
따라서 토크나이저가 병합하는 가장 첫 번째 쌍은 `"u"` 뒤에 오는 `"g"`입니다. `"ug"`가 어휘에 추가되어 어휘는 다음과 같습니다:
```
("h" "ug", 10), ("p" "ug", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "ug" "s", 5)
```
BPE는 다음으로 가장 많이 등장하는 기호 쌍을 식별합니다.
`"u"` 뒤에 오는 `"n"`은 16번 등장해 `"un"` 으로 병합되어 어휘에 추가됩니다.
그 다음으로 빈도수가 놓은 기호 쌍은 `"h"` 뒤에 오는 `"ug"`로 15번 등장합니다.
다시 한 번 `"hug"`로 병합되어 어휘에 추가됩니다.
현재 단계에서 어휘는 `["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"]` 이고, 고유 단어 집합은 다음과 같습니다:
```
("hug", 10), ("p" "ug", 5), ("p" "un", 12), ("b" "un", 4), ("hug" "s", 5)
```
이 시점에서 바이트 페어 인코딩 훈련이 중단된다고 가정하면, 훈련된 병합 규칙은 새로운 단어에 적용됩니다(기본 어휘에 포함된 기호가 새로운 단어에 포함되지 않는 한).
예를 들어, 단어 `"bug"`는 `["b", "ug"]`로 토큰화되지만, `"m"`이 기본 어휘에 없기 때문에 `"mug"`는 `["<unk>", "ug"]`로 토큰화될 것입니다.
훈련 데이터에는 단일 문자가 최소한 한 번 등장하기 때문에 일반적으로 `"m"`과 같은 단일 문자는 `"<unk>"` 기호로 대체되지 않지만, 이모티콘과 같은 특별한 문자인 경우에는 대체될 수 있습니다.
이전에 언급했듯이 어휘 크기(즉 기본 어휘 크기 + 병합 횟수)는 선택해야하는 하이퍼파라미터입니다.
예를 들어 [GPT](model_doc/gpt)의 기본 어휘 크기는 478, 40,000번의 병합 이후에 훈련을 종료하기 때문에 어휘 크기가 40,478입니다.
#### 바이트 수준 BPE (Byte-level BPE)[[bytelevel-bpe]]
가능한 모든 기본 문자를 포함하는 기본 어휘의 크기는 굉장히 커질 수 있습니다. (예: 모든 유니코드 문자를 기본 문자로 간주하는 경우)
더 나은 기본 어휘를 갖도록 [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)는 기본 어휘로 바이트(bytes)를 사용합니다.
이 방식은 모든 기본 문자가 어휘에 포함되도록 하면서 기본 어휘의 크기를 256으로 제한합니다.
구두점을 다루는 추가적인 규칙을 사용해 GPT2 토크나이저는 모든 텍스트를 <unk> 기호 없이 토큰화할 수 있습니다.
[GPT-2](model_doc/gpt)의 어휘 크기는 50,257로 256 바이트 크기의 기본 토큰, 특별한 end-of-text 토큰과 50,000번의 병합으로 학습한 기호로 구성됩니다.
<a id='wordpiece'></a>
### 워드피스 (WordPiece)[[wordpiece]]
워드피스는 [BERT](model_doc/bert), [DistilBERT](model_doc/distilbert), [Electra](model_doc/electra)에 사용된 서브워드 토큰화 알고리즘입니다.
이 알고리즘은 [Japanese and Korean Voice Search (Schuster et al., 2012)](https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/37842.pdf)에서 소개되었고, BPE와 굉장히 유사합니다.
워드피스는 훈련 데이터에 등장하는 모든 문자로 기본 어휘를 초기화한 후, 주어진 병합 규칙에 따라 점진적으로 학습합니다.
BPE와는 대조적으로 워드피스는 가장 빈도수가 높은 기호 쌍을 선택하지 않고, 어휘에 추가되었을 때 훈련 데이터의 우도가 최대화되는 쌍을 선택합니다.
정확히 무슨 의미일까요?
이전 예시를 참조하면, 훈련 데이터의 우도 값을 최대화하는 것은 모든 기호 쌍 중에서 첫 번째 기호와 두 번째 기호의 확률로 나눈 확률이 가장 큰 기호 쌍을 찾는 것과 동일합니다.
예를 들어 `"ug"`의 확률이 `"u"`와 `"g"` 각각으로 쪼개졌을 때 보다 높아야 `"u"` 뒤에 오는 `"g"`는 병합될 것입니다.
직관적으로 워드피스는 두 기호를 병합하여 _잃는_ 것을 평가하여 그만한 _가치_가 있는지 확인한다는 점에서 BPE와 약간 다릅니다.
<a id='unigram'></a>
### 유니그램 (Unigram)[[unigram]]
유니그램은 [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (Kudo, 2018)](https://arxiv.org/pdf/1804.10959.pdf)에서 제안된 서브워드 토큰화 알고리즘입니다.
BPE나 워드피스와 달리 유니그램은 기본 어휘를 많은 수의 기호로 초기화한 후 각 기호를 점진적으로 줄여 더 작은 어휘를 얻습니다.
예를 들어 기본 어휘는 모든 사전 토큰화된 단어와 가장 일반적인 하위 문자열에 해당할 수 있습니다.
유니그램은 transformers 모델에서 직접적으로 사용되지는 않지만, [SentencePiece](#sentencepiece)와 함께 사용됩니다.
각 훈련 단계에서 유니그램 알고리즘은 현재 어휘와 유니그램 언어 모델이 주어졌을 때 훈련 데이터에 대한 손실(흔히 로그 우도로 정의됨)을 정의합니다.
그런 다음 어휘의 각 기호에 대해 알고리즘은 해당 기호를 어휘에서 제거할 경우 전체 손실이 얼마나 증가할지 계산합니다.
이후에 유니그램은 손실 증가율이 가장 낮은 기호의 p(보통 10% 또는 20%) 퍼센트를 제거합니다. (제거되는 기호는 훈련 데이터에 대한 전체 손실에 가장 작은 영향을 미칩니다.)
어휘가 원하는 크기에 도달할 때까지 이 과정을 반복합니다.
유니그램 알고리즘은 항상 기본 문자를 포함해 어떤 단어라도 토큰화할 수 있습니다.
유니그램이 병합 규칙에 기반하지 않기 떄문에 (BPE나 워드피스와는 대조적으로), 해당 알고리즘은 훈련 이후에 새로운 텍스트를 토큰화하는데 여러 가지 방법이 있습니다.
예를 들어, 훈련된 유니그램 토큰화가 다음과 같은 어휘를 가진다면:
```
["b", "g", "h", "n", "p", "s", "u", "ug", "un", "hug"],
```
`"hugs"`는 두 가지로 토큰화할 수 있습니다. `["hug", "s"]`와 `["h", "ug", "s"]` 또는 `["h", "u", "g", "s"]`.
그렇다면 어떤 토큰화 방법을 선택해야 할까요?
유니그램은 어휘를 저장하는 것 외에도 훈련 말뭉치에 각 토큰의 확률을 저장하여 훈련 후 가능한 각 토큰화의 확률을 계산할 수 있도록 합니다.
이 알고리즘은 단순히 실제로 가장 가능성이 높은 토큰화를 선택하지만, 확률에 따라 가능한 토큰화를 샘플링할 수 있는 가능성도 제공합니다.
이러한 확률은 토크나이저가 학습한 손실에 의해 정의됩니다.
단어로 구성된 훈련 데이터를 \\(x_{1}, \dots, x_{N}\\)라 하고, 단어 \\(x_{i}\\)에 대한 가능한 모든 토큰화 결과를 \\(S(x_{i})\\)라 한다면, 전체 손실은 다음과 같이 정의됩니다:
$$\mathcal{L} = -\sum_{i=1}^{N} \log \left ( \sum_{x \in S(x_{i})} p(x) \right )$$
<a id='sentencepiece'></a>
### 센텐스피스 (SentencePiece)[[sentencepiece]]
지금까지 다룬 토큰화 알고리즘은 동일한 문제를 가집니다: 입력 텍스트는 공백을 사용하여 단어를 구분한다고 가정합니다.
하지만, 모든 언어에서 단어를 구분하기 위해 공백을 사용하지 않습니다.
한가지 가능한 해결방안은 특정 언어에 특화된 사전 토크나이저를 사용하는 것입니다. 예를 들어 [XLM](model_doc/xlm)은 특정 중국어, 일본어, 태국어 사전 토크나이저를 사용합니다.
이 문제를 일반적인 방법으로 해결하기 위해, [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (Kudo et al., 2018)](https://arxiv.org/pdf/1808.06226.pdf)는 입력을 스트림으로 처리해 공백를 하나의 문자로 사용합니다.
이후에 BPE 또는 유니그램 알고리즘을 사용해 적절한 어휘를 구성합니다.
[`XLNetTokenizer`]는 센텐스피스를 사용하기 때문에, 위에서 다룬 예시에서 어휘에 `"▁"`가 포함되어있습니다.
모든 토큰을 합친 후 `"▁"`을 공백으로 대체하면 되기 때문에 센텐스피스로 토큰화된 결과는 디코딩하기 수월합니다.
transformers에서 제공하는 센텐스피스 토크나이저를 사용하는 모든 모델은 유니그램과 함께 사용됩니다.
[ALBERT](model_doc/albert), [XLNet](model_doc/xlnet), [Marian](model_doc/marian), [T5](model_doc/t5) 모델이 센텐스피스 토크나이저를 사용합니다. | transformers/docs/source/ko/tokenizer_summary.md/0 | {
"file_path": "transformers/docs/source/ko/tokenizer_summary.md",
"repo_id": "transformers",
"token_count": 15149
} |
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# Guia de Instalação
Neste guia poderá encontrar informações para a instalação do 🤗 Transformers para qualquer biblioteca de
Machine Learning com a qual esteja a trabalhar. Além disso, poderá encontrar informações sobre como gerar cachês e
configurar o 🤗 Transformers para execução em modo offline (opcional).
🤗 Transformers foi testado com Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, e Flax. Para instalar a biblioteca de
deep learning com que deseja trabalhar, siga as instruções correspondentes listadas a seguir:
* [PyTorch](https://pytorch.org/get-started/locally/)
* [TensorFlow 2.0](https://www.tensorflow.org/install/pip)
* [Flax](https://flax.readthedocs.io/en/latest/)
## Instalação pelo Pip
É sugerido instalar o 🤗 Transformers num [ambiente virtual](https://docs.python.org/3/library/venv.html). Se precisar
de mais informações sobre ambientes virtuais em Python, consulte este [guia](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
Um ambiente virtual facilitará a manipulação e organização de projetos e evita problemas de compatibilidade entre dependências.
Comece criando um ambiente virtual no diretório do seu projeto:
```bash
python -m venv .env
```
E para ativar o ambiente virtual:
```bash
source .env/bin/activate
```
Agora É possível instalar o 🤗 Transformers com o comando a seguir:
```bash
pip install transformers
```
Somente para a CPU, é possível instalar o 🤗 Transformers e a biblioteca de deep learning respectiva apenas numa linha.
Por exemplo, para instalar o 🤗 Transformers e o PyTorch, digite:
```bash
pip install transformers[torch]
```
🤗 Transformers e TensorFlow 2.0:
```bash
pip install transformers[tf-cpu]
```
🤗 Transformers e Flax:
```bash
pip install transformers[flax]
```
Por último, verifique se o 🤗 Transformers foi instalado com sucesso usando o seguinte comando para baixar um modelo pré-treinado:
```bash
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"
```
Em seguida, imprima um rótulo e sua pontuação:
```bash
[{'label': 'POSITIVE', 'score': 0.9998704791069031}]
```
## Instalação usando a fonte
Para instalar o 🤗 Transformers a partir da fonte use o seguinte comando:
```bash
pip install git+https://github.com/huggingface/transformers
```
O comando acima instalará a versão `master` mais atual em vez da última versão estável. A versão `master` é útil para
utilizar os últimos updates contidos em 🤗 Transformers. Por exemplo, um erro recente pode ter sido corrigido somente
após a última versão estável, antes que houvesse um novo lançamento. No entanto, há a possibilidade que a versão `master` não esteja estável.
A equipa trata de mantér a versão `master` operacional e a maioria dos erros são resolvidos em poucas horas ou dias.
Se encontrar quaisquer problemas, por favor abra um [Issue](https://github.com/huggingface/transformers/issues) para que o
mesmo possa ser corrigido o mais rápido possível.
Verifique que o 🤗 Transformers está instalado corretamente usando o seguinte comando:
```bash
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
```
## Instalação editável
Uma instalação editável será necessária caso desejas um dos seguintes:
* Usar a versão `master` do código fonte.
* Contribuir ao 🤗 Transformers e precisa testar mudanças ao código.
Para tal, clone o repositório e instale o 🤗 Transformers com os seguintes comandos:
```bash
git clone https://github.com/huggingface/transformers.git
cd transformers
pip install -e .
```
Estes comandos vão ligar o diretório para o qual foi clonado o repositório ao caminho de bibliotecas do Python.
O Python agora buscará dentro dos arquivos que foram clonados além dos caminhos normais da biblioteca.
Por exemplo, se os pacotes do Python se encontram instalados no caminho `~/anaconda3/envs/main/lib/python3.7/site-packages/`,
o Python também buscará módulos no diretório onde clonamos o repositório `~/transformers/`.
<Tip warning={true}>
É necessário manter o diretório `transformers` se desejas continuar usando a biblioteca.
</Tip>
Assim, É possível atualizar sua cópia local para com a última versão do 🤗 Transformers com o seguinte comando:
```bash
cd ~/transformers/
git pull
```
O ambiente de Python que foi criado para a instalação do 🤗 Transformers encontrará a versão `master` em execuções seguintes.
## Instalação usando o Conda
É possível instalar o 🤗 Transformers a partir do canal conda `conda-forge` com o seguinte comando:
```bash
conda install conda-forge::transformers
```
## Configuração do Cachê
Os modelos pré-treinados são baixados e armazenados no cachê local, encontrado em `~/.cache/huggingface/transformers/`.
Este é o diretório padrão determinado pela variável `TRANSFORMERS_CACHE` dentro do shell.
No Windows, este diretório pré-definido é dado por `C:\Users\username\.cache\huggingface\transformers`.
É possível mudar as variáveis dentro do shell em ordem de prioridade para especificar um diretório de cachê diferente:
1. Variável de ambiente do shell (por padrão): `TRANSFORMERS_CACHE`.
2. Variável de ambiente do shell:`HF_HOME` + `transformers/`.
3. Variável de ambiente do shell: `XDG_CACHE_HOME` + `/huggingface/transformers`.
<Tip>
O 🤗 Transformers usará as variáveis de ambiente do shell `PYTORCH_TRANSFORMERS_CACHE` ou `PYTORCH_PRETRAINED_BERT_CACHE`
se estiver vindo de uma versão anterior da biblioteca que tenha configurado essas variáveis de ambiente, a menos que
você especifique a variável de ambiente do shell `TRANSFORMERS_CACHE`.
</Tip>
## Modo Offline
O 🤗 Transformers também pode ser executado num ambiente de firewall ou fora da rede (offline) usando arquivos locais.
Para tal, configure a variável de ambiente de modo que `HF_HUB_OFFLINE=1`.
<Tip>
Você pode adicionar o [🤗 Datasets](https://huggingface.co/docs/datasets/) ao pipeline de treinamento offline declarando
a variável de ambiente `HF_DATASETS_OFFLINE=1`.
</Tip>
Segue um exemplo de execução do programa numa rede padrão com firewall para instâncias externas, usando o seguinte comando:
```bash
python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
```
Execute esse mesmo programa numa instância offline com o seguinte comando:
```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 ...
```
O script agora deve ser executado sem travar ou expirar, pois procurará apenas por arquivos locais.
### Obtendo modelos e tokenizers para uso offline
Outra opção para usar o 🤗 Transformers offline é baixar os arquivos antes e depois apontar para o caminho local onde estão localizados. Existem três maneiras de fazer isso:
* Baixe um arquivo por meio da interface de usuário do [Model Hub](https://huggingface.co/models) clicando no ícone ↓.
* Use o pipeline do [`PreTrainedModel.from_pretrained`] e [`PreTrainedModel.save_pretrained`]:
1. Baixa os arquivos previamente com [`PreTrainedModel.from_pretrained`]:
```py
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("bigscience/T0_3B")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0_3B")
```
2. Salve os arquivos em um diretório específico com [`PreTrainedModel.save_pretrained`]:
```py
>>> tokenizer.save_pretrained("./your/path/bigscience_t0")
>>> model.save_pretrained("./your/path/bigscience_t0")
```
3. Quando estiver offline, acesse os arquivos com [`PreTrainedModel.from_pretrained`] do diretório especificado:
```py
>>> tokenizer = AutoTokenizer.from_pretrained("./your/path/bigscience_t0")
>>> model = AutoModel.from_pretrained("./your/path/bigscience_t0")
```
* Baixando arquivos programaticamente com a biblioteca [huggingface_hub](https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub):
1. Instale a biblioteca [huggingface_hub](https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub) em seu ambiente virtual:
```bash
python -m pip install huggingface_hub
```
2. Utiliza a função [`hf_hub_download`](https://huggingface.co/docs/hub/adding-a-library#download-files-from-the-hub) para baixar um arquivo para um caminho específico. Por exemplo, o comando a seguir baixará o arquivo `config.json` para o modelo [T0](https://huggingface.co/bigscience/T0_3B) no caminho desejado:
```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")
```
Depois que o arquivo for baixado e armazenado no cachê local, especifique seu caminho local para carregá-lo e usá-lo:
```py
>>> from transformers import AutoConfig
>>> config = AutoConfig.from_pretrained("./your/path/bigscience_t0/config.json")
```
<Tip>
Para obter mais detalhes sobre como baixar arquivos armazenados no Hub, consulte a seção [How to download files from the Hub](https://huggingface.co/docs/hub/how-to-downstream).
</Tip>
| transformers/docs/source/pt/installation.md/0 | {
"file_path": "transformers/docs/source/pt/installation.md",
"repo_id": "transformers",
"token_count": 3770
} |
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# 如何创建自定义流水线？
在本指南中，我们将演示如何创建一个自定义流水线并分享到 [Hub](https://hf.co/models)，或将其添加到 🤗 Transformers 库中。
首先，你需要决定流水线将能够接受的原始条目。它可以是字符串、原始字节、字典或任何看起来最可能是期望的输入。
尽量保持输入为纯 Python 语言，因为这样可以更容易地实现兼容性（甚至通过 JSON 在其他语言之间）。
这些将是流水线 (`preprocess`) 的 `inputs`。
然后定义 `outputs`。与 `inputs` 相同的策略。越简单越好。这些将是 `postprocess` 方法的输出。
首先继承基类 `Pipeline`，其中包含实现 `preprocess`、`_forward`、`postprocess` 和 `_sanitize_parameters` 所需的 4 个方法。
```python
from transformers import Pipeline
class MyPipeline(Pipeline):
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "maybe_arg" in kwargs:
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
return preprocess_kwargs, {}, {}
def preprocess(self, inputs, maybe_arg=2):
model_input = Tensor(inputs["input_ids"])
return {"model_input": model_input}
def _forward(self, model_inputs):
# model_inputs == {"model_input": model_input}
outputs = self.model(**model_inputs)
# Maybe {"logits": Tensor(...)}
return outputs
def postprocess(self, model_outputs):
best_class = model_outputs["logits"].softmax(-1)
return best_class
```
这种分解的结构旨在为 CPU/GPU 提供相对无缝的支持，同时支持在不同线程上对 CPU 进行预处理/后处理。
`preprocess` 将接受最初定义的输入，并将其转换为可供模型输入的内容。它可能包含更多信息，通常是一个 `Dict`。
`_forward` 是实现细节，不应直接调用。`forward` 是首选的调用方法，因为它包含保障措施，以确保一切都在预期的设备上运作。
如果任何内容与实际模型相关，它应该属于 `_forward` 方法，其他内容应该在 preprocess/postprocess 中。
`postprocess` 方法将接受 `_forward` 的输出，并将其转换为之前确定的最终输出。
`_sanitize_parameters` 存在是为了允许用户在任何时候传递任何参数，无论是在初始化时 `pipeline(...., maybe_arg=4)`
还是在调用时 `pipe = pipeline(...); output = pipe(...., maybe_arg=4)`。
`_sanitize_parameters` 的返回值是将直接传递给 `preprocess`、`_forward` 和 `postprocess` 的 3 个关键字参数字典。
如果调用方没有使用任何额外参数调用，则不要填写任何内容。这样可以保留函数定义中的默认参数，这总是更"自然"的。
在分类任务中，一个经典的例子是在后处理中使用 `top_k` 参数。
```python
>>> pipe = pipeline("my-new-task")
>>> pipe("This is a test")
[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05}
{"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}]
>>> pipe("This is a test", top_k=2)
[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}]
```
为了实现这一点，我们将更新我们的 `postprocess` 方法，将默认参数设置为 `5`，
并编辑 `_sanitize_parameters` 方法，以允许这个新参数。
```python
def postprocess(self, model_outputs, top_k=5):
best_class = model_outputs["logits"].softmax(-1)
# Add logic to handle top_k
return best_class
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "maybe_arg" in kwargs:
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
postprocess_kwargs = {}
if "top_k" in kwargs:
postprocess_kwargs["top_k"] = kwargs["top_k"]
return preprocess_kwargs, {}, postprocess_kwargs
```
尽量保持简单输入/输出，最好是可 JSON 序列化的，因为这样可以使流水线的使用非常简单，而不需要用户了解新的对象类型。
通常也相对常见地支持许多不同类型的参数以便使用（例如音频文件，可以是文件名、URL 或纯字节）。
## 将其添加到支持的任务列表中
要将你的 `new-task` 注册到支持的任务列表中，你需要将其添加到 `PIPELINE_REGISTRY` 中：
```python
from transformers.pipelines import PIPELINE_REGISTRY
PIPELINE_REGISTRY.register_pipeline(
"new-task",
pipeline_class=MyPipeline,
pt_model=AutoModelForSequenceClassification,
)
```
如果需要，你可以指定一个默认模型，此时它应该带有一个特定的修订版本（可以是分支名称或提交哈希，这里我们使用了 `"abcdef"`），以及类型：
```python
PIPELINE_REGISTRY.register_pipeline(
"new-task",
pipeline_class=MyPipeline,
pt_model=AutoModelForSequenceClassification,
default={"pt": ("user/awesome_model", "abcdef")},
type="text", # current support type: text, audio, image, multimodal
)
```
## 在 Hub 上分享你的流水线
要在 Hub 上分享你的自定义流水线，你只需要将 `Pipeline` 子类的自定义代码保存在一个 Python 文件中。
例如，假设我们想使用一个自定义流水线进行句对分类，如下所示：
```py
import numpy as np
from transformers import Pipeline
def softmax(outputs):
maxes = np.max(outputs, axis=-1, keepdims=True)
shifted_exp = np.exp(outputs - maxes)
return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
class PairClassificationPipeline(Pipeline):
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "second_text" in kwargs:
preprocess_kwargs["second_text"] = kwargs["second_text"]
return preprocess_kwargs, {}, {}
def preprocess(self, text, second_text=None):
return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework)
def _forward(self, model_inputs):
return self.model(**model_inputs)
def postprocess(self, model_outputs):
logits = model_outputs.logits[0].numpy()
probabilities = softmax(logits)
best_class = np.argmax(probabilities)
label = self.model.config.id2label[best_class]
score = probabilities[best_class].item()
logits = logits.tolist()
return {"label": label, "score": score, "logits": logits}
```
这个实现与框架无关，适用于 PyTorch 和 TensorFlow 模型。如果我们将其保存在一个名为
`pair_classification.py` 的文件中，然后我们可以像这样导入并注册它：
```py
from pair_classification import PairClassificationPipeline
from transformers.pipelines import PIPELINE_REGISTRY
from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
PIPELINE_REGISTRY.register_pipeline(
"pair-classification",
pipeline_class=PairClassificationPipeline,
pt_model=AutoModelForSequenceClassification,
tf_model=TFAutoModelForSequenceClassification,
)
```
完成这些步骤后，我们可以将其与预训练模型一起使用。例如，`sgugger/finetuned-bert-mrpc`
已经在 MRPC 数据集上进行了微调，用于将句子对分类为是释义或不是释义。
```py
from transformers import pipeline
classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
```
然后，我们可以通过在 `Repository` 中使用 `save_pretrained` 方法将其分享到 Hub 上：
```py
from huggingface_hub import Repository
repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
classifier.save_pretrained("test-dynamic-pipeline")
repo.push_to_hub()
```
这将会复制包含你定义的 `PairClassificationPipeline` 的文件到文件夹 `"test-dynamic-pipeline"` 中，
同时保存流水线的模型和分词器，然后将所有内容推送到仓库 `{your_username}/test-dynamic-pipeline` 中。
之后，只要提供选项 `trust_remote_code=True`，任何人都可以使用它：
```py
from transformers import pipeline
classifier = pipeline(model="{your_username}/test-dynamic-pipeline", trust_remote_code=True)
```
## 将流水线添加到 🤗 Transformers
如果你想将你的流水线贡献给 🤗 Transformers，你需要在 `pipelines` 子模块中添加一个新模块，
其中包含你的流水线的代码，然后将其添加到 `pipelines/__init__.py` 中定义的任务列表中。
然后，你需要添加测试。创建一个新文件 `tests/test_pipelines_MY_PIPELINE.py`，其中包含其他测试的示例。
`run_pipeline_test` 函数将非常通用，并在每种可能的架构上运行小型随机模型，如 `model_mapping` 和 `tf_model_mapping` 所定义。
这对于测试未来的兼容性非常重要，这意味着如果有人为 `XXXForQuestionAnswering` 添加了一个新模型，
流水线测试将尝试在其上运行。由于模型是随机的，所以不可能检查实际值，这就是为什么有一个帮助函数 `ANY`，它只是尝试匹配流水线的输出类型。
你还 **需要** 实现 2（最好是 4）个测试。
- `test_small_model_pt`：为这个流水线定义一个小型模型（结果是否合理并不重要），并测试流水线的输出。
结果应该与 `test_small_model_tf` 的结果相同。
- `test_small_model_tf`：为这个流水线定义一个小型模型（结果是否合理并不重要），并测试流水线的输出。
结果应该与 `test_small_model_pt` 的结果相同。
- `test_large_model_pt`（可选）：在一个真实的流水线上测试流水线，结果应该是有意义的。
这些测试速度较慢，应该被如此标记。这里的目标是展示流水线，并确保在未来的发布中没有漂移。
- `test_large_model_tf`（可选）：在一个真实的流水线上测试流水线，结果应该是有意义的。
这些测试速度较慢，应该被如此标记。这里的目标是展示流水线，并确保在未来的发布中没有漂移。
| transformers/docs/source/zh/add_new_pipeline.md/0 | {
"file_path": "transformers/docs/source/zh/add_new_pipeline.md",
"repo_id": "transformers",
"token_count": 5640
} |
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# 使用Trainer API进行超参数搜索
🤗 Transformers库提供了一个优化过的[`Trainer`]类，用于训练🤗 Transformers模型，相比于手动编写自己的训练循环，这更容易开始训练。[`Trainer`]提供了超参数搜索的API。本文档展示了如何在示例中启用它。
## 超参数搜索后端
[`Trainer`] 目前支持四种超参数搜索后端：[optuna](https://optuna.org/)，[sigopt](https://sigopt.com/)，[raytune](https://docs.ray.io/en/latest/tune/index.html)，[wandb](https://wandb.ai/site/sweeps)
在使用它们之前，您应该先安装它们作为超参数搜索后端。
```bash
pip install optuna/sigopt/wandb/ray[tune]
```
## 如何在示例中启用超参数搜索
定义超参数搜索空间，不同的后端需要不同的格式。
对于sigopt，请参阅sigopt [object_parameter](https://docs.sigopt.com/ai-module-api-references/api_reference/objects/object_parameter)，它类似于以下内容：
```py
>>> def sigopt_hp_space(trial):
... return [
... {"bounds": {"min": 1e-6, "max": 1e-4}, "name": "learning_rate", "type": "double"},
... {
... "categorical_values": ["16", "32", "64", "128"],
... "name": "per_device_train_batch_size",
... "type": "categorical",
... },
... ]
```
对于optuna，请参阅optuna [object_parameter](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html#sphx-glr-tutorial-10-key-features-002-configurations-py)，它类似于以下内容：
```py
>>> def optuna_hp_space(trial):
... return {
... "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
... "per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [16, 32, 64, 128]),
... }
```
Optuna提供了多目标HPO。您可以在`hyperparameter_search`中传递`direction`参数，并定义自己的`compute_objective`以返回多个目标值。在`hyperparameter_search`中将返回Pareto Front（`List[BestRun]`），您应该参考[test_trainer](https://github.com/huggingface/transformers/blob/main/tests/trainer/test_trainer.py)中的测试用例`TrainerHyperParameterMultiObjectOptunaIntegrationTest`。它类似于以下内容：
```py
>>> best_trials = trainer.hyperparameter_search(
... direction=["minimize", "maximize"],
... backend="optuna",
... hp_space=optuna_hp_space,
... n_trials=20,
... compute_objective=compute_objective,
... )
```
对于raytune，可以参考raytune的[object_parameter](https://docs.ray.io/en/latest/tune/api/search_space.html)，它类似于以下内容：
```py
>>> def ray_hp_space(trial):
... return {
... "learning_rate": tune.loguniform(1e-6, 1e-4),
... "per_device_train_batch_size": tune.choice([16, 32, 64, 128]),
... }
```
对于wandb，可以参考wandb的[object_parameter](https://docs.wandb.ai/guides/sweeps/configuration)，它类似于以下内容：
```py
>>> def wandb_hp_space(trial):
... return {
... "method": "random",
... "metric": {"name": "objective", "goal": "minimize"},
... "parameters": {
... "learning_rate": {"distribution": "uniform", "min": 1e-6, "max": 1e-4},
... "per_device_train_batch_size": {"values": [16, 32, 64, 128]},
... },
... }
```
定义一个`model_init`函数并将其传递给[Trainer]，作为示例：
```py
>>> def model_init(trial):
... return 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,
... use_auth_token=True if model_args.use_auth_token else None,
... )
```
使用你的`model_init`函数、训练参数、训练和测试数据集以及评估函数创建一个[`Trainer`]。
```py
>>> trainer = Trainer(
... model=None,
... args=training_args,
... train_dataset=small_train_dataset,
... eval_dataset=small_eval_dataset,
... compute_metrics=compute_metrics,
... processing_class=tokenizer,
... model_init=model_init,
... data_collator=data_collator,
... )
```
调用超参数搜索，获取最佳试验参数，后端可以是`"optuna"`/`"sigopt"`/`"wandb"`/`"ray"`。方向可以是`"minimize"`或`"maximize"`，表示是否优化更大或更低的目标。
您可以定义自己的compute_objective函数，如果没有定义，将调用默认的compute_objective，并将评估指标（如f1）之和作为目标值返回。
```py
>>> best_trial = trainer.hyperparameter_search(
... direction="maximize",
... backend="optuna",
... hp_space=optuna_hp_space,
... n_trials=20,
... compute_objective=compute_objective,
... )
```
## 针对DDP微调的超参数搜索
目前，Optuna和Sigopt已启用针对DDP的超参数搜索。只有rank-zero进程会进行超参数搜索并将参数传递给其他进程。
| transformers/docs/source/zh/hpo_train.md/0 | {
"file_path": "transformers/docs/source/zh/hpo_train.md",
"repo_id": "transformers",
"token_count": 2832
} |
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# Data Collator
Data collators是一个对象，通过使用数据集元素列表作为输入来形成一个批次。这些元素与 `train_dataset` 或 `eval_dataset` 的元素类型相同。
为了能够构建批次，Data collators可能会应用一些预处理（比如填充）。其中一些（比如[`DataCollatorForLanguageModeling`]）还会在形成的批次上应用一些随机数据增强（比如随机掩码）。
在[示例脚本](../examples)或[示例notebooks](../notebooks)中可以找到使用的示例。
## Default data collator
[[autodoc]] data.data_collator.default_data_collator
## DefaultDataCollator
[[autodoc]] data.data_collator.DefaultDataCollator
## DataCollatorWithPadding
[[autodoc]] data.data_collator.DataCollatorWithPadding
## DataCollatorForTokenClassification
[[autodoc]] data.data_collator.DataCollatorForTokenClassification
## DataCollatorForSeq2Seq
[[autodoc]] data.data_collator.DataCollatorForSeq2Seq
## DataCollatorForLanguageModeling
[[autodoc]] data.data_collator.DataCollatorForLanguageModeling
- numpy_mask_tokens
- tf_mask_tokens
- torch_mask_tokens
## DataCollatorForWholeWordMask
[[autodoc]] data.data_collator.DataCollatorForWholeWordMask
- numpy_mask_tokens
- tf_mask_tokens
- torch_mask_tokens
## DataCollatorForPermutationLanguageModeling
[[autodoc]] data.data_collator.DataCollatorForPermutationLanguageModeling
- numpy_mask_tokens
- tf_mask_tokens
- torch_mask_tokens
| transformers/docs/source/zh/main_classes/data_collator.md/0 | {
"file_path": "transformers/docs/source/zh/main_classes/data_collator.md",
"repo_id": "transformers",
"token_count": 868
} |
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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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# 分享模型
最后两个教程展示了如何使用PyTorch、Keras和 🤗 Accelerate进行分布式设置来微调模型。下一步是将您的模型与社区分享！在Hugging Face，我们相信公开分享知识和资源，能实现人工智能的普及化，让每个人都能受益。我们鼓励您将您的模型与社区分享，以帮助他人节省时间和精力。
在本教程中，您将学习两种在[Model Hub](https://huggingface.co/models)上共享训练好的或微调的模型的方法：
- 通过编程将文件推送到Hub。
- 使用Web界面将文件拖放到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>
要与社区共享模型，您需要在[huggingface.co](https://huggingface.co/join)上拥有一个帐户。您还可以加入现有的组织或创建一个新的组织。
</Tip>
## 仓库功能
Model Hub上的每个仓库都像是一个典型的GitHub仓库。我们的仓库提供版本控制、提交历史记录以及可视化差异的能力。
Model Hub的内置版本控制基于git和[git-lfs](https://git-lfs.github.com/)。换句话说，您可以将一个模型视为一个仓库，从而实现更好的访问控制和可扩展性。版本控制允许使用*修订*方法来固定特定版本的模型，可以使用提交哈希值、标签或分支来标记。
因此，您可以通过`revision`参数加载特定的模型版本：
```py
>>> model = AutoModel.from_pretrained(
... "julien-c/EsperBERTo-small", revision="4c77982" # tag name, or branch name, or commit hash
... )
```
文件也可以轻松地在仓库中编辑，您可以查看提交历史记录以及差异：
## 设置
在将模型共享到Hub之前，您需要拥有Hugging Face的凭证。如果您有访问终端的权限，请在安装🤗 Transformers的虚拟环境中运行以下命令。这将在您的Hugging Face缓存文件夹（默认为`~/.cache/`）中存储您的`access token`：
```bash
huggingface-cli login
```
如果您正在使用像Jupyter或Colaboratory这样的`notebook`，请确保您已安装了[`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library)库。该库允许您以编程方式与Hub进行交互。
```bash
pip install huggingface_hub
```
然后使用`notebook_login`登录到Hub，并按照[这里](https://huggingface.co/settings/token)的链接生成一个token进行登录：
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## 转换模型适用于所有框架
为确保您的模型可以被使用不同框架的人使用，我们建议您将PyTorch和TensorFlow `checkpoints`都转换并上传。如果您跳过此步骤，用户仍然可以从其他框架加载您的模型，但速度会变慢，因为🤗 Transformers需要实时转换`checkpoints`。
为另一个框架转换`checkpoints`很容易。确保您已安装PyTorch和TensorFlow（请参阅[此处](installation)的安装说明），然后在其他框架中找到适合您任务的特定模型。
<frameworkcontent>
<pt>
指定`from_tf=True`将checkpoint从TensorFlow转换为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")
```
</pt>
<tf>
指定`from_pt=True`将checkpoint从PyTorch转换为TensorFlow。
```py
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
```
然后，您可以使用新的checkpoint保存您的新TensorFlow模型：
```py
>>> tf_model.save_pretrained("path/to/awesome-name-you-picked")
```
</tf>
<jax>
如果模型在Flax中可用，您还可以将PyTorch checkpoint转换为Flax：
```py
>>> flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
... "path/to/awesome-name-you-picked", from_pt=True
... )
```
</jax>
</frameworkcontent>
## 在训练过程中推送模型
<frameworkcontent>
<pt>
<Youtube id="Z1-XMy-GNLQ"/>
将模型分享到Hub就像添加一个额外的参数或回调函数一样简单。请记住，在[微调教程](training)中，`TrainingArguments`类是您指定超参数和附加训练选项的地方。其中一项训练选项包括直接将模型推送到Hub的能力。在您的`TrainingArguments`中设置`push_to_hub=True`：
```py
>>> training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True)
```
像往常一样将您的训练参数传递给[`Trainer`]：
```py
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=small_train_dataset,
... eval_dataset=small_eval_dataset,
... compute_metrics=compute_metrics,
... )
```
在您微调完模型后，在[`Trainer`]上调用[`~transformers.Trainer.push_to_hub`]将训练好的模型推送到Hub。🤗 Transformers甚至会自动将训练超参数、训练结果和框架版本添加到你的模型卡片中！
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
使用[`PushToHubCallback`]将模型分享到Hub。在[`PushToHubCallback`]函数中，添加以下内容：
- 一个用于存储模型的输出目录。
- 一个tokenizer。
- `hub_model_id`，即您的Hub用户名和模型名称。
```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"
... )
```
将回调函数添加到 [`fit`](https://keras.io/api/models/model_training_apis/)中，然后🤗 Transformers 会将训练好的模型推送到 Hub：
```py
>>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback)
```
</tf>
</frameworkcontent>
## 使用`push_to_hub`功能
您可以直接在您的模型上调用`push_to_hub`来将其上传到Hub。
在`push_to_hub`中指定你的模型名称：
```py
>>> pt_model.push_to_hub("my-awesome-model")
```
这会在您的用户名下创建一个名为`my-awesome-model`的仓库。用户现在可以使用`from_pretrained`函数加载您的模型：
```py
>>> from transformers import AutoModel
>>> model = AutoModel.from_pretrained("your_username/my-awesome-model")
```
如果您属于一个组织，并希望将您的模型推送到组织名称下，只需将其添加到`repo_id`中：
```py
>>> pt_model.push_to_hub("my-awesome-org/my-awesome-model")
```
`push_to_hub`函数还可以用于向模型仓库添加其他文件。例如，向模型仓库中添加一个`tokenizer`：
```py
>>> tokenizer.push_to_hub("my-awesome-model")
```
或者，您可能希望将您的微调后的PyTorch模型的TensorFlow版本添加进去：
```py
>>> tf_model.push_to_hub("my-awesome-model")
```
现在，当您导航到您的Hugging Face个人资料时，您应该看到您新创建的模型仓库。点击**文件**选项卡将显示您已上传到仓库的所有文件。
有关如何创建和上传文件到仓库的更多详细信息，请参考Hub文档[这里](https://huggingface.co/docs/hub/how-to-upstream)。
## 使用Web界面上传
喜欢无代码方法的用户可以通过Hugging Face的Web界面上传模型。访问[huggingface.co/new](https://huggingface.co/new)创建一个新的仓库：
从这里开始，添加一些关于您的模型的信息：
- 选择仓库的**所有者**。这可以是您本人或者您所属的任何组织。
- 为您的项目选择一个名称，该名称也将成为仓库的名称。
- 选择您的模型是公开还是私有。
- 指定您的模型的许可证使用情况。
现在点击**文件**选项卡，然后点击**添加文件**按钮将一个新文件上传到你的仓库。接着拖放一个文件进行上传，并添加提交信息。
## 添加模型卡片
为了确保用户了解您的模型的能力、限制、潜在偏差和伦理考虑，请在仓库中添加一个模型卡片。模型卡片在`README.md`文件中定义。你可以通过以下方式添加模型卡片：
* 手动创建并上传一个`README.md`文件。
* 在你的模型仓库中点击**编辑模型卡片**按钮。
可以参考DistilBert的[模型卡片](https://huggingface.co/distilbert/distilbert-base-uncased)来了解模型卡片应该包含的信息类型。有关您可以在`README.md`文件中控制的更多选项的细节，例如模型的碳足迹或小部件示例，请参考文档[这里](https://huggingface.co/docs/hub/models-cards)。 | transformers/docs/source/zh/model_sharing.md/0 | {
"file_path": "transformers/docs/source/zh/model_sharing.md",
"repo_id": "transformers",
"token_count": 5355
} |
<!--
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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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# 自动语音识别
[[open-in-colab]]
<Youtube id="TksaY_FDgnk"/>
自动语音识别（ASR）将语音信号转换为文本，将一系列音频输入映射到文本输出。
Siri 和 Alexa 这类虚拟助手使用 ASR 模型来帮助用户日常生活，还有许多其他面向用户的有用应用，如会议实时字幕和会议纪要。
本指南将向您展示如何：
1. 在 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 数据集上对
[Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) 进行微调，以将音频转录为文本。
2. 使用微调后的模型进行推断。
<Tip>
如果您想查看所有与本任务兼容的架构和检查点，最好查看[任务页](https://huggingface.co/tasks/automatic-speech-recognition)。
</Tip>
在开始之前，请确保您已安装所有必要的库：
```bash
pip install transformers datasets evaluate jiwer
```
我们鼓励您登录自己的 Hugging Face 账户，这样您就可以上传并与社区分享您的模型。
出现提示时，输入您的令牌登录：
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## 加载 MInDS-14 数据集
首先从🤗 Datasets 库中加载 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14)
数据集的一个较小子集。这将让您有机会先进行实验，确保一切正常，然后再花更多时间在完整数据集上进行训练。
```py
>>> from datasets import load_dataset, Audio
>>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train[:100]")
```
使用 [`~Dataset.train_test_split`] 方法将数据集的 `train` 拆分为训练集和测试集：
```py
>>> minds = minds.train_test_split(test_size=0.2)
```
然后看看数据集：
```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
})
})
```
虽然数据集包含 `lang_id `和 `english_transcription` 等许多有用的信息，但在本指南中，
您将专注于 `audio` 和 `transcription`。使用 [`~datasets.Dataset.remove_columns`] 方法删除其他列：
```py
>>> minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"])
```
再看看示例：
```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"}
```
有 2 个字段：
- `audio`：由语音信号形成的一维 `array`，用于加载和重新采样音频文件。
- `transcription`：目标文本。
## 预处理
下一步是加载一个 Wav2Vec2 处理器来处理音频信号：
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")
```
MInDS-14 数据集的采样率为 8000kHz（您可以在其[数据集卡片](https://huggingface.co/datasets/PolyAI/minds14)中找到此信息），
这意味着您需要将数据集重新采样为 16000kHz 以使用预训练的 Wav2Vec2 模型：
```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"}
```
如您在上面的 `transcription` 中所看到的，文本包含大小写字符的混合。
Wav2Vec2 分词器仅训练了大写字符，因此您需要确保文本与分词器的词汇表匹配：
```py
>>> def uppercase(example):
... return {"transcription": example["transcription"].upper()}
>>> minds = minds.map(uppercase)
```
现在创建一个预处理函数，该函数应该：
1. 调用 `audio` 列以加载和重新采样音频文件。
2. 从音频文件中提取 `input_values` 并使用处理器对 `transcription` 列执行 tokenizer 操作。
```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
```
要在整个数据集上应用预处理函数，可以使用🤗 Datasets 的 [`~datasets.Dataset.map`] 函数。
您可以通过增加 `num_proc` 参数来加速 `map` 的处理进程数量。
使用 [`~datasets.Dataset.remove_columns`] 方法删除不需要的列：
```py
>>> encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4)
```
🤗 Transformers 没有用于 ASR 的数据整理器，因此您需要调整 [`DataCollatorWithPadding`] 来创建一个示例批次。
它还会动态地将您的文本和标签填充到其批次中最长元素的长度（而不是整个数据集），以使它们具有统一的长度。
虽然可以通过在 `tokenizer` 函数中设置 `padding=True` 来填充文本，但动态填充更有效。
与其他数据整理器不同，这个特定的数据整理器需要对 `input_values` 和 `labels `应用不同的填充方法：
```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
```
现在实例化您的 `DataCollatorForCTCWithPadding`：
```py
>>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding="longest")
```
## 评估
在训练过程中包含一个指标通常有助于评估模型的性能。
您可以通过🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) 库快速加载一个评估方法。
对于这个任务，加载 [word error rate](https://huggingface.co/spaces/evaluate-metric/wer)（WER）指标
（请参阅🤗 Evaluate [快速上手](https://huggingface.co/docs/evaluate/a_quick_tour)以了解如何加载和计算指标）：
```py
>>> import evaluate
>>> wer = evaluate.load("wer")
```
然后创建一个函数，将您的预测和标签传递给 [`~evaluate.EvaluationModule.compute`] 来计算 WER：
```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}
```
您的 `compute_metrics` 函数现在已经准备就绪，当您设置好训练时将返回给此函数。
## 训练
<frameworkcontent>
<pt>
<Tip>
如果您不熟悉使用[`Trainer`]微调模型，请查看这里的基本教程[here](../training#train-with-pytorch-trainer)！
</Tip>
现在您已经准备好开始训练您的模型了！使用 [`AutoModelForCTC`] 加载 Wav2Vec2。
使用 `ctc_loss_reduction` 参数指定要应用的减少方式。通常最好使用平均值而不是默认的求和：
```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,
)
```
此时，只剩下 3 个步骤：
1. 在 [`TrainingArguments`] 中定义您的训练参数。唯一必需的参数是 `output_dir`，用于指定保存模型的位置。
您可以通过设置 `push_to_hub=True` 将此模型推送到 Hub（您需要登录到 Hugging Face 才能上传您的模型）。
在每个 epoch 结束时，[`Trainer`] 将评估 WER 并保存训练检查点。
2. 将训练参数与模型、数据集、分词器、数据整理器和 `compute_metrics` 函数一起传递给 [`Trainer`]。
3. 调用 [`~Trainer.train`] 来微调您的模型。
```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()
```
训练完成后，使用 [`~transformers.Trainer.push_to_hub`] 方法将您的模型分享到 Hub，方便大家使用您的模型：
```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<Tip>
要深入了解如何微调模型进行自动语音识别，
请查看这篇博客[文章](https://huggingface.co/blog/fine-tune-wav2vec2-english)以了解英语 ASR，
还可以参阅[这篇文章](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)以了解多语言 ASR。
</Tip>
## 推断
很好，现在您已经微调了一个模型，您可以用它进行推断了！
加载您想要运行推断的音频文件。请记住，如果需要，将音频文件的采样率重新采样为与模型匹配的采样率！
```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"]
```
尝试使用微调后的模型进行推断的最简单方法是使用 [`pipeline`]。
使用您的模型实例化一个用于自动语音识别的 `pipeline`，并将您的音频文件传递给它：
```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>
转录结果还不错，但可以更好！尝试用更多示例微调您的模型，以获得更好的结果！
</Tip>
如果您愿意，您也可以手动复制 `pipeline` 的结果：
<frameworkcontent>
<pt>
加载一个处理器来预处理音频文件和转录，并将 `input` 返回为 PyTorch 张量：
```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")
```
将您的输入传递给模型并返回 logits：
```py
>>> from transformers import AutoModelForCTC
>>> model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
获取具有最高概率的预测 `input_ids`，并使用处理器将预测的 `input_ids` 解码回文本：
```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/zh/tasks/asr.md/0 | {
"file_path": "transformers/docs/source/zh/tasks/asr.md",
"repo_id": "transformers",
"token_count": 7232
} |
#!/usr/bin/env python
# coding=utf-8
# 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.
"""
Pretraining the library models for denoising language modeling on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be pretrained by this script:
https://huggingface.co/models?filter=bart
"""
# You can also adapt this script on your own denoising language modeling task. Pointers for this are left as comments.
import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import flax
import jax
import jax.numpy as jnp
import nltk
import numpy as np
import optax
from datasets import load_dataset
from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import HfApi
from tqdm import tqdm
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_MASKED_LM_MAPPING,
AutoTokenizer,
BartConfig,
BatchEncoding,
FlaxBartForConditionalGeneration,
HfArgumentParser,
PreTrainedTokenizerBase,
is_tensorboard_available,
set_seed,
)
from transformers.models.bart.modeling_flax_bart import shift_tokens_right
from transformers.utils import send_example_telemetry
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class TrainingArguments:
output_dir: str = field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
)
overwrite_output_dir: bool = field(
default=False,
metadata={
"help": (
"Overwrite the content of the output directory. "
"Use this to continue training if output_dir points to a checkpoint directory."
)
},
)
do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
per_device_train_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
)
learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
push_to_hub: bool = field(
default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
)
hub_model_id: str = field(
default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
)
hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
def __post_init__(self):
if self.output_dir is not None:
self.output_dir = os.path.expanduser(self.output_dir)
def to_dict(self):
"""
Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
the token values by removing their value.
"""
d = asdict(self)
for k, v in d.items():
if isinstance(v, Enum):
d[k] = v.value
if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
d[k] = [x.value for x in v]
if k.endswith("_token"):
d[k] = f"<{k.upper()}>"
return d
@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_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 s3"}
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
dtype: Optional[str] = field(
default="float32",
metadata={
"help": (
"Floating-point format in which the model weights should be initialized and trained. Choose one of"
" `[float32, float16, bfloat16]`."
)
},
)
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`)."
)
},
)
@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)."}
)
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."
)
},
)
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)."},
)
train_ref_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
)
validation_ref_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
)
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"
},
)
max_seq_length: Optional[int] = field(
default=None,
metadata={
"help": (
"The maximum total input sequence length after tokenization and masking. Sequences longer than this"
" will be truncated. Default to the max input length of the model."
)
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
mlm_probability: float = field(
default=0.3, metadata={"help": "Ratio of tokens to mask for span masked language modeling loss"}
)
permute_sentence_ratio: float = field(
default=1.0, metadata={"help": "Ratio of sentences to be permuted in each document"}
)
poisson_lambda: float = field(
default=3.0, metadata={"help": "Mean of Poisson distribution used to generate span-lengths to be masked"}
)
def __post_init__(self):
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]
if extension not in ["csv", "json", "txt"]:
raise ValueError("train_file` should be a csv, json or text file.")
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
if extension not in ["csv", "json", "txt"]:
raise ValueError("`validation_file` should be a csv, json or text file.")
@flax.struct.dataclass
class FlaxDataCollatorForBartDenoisingLM:
"""
Data collator used for BART denoising language modeling. The code is largely copied from
`<https://github.com/morganmcg1/rotobart/blob/main/data_collator.py#L223>`__.
For more information on how BART denoising language modeling works, one can take a look
at the `official paper <https://arxiv.org/pdf/1910.13461.pdf>`__
or the `official code for preprocessing <https://github.com/facebookresearch/fairseq/blob/main/fairseq/data/denoising_dataset.py>`__ .
Args:
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
The tokenizer used for encoding the data
mask_ratio (:obj:`float`):
The probability with which to (randomly) mask tokens in the input
poisson_lambda (:obj:`float`):
Mean parameter of Poisson distribution used to generate span-lengths to be masked
permute_sentence_ratio (:obj:`float`):
Ratio of sentences to be permuted in each document
decoder_start_token_id: (:obj:`int):
The decoder start token id of the model
"""
tokenizer: PreTrainedTokenizerBase
decoder_start_token_id: int
mask_ratio: float = 0.3
poisson_lambda: float = 3.0
permute_sentence_ratio: float = 1.0
def __post_init__(self):
if self.tokenizer.mask_token is None or self.tokenizer.eos_token is None:
raise ValueError(
"This tokenizer does not have a mask token or eos token which is necessary for denoising"
" language modeling. "
)
def __call__(self, examples: List[Dict[str, List[int]]]) -> BatchEncoding:
# convert list to dict and tensorize input
batch = BatchEncoding(
{k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}
)
batch["labels"] = batch["input_ids"].copy()
batch["decoder_input_ids"] = shift_tokens_right(
batch["labels"], self.tokenizer.pad_token_id, self.decoder_start_token_id
)
# permuting sentences
do_permute = False
if self.permute_sentence_ratio > 0.0:
batch["input_ids"] = self.permute_sentences(batch["input_ids"])
do_permute = True
# masking span of tokens (text infilling in the paper)
if self.mask_ratio:
batch["input_ids"], batch["labels"] = self.span_mask_tokens(
batch["input_ids"], batch["labels"], do_permute
)
# ignore pad tokens
batch["attention_mask"] = (batch["input_ids"] != self.tokenizer.pad_token_id).astype(int)
batch["decoder_attention_mask"] = (batch["decoder_input_ids"] != self.tokenizer.pad_token_id).astype(int)
return batch
def permute_sentences(self, input_ids):
"""
Shuffle sentences in each document.
"""
results = input_ids.copy()
# find end locations of sentences
end_sentence_mask = input_ids == self.tokenizer.pad_token_id
sentence_ends = np.argwhere(end_sentence_mask)
sentence_ends[:, 1] += 1
example_has_multiple_sentences, num_sentences = np.unique(sentence_ends[:, 0], return_counts=True)
num_sentences_map = dict(zip(example_has_multiple_sentences, num_sentences))
num_to_permute = np.ceil(num_sentences * self.permute_sentence_ratio).astype(int)
num_to_permute_map = dict(zip(example_has_multiple_sentences, num_to_permute))
sentence_ends = np.split(sentence_ends[:, 1], np.unique(sentence_ends[:, 0], return_index=True)[1][1:])
sentence_ends_map = dict(zip(example_has_multiple_sentences, sentence_ends))
for i in range(input_ids.shape[0]):
if i not in example_has_multiple_sentences:
continue
substitutions = np.random.permutation(num_sentences_map[i])[: num_to_permute_map[i]]
ordering = np.arange(0, num_sentences_map[i])
ordering[substitutions] = substitutions[np.random.permutation(num_to_permute_map[i])]
# write shuffled sentences into results
index = 0
for j in ordering:
sentence = input_ids[i, (sentence_ends_map[i][j - 1] if j > 0 else 0) : sentence_ends_map[i][j]]
results[i, index : index + sentence.shape[0]] = sentence
index += sentence.shape[0]
return results
def span_mask_tokens(self, input_ids, labels, do_permute):
"""
Sampling text spans with span lengths drawn from a Poisson distribution and masking them.
"""
special_tokens_mask_labels = [
self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
]
special_tokens_mask_inputs = [
self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in input_ids.tolist()
]
special_tokens_mask_labels = np.array(special_tokens_mask_labels, dtype=bool)
special_tokens_mask_inputs = np.array(special_tokens_mask_inputs, dtype=bool)
# determine how many tokens we need to mask in total
is_token_mask = ~(input_ids == self.tokenizer.pad_token_id) & ~special_tokens_mask_inputs
num_tokens_to_mask = int(math.ceil(is_token_mask.astype(float).sum() * self.mask_ratio))
if num_tokens_to_mask == 0:
return input_ids, labels
# generate a sufficient number of span lengths
span_lengths = np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,))
while np.cumsum(span_lengths, 0)[-1] < num_tokens_to_mask:
span_lengths = np.concatenate(
[span_lengths, np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,))]
)
# remove all spans of length 0
# note that BART inserts additional mask tokens where length == 0,
# which we do not implement for now as it adds additional complexity
span_lengths = span_lengths[span_lengths > 0]
# trim to about num_tokens_to_mask tokens
cutoff_idx = np.argmin(np.abs(np.cumsum(span_lengths, 0) - num_tokens_to_mask)) + 1
span_lengths = span_lengths[:cutoff_idx]
# randomly choose starting positions for masking
token_indices = np.argwhere(is_token_mask == 1)
span_starts = np.random.permutation(token_indices.shape[0])[: span_lengths.shape[0]]
# prepare mask
masked_indices = np.array(token_indices[span_starts])
mask = np.full_like(input_ids, fill_value=False)
# mask starting positions
for mi in masked_indices:
mask[tuple(mi)] = True
span_lengths -= 1
# fill up spans
max_index = input_ids.shape[1] - 1
remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index)
while np.any(remaining):
masked_indices[remaining, 1] += 1
for mi in masked_indices:
mask[tuple(mi)] = True
span_lengths -= 1
remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index)
# place the mask tokens
mask[np.where(special_tokens_mask_inputs)] = False
input_ids[np.where(mask)] = self.tokenizer.mask_token_id
if not do_permute:
labels[np.where(mask == 0)] = -100
else:
labels[np.where(special_tokens_mask_labels)] = -100
# remove mask tokens that are not starts of spans
to_remove = (mask == 1) & np.roll((mask == 1), 1, 1)
new_input_ids = np.full_like(input_ids, fill_value=self.tokenizer.pad_token_id)
for i, example in enumerate(input_ids):
new_example = example[~to_remove[i]]
new_input_ids[i, : new_example.shape[0]] = new_example
return new_input_ids, labels
def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray:
"""Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by
the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned."""
num_samples = len(samples_idx)
if drop_last:
samples_to_remove = num_samples % batch_size
if samples_to_remove != 0:
samples_idx = samples_idx[:-samples_to_remove]
sections_split = num_samples // batch_size
samples_idx = samples_idx.reshape((sections_split, batch_size))
else:
sections_split = math.ceil(num_samples / batch_size)
samples_idx = np.array_split(samples_idx, sections_split)
return samples_idx
def write_train_metric(summary_writer, train_metrics, train_time, step):
summary_writer.scalar("train_time", train_time, step)
train_metrics = get_metrics(train_metrics)
for key, vals in train_metrics.items():
tag = f"train_{key}"
for i, val in enumerate(vals):
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
def write_eval_metric(summary_writer, eval_metrics, step):
for metric_name, value in eval_metrics.items():
summary_writer.scalar(f"eval_{metric_name}", value, step)
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_bart_dlm", model_args, data_args, framework="flax")
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."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
level=logging.INFO,
datefmt="[%X]",
)
# Log on each process the small summary:
logger = logging.getLogger(__name__)
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# Handle the repository creation
if training_args.push_to_hub:
# Retrieve of infer repo_name
repo_name = training_args.hub_model_id
if repo_name is None:
repo_name = Path(training_args.output_dir).absolute().name
# Create repo and retrieve repo_id
api = HfApi()
repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
# 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).
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
num_proc=data_args.preprocessing_num_workers,
trust_remote_code=data_args.trust_remote_code,
)
if "validation" not in datasets.keys():
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,
num_proc=data_args.preprocessing_num_workers,
trust_remote_code=data_args.trust_remote_code,
)
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,
num_proc=data_args.preprocessing_num_workers,
trust_remote_code=data_args.trust_remote_code,
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
num_proc=data_args.preprocessing_num_workers,
)
if "validation" not in datasets.keys():
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,
num_proc=data_args.preprocessing_num_workers,
)
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,
num_proc=data_args.preprocessing_num_workers,
)
# 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.
# Load pretrained model and tokenizer
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
token=model_args.token,
)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
token=model_args.token,
)
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.config_name:
config = BartConfig.from_pretrained(
model_args.config_name,
cache_dir=model_args.cache_dir,
vocab_size=len(tokenizer),
token=model_args.token,
)
elif model_args.model_name_or_path:
config = BartConfig.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = datasets["train"].column_names
else:
column_names = datasets["validation"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
# Use Punkt Sentence Tokenizer to divide a document into a list of sentences
nltk.download("punkt")
sentence_tokenizer = nltk.data.load("tokenizers/punkt/english.pickle")
def sentence_split_function(example):
sents = sentence_tokenizer.tokenize(example["text"])
# use pad token as end of sentence indicator
new_text = tokenizer.bos_token + f"{tokenizer.pad_token}".join(sents) + tokenizer.eos_token
return {"text": new_text}
split_datasets = datasets.map(
sentence_split_function,
batched=False,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
)
# Tokenize every text, then concatenate them together before splitting them in smaller parts.
# Since we make sure that all sequences are of the same length, no attention_mask is needed.
def tokenize_function(examples):
return tokenizer(examples[text_column_name], add_special_tokens=False, return_attention_mask=False)
tokenized_datasets = split_datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=text_column_name,
load_from_cache_file=not data_args.overwrite_cache,
)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of
# max_seq_length.
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, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= max_seq_length:
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
# 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
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
# Enable tensorboard only on the master node
has_tensorboard = is_tensorboard_available()
if has_tensorboard and jax.process_index() == 0:
try:
from flax.metrics.tensorboard import SummaryWriter
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
except ImportError as ie:
has_tensorboard = False
logger.warning(
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
)
else:
logger.warning(
"Unable to display metrics through TensorBoard because the package is not installed: "
"Please run pip install tensorboard to enable."
)
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
dropout_rngs = jax.random.split(rng, jax.local_device_count())
if model_args.model_name_or_path:
model = FlaxBartForConditionalGeneration.from_pretrained(
model_args.model_name_or_path,
config=config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
token=model_args.token,
)
else:
config.vocab_size = len(tokenizer)
model = FlaxBartForConditionalGeneration(
config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
)
# Data collator
# This one will take care of randomly masking the tokens and permuting the sentences.
data_collator = FlaxDataCollatorForBartDenoisingLM(
tokenizer=tokenizer,
decoder_start_token_id=model.config.decoder_start_token_id,
mask_ratio=data_args.mlm_probability,
poisson_lambda=data_args.poisson_lambda,
permute_sentence_ratio=data_args.permute_sentence_ratio,
)
# Store some constant
num_epochs = int(training_args.num_train_epochs)
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
eval_batch_size = per_device_eval_batch_size * jax.device_count()
num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs
# Create learning rate schedule
warmup_fn = optax.linear_schedule(
init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
)
decay_fn = optax.linear_schedule(
init_value=training_args.learning_rate,
end_value=0,
transition_steps=num_train_steps - training_args.warmup_steps,
)
linear_decay_lr_schedule_fn = optax.join_schedules(
schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
)
# We use Optax's "masking" functionality to not apply weight decay
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
# mask boolean with the same structure as the parameters.
# The mask is True for parameters that should be decayed.
def decay_mask_fn(params):
flat_params = traverse_util.flatten_dict(params)
# find out all LayerNorm parameters
layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
layer_norm_named_params = {
layer[-2:]
for layer_norm_name in layer_norm_candidates
for layer in flat_params.keys()
if layer_norm_name in "".join(layer).lower()
}
flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
return traverse_util.unflatten_dict(flat_mask)
# create adam optimizer
if training_args.adafactor:
# We use the default parameters here to initialize adafactor,
# For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
optimizer = optax.adafactor(
learning_rate=linear_decay_lr_schedule_fn,
)
else:
optimizer = optax.adamw(
learning_rate=linear_decay_lr_schedule_fn,
b1=training_args.adam_beta1,
b2=training_args.adam_beta2,
weight_decay=training_args.weight_decay,
mask=decay_mask_fn,
)
# Setup train state
state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer)
# Define gradient update step fn
def train_step(state, batch, dropout_rng):
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
def loss_fn(params):
labels = batch.pop("labels")
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
# compute loss, ignore padded input tokens and special tokens
label_mask = jnp.where(labels > 0, 1.0, 0.0)
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
# take average
loss = loss.sum()
num_labels = label_mask.sum()
return loss, num_labels
grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
(loss, num_labels), grad = grad_fn(state.params)
num_labels = jax.lax.psum(num_labels, "batch")
# true loss = total loss / total samples
loss = jax.lax.psum(loss, "batch")
loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
# true grad = total grad / total samples
grad = jax.lax.psum(grad, "batch")
grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
new_state = state.apply_gradients(grads=grad)
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
return new_state, metrics, new_dropout_rng
# Create parallel version of the train step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
# Define eval fn
def eval_step(params, batch):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
# compute loss, ignore padded input tokens and special tokens
label_mask = jnp.where(labels > 0, 1.0, 0.0)
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
# compute accuracy
accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask
# summarize metrics
metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()}
metrics = jax.lax.psum(metrics, axis_name="batch")
return metrics
p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
# Replicate the train state on each device
state = jax_utils.replicate(state)
train_time = 0
epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0)
for epoch in epochs:
# ======================== Training ================================
train_start = time.time()
train_metrics = []
# Create sampling rng
rng, input_rng = jax.random.split(rng)
# Generate an epoch by shuffling sampling indices from the train dataset
num_train_samples = len(tokenized_datasets["train"])
# Avoid using jax.numpy here in case of TPU training
train_samples_idx = np.random.permutation(np.arange(num_train_samples))
train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size)
# Gather the indexes for creating the batch and do a training step
for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)):
samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
model_inputs = data_collator(samples)
# Model forward
model_inputs = shard(model_inputs.data)
state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
train_metrics.append(train_metric)
cur_step = epoch * (num_train_samples // train_batch_size) + step
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
# Save metrics
train_metric = jax_utils.unreplicate(train_metric)
train_time += time.time() - train_start
if has_tensorboard and jax.process_index() == 0:
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
epochs.write(
f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate:"
f" {train_metric['learning_rate']})"
)
train_metrics = []
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
# ======================== Evaluating ==============================
num_eval_samples = len(tokenized_datasets["validation"])
# Avoid using jax.numpy here in case of TPU training
eval_samples_idx = np.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
eval_metrics = []
for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
model_inputs = data_collator(samples)
# Model forward
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
)
eval_metrics.append(metrics)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics)
eval_normalizer = eval_metrics.pop("normalizer")
eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)
# Update progress bar
epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
# Save metrics
if has_tensorboard and jax.process_index() == 0:
write_eval_metric(summary_writer, eval_metrics, cur_step)
if cur_step % training_args.save_steps == 0 and cur_step > 0:
# save checkpoint after each epoch and push checkpoint to the hub
if jax.process_index() == 0:
params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
model.save_pretrained(training_args.output_dir, params=params)
tokenizer.save_pretrained(training_args.output_dir)
if training_args.push_to_hub:
api.upload_folder(
commit_message=f"Saving weights and logs of step {cur_step}",
folder_path=training_args.output_dir,
repo_id=repo_id,
repo_type="model",
token=training_args.hub_token,
)
# Eval after training
if training_args.do_eval:
num_eval_samples = len(tokenized_datasets["validation"])
# Avoid using jax.numpy here in case of TPU training
eval_samples_idx = np.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
eval_metrics = []
for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
model_inputs = data_collator(samples)
# Model forward
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
)
eval_metrics.append(metrics)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_util.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics)
eval_normalizer = eval_metrics.pop("normalizer")
eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)
try:
perplexity = math.exp(eval_metrics["loss"])
except OverflowError:
perplexity = float("inf")
eval_metrics["perplexity"] = perplexity
if jax.process_index() == 0:
eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
path = os.path.join(training_args.output_dir, "eval_results.json")
with open(path, "w") as f:
json.dump(eval_metrics, f, indent=4, sort_keys=True)
if __name__ == "__main__":
main()
| transformers/examples/flax/language-modeling/run_bart_dlm_flax.py/0 | {
"file_path": "transformers/examples/flax/language-modeling/run_bart_dlm_flax.py",
"repo_id": "transformers",
"token_count": 18514
} |
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# 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 argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
SRC_DIRS = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"text-classification",
"language-modeling",
"summarization",
"token-classification",
"question-answering",
"speech-recognition",
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_flax_speech_recognition_seq2seq
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_t5_mlm_flax
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
def get_setup_file():
parser = argparse.ArgumentParser()
parser.add_argument("-f")
args = parser.parse_args()
return args.f
def get_results(output_dir, split="eval"):
path = os.path.join(output_dir, f"{split}_results.json")
if os.path.exists(path):
with open(path, "r") as f:
return json.load(f)
raise ValueError(f"can't find {path}")
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class ExamplesTests(TestCasePlus):
def test_run_glue(self):
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_glue.py
--model_name_or_path distilbert/distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(sys, "argv", testargs):
run_flax_glue.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
@slow
def test_run_clm(self):
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_clm_flax.py
--model_name_or_path distilbert/distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(sys, "argv", testargs):
run_clm_flax.main()
result = get_results(tmp_dir)
self.assertLess(result["eval_perplexity"], 100)
@slow
def test_run_summarization(self):
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_summarization.py
--model_name_or_path google-t5/t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(sys, "argv", testargs):
run_summarization_flax.main()
result = get_results(tmp_dir, split="test")
self.assertGreaterEqual(result["test_rouge1"], 10)
self.assertGreaterEqual(result["test_rouge2"], 2)
self.assertGreaterEqual(result["test_rougeL"], 7)
self.assertGreaterEqual(result["test_rougeLsum"], 7)
@slow
def test_run_mlm(self):
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_mlm.py
--model_name_or_path distilbert/distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(sys, "argv", testargs):
run_mlm_flax.main()
result = get_results(tmp_dir)
self.assertLess(result["eval_perplexity"], 42)
@slow
def test_run_t5_mlm(self):
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_t5_mlm_flax.py
--model_name_or_path google-t5/t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(sys, "argv", testargs):
run_t5_mlm_flax.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.42)
@slow
def test_run_ner(self):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
epochs = 7 if get_gpu_count() > 1 else 2
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_flax_ner.py
--model_name_or_path google-bert/bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(sys, "argv", testargs):
run_flax_ner.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
self.assertGreaterEqual(result["eval_f1"], 0.3)
@slow
def test_run_qa(self):
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_qa.py
--model_name_or_path google-bert/bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(sys, "argv", testargs):
run_qa.main()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_f1"], 30)
self.assertGreaterEqual(result["eval_exact"], 30)
@slow
def test_run_flax_speech_recognition_seq2seq(self):
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_flax_speech_recognition_seq2seq.py
--model_name_or_path openai/whisper-tiny.en
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config clean
--train_split_name validation
--eval_split_name validation
--trust_remote_code
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=2
--max_train_samples 10
--max_eval_samples 10
--warmup_steps=8
--do_train
--do_eval
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(sys, "argv", testargs):
run_flax_speech_recognition_seq2seq.main()
result = get_results(tmp_dir, split="eval")
self.assertLessEqual(result["eval_wer"], 0.05)
| transformers/examples/flax/test_flax_examples.py/0 | {
"file_path": "transformers/examples/flax/test_flax_examples.py",
"repo_id": "transformers",
"token_count": 4835
} |
# 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.
"""Multiple choice fine-tuning: utilities to work with multiple choice tasks of reading comprehension"""
import csv
import glob
import json
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional
import tqdm
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
logger = logging.getLogger(__name__)
@dataclass(frozen=True)
class InputExample:
"""
A single training/test example for multiple choice
Args:
example_id: Unique id for the example.
question: string. The untokenized text of the second sequence (question).
contexts: list of str. The untokenized text of the first sequence (context of corresponding question).
endings: list of str. multiple choice's options. Its length must be equal to contexts' length.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
example_id: str
question: str
contexts: List[str]
endings: List[str]
label: Optional[str]
@dataclass(frozen=True)
class InputFeatures:
"""
A single set of features of data.
Property names are the same names as the corresponding inputs to a model.
"""
example_id: str
input_ids: List[List[int]]
attention_mask: Optional[List[List[int]]]
token_type_ids: Optional[List[List[int]]]
label: Optional[int]
class Split(Enum):
train = "train"
dev = "dev"
test = "test"
if is_torch_available():
import torch
from torch.utils.data import Dataset
class MultipleChoiceDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach
soon.
"""
features: List[InputFeatures]
def __init__(
self,
data_dir: str,
tokenizer: PreTrainedTokenizer,
task: str,
max_seq_length: Optional[int] = None,
overwrite_cache=False,
mode: Split = Split.train,
):
processor = processors[task]()
cached_features_file = os.path.join(
data_dir,
"cached_{}_{}_{}_{}".format(
mode.value,
tokenizer.__class__.__name__,
str(max_seq_length),
task,
),
)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not overwrite_cache:
logger.info(f"Loading features from cached file {cached_features_file}")
self.features = torch.load(cached_features_file)
else:
logger.info(f"Creating features from dataset file at {data_dir}")
label_list = processor.get_labels()
if mode == Split.dev:
examples = processor.get_dev_examples(data_dir)
elif mode == Split.test:
examples = processor.get_test_examples(data_dir)
else:
examples = processor.get_train_examples(data_dir)
logger.info("Training examples: %s", len(examples))
self.features = convert_examples_to_features(
examples,
label_list,
max_seq_length,
tokenizer,
)
logger.info("Saving features into cached file %s", cached_features_file)
torch.save(self.features, cached_features_file)
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> InputFeatures:
return self.features[i]
if is_tf_available():
import tensorflow as tf
class TFMultipleChoiceDataset:
"""
This will be superseded by a framework-agnostic approach
soon.
"""
features: List[InputFeatures]
def __init__(
self,
data_dir: str,
tokenizer: PreTrainedTokenizer,
task: str,
max_seq_length: Optional[int] = 128,
overwrite_cache=False,
mode: Split = Split.train,
):
processor = processors[task]()
logger.info(f"Creating features from dataset file at {data_dir}")
label_list = processor.get_labels()
if mode == Split.dev:
examples = processor.get_dev_examples(data_dir)
elif mode == Split.test:
examples = processor.get_test_examples(data_dir)
else:
examples = processor.get_train_examples(data_dir)
logger.info("Training examples: %s", len(examples))
self.features = convert_examples_to_features(
examples,
label_list,
max_seq_length,
tokenizer,
)
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
if ex_index % 10000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(examples)))
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
self.dataset = tf.data.Dataset.from_generator(
gen,
(
{
"example_id": tf.int32,
"input_ids": tf.int32,
"attention_mask": tf.int32,
"token_type_ids": tf.int32,
},
tf.int64,
),
(
{
"example_id": tf.TensorShape([]),
"input_ids": tf.TensorShape([None, None]),
"attention_mask": tf.TensorShape([None, None]),
"token_type_ids": tf.TensorShape([None, None]),
},
tf.TensorShape([]),
),
)
def get_dataset(self):
self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))
return self.dataset
def __len__(self):
return len(self.features)
def __getitem__(self, i) -> InputFeatures:
return self.features[i]
class DataProcessor:
"""Base class for data converters for multiple choice data sets."""
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_test_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the test set."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
class RaceProcessor(DataProcessor):
"""Processor for the RACE data set."""
def get_train_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} train".format(data_dir))
high = os.path.join(data_dir, "train/high")
middle = os.path.join(data_dir, "train/middle")
high = self._read_txt(high)
middle = self._read_txt(middle)
return self._create_examples(high + middle, "train")
def get_dev_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} dev".format(data_dir))
high = os.path.join(data_dir, "dev/high")
middle = os.path.join(data_dir, "dev/middle")
high = self._read_txt(high)
middle = self._read_txt(middle)
return self._create_examples(high + middle, "dev")
def get_test_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} test".format(data_dir))
high = os.path.join(data_dir, "test/high")
middle = os.path.join(data_dir, "test/middle")
high = self._read_txt(high)
middle = self._read_txt(middle)
return self._create_examples(high + middle, "test")
def get_labels(self):
"""See base class."""
return ["0", "1", "2", "3"]
def _read_txt(self, input_dir):
lines = []
files = glob.glob(input_dir + "/*txt")
for file in tqdm.tqdm(files, desc="read files"):
with open(file, "r", encoding="utf-8") as fin:
data_raw = json.load(fin)
data_raw["race_id"] = file
lines.append(data_raw)
return lines
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for _, data_raw in enumerate(lines):
race_id = "%s-%s" % (set_type, data_raw["race_id"])
article = data_raw["article"]
for i in range(len(data_raw["answers"])):
truth = str(ord(data_raw["answers"][i]) - ord("A"))
question = data_raw["questions"][i]
options = data_raw["options"][i]
examples.append(
InputExample(
example_id=race_id,
question=question,
contexts=[article, article, article, article], # this is not efficient but convenient
endings=[options[0], options[1], options[2], options[3]],
label=truth,
)
)
return examples
class SynonymProcessor(DataProcessor):
"""Processor for the Synonym data set."""
def get_train_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} train".format(data_dir))
return self._create_examples(self._read_csv(os.path.join(data_dir, "mctrain.csv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} dev".format(data_dir))
return self._create_examples(self._read_csv(os.path.join(data_dir, "mchp.csv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} dev".format(data_dir))
return self._create_examples(self._read_csv(os.path.join(data_dir, "mctest.csv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1", "2", "3", "4"]
def _read_csv(self, input_file):
with open(input_file, "r", encoding="utf-8") as f:
return list(csv.reader(f))
def _create_examples(self, lines: List[List[str]], type: str):
"""Creates examples for the training and dev sets."""
examples = [
InputExample(
example_id=line[0],
question="", # in the swag dataset, the
# common beginning of each
# choice is stored in "sent2".
contexts=[line[1], line[1], line[1], line[1], line[1]],
endings=[line[2], line[3], line[4], line[5], line[6]],
label=line[7],
)
for line in lines # we skip the line with the column names
]
return examples
class SwagProcessor(DataProcessor):
"""Processor for the SWAG data set."""
def get_train_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} train".format(data_dir))
return self._create_examples(self._read_csv(os.path.join(data_dir, "train.csv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} dev".format(data_dir))
return self._create_examples(self._read_csv(os.path.join(data_dir, "val.csv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} dev".format(data_dir))
raise ValueError(
"For swag testing, the input file does not contain a label column. It can not be tested in current code "
"setting!"
)
return self._create_examples(self._read_csv(os.path.join(data_dir, "test.csv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1", "2", "3"]
def _read_csv(self, input_file):
with open(input_file, "r", encoding="utf-8") as f:
return list(csv.reader(f))
def _create_examples(self, lines: List[List[str]], type: str):
"""Creates examples for the training and dev sets."""
if type == "train" and lines[0][-1] != "label":
raise ValueError("For training, the input file must contain a label column.")
examples = [
InputExample(
example_id=line[2],
question=line[5], # in the swag dataset, the
# common beginning of each
# choice is stored in "sent2".
contexts=[line[4], line[4], line[4], line[4]],
endings=[line[7], line[8], line[9], line[10]],
label=line[11],
)
for line in lines[1:] # we skip the line with the column names
]
return examples
class ArcProcessor(DataProcessor):
"""Processor for the ARC data set (request from allennlp)."""
def get_train_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} train".format(data_dir))
return self._create_examples(self._read_json(os.path.join(data_dir, "train.jsonl")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
logger.info("LOOKING AT {} dev".format(data_dir))
return self._create_examples(self._read_json(os.path.join(data_dir, "dev.jsonl")), "dev")
def get_test_examples(self, data_dir):
logger.info("LOOKING AT {} test".format(data_dir))
return self._create_examples(self._read_json(os.path.join(data_dir, "test.jsonl")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1", "2", "3"]
def _read_json(self, input_file):
with open(input_file, "r", encoding="utf-8") as fin:
lines = fin.readlines()
return lines
def _create_examples(self, lines, type):
"""Creates examples for the training and dev sets."""
# There are two types of labels. They should be normalized
def normalize(truth):
if truth in "ABCD":
return ord(truth) - ord("A")
elif truth in "1234":
return int(truth) - 1
else:
logger.info("truth ERROR! %s", str(truth))
return None
examples = []
three_choice = 0
four_choice = 0
five_choice = 0
other_choices = 0
# we deleted example which has more than or less than four choices
for line in tqdm.tqdm(lines, desc="read arc data"):
data_raw = json.loads(line.strip("\n"))
if len(data_raw["question"]["choices"]) == 3:
three_choice += 1
continue
elif len(data_raw["question"]["choices"]) == 5:
five_choice += 1
continue
elif len(data_raw["question"]["choices"]) != 4:
other_choices += 1
continue
four_choice += 1
truth = str(normalize(data_raw["answerKey"]))
assert truth != "None"
question_choices = data_raw["question"]
question = question_choices["stem"]
id = data_raw["id"]
options = question_choices["choices"]
if len(options) == 4:
examples.append(
InputExample(
example_id=id,
question=question,
contexts=[
options[0]["para"].replace("_", ""),
options[1]["para"].replace("_", ""),
options[2]["para"].replace("_", ""),
options[3]["para"].replace("_", ""),
],
endings=[options[0]["text"], options[1]["text"], options[2]["text"], options[3]["text"]],
label=truth,
)
)
if type == "train":
assert len(examples) > 1
assert examples[0].label is not None
logger.info("len examples: %s}", str(len(examples)))
logger.info("Three choices: %s", str(three_choice))
logger.info("Five choices: %s", str(five_choice))
logger.info("Other choices: %s", str(other_choices))
logger.info("four choices: %s", str(four_choice))
return examples
def convert_examples_to_features(
examples: List[InputExample],
label_list: List[str],
max_length: int,
tokenizer: PreTrainedTokenizer,
) -> List[InputFeatures]:
"""
Loads a data file into a list of `InputFeatures`
"""
label_map = {label: i for i, label in enumerate(label_list)}
features = []
for ex_index, example in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
if ex_index % 10000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(examples)))
choices_inputs = []
for ending_idx, (context, ending) in enumerate(zip(example.contexts, example.endings)):
text_a = context
if example.question.find("_") != -1:
# this is for cloze question
text_b = example.question.replace("_", ending)
else:
text_b = example.question + " " + ending
inputs = tokenizer(
text_a,
text_b,
add_special_tokens=True,
max_length=max_length,
padding="max_length",
truncation=True,
return_overflowing_tokens=True,
)
if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0:
logger.info(
"Attention! you are cropping tokens (swag task is ok). "
"If you are training ARC and RACE and you are poping question + options, "
"you need to try to use a bigger max seq length!"
)
choices_inputs.append(inputs)
label = label_map[example.label]
input_ids = [x["input_ids"] for x in choices_inputs]
attention_mask = (
[x["attention_mask"] for x in choices_inputs] if "attention_mask" in choices_inputs[0] else None
)
token_type_ids = (
[x["token_type_ids"] for x in choices_inputs] if "token_type_ids" in choices_inputs[0] else None
)
features.append(
InputFeatures(
example_id=example.example_id,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
label=label,
)
)
for f in features[:2]:
logger.info("*** Example ***")
logger.info("feature: %s" % f)
return features
processors = {"race": RaceProcessor, "swag": SwagProcessor, "arc": ArcProcessor, "syn": SynonymProcessor}
MULTIPLE_CHOICE_TASKS_NUM_LABELS = {"race", 4, "swag", 4, "arc", 4, "syn", 5}
| transformers/examples/legacy/multiple_choice/utils_multiple_choice.py/0 | {
"file_path": "transformers/examples/legacy/multiple_choice/utils_multiple_choice.py",
"repo_id": "transformers",
"token_count": 10031
} |
#!/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.
"""PyTorch Transformer XL model evaluation script.
Adapted from https://github.com/kimiyoung/transformer-xl.
In particular https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/eval.py
This script with default values evaluates a pretrained Transformer-XL on WikiText 103
"""
import argparse
import logging
import math
import time
import torch
from transformers import TransfoXLCorpus, TransfoXLLMHeadModel
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 main():
parser = argparse.ArgumentParser(description="PyTorch Transformer Language Model")
parser.add_argument("--model_name", type=str, default="transfo-xl/transfo-xl-wt103", help="pretrained model name")
parser.add_argument(
"--split", type=str, default="test", choices=["all", "valid", "test"], help="which split to evaluate"
)
parser.add_argument("--batch_size", type=int, default=10, help="batch size")
parser.add_argument("--tgt_len", type=int, default=128, help="number of tokens to predict")
parser.add_argument("--ext_len", type=int, default=0, help="length of the extended context")
parser.add_argument("--mem_len", type=int, default=1600, help="length of the retained previous heads")
parser.add_argument("--clamp_len", type=int, default=1000, help="max positional embedding index")
parser.add_argument("--no_cuda", action="store_true", help="Do not use CUDA even though CUA is available")
parser.add_argument("--work_dir", type=str, required=True, help="path to the work_dir")
parser.add_argument("--no_log", action="store_true", help="do not log the eval result")
parser.add_argument("--same_length", action="store_true", help="set same length attention with masking")
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()
assert args.ext_len >= 0, "extended context length must be non-negative"
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()
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
logger.info("device: {}".format(device))
# Load a pre-processed dataset
# You can also build the corpus yourself using TransfoXLCorpus methods
# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
# and tokenizing the dataset
# The pre-processed corpus is a convertion (using the conversion script )
corpus = TransfoXLCorpus.from_pretrained(args.model_name)
va_iter = corpus.get_iterator("valid", args.batch_size, args.tgt_len, device=device, ext_len=args.ext_len)
te_iter = corpus.get_iterator("test", args.batch_size, args.tgt_len, device=device, ext_len=args.ext_len)
# Load a pre-trained model
model = TransfoXLLMHeadModel.from_pretrained(args.model_name)
model.to(device)
logger.info(
"Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
args.batch_size, args.tgt_len, args.ext_len, args.mem_len, args.clamp_len
)
)
model.reset_memory_length(args.mem_len)
if args.clamp_len > 0:
model.clamp_len = args.clamp_len
if args.same_length:
model.same_length = True
###############################################################################
# Evaluation code
###############################################################################
def evaluate(eval_iter):
# Turn on evaluation mode which disables dropout.
model.eval()
total_len, total_loss = 0, 0.0
start_time = time.time()
with torch.no_grad():
mems = None
for idx, (data, target, seq_len) in enumerate(eval_iter):
ret = model(data, lm_labels=target, mems=mems)
loss, _, mems = ret
loss = loss.mean()
total_loss += seq_len * loss.item()
total_len += seq_len
total_time = time.time() - start_time
logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(total_time, 1000 * total_time / (idx + 1)))
return total_loss / total_len
# Run on test data.
if args.split == "all":
test_loss = evaluate(te_iter)
valid_loss = evaluate(va_iter)
elif args.split == "valid":
valid_loss = evaluate(va_iter)
test_loss = None
elif args.split == "test":
test_loss = evaluate(te_iter)
valid_loss = None
def format_log(loss, split):
log_str = "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(split, loss, math.exp(loss))
return log_str
log_str = ""
if valid_loss is not None:
log_str += format_log(valid_loss, "valid")
if test_loss is not None:
log_str += format_log(test_loss, "test")
logger.info("=" * 100)
logger.info(log_str)
logger.info("=" * 100)
if __name__ == "__main__":
main()
| transformers/examples/legacy/run_transfo_xl.py/0 | {
"file_path": "transformers/examples/legacy/run_transfo_xl.py",
"repo_id": "transformers",
"token_count": 2273
} |
import sys
from transformers import AutoTokenizer
dataset = sys.argv[1]
model_name_or_path = sys.argv[2]
max_len = int(sys.argv[3])
subword_len_counter = 0
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()
with open(dataset, "rt") as f_p:
for line in f_p:
line = line.rstrip()
if not line:
print(line)
subword_len_counter = 0
continue
token = line.split()[0]
current_subwords_len = len(tokenizer.tokenize(token))
# Token contains strange control characters like \x96 or \x95
# Just filter out the complete line
if current_subwords_len == 0:
continue
if (subword_len_counter + current_subwords_len) > max_len:
print("")
print(line)
subword_len_counter = current_subwords_len
continue
subword_len_counter += current_subwords_len
print(line)
| transformers/examples/legacy/token-classification/scripts/preprocess.py/0 | {
"file_path": "transformers/examples/legacy/token-classification/scripts/preprocess.py",
"repo_id": "transformers",
"token_count": 452
} |
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# This file was automatically generated from examples/modular-transformers/modular_multimodal2.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_multimodal2.py file directly. One of our CI enforces this.
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from typing import Optional, Tuple, Union
import torch
from torch import nn
from transformers.utils import add_start_docstrings
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import is_torch_greater_or_equal_than_2_2
from ...utils import (
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
is_flash_attn_greater_or_equal_2_10,
logging,
replace_return_docstrings,
torch_int,
)
from .configuration_multimodal2 import Multimodal2Config, Multimodal2VisionConfig
if is_flash_attn_2_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
class Multimodal2VisionAttention(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 Multimodal2VisionSdpaAttention(Multimodal2VisionAttention):
"""
SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`Multimodal2VisionAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from Multimodal2VisionAttention.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(
"Multimodal2VisionModel is using Multimodal2VisionSdpaAttention, 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,
)
# MULTIMODAL2_VISION 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()
# MULTIMODAL2_VISION 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 Multimodal2VisionFlashAttention2(Multimodal2VisionAttention):
"""
Multimodal2VisionAttention flash attention module. This module inherits from `Multimodal2VisionAttention` 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 Multimodal2VisionMLP(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
MULTIMODAL2_VISION_ATTENTION_CLASSES = {
"eager": Multimodal2VisionAttention,
"sdpa": Multimodal2VisionSdpaAttention,
"flash_attention_2": Multimodal2VisionFlashAttention2,
}
class Multimodal2VisionEncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = MULTIMODAL2_VISION_ATTENTION_CLASSES[config._attn_implementation](config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = Multimodal2VisionMLP(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
class Multimodal2VisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Multimodal2VisionEncoderLayer`].
Args:
config: Multimodal2VisionConfig
"""
def __init__(self, config):
super().__init__()
self.config = config
self.layers = nn.ModuleList([Multimodal2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class Multimodal2VisionEmbeddings(nn.Module):
def __init__(self, config: Multimodal2VisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1] - 1
position_embedding = self.position_embedding.weight.unsqueeze(0)
num_positions = position_embedding.shape[1] - 1
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embedding(self.position_ids)
class_pos_embed = position_embedding[:, :1]
patch_pos_embed = position_embedding[:, 1:]
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size}*{self.image_size})."
)
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
MULTIMODAL2_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`Multimodal2ImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*, defaults `False`):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class Multimodal2VisionTransformer(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = Multimodal2VisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.encoder = Multimodal2VisionEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
@add_start_docstrings_to_model_forward(MULTIMODAL2_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Multimodal2VisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class Multimodal2VisionPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Multimodal2Config
base_model_prefix = "multimodal2_vision"
supports_gradient_checkpointing = True
_supports_sdpa = True
_supports_flash_attn_2 = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, Multimodal2VisionMLP):
pass
MULTIMODAL2_VISION_START_DOCSTRING = "doc"
@add_start_docstrings("New doc", MULTIMODAL2_VISION_START_DOCSTRING)
class Multimodal2VisionModel(Multimodal2VisionPreTrainedModel):
config_class = Multimodal2VisionConfig
main_input_name = "pixel_values"
_no_split_modules = ["Multimodal2VisionEncoderLayer"]
def __init__(self, config: Multimodal2VisionConfig):
super().__init__(config)
self.vision_model = Multimodal2VisionTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.vision_model.embeddings.patch_embedding
@add_start_docstrings_to_model_forward(MULTIMODAL2_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Multimodal2VisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Multimodal2VisionModel
>>> model = Multimodal2VisionModel.from_pretrained("openai/multimodal2-vit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("openai/multimodal2-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled CLS states
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
return self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
| transformers/examples/modular-transformers/modeling_multimodal2.py/0 | {
"file_path": "transformers/examples/modular-transformers/modeling_multimodal2.py",
"repo_id": "transformers",
"token_count": 13650
} |
from typing import ClassVar, List, Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
from ...cache_utils import Cache
class NewTaskModelForNewTask(PaliGemmaForConditionalGeneration):
main_input_name: ClassVar[str] = "doc_input_ids" # transformers-related
def __init__(self, config):
super().__init__(config=config)
self.embedding_dim = self.config.embedding_dim
self.custom_text_proj = nn.Linear(self.config.text_config.hidden_size, self.embedding_dim)
if self.language_model._tied_weights_keys is not None:
self._tied_weights_keys = [f"model.language_model.{k}" for k in self.language_model._tied_weights_keys]
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None,
token_type_ids: Optional[torch.LongTensor] = None,
cache_position: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
num_logits_to_keep: int = 0,
):
r"""
Returns:
"""
vlm_outputs = super().forward(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
token_type_ids=token_type_ids,
cache_position=cache_position,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=True,
num_logits_to_keep=num_logits_to_keep,
)
last_hidden_states = vlm_outputs.hidden_states[-1] # (batch_size, sequence_length, hidden_size)
proj = self.custom_text_proj(last_hidden_states) # (batch_size, sequence_length, dim)
# L2 normalization
embeddings = proj / proj.norm(dim=-1, keepdim=True) # (batch_size, sequence_length, dim)
embeddings = embeddings * attention_mask.unsqueeze(-1) # (batch_size, sequence_length, dim)
return (embeddings,) + vlm_outputs
def resize_token_embeddings(
self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, mean_resizing=True
) -> nn.Embedding:
model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
# Update vocab size
self.config.text_config.vocab_size = model_embeds.num_embeddings
self.config.vocab_size = model_embeds.num_embeddings
self.vocab_size = model_embeds.num_embeddings
return model_embeds
| transformers/examples/modular-transformers/modular_new_task_model.py/0 | {
"file_path": "transformers/examples/modular-transformers/modular_new_task_model.py",
"repo_id": "transformers",
"token_count": 1424
} |
# 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.
"""Finetuning any 🤗 Transformers model for image classification leveraging 🤗 Accelerate."""
import argparse
import json
import logging
import math
import os
from pathlib import Path
import datasets
import evaluate
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import HfApi
from torch.utils.data import DataLoader
from torchvision.transforms import (
CenterCrop,
Compose,
Lambda,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, AutoImageProcessor, AutoModelForImageClassification, SchedulerType, get_scheduler
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")
logger = get_logger(__name__)
require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
def parse_args():
parser = argparse.ArgumentParser(description="Fine-tune a Transformers model on an image classification dataset")
parser.add_argument(
"--dataset_name",
type=str,
default="cifar10",
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset)."
),
)
parser.add_argument("--train_dir", type=str, default=None, help="A folder containing the training data.")
parser.add_argument("--validation_dir", type=str, default=None, help="A folder containing the validation data.")
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--max_eval_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
),
)
parser.add_argument(
"--train_val_split",
type=float,
default=0.15,
help="Percent to split off of train for validation",
)
parser.add_argument(
"--model_name_or_path",
type=str,
help="Path to pretrained model or model identifier from huggingface.co/models.",
default="google/vit-base-patch16-224-in21k",
)
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("--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.")
parser.add_argument(
"--trust_remote_code",
action="store_true",
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."
),
)
parser.add_argument(
"--checkpointing_steps",
type=str,
default=None,
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help="If the training should continue from a checkpoint folder.",
)
parser.add_argument(
"--with_tracking",
action="store_true",
help="Whether to enable experiment trackers for logging.",
)
parser.add_argument(
"--report_to",
type=str,
default="all",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
"Only applicable when `--with_tracking` is passed."
),
)
parser.add_argument(
"--ignore_mismatched_sizes",
action="store_true",
help="Whether or not to enable to load a pretrained model whose head dimensions are different.",
)
parser.add_argument(
"--image_column_name",
type=str,
default="image",
help="The name of the dataset column containing the image data. Defaults to 'image'.",
)
parser.add_argument(
"--label_column_name",
type=str,
default="label",
help="The name of the dataset column containing the labels. Defaults to 'label'.",
)
args = parser.parse_args()
# Sanity checks
if args.dataset_name is None and args.train_dir is None and args.validation_dir is None:
raise ValueError("Need either a dataset name or a training/validation folder.")
if args.push_to_hub or args.with_tracking:
if args.output_dir is None:
raise ValueError(
"Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified."
)
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
return args
def main():
args = parse_args()
# 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_image_classification_no_trainer", args)
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
# in the environment
accelerator_log_kwargs = {}
if args.with_tracking:
accelerator_log_kwargs["log_with"] = args.report_to
accelerator_log_kwargs["project_dir"] = args.output_dir
accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
logger.info(accelerator.state)
# 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, main_process_only=False)
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
api = HfApi()
repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
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 training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees 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.
dataset = load_dataset(args.dataset_name, trust_remote_code=args.trust_remote_code)
else:
data_files = {}
if args.train_dir is not None:
data_files["train"] = os.path.join(args.train_dir, "**")
if args.validation_dir is not None:
data_files["validation"] = os.path.join(args.validation_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder.
dataset_column_names = dataset["train"].column_names if "train" in dataset else dataset["validation"].column_names
if args.image_column_name not in dataset_column_names:
raise ValueError(
f"--image_column_name {args.image_column_name} not found in dataset '{args.dataset_name}'. "
"Make sure to set `--image_column_name` to the correct audio column - one of "
f"{', '.join(dataset_column_names)}."
)
if args.label_column_name not in dataset_column_names:
raise ValueError(
f"--label_column_name {args.label_column_name} not found in dataset '{args.dataset_name}'. "
"Make sure to set `--label_column_name` to the correct text column - one of "
f"{', '.join(dataset_column_names)}."
)
# If we don't have a validation split, split off a percentage of train as validation.
args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split
if isinstance(args.train_val_split, float) and args.train_val_split > 0.0:
split = dataset["train"].train_test_split(args.train_val_split)
dataset["train"] = split["train"]
dataset["validation"] = split["test"]
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
labels = dataset["train"].features[args.label_column_name].names
label2id = {label: str(i) for i, label in enumerate(labels)}
id2label = {str(i): label for i, label in enumerate(labels)}
# Load pretrained model and image processor
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
args.model_name_or_path,
num_labels=len(labels),
id2label=id2label,
label2id=label2id,
finetuning_task="image-classification",
trust_remote_code=args.trust_remote_code,
)
image_processor = AutoImageProcessor.from_pretrained(
args.model_name_or_path,
trust_remote_code=args.trust_remote_code,
)
model = AutoModelForImageClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
ignore_mismatched_sizes=args.ignore_mismatched_sizes,
trust_remote_code=args.trust_remote_code,
)
# Preprocessing the datasets
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
size = image_processor.size["shortest_edge"]
else:
size = (image_processor.size["height"], image_processor.size["width"])
normalize = (
Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
if hasattr(image_processor, "image_mean") and hasattr(image_processor, "image_std")
else Lambda(lambda x: x)
)
train_transforms = Compose(
[
RandomResizedCrop(size),
RandomHorizontalFlip(),
ToTensor(),
normalize,
]
)
val_transforms = Compose(
[
Resize(size),
CenterCrop(size),
ToTensor(),
normalize,
]
)
def preprocess_train(example_batch):
"""Apply _train_transforms across a batch."""
example_batch["pixel_values"] = [
train_transforms(image.convert("RGB")) for image in example_batch[args.image_column_name]
]
return example_batch
def preprocess_val(example_batch):
"""Apply _val_transforms across a batch."""
example_batch["pixel_values"] = [
val_transforms(image.convert("RGB")) for image in example_batch[args.image_column_name]
]
return example_batch
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
if args.max_eval_samples is not None:
dataset["validation"] = dataset["validation"].shuffle(seed=args.seed).select(range(args.max_eval_samples))
# Set the validation transforms
eval_dataset = dataset["validation"].with_transform(preprocess_val)
# DataLoaders creation:
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
labels = torch.tensor([example[args.label_column_name] for example in examples])
return {"pixel_values": pixel_values, "labels": labels}
train_dataloader = DataLoader(
train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.per_device_train_batch_size
)
eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, 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 = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
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
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps
if overrode_max_train_steps
else args.max_train_steps * accelerator.num_processes,
)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Figure out how many steps we should save the Accelerator states
checkpointing_steps = args.checkpointing_steps
if checkpointing_steps is not None and checkpointing_steps.isdigit():
checkpointing_steps = int(checkpointing_steps)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if args.with_tracking:
experiment_config = vars(args)
# TensorBoard cannot log Enums, need the raw value
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
accelerator.init_trackers("image_classification_no_trainer", experiment_config)
# Get the metric function
metric = evaluate.load("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
starting_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
checkpoint_path = args.resume_from_checkpoint
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
dirs.sort(key=os.path.getctime)
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
checkpoint_path = path
path = os.path.basename(checkpoint_path)
accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
accelerator.load_state(checkpoint_path)
# Extract `epoch_{i}` or `step_{i}`
training_difference = os.path.splitext(path)[0]
if "epoch" in training_difference:
starting_epoch = int(training_difference.replace("epoch_", "")) + 1
resume_step = None
completed_steps = starting_epoch * num_update_steps_per_epoch
else:
# need to multiply `gradient_accumulation_steps` to reflect real steps
resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
starting_epoch = resume_step // len(train_dataloader)
completed_steps = resume_step // args.gradient_accumulation_steps
resume_step -= starting_epoch * len(train_dataloader)
# update the progress_bar if load from checkpoint
progress_bar.update(completed_steps)
for epoch in range(starting_epoch, args.num_train_epochs):
model.train()
if args.with_tracking:
total_loss = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We skip the first `n` batches in the dataloader when resuming from a checkpoint
active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
else:
active_dataloader = train_dataloader
for step, batch in enumerate(active_dataloader):
with accelerator.accumulate(model):
outputs = model(**batch)
loss = outputs.loss
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
completed_steps += 1
if isinstance(checkpointing_steps, int):
if completed_steps % checkpointing_steps == 0 and accelerator.sync_gradients:
output_dir = f"step_{completed_steps}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
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,
is_main_process=accelerator.is_main_process,
save_function=accelerator.save,
)
if accelerator.is_main_process:
image_processor.save_pretrained(args.output_dir)
api.upload_folder(
commit_message=f"Training in progress epoch {epoch}",
folder_path=args.output_dir,
repo_id=repo_id,
repo_type="model",
token=args.hub_token,
)
if completed_steps >= args.max_train_steps:
break
model.eval()
for step, batch in enumerate(eval_dataloader):
with torch.no_grad():
outputs = model(**batch)
predictions = outputs.logits.argmax(dim=-1)
predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=predictions,
references=references,
)
eval_metric = metric.compute()
logger.info(f"epoch {epoch}: {eval_metric}")
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric,
"train_loss": total_loss.item() / len(train_dataloader),
"epoch": epoch,
"step": completed_steps,
},
step=completed_steps,
)
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, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
image_processor.save_pretrained(args.output_dir)
api.upload_folder(
commit_message=f"Training in progress epoch {epoch}",
folder_path=args.output_dir,
repo_id=repo_id,
repo_type="model",
token=args.hub_token,
)
if args.checkpointing_steps == "epoch":
output_dir = f"epoch_{epoch}"
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
if args.with_tracking:
accelerator.end_training()
if args.output_dir is not None:
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
)
if accelerator.is_main_process:
image_processor.save_pretrained(args.output_dir)
if args.push_to_hub:
api.upload_folder(
commit_message="End of training",
folder_path=args.output_dir,
repo_id=repo_id,
repo_type="model",
token=args.hub_token,
)
all_results = {f"eval_{k}": v for k, v in eval_metric.items()}
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
json.dump(all_results, f)
if __name__ == "__main__":
main()
| transformers/examples/pytorch/image-classification/run_image_classification_no_trainer.py/0 | {
"file_path": "transformers/examples/pytorch/image-classification/run_image_classification_no_trainer.py",
"repo_id": "transformers",
"token_count": 11490
} |
#!/usr/bin/env python
# coding=utf-8
# 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.
"""
Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) 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=fill-mask
"""
# You can also adapt this script on your own masked 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 torch
from datasets import load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
is_torch_xla_available,
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>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_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."
)
},
)
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)."},
)
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"
},
)
max_seq_length: Optional[int] = field(
default=None,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated."
)
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
mlm_probability: float = field(
default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
)
line_by_line: bool = field(
default=False,
metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
)
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."
)
},
)
streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
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]
if extension not in ["csv", "json", "txt"]:
raise ValueError("`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]
if extension not in ["csv", "json", "txt"]:
raise ValueError("`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_mlm", 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}"
)
# Set the verbosity to info of the Transformers logger (on main process only):
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)
# 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. 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 = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
if extension == "txt":
extension = "text"
raw_datasets = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
# 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,
)
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,
)
# 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.
# 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 = AutoModelForMaskedLM.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,
)
else:
logger.info("Training new model from scratch")
model = AutoModelForMaskedLM.from_config(config, trust_remote_code=model_args.trust_remote_code)
# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
# on a small vocab and want a smaller embedding size, remove this test.
embedding_size = model.get_input_embeddings().weight.shape[0]
if len(tokenizer) > embedding_size:
model.resize_token_embeddings(len(tokenizer))
# 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]
if data_args.max_seq_length is None:
max_seq_length = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`."
)
max_seq_length = 1024
else:
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)
if data_args.line_by_line:
# When using line_by_line, we just tokenize each nonempty line.
padding = "max_length" if data_args.pad_to_max_length else False
def tokenize_function(examples):
# Remove empty lines
examples[text_column_name] = [
line for line in examples[text_column_name] if len(line) > 0 and not line.isspace()
]
return tokenizer(
examples[text_column_name],
padding=padding,
truncation=True,
max_length=max_seq_length,
# We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
# receives the `special_tokens_mask`.
return_special_tokens_mask=True,
)
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=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset line_by_line",
)
else:
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
remove_columns=[text_column_name],
)
else:
# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
# We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
# efficient when it receives the `special_tokens_mask`.
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
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 every text in dataset",
)
else:
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
remove_columns=column_names,
)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of
# max_seq_length.
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 < max_seq_length 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 // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
# 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
with training_args.main_process_first(desc="grouping texts together"):
if not data_args.streaming:
tokenized_datasets = tokenized_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 {max_seq_length}",
)
else:
tokenized_datasets = tokenized_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 = tokenized_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 = tokenized_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", cache_dir=model_args.cache_dir)
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
labels = labels.reshape(-1)
preds = preds.reshape(-1)
mask = labels != -100
labels = labels[mask]
preds = preds[mask]
return metric.compute(predictions=preds, references=labels)
# Data collator
# This one will take care of randomly masking the tokens.
pad_to_multiple_of_8 = data_args.line_by_line and training_args.fp16 and not data_args.pad_to_max_length
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm_probability=data_args.mlm_probability,
pad_to_multiple_of=8 if pad_to_multiple_of_8 else 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,
processing_class=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics if training_args.do_eval and not is_torch_xla_available() else None,
preprocess_logits_for_metrics=preprocess_logits_for_metrics
if training_args.do_eval and not is_torch_xla_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": "fill-mask"}
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_mlm.py/0 | {
"file_path": "transformers/examples/pytorch/language-modeling/run_mlm.py",
"repo_id": "transformers",
"token_count": 12786
} |
#! /usr/bin/python3
import argparse
import logging
import os
import sys
from collections import namedtuple
import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm
from transformers import BertTokenizer
from .utils_summarization import (
CNNDMDataset,
build_mask,
compute_token_type_ids,
encode_for_summarization,
truncate_or_pad,
)
logger = logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
Batch = namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])
def evaluate(args):
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased", do_lower_case=True)
model = BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
model.to(args.device)
model.eval()
symbols = {
"BOS": tokenizer.vocab["[unused0]"],
"EOS": tokenizer.vocab["[unused1]"],
"PAD": tokenizer.vocab["[PAD]"],
}
if args.compute_rouge:
reference_summaries = []
generated_summaries = []
import nltk
import rouge
nltk.download("punkt")
rouge_evaluator = rouge.Rouge(
metrics=["rouge-n", "rouge-l"],
max_n=2,
limit_length=True,
length_limit=args.beam_size,
length_limit_type="words",
apply_avg=True,
apply_best=False,
alpha=0.5, # Default F1_score
weight_factor=1.2,
stemming=True,
)
# these (unused) arguments are defined to keep the compatibility
# with the legacy code and will be deleted in a next iteration.
args.result_path = ""
args.temp_dir = ""
data_iterator = build_data_iterator(args, tokenizer)
predictor = build_predictor(args, tokenizer, symbols, model)
logger.info("***** Running evaluation *****")
logger.info(" Number examples = %d", len(data_iterator.dataset))
logger.info(" Batch size = %d", args.batch_size)
logger.info("")
logger.info("***** Beam Search parameters *****")
logger.info(" Beam size = %d", args.beam_size)
logger.info(" Minimum length = %d", args.min_length)
logger.info(" Maximum length = %d", args.max_length)
logger.info(" Alpha (length penalty) = %.2f", args.alpha)
logger.info(" Trigrams %s be blocked", ("will" if args.block_trigram else "will NOT"))
for batch in tqdm(data_iterator):
batch_data = predictor.translate_batch(batch)
translations = predictor.from_batch(batch_data)
summaries = [format_summary(t) for t in translations]
save_summaries(summaries, args.summaries_output_dir, batch.document_names)
if args.compute_rouge:
reference_summaries += batch.tgt_str
generated_summaries += summaries
if args.compute_rouge:
scores = rouge_evaluator.get_scores(generated_summaries, reference_summaries)
str_scores = format_rouge_scores(scores)
save_rouge_scores(str_scores)
print(str_scores)
def save_summaries(summaries, path, original_document_name):
"""Write the summaries in fies that are prefixed by the original
files' name with the `_summary` appended.
Attributes:
original_document_names: List[string]
Name of the document that was summarized.
path: string
Path were the summaries will be written
summaries: List[string]
The summaries that we produced.
"""
for summary, document_name in zip(summaries, original_document_name):
# Prepare the summary file's name
if "." in document_name:
bare_document_name = ".".join(document_name.split(".")[:-1])
extension = document_name.split(".")[-1]
name = bare_document_name + "_summary." + extension
else:
name = document_name + "_summary"
file_path = os.path.join(path, name)
with open(file_path, "w") as output:
output.write(summary)
def format_summary(translation):
"""Transforms the output of the `from_batch` function
into nicely formatted summaries.
"""
raw_summary, _, _ = translation
summary = (
raw_summary.replace("[unused0]", "")
.replace("[unused3]", "")
.replace("[PAD]", "")
.replace("[unused1]", "")
.replace(r" +", " ")
.replace(" [unused2] ", ". ")
.replace("[unused2]", "")
.strip()
)
return summary
def format_rouge_scores(scores):
return """\n
****** ROUGE SCORES ******
** ROUGE 1
F1 >> {:.3f}
Precision >> {:.3f}
Recall >> {:.3f}
** ROUGE 2
F1 >> {:.3f}
Precision >> {:.3f}
Recall >> {:.3f}
** ROUGE L
F1 >> {:.3f}
Precision >> {:.3f}
Recall >> {:.3f}""".format(
scores["rouge-1"]["f"],
scores["rouge-1"]["p"],
scores["rouge-1"]["r"],
scores["rouge-2"]["f"],
scores["rouge-2"]["p"],
scores["rouge-2"]["r"],
scores["rouge-l"]["f"],
scores["rouge-l"]["p"],
scores["rouge-l"]["r"],
)
def save_rouge_scores(str_scores):
with open("rouge_scores.txt", "w") as output:
output.write(str_scores)
#
# LOAD the dataset
#
def build_data_iterator(args, tokenizer):
dataset = load_and_cache_examples(args, tokenizer)
sampler = SequentialSampler(dataset)
def collate_fn(data):
return collate(data, tokenizer, block_size=512, device=args.device)
iterator = DataLoader(
dataset,
sampler=sampler,
batch_size=args.batch_size,
collate_fn=collate_fn,
)
return iterator
def load_and_cache_examples(args, tokenizer):
dataset = CNNDMDataset(args.documents_dir)
return dataset
def collate(data, tokenizer, block_size, device):
"""Collate formats the data passed to the data loader.
In particular we tokenize the data batch after batch to avoid keeping them
all in memory. We output the data as a namedtuple to fit the original BertAbs's
API.
"""
data = [x for x in data if not len(x[1]) == 0] # remove empty_files
names = [name for name, _, _ in data]
summaries = [" ".join(summary_list) for _, _, summary_list in data]
encoded_text = [encode_for_summarization(story, summary, tokenizer) for _, story, summary in data]
encoded_stories = torch.tensor(
[truncate_or_pad(story, block_size, tokenizer.pad_token_id) for story, _ in encoded_text]
)
encoder_token_type_ids = compute_token_type_ids(encoded_stories, tokenizer.cls_token_id)
encoder_mask = build_mask(encoded_stories, tokenizer.pad_token_id)
batch = Batch(
document_names=names,
batch_size=len(encoded_stories),
src=encoded_stories.to(device),
segs=encoder_token_type_ids.to(device),
mask_src=encoder_mask.to(device),
tgt_str=summaries,
)
return batch
def decode_summary(summary_tokens, tokenizer):
"""Decode the summary and return it in a format
suitable for evaluation.
"""
summary_tokens = summary_tokens.to("cpu").numpy()
summary = tokenizer.decode(summary_tokens)
sentences = summary.split(".")
sentences = [s + "." for s in sentences]
return sentences
def main():
"""The main function defines the interface with the users."""
parser = argparse.ArgumentParser()
parser.add_argument(
"--documents_dir",
default=None,
type=str,
required=True,
help="The folder where the documents to summarize are located.",
)
parser.add_argument(
"--summaries_output_dir",
default=None,
type=str,
required=False,
help="The folder in wich the summaries should be written. Defaults to the folder where the documents are",
)
parser.add_argument(
"--compute_rouge",
default=False,
type=bool,
required=False,
help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.",
)
# EVALUATION options
parser.add_argument(
"--no_cuda",
default=False,
type=bool,
help="Whether to force the execution on CPU.",
)
parser.add_argument(
"--batch_size",
default=4,
type=int,
help="Batch size per GPU/CPU for training.",
)
# BEAM SEARCH arguments
parser.add_argument(
"--min_length",
default=50,
type=int,
help="Minimum number of tokens for the summaries.",
)
parser.add_argument(
"--max_length",
default=200,
type=int,
help="Maixmum number of tokens for the summaries.",
)
parser.add_argument(
"--beam_size",
default=5,
type=int,
help="The number of beams to start with for each example.",
)
parser.add_argument(
"--alpha",
default=0.95,
type=float,
help="The value of alpha for the length penalty in the beam search.",
)
parser.add_argument(
"--block_trigram",
default=True,
type=bool,
help="Whether to block the existence of repeating trigrams in the text generated by beam search.",
)
args = parser.parse_args()
# Select device (distibuted not available)
args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
# Check the existence of directories
if not args.summaries_output_dir:
args.summaries_output_dir = args.documents_dir
if not documents_dir_is_valid(args.documents_dir):
raise FileNotFoundError(
"We could not find the directory you specified for the documents to summarize, or it was empty. Please"
" specify a valid path."
)
os.makedirs(args.summaries_output_dir, exist_ok=True)
evaluate(args)
def documents_dir_is_valid(path):
if not os.path.exists(path):
return False
file_list = os.listdir(path)
if len(file_list) == 0:
return False
return True
if __name__ == "__main__":
main()
| transformers/examples/research_projects/bertabs/run_summarization.py/0 | {
"file_path": "transformers/examples/research_projects/bertabs/run_summarization.py",
"repo_id": "transformers",
"token_count": 4319
} |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
NON_ALPHA = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
MIN_NUM_TOKENS = 10
NUM_PERM = 256
def get_min_hash(tokens: List[str]) -> Optional[MinHash]:
"""Compute the MinHash of a code snippet."""
if len(tokens) < MIN_NUM_TOKENS:
return None
min_hash = MinHash(num_perm=NUM_PERM)
for token in set(tokens):
min_hash.update(token.encode())
return min_hash
def get_tokens(code: str) -> Set[str]:
"""Tokenize a code snippet."""
return {t for t in NON_ALPHA.split(code) if len(t.strip()) > 0}
class DuplicationIndex:
def __init__(
self,
*,
duplication_jaccard_threshold: float = 0.85,
):
self._duplication_jaccard_threshold = duplication_jaccard_threshold
self._num_perm = NUM_PERM
self._index = MinHashLSH(threshold=self._duplication_jaccard_threshold, num_perm=self._num_perm)
self._duplicate_clusters = defaultdict(set)
def add(self, code_key: Tuple, min_hash: MinHash) -> None:
"""Add a key to _index (MinHashLSH)
the min_hash is used to query closest matches based on the jaccard_threshold.
The new key is either added to a existing cluster of one close match,
or a new cluster is created. The clusters created in this way, depend on the order of add.
Args:
code_key (Tuple of (index, repo_name, path)):
Theoritically any hasbale key. Here we use a tuple to retrieve the information later.
min_hash: MinHash of the code_key.
"""
close_duplicates = self._index.query(min_hash)
if code_key in self._index.keys:
print(f"Duplicate key {code_key}")
return
self._index.insert(code_key, min_hash)
if len(close_duplicates) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(code_key)
break
else:
self._duplicate_clusters[close_duplicates[0]].add(code_key)
def get_duplicate_clusters(self) -> List[List[Dict]]:
"""Export the duplicate clusters.
For each cluster, the first element is the base element of the cluster.
The base element has an estimation jaccard similarity higher than the threshold with all the other elements.
Returns:
duplicate_clusters (List[List[Dict]]):
List of duplicate clusters.
"""
duplicate_clusters = []
for base, duplicates in self._duplicate_clusters.items():
cluster = [base] + list(duplicates)
# reformat the cluster to be a list of dict
cluster = [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
duplicate_clusters.append(cluster)
return duplicate_clusters
def save(self, filepath) -> None:
duplicate_clusters = self.get_duplicate_clusters()
with open(filepath, "w") as f:
json.dump(duplicate_clusters, f)
def _compute_min_hash(element):
index, data = element
min_hash = get_min_hash([t for t in NON_ALPHA.split(data["content"]) if len(t.strip()) > 0])
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def minhash_iter(dataset_iterator: Type[Dataset]):
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash,
ThreadedIterator(dataset_iterator, max_queue_size=10000),
chunksize=100,
):
if data is not None:
yield data
def make_duplicate_clusters(dataset_iterator: Type[Dataset], jaccard_threshold: float):
"""Find duplicate clusters in the dataset in two steps:
1. Compute MinHash for each code snippet. MinHash is a tool for fast jaccard similarity estimation.
This step is computed using an asynchronous multiprocessing pool, minhash_iter
2. Find duplicate clusters. The computed MinHash is added sequentially to the DuplicationIndex.
This step cannot be parallelized. So using asynchronous thread in the previous step helps to speed up the process.
"""
di = DuplicationIndex(duplication_jaccard_threshold=jaccard_threshold)
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(dataset_iterator)), max_queue_size=100)):
di.add(filename, min_hash)
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def jaccard_similarity(code1: str, code2: str) -> float:
"""Compute the Jaccard similarity of two code snippets."""
tokens1 = get_tokens(code1)
tokens2 = get_tokens(code2)
return len(tokens1 & tokens2) / len(tokens1 | tokens2)
_shared_dataset = None
def _find_cluster_extremes_shared(cluster, jaccard_threshold):
"""Find a reduced cluster such that each code in the origin cluster is similar to at least one code in the reduced cluster.
Two codes are similar if their Jaccard similarity is above the threshold.
Args:
cluster (List[dict]):
cluster is a list of dict, each dict contains the following keys:
- base_index
- repo_name
- path
This is a typical output of DuplicationIndex.get_duplicate_clusters()
jaccard_threshold (float):
threshold for Jaccard similarity.
Two codes are similar if their Jaccard similarity is above the threshold.
Returns:
extremes (List[dict]):
A reduced representation of the cluster. The field copies is added to each dict.
The copies field indicates the number of similar codes in the cluster for a extreme.
"""
extremes = []
for element1 in cluster:
code1 = _shared_dataset[element1["base_index"]]["content"]
for element2 in extremes:
code2 = _shared_dataset[element2["base_index"]]["content"]
if jaccard_similarity(code1, code2) >= jaccard_threshold:
element2["copies"] += 1
break
else:
element1["copies"] = 1
extremes.append(element1)
return extremes
def find_extremes(cluster_list, dataset, jaccard_threshold):
"""Call the _find_cluster_extremes_shared function in a parallel fashion.
Args:
cluster_list (List[List[Dict]]):
each cluster is a list of dicts with the key base_index,
referring to the index of the base code in the dataset.
dataset (Type[Dataset]):
dataset is used to access the content of the code snippets,
using the base_index from the cluster_list.
dataset is shared between all the processes using a glabal variable (any other way to share the dataset?),
otherwise the multi processing is not speeded up.
jaccard_threshold (float):
the threshold for the jaccard similarity. The default value is 0.85
Returns:
extremes_list (List[Dict]):
Each cluster is reduced to extremes.
See _find_cluster_extremes_shared for the definition of extremes.
"""
global _shared_dataset
_shared_dataset = dataset
extremes_list = []
f = partial(_find_cluster_extremes_shared, jaccard_threshold=jaccard_threshold)
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
f,
cluster_list,
),
total=len(cluster_list),
):
extremes_list.append(extremes)
return extremes_list
def deduplicate_dataset(
dataset: Type[Dataset], jaccard_threshold: float = 0.85
) -> Tuple[Type[Dataset], List[List[Dict]]]:
"""Deduplicate the dataset using minhash and jaccard similarity.
This function first generate duplicate clusters, then each cluster
is reduced to the extremes that are similar to the other elements in the cluster.
Codes are called similar if their Jaccard similarity is greater than jaccard_threshold (0.85 default).
Args:
dataset (Type[Dataset]):
The dataset to deduplicate.
jaccard_threshold (float, default=0.85):
jaccard threshold to determine if two codes are similar
Returns:
ds_dedup (Type[Dataset]):
The deduplicated dataset.
duplicate_clusters (List[List[Dict]]):
The list of duplicate clusters.
Each cluster is a list of dicts with the following keys:
- base_index : int
The index of the code in the original dataset.
- repo_name : str
- path : str
- copies : int
The number of copies of the code in the cluster. (find_cluster_extremes)
- is_extreme : bool
Whether the code is an extreme in the cluster.
All the codes in the cluster are removed from the dataset except the extremes.
Example:
>>> from datasets import load_dataset
>>> from minhash_deduplication import deduplicate_dataset
>>> ds = load_dataset("lvwerra/codeparrot-clean", split="train")
>>> ds_dedup, duplicate_clusters = deduplicate_dataset(ds, jaccard_threshold=0.85)
"""
duplicate_clusters = make_duplicate_clusters(dataset, jaccard_threshold)
duplicate_indices = {x["base_index"] for cluster in duplicate_clusters for x in cluster}
extreme_dict = {}
extremes_clusters = find_extremes(duplicate_clusters, dataset, jaccard_threshold)
for extremes in extremes_clusters:
for element in extremes:
extreme_dict[element["base_index"]] = element
remove_indices = duplicate_indices - set(extreme_dict.keys())
ds_filter = dataset.filter(lambda x, idx: idx not in remove_indices, with_indices=True)
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
element["is_extreme"] = element["base_index"] in extreme_dict
if element["is_extreme"]:
element["copies"] = extreme_dict[element["base_index"]]["copies"]
print(f"Original dataset size: {len(dataset)}")
print(f"Number of duplicate clusters: {len(duplicate_clusters)}")
print(f"Files in duplicate cluster: {len(duplicate_indices)}")
print(f"Unique files in duplicate cluster: {len(extreme_dict)}")
print(f"Filtered dataset size: {len(ds_filter)}")
return ds_filter, duplicate_clusters
| transformers/examples/research_projects/codeparrot/scripts/minhash_deduplication.py/0 | {
"file_path": "transformers/examples/research_projects/codeparrot/scripts/minhash_deduplication.py",
"repo_id": "transformers",
"token_count": 4391
} |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
def get_setup_file():
parser = argparse.ArgumentParser()
parser.add_argument("-f")
args = parser.parse_args()
return args.f
class DeeBertTests(TestCasePlus):
def setup(self) -> None:
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
def run_and_check(self, args):
n_gpu = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0, "run_glue_deebert.py")
with patch.object(sys, "argv", args):
result = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(value, 0.666)
@slow
@require_torch_non_multi_gpu
def test_glue_deebert_train(self):
train_args = """
--model_type roberta
--model_name_or_path FacebookAI/roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/FacebookAI/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
""".split()
self.run_and_check(train_args)
eval_args = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/FacebookAI/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/FacebookAI/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(eval_args)
entropy_eval_args = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/FacebookAI/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/FacebookAI/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(entropy_eval_args)
| transformers/examples/research_projects/deebert/test_glue_deebert.py/0 | {
"file_path": "transformers/examples/research_projects/deebert/test_glue_deebert.py",
"repo_id": "transformers",
"token_count": 1881
} |
# How to propose a Flax/JAX + Transformers project
Great that you've opened this document!
While we at 🤗 are proposing a couple of projects, we strongly
believe that the community can come up with much more **creative**, **fun**, and
**impactful** projects on their own. This being said, we are really looking forward
to seeing your project proposal!
## What a project should be about
The proposed project should fall into the machine learning fields of **Natural Language Processing (NLP)** and/or **Computer Vision (CV)** (possibly also **Speech Recognition (ASR)** depending on whether Speech Recognition models are available in Flax in due time) and aim at solving a specific task.
Possible tasks can belong to:
* text classification
* text generation
* image recognition
* image processing
* image captioning
* audio classification
* and other tasks you can think of!
The clearer a task is defined, the better your project proposal is.
*E.g.* "Using a T5 model to learn grammar correction in French" or "Adapting a pre-trained CLIP model for zero-shot image classification in Spanish" are **well-defined and clear** project proposals, while something like "Train a language model" or "Image classification" are **too vague**.
There is no limit to your creativity as long as the project is feasible and ethical.
The more creative & specific your project proposal, the more interesting it will be,
and the more likely will you find motivated team members to work on your project!
To get an idea of how to formulate your project proposals, you can browse through
existing project proposals on the [forum](https://discuss.huggingface.co/c/flax-jax-projects/22).
## How to submit a project proposal
First, you should make sure that you are [logged in](https://huggingface.co/login?sso=bm9uY2U9OTRlNjZjZmZhYjMwMmJmMWMyYjc5MmFiMTMyMzY5ODYmcmV0dXJuX3Nzb191cmw9aHR0cHMlM0ElMkYlMkZkaXNjdXNzLmh1Z2dpbmdmYWNlLmNvJTJGc2Vzc2lvbiUyRnNzb19sb2dpbg%3D%3D&sig=429ad8924bcb33c40f9823027ea749abb55d393f4f58924f36a2dba3ab0a48da) with your Hugging Face account on the forum.
Second, make sure that your project idea doesn't already exist by checking [existing projects](https://discuss.huggingface.co/c/flax-jax-projects/22).
If your project already exists - great! This means that you can comment and improve
the existing idea and join the project to form a team! If your project idea already
exists for a different language, feel free to submit the same project idea, just in
a different language.
Third, having ensured that your project doesn't exist, click on the *"New Topic"*
button on the [Flax/JAX Projects Forum category](https://discuss.huggingface.co/c/flax-jax-projects/22) to create a new project proposal.
Fourth, make sure that your project proposal includes the following information:
1. *A clear description of the project*
2. *In which language should the project be conducted?* English, German, Chinese, ...? It can also be a multi-lingual project
3. *Which model should be used?* If you want to adapt an existing model, you can add the link to one of the 4000 available checkpoints in JAX [here](https://huggingface.co/models?filter=jax) If you want to train a model from scratch, you can simply state the model architecture to be used, *e.g.* BERT, CLIP, etc. You can also base your project on a model that is not part of transformers. For an overview of libraries based on JAX, you can take a look at [awesome-jax](https://github.com/n2cholas/awesome-jax#awesome-jax-). **Note** that for a project that is not based on Transformers it will be more difficult for the 🤗 team to help you. Also have a look at the section [Quickstart Flax & Jax in Transformers](https://github.com/huggingface/transformers/tree/main/examples/research_projects/jax-projects#quickstart-flax-and-jax-in-transformers) to see what model architectures are currently supported in 🤗 Transformers.
4. *What data should be used?* It is important to state at least what kind of data you would like to use. Ideally, you can already point to publicly available data or a dataset in the 🤗 Datasets library.
5. *Are similar training scripts available in Flax/JAX?* It would be important to find similar training scripts that already exist in Flax/JAX. *E.g.* if you are working on a Seq-to-Seq task, you can make use of the [`run_summarization_flax.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/summarization/run_summarization_flax.py) script which is very similar to any seq2seq training. Also have a look at the section [Quickstart Flax & Jax in Transformers](https://github.com/huggingface/transformers/tree/main/examples/research_projects/jax-projects#quickstart-flax-and-jax-in-transformers) to see what training scripts are currently supported in 🤗 Transformers.
6. *(Optionally) What are possible challenges?* List possible difficulties with your project. *E.g.* If you know that training convergence usually takes a lot of time, it is worth stating this here!
7. *(Optionally) What is the desired project outcome?* - How would you like to demo your project? One could *e.g.* create a Streamlit application.
8. *(Optionally) Links to read upon* - Can you provide any links that would help the reader to better understand your project idea?
Feel free to copy-paste the following format for your project proposal and fill out the respective sections:
```
# <FILL ME: Name of project>
<FILL ME: A clear description of the project>
## 2. Language
The model will be trained in <FILL ME: which language?>.
## 3. Model
<FILL ME: 3. Which model should be used?>
## 4. Datasets
<FILL ME: 4. Which data should be used?>
Possible links to publicly available datasets include:
- <FILL ME: Link 1 to dataset>
- <FILL ME: Link 2 to dataset>
- <FILL ME: Link 3 to dataset>
## 5. Training scripts
<FILL ME: 5. Are there publicly available training scripts that can be used/tweaked for the project?>
We can make use of <FILL ME: link to training script> to train the model.>
## 6. (Optional) Challenges
<(Optionally) FILL ME: 6. What are possible challenges?>
## 7. (Optional) Desired project outcome
<(Optionally) FILL ME: 7. What is the desired project outcome? A demo?>
## 8. (Optional) Reads
The following links can be useful to better understand the project and
what has previously been done.
- <FILL ME: Link 1 to read>
- <FILL ME: Link 2 to read>
- <FILL ME: Link 3 to read>
```
To see how a proposed project looks like, please have a look at submitted project
proposals [here](https://discuss.huggingface.co/c/flax-jax-projects/22).
## Will my project proposal be selected?
Having submitted a project proposal, you can now promote your idea in the Slack channel `#flax-jax-community-week` to try to convince other participants to join your project!
Once other people have joined your project, one of the organizers (`@Suzana, @valhalla, @osanseviero, @patrickvonplaten`) will officially create a team for your project and add your project to [this google sheet](https://docs.google.com/spreadsheets/d/1GpHebL7qrwJOc9olTpIPgjf8vOS0jNb6zR_B8x_Jtik/edit?usp=sharing).
| transformers/examples/research_projects/jax-projects/HOW_TO_PROPOSE_PROJECT.md/0 | {
"file_path": "transformers/examples/research_projects/jax-projects/HOW_TO_PROPOSE_PROJECT.md",
"repo_id": "transformers",
"token_count": 2070
} |
<!---
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.
-->
# Model parallel language model training example
The following example showcases how to train/fine-tune GPTNeo model with model parallelism using
the JAX/Flax backend and the [`pjit`](https://jax.readthedocs.io/en/latest/jax.experimental.pjit.html) transformation.
> Note: The example is experimental and might have bugs. Also currently it only supports single V3-8.
The `partition.py` file defines the `PyTree` of `ParitionSpec` for the GPTNeo model which describes how the model will be sharded.
The actual sharding is auto-matically handled by `pjit`. The weights are sharded across all local devices.
To adapt the script for other models, we need to also change the `ParitionSpec` accordingly.
TODO: Add more explantion.
Before training, let's prepare our model first. To be able to shard the model, the sharded dimension needs to be a multiple of devices it'll be sharded on. But GPTNeo's vocab size is 50257, so we need to resize the embeddings accordingly.
```python
from transformers import FlaxGPTNeoForCausalLM, GPTNeoConfig
model = FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
emb = jnp.zeros((50264, model.config.hidden_size))
# update the first 50257 weights using pre-trained weights
emb = emb.at[:50257, :].set(model.params["transformer"]["wte"]["embedding"])
params = model.params
params["transformer"]["wte"]["embedding"] = emb
# initialize a random model with the right vocab_size
config = GPTNeoConfig.from_pretrained("EleutherAI/gpt-neo-1.3B", vocab_size=50264)
model = FlaxGPTNeoForCausalLM(config)
# assign the pre-trained weights and save the model.
model.params = params
model.save_pretrained("gpt-neo-1.3B")
```
### Train Model
```bash
python run_clm_mp.py \
--model_name_or_path gpt-neo-1.3B \
--tokenizer_name openai-community/gpt2 \
--dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
--do_train --do_eval \
--block_size 1024 \
--num_train_epochs 5 \
--learning_rate 4e-6 \
--per_device_train_batch_size 3 --per_device_eval_batch_size 3 \
--overwrite_output_dir --output_dir ~/tmp/flax-clm \
--cache_dir ~/datasets_cache/wikitext --dtype bfloat16 \
--logging_steps 96 --eval_steps 96
``` | transformers/examples/research_projects/jax-projects/model_parallel/README.md/0 | {
"file_path": "transformers/examples/research_projects/jax-projects/model_parallel/README.md",
"repo_id": "transformers",
"token_count": 918
} |
<jupyter_start><jupyter_code># %pip install-r requirements.txt
from IPython.display import clear_output, Image, display
import PIL.Image
import io
import json
import torch
import numpy as np
from processing_image import Preprocess
from visualizing_image import SingleImageViz
from modeling_frcnn import GeneralizedRCNN
from utils import Config
import utils
from transformers import LxmertForQuestionAnswering, LxmertTokenizer
import wget
import pickle
import os
# URL = "https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/images/input.jpg",
URL = "https://vqa.cloudcv.org/media/test2014/COCO_test2014_000000262567.jpg"
OBJ_URL = "https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/objects_vocab.txt"
ATTR_URL = "https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/attributes_vocab.txt"
GQA_URL = "https://raw.githubusercontent.com/airsplay/lxmert/master/data/gqa/trainval_label2ans.json"
VQA_URL = "https://raw.githubusercontent.com/airsplay/lxmert/master/data/vqa/trainval_label2ans.json"
# for visualizing output
def showarray(a, fmt="jpeg"):
a = np.uint8(np.clip(a, 0, 255))
f = io.BytesIO()
PIL.Image.fromarray(a).save(f, fmt)
display(Image(data=f.getvalue()))
# load object, attribute, and answer labels
objids = utils.get_data(OBJ_URL)
attrids = utils.get_data(ATTR_URL)
gqa_answers = utils.get_data(GQA_URL)
vqa_answers = utils.get_data(VQA_URL)
# load models and model components
frcnn_cfg = Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
frcnn = GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned", config=frcnn_cfg)
image_preprocess = Preprocess(frcnn_cfg)
lxmert_tokenizer = LxmertTokenizer.from_pretrained("unc-nlp/lxmert-base-uncased")
lxmert_gqa = LxmertForQuestionAnswering.from_pretrained("unc-nlp/lxmert-gqa-uncased")
lxmert_vqa = LxmertForQuestionAnswering.from_pretrained("unc-nlp/lxmert-vqa-uncased")
# image viz
frcnn_visualizer = SingleImageViz(URL, id2obj=objids, id2attr=attrids)
# run frcnn
images, sizes, scales_yx = image_preprocess(URL)
output_dict = frcnn(
images,
sizes,
scales_yx=scales_yx,
padding="max_detections",
max_detections=frcnn_cfg.max_detections,
return_tensors="pt",
)
# add boxes and labels to the image
frcnn_visualizer.draw_boxes(
output_dict.get("boxes"),
output_dict.pop("obj_ids"),
output_dict.pop("obj_probs"),
output_dict.pop("attr_ids"),
output_dict.pop("attr_probs"),
)
showarray(frcnn_visualizer._get_buffer())
test_questions_for_url1 = [
"Where is this scene?",
"what is the man riding?",
"What is the man wearing?",
"What is the color of the horse?",
]
test_questions_for_url2 = [
"Where is the cat?",
"What is near the disk?",
"What is the color of the table?",
"What is the color of the cat?",
"What is the shape of the monitor?",
]
# Very important that the boxes are normalized
normalized_boxes = output_dict.get("normalized_boxes")
features = output_dict.get("roi_features")
for test_question in test_questions_for_url2:
# run lxmert
test_question = [test_question]
inputs = lxmert_tokenizer(
test_question,
padding="max_length",
max_length=20,
truncation=True,
return_token_type_ids=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
# run lxmert(s)
output_gqa = lxmert_gqa(
input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
visual_feats=features,
visual_pos=normalized_boxes,
token_type_ids=inputs.token_type_ids,
output_attentions=False,
)
output_vqa = lxmert_vqa(
input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
visual_feats=features,
visual_pos=normalized_boxes,
token_type_ids=inputs.token_type_ids,
output_attentions=False,
)
# get prediction
pred_vqa = output_vqa["question_answering_score"].argmax(-1)
pred_gqa = output_gqa["question_answering_score"].argmax(-1)
print("Question:", test_question)
print("prediction from LXMERT GQA:", gqa_answers[pred_gqa])
print("prediction from LXMERT VQA:", vqa_answers[pred_vqa])<jupyter_output>Question: ['Where is the cat?']
prediction from LXMERT GQA: desk
prediction from LXMERT VQA: desk
Question: ['What is near the disk?']
prediction from LXMERT GQA: can
prediction from LXMERT VQA: cat
Question: ['What is the color of the table?']
prediction from LXMERT GQA: brown
prediction from LXMERT VQA: brown
Question: ['What is the color of the cat?']
prediction from LXMERT GQA: black
prediction from LXMERT VQA: black and white
Question: ['What is the shape of the monitor?']
prediction from LXMERT GQA: square
prediction from LXMERT VQA: rectangle | transformers/examples/research_projects/lxmert/demo.ipynb/0 | {
"file_path": "transformers/examples/research_projects/lxmert/demo.ipynb",
"repo_id": "transformers",
"token_count": 1973
} |
# Copyright 2020-present, 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.
"""
Once a model has been fine-pruned, the weights that are masked during the forward pass can be pruned once for all.
For instance, once the a model from the :class:`~emmental.MaskedBertForSequenceClassification` is trained, it can be saved (and then loaded)
as a standard :class:`~transformers.BertForSequenceClassification`.
"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def main(args):
pruning_method = args.pruning_method
threshold = args.threshold
model_name_or_path = args.model_name_or_path.rstrip("/")
target_model_path = args.target_model_path
print(f"Load fine-pruned model from {model_name_or_path}")
model = torch.load(os.path.join(model_name_or_path, "pytorch_model.bin"))
pruned_model = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
pruned_model[name] = tensor
print(f"Copied layer {name}")
elif "classifier" in name or "qa_output" in name:
pruned_model[name] = tensor
print(f"Copied layer {name}")
elif "bias" in name:
pruned_model[name] = tensor
print(f"Copied layer {name}")
else:
if pruning_method == "magnitude":
mask = MagnitudeBinarizer.apply(inputs=tensor, threshold=threshold)
pruned_model[name] = tensor * mask
print(f"Pruned layer {name}")
elif pruning_method == "topK":
if "mask_scores" in name:
continue
prefix_ = name[:-6]
scores = model[f"{prefix_}mask_scores"]
mask = TopKBinarizer.apply(scores, threshold)
pruned_model[name] = tensor * mask
print(f"Pruned layer {name}")
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
prefix_ = name[:-6]
scores = model[f"{prefix_}mask_scores"]
mask = ThresholdBinarizer.apply(scores, threshold, True)
pruned_model[name] = tensor * mask
print(f"Pruned layer {name}")
elif pruning_method == "l0":
if "mask_scores" in name:
continue
prefix_ = name[:-6]
scores = model[f"{prefix_}mask_scores"]
l, r = -0.1, 1.1
s = torch.sigmoid(scores)
s_bar = s * (r - l) + l
mask = s_bar.clamp(min=0.0, max=1.0)
pruned_model[name] = tensor * mask
print(f"Pruned layer {name}")
else:
raise ValueError("Unknown pruning method")
if target_model_path is None:
target_model_path = os.path.join(
os.path.dirname(model_name_or_path), f"bertarized_{os.path.basename(model_name_or_path)}"
)
if not os.path.isdir(target_model_path):
shutil.copytree(model_name_or_path, target_model_path)
print(f"\nCreated folder {target_model_path}")
torch.save(pruned_model, os.path.join(target_model_path, "pytorch_model.bin"))
print("\nPruned model saved! See you later!")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help=(
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
" sigmoied_threshold = Soft movement pruning)"
),
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help=(
"For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model. "
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared. "
"Not needed for `l0`"
),
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
args = parser.parse_args()
main(args)
| transformers/examples/research_projects/movement-pruning/bertarize.py/0 | {
"file_path": "transformers/examples/research_projects/movement-pruning/bertarize.py",
"repo_id": "transformers",
"token_count": 2329
} |
# Performer fine-tuning
Example authors: @TevenLeScao, @Patrickvonplaten
Paper authors: Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller
## Requirements
`datasets`, `flax` and `jax`. `wandb` integration is built-in if you want to use it.
## Examples
`sanity_script.sh` will launch performer fine-tuning from the google-bert/bert-base-cased checkpoint on the Simple Wikipedia dataset (a small, easy-language English Wikipedia) from `datasets`.
`full_script.sh` will launch performer fine-tuning from the google-bert/bert-large-cased checkpoint on the English Wikipedia dataset from `datasets`.
Here are a few key arguments:
- Remove the `--performer` argument to use a standard Bert model.
- Add `--reinitialize` to start from a blank model rather than a Bert checkpoint.
- You may change the Bert size by passing a different [checkpoint](https://huggingface.co/transformers/pretrained_models.html) to the `--model_name_or_path` argument.
- Passing your user name to the `--wandb_user_name` argument will trigger weights and biases logging.
- You can choose a dataset with `--dataset_name` and `--dataset_config`. Our [viewer](https://huggingface.co/datasets/viewer/) will help you find what you need. | transformers/examples/research_projects/performer/README.md/0 | {
"file_path": "transformers/examples/research_projects/performer/README.md",
"repo_id": "transformers",
"token_count": 414
} |
import os
import time
import numpy as np
import onnxruntime as ort
os.environ["ORT_TENSORRT_INT8_ENABLE"] = "1"
os.environ["ORT_TENSORRT_INT8_USE_NATIVE_CALIBRATION_TABLE"] = "0"
os.environ["ORT_TENSORRT_ENGINE_CACHE_ENABLE"] = "1"
sess_opt = ort.SessionOptions()
sess_opt.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
execution_provider = ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
sess = ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
run_opt = ort.RunOptions()
sequence = 128
batch = 1
input_ids = np.ones((batch, sequence), dtype=np.int64)
attention_mask = np.ones((batch, sequence), dtype=np.int64)
token_type_ids = np.ones((batch, sequence), dtype=np.int64)
print("Warm up phase...")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("Start inference...")
start_time = time.time()
max_iters = 2000
predict = {}
for iter in range(max_iters):
predict = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))
| transformers/examples/research_projects/quantization-qdqbert/ort-infer-benchmark.py/0 | {
"file_path": "transformers/examples/research_projects/quantization-qdqbert/ort-infer-benchmark.py",
"repo_id": "transformers",
"token_count": 644
} |
"""Evaluation script for RAG models."""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, f1_score # noqa: E402 # isort:skip
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def infer_model_type(model_name_or_path):
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
return max(metric_fn(prediction, gt) for gt in ground_truths)
def get_scores(args, preds_path, gold_data_path):
hypos = [line.strip() for line in open(preds_path, "r").readlines()]
answers = []
if args.gold_data_mode == "qa":
data = pd.read_csv(gold_data_path, sep="\t", header=None)
for answer_list in data[1]:
ground_truths = ast.literal_eval(answer_list)
answers.append(ground_truths)
else:
references = [line.strip() for line in open(gold_data_path, "r").readlines()]
answers = [[reference] for reference in references]
f1 = em = total = 0
for prediction, ground_truths in zip(hypos, answers):
total += 1
em += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)
em = 100.0 * em / total
f1 = 100.0 * f1 / total
logger.info(f"F1: {f1:.2f}")
logger.info(f"EM: {em:.2f}")
def get_precision_at_k(args, preds_path, gold_data_path):
k = args.k
hypos = [line.strip() for line in open(preds_path, "r").readlines()]
references = [line.strip() for line in open(gold_data_path, "r").readlines()]
em = total = 0
for hypo, reference in zip(hypos, references):
hypo_provenance = set(hypo.split("\t")[:k])
ref_provenance = set(reference.split("\t"))
total += 1
em += len(hypo_provenance & ref_provenance) / k
em = 100.0 * em / total
logger.info(f"Precision@{k}: {em: .2f}")
def evaluate_batch_retrieval(args, rag_model, questions):
def strip_title(title):
if title.startswith('"'):
title = title[1:]
if title.endswith('"'):
title = title[:-1]
return title
retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
questions,
return_tensors="pt",
padding=True,
truncation=True,
)["input_ids"].to(args.device)
question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids)
question_enc_pool_output = question_enc_outputs[0]
result = rag_model.retriever(
retriever_input_ids,
question_enc_pool_output.cpu().detach().to(torch.float32).numpy(),
prefix=rag_model.rag.generator.config.prefix,
n_docs=rag_model.config.n_docs,
return_tensors="pt",
)
all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
provenance_strings = []
for docs in all_docs:
provenance = [strip_title(title) for title in docs["title"]]
provenance_strings.append("\t".join(provenance))
return provenance_strings
def evaluate_batch_e2e(args, rag_model, questions):
with torch.no_grad():
inputs_dict = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
questions, return_tensors="pt", padding=True, truncation=True
)
input_ids = inputs_dict.input_ids.to(args.device)
attention_mask = inputs_dict.attention_mask.to(args.device)
outputs = rag_model.generate( # rag_model overwrites generate
input_ids,
attention_mask=attention_mask,
num_beams=args.num_beams,
min_length=args.min_length,
max_length=args.max_length,
early_stopping=False,
num_return_sequences=1,
bad_words_ids=[[0, 0]], # BART likes to repeat BOS tokens, dont allow it to generate more than one
)
answers = rag_model.retriever.generator_tokenizer.batch_decode(outputs, skip_special_tokens=True)
if args.print_predictions:
for q, a in zip(questions, answers):
logger.info("Q: {} - A: {}".format(q, a))
return answers
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_type",
choices=["rag_sequence", "rag_token", "bart"],
type=str,
help=(
"RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
" model_name_or_path"
),
)
parser.add_argument(
"--index_name",
default=None,
choices=["exact", "compressed", "legacy"],
type=str,
help="RAG model retriever type",
)
parser.add_argument(
"--index_path",
default=None,
type=str,
help="Path to the retrieval index",
)
parser.add_argument("--n_docs", default=5, type=int, help="Number of retrieved docs")
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pretrained checkpoints or model identifier from huggingface.co/models",
)
parser.add_argument(
"--eval_mode",
choices=["e2e", "retrieval"],
default="e2e",
type=str,
help=(
"Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
" precision@k."
),
)
parser.add_argument("--k", default=1, type=int, help="k for the precision@k calculation")
parser.add_argument(
"--evaluation_set",
default=None,
type=str,
required=True,
help="Path to a file containing evaluation samples",
)
parser.add_argument(
"--gold_data_path",
default=None,
type=str,
required=True,
help="Path to a tab-separated file with gold samples",
)
parser.add_argument(
"--gold_data_mode",
default="qa",
type=str,
choices=["qa", "ans"],
help=(
"Format of the gold data file"
"qa - a single line in the following format: question [tab] answer_list"
"ans - a single line of the gold file contains the expected answer string"
),
)
parser.add_argument(
"--predictions_path",
type=str,
default="predictions.txt",
help="Name of the predictions file, to be stored in the checkpoints directory",
)
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument(
"--eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.",
)
parser.add_argument(
"--recalculate",
help="Recalculate predictions even if the prediction file exists",
action="store_true",
)
parser.add_argument(
"--num_beams",
default=4,
type=int,
help="Number of beams to be used when generating answers",
)
parser.add_argument("--min_length", default=1, type=int, help="Min length of the generated answers")
parser.add_argument("--max_length", default=50, type=int, help="Max length of the generated answers")
parser.add_argument(
"--print_predictions",
action="store_true",
help="If True, prints predictions while evaluating.",
)
parser.add_argument(
"--print_docs",
action="store_true",
help="If True, prints docs retried while generating.",
)
args = parser.parse_args()
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
return args
def main(args):
model_kwargs = {}
if args.model_type is None:
args.model_type = infer_model_type(args.model_name_or_path)
assert args.model_type is not None
if args.model_type.startswith("rag"):
model_class = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
model_kwargs["n_docs"] = args.n_docs
if args.index_name is not None:
model_kwargs["index_name"] = args.index_name
if args.index_path is not None:
model_kwargs["index_path"] = args.index_path
else:
model_class = BartForConditionalGeneration
checkpoints = (
[f.path for f in os.scandir(args.model_name_or_path) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("Evaluate the following checkpoints: %s", checkpoints)
score_fn = get_scores if args.eval_mode == "e2e" else get_precision_at_k
evaluate_batch_fn = evaluate_batch_e2e if args.eval_mode == "e2e" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path) and (not args.recalculate):
logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path))
score_fn(args, args.predictions_path, args.gold_data_path)
continue
logger.info("***** Running evaluation for {} *****".format(checkpoint))
logger.info(" Batch size = %d", args.eval_batch_size)
logger.info(" Predictions will be stored under {}".format(args.predictions_path))
if args.model_type.startswith("rag"):
retriever = RagRetriever.from_pretrained(checkpoint, **model_kwargs)
model = model_class.from_pretrained(checkpoint, retriever=retriever, **model_kwargs)
model.retriever.init_retrieval()
else:
model = model_class.from_pretrained(checkpoint, **model_kwargs)
model.to(args.device)
with open(args.evaluation_set, "r") as eval_file, open(args.predictions_path, "w") as preds_file:
questions = []
for line in tqdm(eval_file):
questions.append(line.strip())
if len(questions) == args.eval_batch_size:
answers = evaluate_batch_fn(args, model, questions)
preds_file.write("\n".join(answers) + "\n")
preds_file.flush()
questions = []
if len(questions) > 0:
answers = evaluate_batch_fn(args, model, questions)
preds_file.write("\n".join(answers))
preds_file.flush()
score_fn(args, args.predictions_path, args.gold_data_path)
if __name__ == "__main__":
args = get_args()
main(args)
| transformers/examples/research_projects/rag/eval_rag.py/0 | {
"file_path": "transformers/examples/research_projects/rag/eval_rag.py",
"repo_id": "transformers",
"token_count": 4856
} |
# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# 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 sequence classification."""
import argparse
import dataclasses
import json
import logging
import math
import os
import random
import shutil
from typing import List, Optional
import datasets
import numpy as np
import pandas as pd
import torch
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from transformers import (
AdamW,
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
default_data_collator,
get_scheduler,
set_seed,
)
from transformers.file_utils import ExplicitEnum
from transformers.trainer_utils import IntervalStrategy
logger = logging.getLogger(__name__)
class Split(ExplicitEnum):
TRAIN = "train"
EVAL = "eval"
TEST = "test"
INFER = "infer"
@dataclasses.dataclass
class FTModelArguments:
"""Arguments pertaining to which config/tokenizer/model we are going to fine-tune from."""
model_name_or_path: str = dataclasses.field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}
)
use_fast_tokenizer: Optional[bool] = dataclasses.field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
cache_dir: Optional[str] = dataclasses.field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."},
)
@dataclasses.dataclass
class FTDataArguments:
"""Arguments pertaining to what data we are going to input our model for training and evaluation."""
train_file: str = dataclasses.field(
default=None, metadata={"help": "A csv or a json file containing the training data."}
)
eval_file: Optional[str] = dataclasses.field(
default=None, metadata={"help": "A csv or a json file containing the validation data."}
)
test_file: Optional[str] = dataclasses.field(
default=None, metadata={"help": "A csv or a json file containing the test data."}
)
infer_file: Optional[str] = dataclasses.field(
default=None, metadata={"help": "A csv or a json file containing the data to predict on."}
)
task_name: Optional[str] = dataclasses.field(
default=None,
metadata={"help": "The name of the task to train on."},
)
label_list: Optional[List[str]] = dataclasses.field(
default=None, metadata={"help": "The list of labels for the task."}
)
max_length: Optional[int] = dataclasses.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."
)
},
)
pad_to_max_length: Optional[bool] = dataclasses.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."
)
},
)
@dataclasses.dataclass
class FTTrainingArguments:
"""Training arguments pertaining to the training loop itself."""
output_dir: str = dataclasses.field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."}
)
do_train: Optional[bool] = dataclasses.field(
default=False,
metadata={"help": "Whether to run training or not."},
)
do_eval: Optional[bool] = dataclasses.field(
default=False,
metadata={"help": "Whether to run evaluation on the validation set or not."},
)
do_predict: Optional[bool] = dataclasses.field(
default=False,
metadata={"help": "Whether to run inference on the inference set or not."},
)
seed: Optional[int] = dataclasses.field(
default=42,
metadata={"help": "Random seed that will be set at the beginning of training."},
)
per_device_train_batch_size: Optional[int] = dataclasses.field(
default=8,
metadata={"help": "The batch size per GPU/TPU core/CPU for training."},
)
per_device_eval_batch_size: Optional[int] = dataclasses.field(
default=8,
metadata={"help": "The batch size per GPU/TPU core/CPU for evaluation."},
)
weight_decay: Optional[float] = dataclasses.field(
default=0.0,
metadata={
"help": (
"The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in"
" [`AdamW`] optimizer."
)
},
)
learning_rate: Optional[float] = dataclasses.field(
default=5e-5,
metadata={"help": "The initial learning rate for [`AdamW`] optimizer."},
)
gradient_accumulation_steps: Optional[int] = dataclasses.field(
default=1,
metadata={
"help": (
"Number of updates steps to accumulate the gradients for, before performing a backward/update pass."
)
},
)
max_steps: Optional[int] = dataclasses.field(
default=-1,
metadata={
"help": (
"If set to a positive number, the total number of training steps to perform. Overrides"
" `num_train_epochs`."
)
},
)
lr_scheduler_type: Optional[str] = dataclasses.field(
default="linear", metadata={"help": "The scheduler type to use."}
)
warmup_steps: Optional[int] = dataclasses.field(
default=1,
metadata={
"help": (
"Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of"
" `warmup_ratio`."
)
},
)
eval_strategy: Optional[str] = dataclasses.field(
default="no",
metadata={
"help": 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
},
)
eval_steps: Optional[int] = dataclasses.field(
default=1,
metadata={"help": 'Number of update steps between two evaluations if `eval_strategy="steps"`.'},
)
eval_metric: Optional[str] = dataclasses.field(
default="accuracy", metadata={"help": "The evaluation metric used for the task."}
)
keep_checkpoint_max: Optional[int] = dataclasses.field(
default=1,
metadata={"help": "The maximum number of best checkpoint files to keep."},
)
early_stopping_patience: Optional[int] = dataclasses.field(
default=10,
metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."},
)
early_stopping_threshold: Optional[float] = dataclasses.field(
default=0.0,
metadata={
"help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
},
)
def train(args, accelerator, model, tokenizer, train_dataloader, optimizer, lr_scheduler, eval_dataloader=None):
"""Train a model on the given training data."""
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(" Num examples = %d", args.num_examples[Split.TRAIN.value])
logger.info(" Instantaneous batch size per device = %d", args.per_device_train_batch_size)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", total_batch_size)
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", args.max_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_steps), disable=not accelerator.is_local_main_process)
checkpoints = None
eval_results = None
best_checkpoint = None
best_eval_result = None
early_stopping_patience_counter = 0
should_training_stop = False
epoch = 0
completed_steps = 0
train_loss = 0.0
model.zero_grad()
for _ in range(args.num_train_epochs):
epoch += 1
model.train()
for step, batch in enumerate(train_dataloader):
outputs = model(**batch)
loss = outputs.loss
loss = loss / args.gradient_accumulation_steps
accelerator.backward(loss)
train_loss += loss.item()
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
# Evaluate during training
if (
eval_dataloader is not None
and args.eval_strategy == IntervalStrategy.STEPS.value
and args.eval_steps > 0
and completed_steps % args.eval_steps == 0
):
accelerator.wait_for_everyone()
new_checkpoint = f"checkpoint-{IntervalStrategy.STEPS.value}-{completed_steps}"
new_eval_result = evaluate(args, accelerator, eval_dataloader, "eval", model, new_checkpoint)[
args.eval_metric
]
logger.info(
"Evaluation result at step %d: %s = %f", completed_steps, args.eval_metric, new_eval_result
)
if checkpoints is None:
checkpoints = np.array([new_checkpoint])
eval_results = np.array([new_eval_result])
best_checkpoint = new_checkpoint
best_eval_result = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
best_checkpoint = new_checkpoint
best_eval_result = new_eval_result
early_stopping_patience_counter = 0
else:
if new_eval_result == best_eval_result:
best_checkpoint = new_checkpoint
best_eval_result = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
should_training_stop = True
checkpoints = np.append(checkpoints, [new_checkpoint], axis=0)
eval_results = np.append(eval_results, [new_eval_result], axis=0)
sorted_ids = np.argsort(eval_results)
eval_results = eval_results[sorted_ids]
checkpoints = checkpoints[sorted_ids]
if len(checkpoints) > args.keep_checkpoint_max:
# Delete the current worst checkpoint
checkpoint_to_remove, *checkpoints = checkpoints
eval_results = eval_results[1:]
if checkpoint_to_remove != new_checkpoint:
if accelerator.is_main_process:
shutil.rmtree(os.path.join(args.output_dir, checkpoint_to_remove), ignore_errors=True)
accelerator.wait_for_everyone()
if new_checkpoint in checkpoints:
# Save model checkpoint
checkpoint_output_dir = os.path.join(args.output_dir, new_checkpoint)
if accelerator.is_main_process:
if not os.path.exists(checkpoint_output_dir):
os.makedirs(checkpoint_output_dir)
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(checkpoint_output_dir, save_function=accelerator.save)
if accelerator.is_main_process:
tokenizer.save_pretrained(checkpoint_output_dir)
logger.info("Saving model checkpoint to %s", checkpoint_output_dir)
if completed_steps >= args.max_steps:
break
if should_training_stop:
break
# Evaluate during training
if eval_dataloader is not None and args.eval_strategy == IntervalStrategy.EPOCH.value:
accelerator.wait_for_everyone()
new_checkpoint = f"checkpoint-{IntervalStrategy.EPOCH.value}-{epoch}"
new_eval_result = evaluate(args, accelerator, eval_dataloader, "eval", model, new_checkpoint)[
args.eval_metric
]
logger.info("Evaluation result at epoch %d: %s = %f", epoch, args.eval_metric, new_eval_result)
if checkpoints is None:
checkpoints = np.array([new_checkpoint])
eval_results = np.array([new_eval_result])
best_checkpoint = new_checkpoint
best_eval_result = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
best_checkpoint = new_checkpoint
best_eval_result = new_eval_result
early_stopping_patience_counter = 0
else:
if new_eval_result == best_eval_result:
best_checkpoint = new_checkpoint
best_eval_result = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
should_training_stop = True
checkpoints = np.append(checkpoints, [new_checkpoint], axis=0)
eval_results = np.append(eval_results, [new_eval_result], axis=0)
sorted_ids = np.argsort(eval_results)
eval_results = eval_results[sorted_ids]
checkpoints = checkpoints[sorted_ids]
if len(checkpoints) > args.keep_checkpoint_max:
# Delete the current worst checkpoint
checkpoint_to_remove, *checkpoints = checkpoints
eval_results = eval_results[1:]
if checkpoint_to_remove != new_checkpoint:
if accelerator.is_main_process:
shutil.rmtree(os.path.join(args.output_dir, checkpoint_to_remove), ignore_errors=True)
accelerator.wait_for_everyone()
if new_checkpoint in checkpoints:
# Save model checkpoint
checkpoint_output_dir = os.path.join(args.output_dir, new_checkpoint)
if accelerator.is_main_process:
if not os.path.exists(checkpoint_output_dir):
os.makedirs(checkpoint_output_dir)
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(checkpoint_output_dir, save_function=accelerator.save)
if accelerator.is_main_process:
tokenizer.save_pretrained(checkpoint_output_dir)
logger.info("Saving model checkpoint to %s", checkpoint_output_dir)
if completed_steps >= args.max_steps:
break
if should_training_stop:
break
if best_checkpoint is not None:
# Save the best checkpoint
logger.info("Best checkpoint: %s", best_checkpoint)
logger.info("Best evaluation result: %s = %f", args.eval_metric, best_eval_result)
best_checkpoint_output_dir = os.path.join(args.output_dir, best_checkpoint)
if accelerator.is_main_process:
shutil.move(best_checkpoint_output_dir, os.path.join(args.output_dir, "best-checkpoint"))
shutil.rmtree(best_checkpoint_output_dir, ignore_errors=True)
accelerator.wait_for_everyone()
else:
# Assume that the last checkpoint is the best checkpoint and save it
checkpoint_output_dir = os.path.join(args.output_dir, "best-checkpoint")
if not os.path.exists(checkpoint_output_dir):
os.makedirs(checkpoint_output_dir)
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(checkpoint_output_dir, save_function=accelerator.save)
if accelerator.is_main_process:
tokenizer.save_pretrained(checkpoint_output_dir)
logger.info("Saving model checkpoint to %s", checkpoint_output_dir)
return completed_steps, train_loss / completed_steps
def evaluate(args, accelerator, dataloader, eval_set, model, checkpoint, has_labels=True, write_to_file=True):
"""Evaluate a model checkpoint on the given evaluation data."""
num_examples = args.num_examples[eval_set]
eval_metric = None
completed_steps = 0
eval_loss = 0.0
all_predictions = None
all_references = None
all_probabilities = None
if has_labels:
# Get the metric function
eval_metric = load_metric(args.eval_metric)
eval_results = {}
model.eval()
for _, batch in enumerate(dataloader):
with torch.no_grad():
outputs = model(**batch)
eval_loss += outputs.loss.item()
logits = outputs.logits
predictions = logits.argmax(dim=-1) if not args.is_regression else logits.squeeze()
predictions = accelerator.gather(predictions)
if all_predictions is None:
all_predictions = predictions.detach().cpu().numpy()
else:
all_predictions = np.append(all_predictions, predictions.detach().cpu().numpy(), axis=0)
if not args.is_regression:
probabilities = logits.softmax(dim=-1).max(dim=-1).values
probabilities = accelerator.gather(probabilities)
if all_probabilities is None:
all_probabilities = probabilities.detach().cpu().numpy()
else:
all_probabilities = np.append(all_probabilities, probabilities.detach().cpu().numpy(), axis=0)
if has_labels:
references = batch["labels"]
references = accelerator.gather(references)
if all_references is None:
all_references = references.detach().cpu().numpy()
else:
all_references = np.append(all_references, references.detach().cpu().numpy(), axis=0)
eval_metric.add_batch(
predictions=predictions,
references=references,
)
completed_steps += 1
if has_labels:
eval_results.update(eval_metric.compute())
eval_results["completed_steps"] = completed_steps
eval_results["avg_eval_loss"] = eval_loss / completed_steps
if write_to_file:
accelerator.wait_for_everyone()
if accelerator.is_main_process:
results_file = os.path.join(args.output_dir, f"{eval_set}_results_{checkpoint}.json")
with open(results_file, "w") as f:
json.dump(eval_results, f, indent=4, sort_keys=True)
if write_to_file:
accelerator.wait_for_everyone()
if accelerator.is_main_process:
output_file = os.path.join(args.output_dir, f"{eval_set}_output_{checkpoint}.csv")
if not args.is_regression:
assert len(all_predictions) == len(all_probabilities)
df = pd.DataFrame(list(zip(all_predictions, all_probabilities)), columns=["prediction", "probability"])
else:
df = pd.DataFrame(all_predictions, columns=["prediction"])
df = df.head(num_examples)
df.to_csv(output_file, header=True, index=False)
return eval_results
def load_from_pretrained(args, pretrained_model_name_or_path):
"""Load the pretrained model and tokenizer."""
# In distributed training, the .from_pretrained methods guarantee that only
# one local process can concurrently perform this procedure.
config = AutoConfig.from_pretrained(
pretrained_model_name_or_path,
num_labels=args.num_labels if hasattr(args, "num_labels") else None,
finetuning_task=args.task_name.lower(),
cache_dir=args.cache_dir,
)
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path, use_fast=args.use_fast_tokenizer, cache_dir=args.cache_dir
)
model = AutoModelForSequenceClassification.from_pretrained(
pretrained_model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
ignore_mismatched_sizes=True,
cache_dir=args.cache_dir,
)
return config, tokenizer, model
def finetune(accelerator, model_name_or_path, train_file, output_dir, **kwargs):
"""Fine-tuning a pre-trained model on a downstream task.
Args:
accelerator: An instance of an accelerator for distributed training (on
multi-GPU, TPU) or mixed precision training.
model_name_or_path: Path to pretrained model or model identifier from
huggingface.co/models.
train_file: A csv or a json file containing the training data.
output_dir: The output directory where the model predictions and checkpoints
will be written.
**kwargs: Dictionary of key/value pairs with which to update the
configuration object after loading. The values in kwargs of any keys which
are configuration attributes will be used to override the loaded values.
"""
# 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)
model_args = FTModelArguments(model_name_or_path=model_name_or_path)
data_args = FTDataArguments(train_file=train_file)
training_args = FTTrainingArguments(output_dir=output_dir)
args = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(arg_class).items():
setattr(args, key, value)
for key, value in kwargs.items():
if hasattr(args, key):
setattr(args, key, value)
# Sanity checks
data_files = {}
args.data_file_extension = None
# You need to provide the training data as we always run training
args.do_train = True
assert args.train_file is not None
data_files[Split.TRAIN.value] = args.train_file
if args.do_eval or args.eval_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
data_files[Split.EVAL.value] = args.eval_file
if args.do_eval and args.test_file is not None:
data_files[Split.TEST.value] = args.test_file
if args.do_predict:
assert args.infer_file is not None
data_files[Split.INFER.value] = args.infer_file
for key in data_files:
extension = data_files[key].split(".")[-1]
assert extension in ["csv", "json"], f"`{key}_file` should be a csv or a json file."
if args.data_file_extension is None:
args.data_file_extension = extension
else:
assert extension == args.data_file_extension, f"`{key}_file` should be a {args.data_file_extension} file`."
assert (
args.eval_metric in datasets.list_metrics()
), f"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."
# Handle the output directory creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# You need to provide your CSV/JSON data files.
#
# For CSV/JSON files, this script will use as labels the column called 'label'
# and as pair of sentences the sentences in columns called 'sentence1' and
# 'sentence2' if these columns exist or the first two columns not named
# 'label' if at least two columns are provided.
#
# If the CSVs/JSONs contain only one non-label column, the script does single
# sentence classification on this single column.
#
# In distributed training, the load_dataset function guarantees that only one
# local process can download the dataset.
# Loading the dataset from local csv or json files.
raw_datasets = load_dataset(args.data_file_extension, data_files=data_files)
# Labels
is_regression = raw_datasets[Split.TRAIN.value].features["label"].dtype in ["float32", "float64"]
args.is_regression = is_regression
if args.is_regression:
label_list = None
num_labels = 1
else:
label_list = args.label_list
assert label_list is not None
label_list.sort() # Let's sort it for determinism
num_labels = len(label_list)
args.num_labels = num_labels
# Load pre-trained model
config, tokenizer, model = load_from_pretrained(args, args.model_name_or_path)
# Preprocessing the datasets
non_label_column_names = [name for name in raw_datasets[Split.TRAIN.value].column_names if name != "label"]
if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
sentence1_key, sentence2_key = "sentence1", "sentence2"
else:
if len(non_label_column_names) >= 2:
sentence1_key, sentence2_key = non_label_column_names[:2]
else:
sentence1_key, sentence2_key = non_label_column_names[0], None
label_to_id = {v: i for i, v in enumerate(label_list)}
config.label2id = label_to_id
config.id2label = {id: label for label, id in config.label2id.items()}
padding = "max_length" if args.pad_to_max_length else False
def preprocess_function(examples):
# Tokenize the texts
texts = (
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
)
result = tokenizer(*texts, padding=padding, max_length=args.max_length, truncation=True)
if "label" in examples:
if label_to_id is not None:
# Map labels to IDs (not necessary for GLUE tasks)
result["labels"] = [label_to_id[l] for l in examples["label"]]
else:
# In all cases, rename the column to labels because the model will
# expect that.
result["labels"] = examples["label"]
return result
with accelerator.main_process_first():
processed_datasets = raw_datasets.map(
preprocess_function,
batched=True,
remove_columns=raw_datasets[Split.TRAIN.value].column_names,
desc="Running tokenizer on dataset",
)
num_examples = {}
splits = [s.value for s in Split]
for split in splits:
if split in processed_datasets:
num_examples[split] = len(processed_datasets[split])
args.num_examples = num_examples
train_dataset = processed_datasets[Split.TRAIN.value]
eval_dataset = processed_datasets[Split.EVAL.value] if Split.EVAL.value in processed_datasets else None
test_dataset = processed_datasets[Split.TEST.value] if Split.TEST.value in processed_datasets else None
infer_dataset = processed_datasets[Split.INFER.value] if Split.INFER.value in processed_datasets else None
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info("Sample %d of the training set: %s.", index, 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, `DataCollatorWithPadding` 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 = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=pad_to_multiple_of)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.per_device_train_batch_size,
shuffle=True,
collate_fn=data_collator,
)
eval_dataloader, test_dataloader, infer_dataloader = None, None, None
if eval_dataset is not None:
eval_dataloader = DataLoader(
eval_dataset, batch_size=args.per_device_eval_batch_size, collate_fn=data_collator
)
if test_dataset is not None:
test_dataloader = DataLoader(
test_dataset, batch_size=args.per_device_eval_batch_size, collate_fn=data_collator
)
if infer_dataset is not None:
infer_dataloader = DataLoader(
infer_dataset, batch_size=args.per_device_eval_batch_size, collate_fn=data_collator
)
# 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)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader, test_dataloader, infer_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader, test_dataloader, infer_dataloader
)
# Note -> the training dataloader needs to be prepared before we grab its
# 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_steps == -1:
args.max_steps = args.num_train_epochs * num_update_steps_per_epoch
else:
args.num_train_epochs = math.ceil(args.max_steps / num_update_steps_per_epoch)
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps,
)
# Train
completed_steps, avg_train_loss = train(
args, accelerator, model, tokenizer, train_dataloader, optimizer, lr_scheduler, eval_dataloader
)
accelerator.wait_for_everyone()
logger.info("Training job completed: completed_steps = %d, avg_train_loss = %f", completed_steps, avg_train_loss)
args.model_name_or_path = os.path.join(args.output_dir, "best-checkpoint")
logger.info("Loading the best checkpoint: %s", args.model_name_or_path)
config, tokenizer, model = load_from_pretrained(args, args.model_name_or_path)
model = accelerator.prepare(model)
if args.do_eval:
# Evaluate
if eval_dataloader is not None:
logger.info("***** Running evaluation on the eval data using the best checkpoint *****")
eval_results = evaluate(args, accelerator, eval_dataloader, Split.EVAL.value, model, "best-checkpoint")
avg_eval_loss = eval_results["avg_eval_loss"]
eval_metric = eval_results[args.eval_metric]
logger.info("Evaluation job completed: avg_eval_loss = %f", avg_eval_loss)
logger.info("Evaluation result for the best checkpoint: %s = %f", args.eval_metric, eval_metric)
if test_dataloader is not None:
logger.info("***** Running evaluation on the test data using the best checkpoint *****")
eval_results = evaluate(args, accelerator, test_dataloader, Split.TEST.value, model, "best-checkpoint")
avg_eval_loss = eval_results["avg_eval_loss"]
eval_metric = eval_results[args.eval_metric]
logger.info("Test job completed: avg_test_loss = %f", avg_eval_loss)
logger.info("Test result for the best checkpoint: %s = %f", args.eval_metric, eval_metric)
if args.do_predict:
# Predict
if infer_dataloader is not None:
logger.info("***** Running inference using the best checkpoint *****")
evaluate(
args, accelerator, infer_dataloader, Split.INFER.value, model, "best-checkpoint", has_labels=False
)
logger.info("Inference job completed.")
# Release all references to the internal objects stored and call the garbage
# collector. You should call this method between two trainings with different
# models/optimizers.
accelerator.free_memory()
| transformers/examples/research_projects/self-training-text-classification/finetuning.py/0 | {
"file_path": "transformers/examples/research_projects/self-training-text-classification/finetuning.py",
"repo_id": "transformers",
"token_count": 14818
} |
# the proper usage is documented in the README, you need to specify data_dir, output_dir and model_name_or_path
# run ./finetune.sh --help to see all the possible options
python finetune.py \
--learning_rate=3e-5 \
--fp16 \
--gpus 1 \
--do_train \
--do_predict \
--n_val 1000 \
--val_check_interval 0.1 \
"$@"
| transformers/examples/research_projects/seq2seq-distillation/finetune.sh/0 | {
"file_path": "transformers/examples/research_projects/seq2seq-distillation/finetune.sh",
"repo_id": "transformers",
"token_count": 138
} |
"""
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal, Huggingface team :)
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 copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cv2
import numpy as np
import requests
import wget
from filelock import FileLock
from huggingface_hub.utils import insecure_hashlib
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
_torch_available = True
except ImportError:
_torch_available = False
try:
from torch.hub import _get_torch_home
torch_cache_home = _get_torch_home()
except ImportError:
torch_cache_home = os.path.expanduser(
os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
)
default_cache_path = os.path.join(torch_cache_home, "transformers")
CLOUDFRONT_DISTRIB_PREFIX = "https://cdn.huggingface.co"
S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
PATH = "/".join(str(Path(__file__).resolve()).split("/")[:-1])
CONFIG = os.path.join(PATH, "config.yaml")
ATTRIBUTES = os.path.join(PATH, "attributes.txt")
OBJECTS = os.path.join(PATH, "objects.txt")
PYTORCH_PRETRAINED_BERT_CACHE = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
PYTORCH_TRANSFORMERS_CACHE = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
TRANSFORMERS_CACHE = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
WEIGHTS_NAME = "pytorch_model.bin"
CONFIG_NAME = "config.yaml"
def load_labels(objs=OBJECTS, attrs=ATTRIBUTES):
vg_classes = []
with open(objs) as f:
for object in f.readlines():
vg_classes.append(object.split(",")[0].lower().strip())
vg_attrs = []
with open(attrs) as f:
for object in f.readlines():
vg_attrs.append(object.split(",")[0].lower().strip())
return vg_classes, vg_attrs
def load_checkpoint(ckp):
r = OrderedDict()
with open(ckp, "rb") as f:
ckp = pkl.load(f)["model"]
for k in copy.deepcopy(list(ckp.keys())):
v = ckp.pop(k)
if isinstance(v, np.ndarray):
v = torch.tensor(v)
else:
assert isinstance(v, torch.tensor), type(v)
r[k] = v
return r
class Config:
_pointer = {}
def __init__(self, dictionary: dict, name: str = "root", level=0):
self._name = name
self._level = level
d = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
k = copy.deepcopy(k)
v = copy.deepcopy(v)
if isinstance(v, dict):
v = Config(v, name=k, level=level + 1)
d[k] = v
setattr(self, k, v)
self._pointer = d
def __repr__(self):
return str(list((self._pointer.keys())))
def __setattr__(self, key, val):
self.__dict__[key] = val
self.__dict__[key.upper()] = val
levels = key.split(".")
last_level = len(levels) - 1
pointer = self._pointer
if len(levels) > 1:
for i, l in enumerate(levels):
if hasattr(self, l) and isinstance(getattr(self, l), Config):
setattr(getattr(self, l), ".".join(levels[i:]), val)
if l == last_level:
pointer[l] = val
else:
pointer = pointer[l]
def to_dict(self):
return self._pointer
def dump_yaml(self, data, file_name):
with open(f"{file_name}", "w") as stream:
dump(data, stream)
def dump_json(self, data, file_name):
with open(f"{file_name}", "w") as stream:
json.dump(data, stream)
@staticmethod
def load_yaml(config):
with open(config) as stream:
data = load(stream, Loader=Loader)
return data
def __str__(self):
t = " "
if self._name != "root":
r = f"{t * (self._level-1)}{self._name}:\n"
else:
r = ""
level = self._level
for i, (k, v) in enumerate(self._pointer.items()):
if isinstance(v, Config):
r += f"{t * (self._level)}{v}\n"
self._level += 1
else:
r += f"{t * (self._level)}{k}: {v} ({type(v).__name__})\n"
self._level = level
return r[:-1]
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
return cls(config_dict)
@classmethod
def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs):
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
if os.path.isdir(pretrained_model_name_or_path):
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
config_file = pretrained_model_name_or_path
else:
config_file = hf_bucket_url(pretrained_model_name_or_path, filename=CONFIG_NAME, use_cdn=False)
try:
# Load from URL or cache if already cached
resolved_config_file = cached_path(
config_file,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
)
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
config_file = Config.load_yaml(resolved_config_file)
except EnvironmentError:
msg = "Can't load config for"
raise EnvironmentError(msg)
if resolved_config_file == config_file:
print("loading configuration file from path")
else:
print("loading configuration file cache")
return Config.load_yaml(resolved_config_file), kwargs
# quick compare tensors
def compare(in_tensor):
out_tensor = torch.load("dump.pt", map_location=in_tensor.device)
n1 = in_tensor.numpy()
n2 = out_tensor.numpy()[0]
print(n1.shape, n1[0, 0, :5])
print(n2.shape, n2[0, 0, :5])
assert np.allclose(n1, n2, rtol=0.01, atol=0.1), (
f"{sum([1 for x in np.isclose(n1, n2, rtol=0.01, atol=0.1).flatten() if x is False])/len(n1.flatten())*100:.4f} %"
" element-wise mismatch"
)
raise Exception("tensors are all good")
# Hugging face functions below
def is_remote_url(url_or_filename):
parsed = urlparse(url_or_filename)
return parsed.scheme in ("http", "https")
def hf_bucket_url(model_id: str, filename: str, use_cdn=True) -> str:
endpoint = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
legacy_format = "/" not in model_id
if legacy_format:
return f"{endpoint}/{model_id}-{filename}"
else:
return f"{endpoint}/{model_id}/{filename}"
def http_get(
url,
temp_file,
proxies=None,
resume_size=0,
user_agent=None,
):
ua = "python/{}".format(sys.version.split()[0])
if _torch_available:
ua += "; torch/{}".format(torch.__version__)
if isinstance(user_agent, dict):
ua += "; " + "; ".join("{}/{}".format(k, v) for k, v in user_agent.items())
elif isinstance(user_agent, str):
ua += "; " + user_agent
headers = {"user-agent": ua}
if resume_size > 0:
headers["Range"] = "bytes=%d-" % (resume_size,)
response = requests.get(url, stream=True, proxies=proxies, headers=headers)
if response.status_code == 416: # Range not satisfiable
return
content_length = response.headers.get("Content-Length")
total = resume_size + int(content_length) if content_length is not None else None
progress = tqdm(
unit="B",
unit_scale=True,
total=total,
initial=resume_size,
desc="Downloading",
)
for chunk in response.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
progress.update(len(chunk))
temp_file.write(chunk)
progress.close()
def get_from_cache(
url,
cache_dir=None,
force_download=False,
proxies=None,
etag_timeout=10,
resume_download=False,
user_agent=None,
local_files_only=False,
):
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
os.makedirs(cache_dir, exist_ok=True)
etag = None
if not local_files_only:
try:
response = requests.head(url, allow_redirects=True, proxies=proxies, timeout=etag_timeout)
if response.status_code == 200:
etag = response.headers.get("ETag")
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
filename = url_to_filename(url, etag)
# get cache path to put the file
cache_path = os.path.join(cache_dir, filename)
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(cache_path):
return cache_path
else:
matching_files = [
file
for file in fnmatch.filter(os.listdir(cache_dir), filename + ".*")
if not file.endswith(".json") and not file.endswith(".lock")
]
if len(matching_files) > 0:
return os.path.join(cache_dir, matching_files[-1])
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
"Cannot find the requested files in the cached path and outgoing traffic has been"
" disabled. To enable model look-ups and downloads online, set 'local_files_only'"
" to False."
)
return None
# From now on, etag is not None.
if os.path.exists(cache_path) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
lock_path = cache_path + ".lock"
with FileLock(lock_path):
# If the download just completed while the lock was activated.
if os.path.exists(cache_path) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
incomplete_path = cache_path + ".incomplete"
@contextmanager
def _resumable_file_manager():
with open(incomplete_path, "a+b") as f:
yield f
temp_file_manager = _resumable_file_manager
if os.path.exists(incomplete_path):
resume_size = os.stat(incomplete_path).st_size
else:
resume_size = 0
else:
temp_file_manager = partial(tempfile.NamedTemporaryFile, dir=cache_dir, delete=False)
resume_size = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
"%s not found in cache or force_download set to True, downloading to %s",
url,
temp_file.name,
)
http_get(
url,
temp_file,
proxies=proxies,
resume_size=resume_size,
user_agent=user_agent,
)
os.replace(temp_file.name, cache_path)
meta = {"url": url, "etag": etag}
meta_path = cache_path + ".json"
with open(meta_path, "w") as meta_file:
json.dump(meta, meta_file)
return cache_path
def url_to_filename(url, etag=None):
url_bytes = url.encode("utf-8")
url_hash = insecure_hashlib.sha256(url_bytes)
filename = url_hash.hexdigest()
if etag:
etag_bytes = etag.encode("utf-8")
etag_hash = insecure_hashlib.sha256(etag_bytes)
filename += "." + etag_hash.hexdigest()
if url.endswith(".h5"):
filename += ".h5"
return filename
def cached_path(
url_or_filename,
cache_dir=None,
force_download=False,
proxies=None,
resume_download=False,
user_agent=None,
extract_compressed_file=False,
force_extract=False,
local_files_only=False,
):
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
if isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if is_remote_url(url_or_filename):
# URL, so get it from the cache (downloading if necessary)
output_path = get_from_cache(
url_or_filename,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
user_agent=user_agent,
local_files_only=local_files_only,
)
elif os.path.exists(url_or_filename):
# File, and it exists.
output_path = url_or_filename
elif urlparse(url_or_filename).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(url_or_filename))
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
if extract_compressed_file:
if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
output_dir, output_file = os.path.split(output_path)
output_extract_dir_name = output_file.replace(".", "-") + "-extracted"
output_path_extracted = os.path.join(output_dir, output_extract_dir_name)
if os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
lock_path = output_path + ".lock"
with FileLock(lock_path):
shutil.rmtree(output_path_extracted, ignore_errors=True)
os.makedirs(output_path_extracted)
if is_zipfile(output_path):
with ZipFile(output_path, "r") as zip_file:
zip_file.extractall(output_path_extracted)
zip_file.close()
elif tarfile.is_tarfile(output_path):
tar_file = tarfile.open(output_path)
tar_file.extractall(output_path_extracted)
tar_file.close()
else:
raise EnvironmentError("Archive format of {} could not be identified".format(output_path))
return output_path_extracted
return output_path
def get_data(query, delim=","):
assert isinstance(query, str)
if os.path.isfile(query):
with open(query) as f:
data = eval(f.read())
else:
req = requests.get(query)
try:
data = requests.json()
except Exception:
data = req.content.decode()
assert data is not None, "could not connect"
try:
data = eval(data)
except Exception:
data = data.split("\n")
req.close()
return data
def get_image_from_url(url):
response = requests.get(url)
img = np.array(Image.open(BytesIO(response.content)))
return img
# to load legacy frcnn checkpoint from detectron
def load_frcnn_pkl_from_url(url):
fn = url.split("/")[-1]
if fn not in os.listdir(os.getcwd()):
wget.download(url)
with open(fn, "rb") as stream:
weights = pkl.load(stream)
model = weights.pop("model")
new = {}
for k, v in model.items():
new[k] = torch.from_numpy(v)
if "running_var" in k:
zero = torch.tensor([0])
k2 = k.replace("running_var", "num_batches_tracked")
new[k2] = zero
return new
def get_demo_path():
print(f"{os.path.abspath(os.path.join(PATH, os.pardir))}/demo.ipynb")
def img_tensorize(im, input_format="RGB"):
assert isinstance(im, str)
if os.path.isfile(im):
img = cv2.imread(im)
else:
img = get_image_from_url(im)
assert img is not None, f"could not connect to: {im}"
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if input_format == "RGB":
img = img[:, :, ::-1]
return img
def chunk(images, batch=1):
return (images[i : i + batch] for i in range(0, len(images), batch))
| transformers/examples/research_projects/visual_bert/utils.py/0 | {
"file_path": "transformers/examples/research_projects/visual_bert/utils.py",
"repo_id": "transformers",
"token_count": 8363
} |
#!/usr/bin/env bash
python run_asr.py \
--output_dir="./wav2vec2-large-lv60-timit-asr" \
--num_train_epochs="30" \
--per_device_train_batch_size="2" \
--per_device_eval_batch_size="2" \
--gradient_accumulation_steps="4" \
--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-large-lv60" \
--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_large_lv60_timit_asr.sh/0 | {
"file_path": "transformers/examples/research_projects/wav2vec2/finetune_large_lv60_timit_asr.sh",
"repo_id": "transformers",
"token_count": 275
} |
<!---
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.
-->
# Examples
This folder contains actively maintained examples of the use of 🤗 Transformers organized into different ML tasks. All examples in this folder are **TensorFlow** examples and are written using native Keras. If you've previously only used 🤗 Transformers via `TFTrainer`, we highly recommend taking a look at the new style - we think it's a big improvement!
In addition, all scripts here now support the [🤗 Datasets](https://github.com/huggingface/datasets) library - you can grab entire datasets just by changing one command-line argument!
## A note on code folding
Most of these examples have been formatted with #region blocks. In IDEs such as PyCharm and VSCode, these blocks mark
named regions of code that can be folded for easier viewing. If you find any of these scripts overwhelming or difficult
to follow, we highly recommend beginning with all regions folded and then examining regions one at a time!
## The Big Table of Tasks
Here is the list of all our examples:
| Task | Example datasets |
|---|---|
| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling) | WikiText-2
| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice) | SWAG
| [**`question-answering`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) | SQuAD
| [**`summarization`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization) | XSum
| [**`text-classification`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification) | GLUE
| [**`token-classification`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) | CoNLL NER
| [**`translation`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/translation) | WMT
## Coming soon
- **Colab notebooks** to easily run through these scripts!
| transformers/examples/tensorflow/README.md/0 | {
"file_path": "transformers/examples/tensorflow/README.md",
"repo_id": "transformers",
"token_count": 731
} |
<!---
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.
-->
# Token classification
Fine-tuning the library models for token classification task such as Named Entity Recognition (NER), Parts-of-speech
tagging (POS) or phrase extraction (CHUNKS). The main script `run_ner.py` leverages the [🤗 Datasets](https://github.com/huggingface/datasets) library. You can easily
customize it to your needs if you need extra processing on your datasets.
It will either run on a datasets hosted on our [hub](https://huggingface.co/datasets) or with your own text files for
training and validation, you might just need to add some tweaks in the data preprocessing.
The following example fine-tunes BERT on CoNLL-2003:
```bash
python run_ner.py \
--model_name_or_path google-bert/bert-base-uncased \
--dataset_name conll2003 \
--output_dir /tmp/test-ner
```
To run on your own training and validation files, use the following command:
```bash
python run_ner.py \
--model_name_or_path google-bert/bert-base-uncased \
--train_file path_to_train_file \
--validation_file path_to_validation_file \
--output_dir /tmp/test-ner
```
**Note:** This script only works with models that have a fast tokenizer (backed by the [🤗 Tokenizers](https://github.com/huggingface/tokenizers) library) as it
uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
[this table](https://huggingface.co/transformers/index.html#supported-frameworks).
| transformers/examples/tensorflow/token-classification/README.md/0 | {
"file_path": "transformers/examples/tensorflow/token-classification/README.md",
"repo_id": "transformers",
"token_count": 579
} |
# 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 math
import tensorflow as tf
from packaging.version import parse
try:
import tf_keras as keras
except (ModuleNotFoundError, ImportError):
import keras
if parse(keras.__version__).major > 2:
raise ValueError(
"Your currently installed version of Keras is Keras 3, but this is not yet supported in "
"Transformers. Please install the backwards-compatible tf-keras package with "
"`pip install tf-keras`."
)
def _gelu(x):
"""
Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
initially created. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see
https://arxiv.org/abs/1606.08415
"""
x = tf.convert_to_tensor(x)
cdf = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0), x.dtype)))
return x * cdf
def _gelu_new(x):
"""
Gaussian Error Linear Unit. This is a smoother version of the GELU. Original paper: https://arxiv.org/abs/1606.0841
Args:
x: float Tensor to perform activation
Returns:
`x` with the GELU activation applied.
"""
x = tf.convert_to_tensor(x)
pi = tf.cast(math.pi, x.dtype)
coeff = tf.cast(0.044715, x.dtype)
cdf = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi) * (x + coeff * tf.pow(x, 3))))
return x * cdf
def mish(x):
x = tf.convert_to_tensor(x)
return x * tf.tanh(tf.math.softplus(x))
def gelu_fast(x):
x = tf.convert_to_tensor(x)
coeff1 = tf.cast(0.044715, x.dtype)
coeff2 = tf.cast(0.7978845608, x.dtype)
return 0.5 * x * (1.0 + tf.tanh(x * coeff2 * (1.0 + coeff1 * x * x)))
def quick_gelu(x):
x = tf.convert_to_tensor(x)
coeff = tf.cast(1.702, x.dtype)
return x * tf.math.sigmoid(coeff * x)
def gelu_10(x):
"""
Clip the range of possible GeLU outputs between [-10, 10]. This is especially useful for quantization purpose, as
it allows mapping 2 negatives values in the GeLU spectrum. For more information on this trick, please refer to
https://arxiv.org/abs/2004.09602
Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
initially created. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see
https://arxiv.org/abs/1606.08415 :param x: :return:
"""
return tf.clip_by_value(_gelu(x), -10, 10)
def glu(x, axis=-1):
"""
Gated Linear Unit. Implementation as defined in the original paper (see https://arxiv.org/abs/1612.08083), where
the input `x` is split in two halves across a dimension (`axis`), A and B, returning A * sigmoid(B).
Args:
`x`: float Tensor to perform activation
`axis`: dimension across which `x` be split in half
Returns:
`x` with the GLU activation applied (with its size halved across the dimension `axis`).
"""
a, b = tf.split(x, 2, axis=axis)
return a * tf.math.sigmoid(b)
if parse(tf.version.VERSION) >= parse("2.4"):
def approximate_gelu_wrap(x):
return keras.activations.gelu(x, approximate=True)
gelu = keras.activations.gelu
gelu_new = approximate_gelu_wrap
else:
gelu = _gelu
gelu_new = _gelu_new
ACT2FN = {
"gelu": gelu,
"gelu_10": gelu_10,
"gelu_fast": gelu_fast,
"gelu_new": gelu_new,
"glu": glu,
"mish": mish,
"quick_gelu": quick_gelu,
"relu": keras.activations.relu,
"sigmoid": keras.activations.sigmoid,
"silu": keras.activations.swish,
"swish": keras.activations.swish,
"tanh": keras.activations.tanh,
}
def get_tf_activation(activation_string):
if activation_string in ACT2FN:
return ACT2FN[activation_string]
else:
raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}")
| transformers/src/transformers/activations_tf.py/0 | {
"file_path": "transformers/src/transformers/activations_tf.py",
"repo_id": "transformers",
"token_count": 1836
} |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 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.
from ..models.auto import AutoModelForSeq2SeqLM, AutoTokenizer
from .tools import PipelineTool
LANGUAGE_CODES = {
"Acehnese Arabic": "ace_Arab",
"Acehnese Latin": "ace_Latn",
"Mesopotamian Arabic": "acm_Arab",
"Ta'izzi-Adeni Arabic": "acq_Arab",
"Tunisian Arabic": "aeb_Arab",
"Afrikaans": "afr_Latn",
"South Levantine Arabic": "ajp_Arab",
"Akan": "aka_Latn",
"Amharic": "amh_Ethi",
"North Levantine Arabic": "apc_Arab",
"Modern Standard Arabic": "arb_Arab",
"Modern Standard Arabic Romanized": "arb_Latn",
"Najdi Arabic": "ars_Arab",
"Moroccan Arabic": "ary_Arab",
"Egyptian Arabic": "arz_Arab",
"Assamese": "asm_Beng",
"Asturian": "ast_Latn",
"Awadhi": "awa_Deva",
"Central Aymara": "ayr_Latn",
"South Azerbaijani": "azb_Arab",
"North Azerbaijani": "azj_Latn",
"Bashkir": "bak_Cyrl",
"Bambara": "bam_Latn",
"Balinese": "ban_Latn",
"Belarusian": "bel_Cyrl",
"Bemba": "bem_Latn",
"Bengali": "ben_Beng",
"Bhojpuri": "bho_Deva",
"Banjar Arabic": "bjn_Arab",
"Banjar Latin": "bjn_Latn",
"Standard Tibetan": "bod_Tibt",
"Bosnian": "bos_Latn",
"Buginese": "bug_Latn",
"Bulgarian": "bul_Cyrl",
"Catalan": "cat_Latn",
"Cebuano": "ceb_Latn",
"Czech": "ces_Latn",
"Chokwe": "cjk_Latn",
"Central Kurdish": "ckb_Arab",
"Crimean Tatar": "crh_Latn",
"Welsh": "cym_Latn",
"Danish": "dan_Latn",
"German": "deu_Latn",
"Southwestern Dinka": "dik_Latn",
"Dyula": "dyu_Latn",
"Dzongkha": "dzo_Tibt",
"Greek": "ell_Grek",
"English": "eng_Latn",
"Esperanto": "epo_Latn",
"Estonian": "est_Latn",
"Basque": "eus_Latn",
"Ewe": "ewe_Latn",
"Faroese": "fao_Latn",
"Fijian": "fij_Latn",
"Finnish": "fin_Latn",
"Fon": "fon_Latn",
"French": "fra_Latn",
"Friulian": "fur_Latn",
"Nigerian Fulfulde": "fuv_Latn",
"Scottish Gaelic": "gla_Latn",
"Irish": "gle_Latn",
"Galician": "glg_Latn",
"Guarani": "grn_Latn",
"Gujarati": "guj_Gujr",
"Haitian Creole": "hat_Latn",
"Hausa": "hau_Latn",
"Hebrew": "heb_Hebr",
"Hindi": "hin_Deva",
"Chhattisgarhi": "hne_Deva",
"Croatian": "hrv_Latn",
"Hungarian": "hun_Latn",
"Armenian": "hye_Armn",
"Igbo": "ibo_Latn",
"Ilocano": "ilo_Latn",
"Indonesian": "ind_Latn",
"Icelandic": "isl_Latn",
"Italian": "ita_Latn",
"Javanese": "jav_Latn",
"Japanese": "jpn_Jpan",
"Kabyle": "kab_Latn",
"Jingpho": "kac_Latn",
"Kamba": "kam_Latn",
"Kannada": "kan_Knda",
"Kashmiri Arabic": "kas_Arab",
"Kashmiri Devanagari": "kas_Deva",
"Georgian": "kat_Geor",
"Central Kanuri Arabic": "knc_Arab",
"Central Kanuri Latin": "knc_Latn",
"Kazakh": "kaz_Cyrl",
"Kabiyè": "kbp_Latn",
"Kabuverdianu": "kea_Latn",
"Khmer": "khm_Khmr",
"Kikuyu": "kik_Latn",
"Kinyarwanda": "kin_Latn",
"Kyrgyz": "kir_Cyrl",
"Kimbundu": "kmb_Latn",
"Northern Kurdish": "kmr_Latn",
"Kikongo": "kon_Latn",
"Korean": "kor_Hang",
"Lao": "lao_Laoo",
"Ligurian": "lij_Latn",
"Limburgish": "lim_Latn",
"Lingala": "lin_Latn",
"Lithuanian": "lit_Latn",
"Lombard": "lmo_Latn",
"Latgalian": "ltg_Latn",
"Luxembourgish": "ltz_Latn",
"Luba-Kasai": "lua_Latn",
"Ganda": "lug_Latn",
"Luo": "luo_Latn",
"Mizo": "lus_Latn",
"Standard Latvian": "lvs_Latn",
"Magahi": "mag_Deva",
"Maithili": "mai_Deva",
"Malayalam": "mal_Mlym",
"Marathi": "mar_Deva",
"Minangkabau Arabic ": "min_Arab",
"Minangkabau Latin": "min_Latn",
"Macedonian": "mkd_Cyrl",
"Plateau Malagasy": "plt_Latn",
"Maltese": "mlt_Latn",
"Meitei Bengali": "mni_Beng",
"Halh Mongolian": "khk_Cyrl",
"Mossi": "mos_Latn",
"Maori": "mri_Latn",
"Burmese": "mya_Mymr",
"Dutch": "nld_Latn",
"Norwegian Nynorsk": "nno_Latn",
"Norwegian Bokmål": "nob_Latn",
"Nepali": "npi_Deva",
"Northern Sotho": "nso_Latn",
"Nuer": "nus_Latn",
"Nyanja": "nya_Latn",
"Occitan": "oci_Latn",
"West Central Oromo": "gaz_Latn",
"Odia": "ory_Orya",
"Pangasinan": "pag_Latn",
"Eastern Panjabi": "pan_Guru",
"Papiamento": "pap_Latn",
"Western Persian": "pes_Arab",
"Polish": "pol_Latn",
"Portuguese": "por_Latn",
"Dari": "prs_Arab",
"Southern Pashto": "pbt_Arab",
"Ayacucho Quechua": "quy_Latn",
"Romanian": "ron_Latn",
"Rundi": "run_Latn",
"Russian": "rus_Cyrl",
"Sango": "sag_Latn",
"Sanskrit": "san_Deva",
"Santali": "sat_Olck",
"Sicilian": "scn_Latn",
"Shan": "shn_Mymr",
"Sinhala": "sin_Sinh",
"Slovak": "slk_Latn",
"Slovenian": "slv_Latn",
"Samoan": "smo_Latn",
"Shona": "sna_Latn",
"Sindhi": "snd_Arab",
"Somali": "som_Latn",
"Southern Sotho": "sot_Latn",
"Spanish": "spa_Latn",
"Tosk Albanian": "als_Latn",
"Sardinian": "srd_Latn",
"Serbian": "srp_Cyrl",
"Swati": "ssw_Latn",
"Sundanese": "sun_Latn",
"Swedish": "swe_Latn",
"Swahili": "swh_Latn",
"Silesian": "szl_Latn",
"Tamil": "tam_Taml",
"Tatar": "tat_Cyrl",
"Telugu": "tel_Telu",
"Tajik": "tgk_Cyrl",
"Tagalog": "tgl_Latn",
"Thai": "tha_Thai",
"Tigrinya": "tir_Ethi",
"Tamasheq Latin": "taq_Latn",
"Tamasheq Tifinagh": "taq_Tfng",
"Tok Pisin": "tpi_Latn",
"Tswana": "tsn_Latn",
"Tsonga": "tso_Latn",
"Turkmen": "tuk_Latn",
"Tumbuka": "tum_Latn",
"Turkish": "tur_Latn",
"Twi": "twi_Latn",
"Central Atlas Tamazight": "tzm_Tfng",
"Uyghur": "uig_Arab",
"Ukrainian": "ukr_Cyrl",
"Umbundu": "umb_Latn",
"Urdu": "urd_Arab",
"Northern Uzbek": "uzn_Latn",
"Venetian": "vec_Latn",
"Vietnamese": "vie_Latn",
"Waray": "war_Latn",
"Wolof": "wol_Latn",
"Xhosa": "xho_Latn",
"Eastern Yiddish": "ydd_Hebr",
"Yoruba": "yor_Latn",
"Yue Chinese": "yue_Hant",
"Chinese Simplified": "zho_Hans",
"Chinese Traditional": "zho_Hant",
"Standard Malay": "zsm_Latn",
"Zulu": "zul_Latn",
}
class TranslationTool(PipelineTool):
"""
Example:
```py
from transformers.agents import TranslationTool
translator = TranslationTool()
translator("This is a super nice API!", src_lang="English", tgt_lang="French")
```
"""
lang_to_code = LANGUAGE_CODES
default_checkpoint = "facebook/nllb-200-distilled-600M"
description = (
"This is a tool that translates text from a language to another."
f"Both `src_lang`and `tgt_lang` should belong to this list of languages: {list(lang_to_code.keys())}."
)
name = "translator"
pre_processor_class = AutoTokenizer
model_class = AutoModelForSeq2SeqLM
inputs = {
"text": {"type": "string", "description": "The text to translate"},
"src_lang": {
"type": "string",
"description": "The language of the text to translate. Written in plain English, such as 'Romanian', or 'Albanian'",
},
"tgt_lang": {
"type": "string",
"description": "The language for the desired ouput language. Written in plain English, such as 'Romanian', or 'Albanian'",
},
}
output_type = "string"
def encode(self, text, src_lang, tgt_lang):
if src_lang not in self.lang_to_code:
raise ValueError(f"{src_lang} is not a supported language.")
if tgt_lang not in self.lang_to_code:
raise ValueError(f"{tgt_lang} is not a supported language.")
src_lang = self.lang_to_code[src_lang]
tgt_lang = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
text, return_tensors="pt", src_lang=src_lang, tgt_lang=tgt_lang
)
def forward(self, inputs):
return self.model.generate(**inputs)
def decode(self, outputs):
return self.post_processor.decode(outputs[0].tolist(), skip_special_tokens=True)
| transformers/src/transformers/agents/translation.py/0 | {
"file_path": "transformers/src/transformers/agents/translation.py",
"repo_id": "transformers",
"token_count": 4239
} |
# 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.
"""Configuration base class and utilities."""
import copy
import json
import os
import re
import warnings
from typing import Any, Dict, List, Optional, Tuple, Union
from packaging import version
from . import __version__
from .dynamic_module_utils import custom_object_save
from .modeling_gguf_pytorch_utils import load_gguf_checkpoint
from .utils import (
CONFIG_NAME,
PushToHubMixin,
add_model_info_to_auto_map,
add_model_info_to_custom_pipelines,
cached_file,
copy_func,
download_url,
extract_commit_hash,
is_remote_url,
is_torch_available,
logging,
)
from .utils.generic import is_timm_config_dict
logger = logging.get_logger(__name__)
_re_configuration_file = re.compile(r"config\.(.*)\.json")
class PretrainedConfig(PushToHubMixin):
# no-format
r"""
Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
methods for loading/downloading/saving configurations.
<Tip>
A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
initialize a model does **not** load the model weights. It only affects the model's configuration.
</Tip>
Class attributes (overridden by derived classes):
- **model_type** (`str`) -- An identifier for the model type, serialized into the JSON file, and used to recreate
the correct object in [`~transformers.AutoConfig`].
- **is_composition** (`bool`) -- Whether the config class is composed of multiple sub-configs. In this case the
config has to be initialized from two or more configs of type [`~transformers.PretrainedConfig`] like:
[`~transformers.EncoderDecoderConfig`] or [`~RagConfig`].
- **keys_to_ignore_at_inference** (`List[str]`) -- A list of keys to ignore by default when looking at dictionary
outputs of the model during inference.
- **attribute_map** (`Dict[str, str]`) -- A dict that maps model specific attribute names to the standardized
naming of attributes.
- **base_model_tp_plan** (`Dict[str, Any]`) -- A dict that maps sub-modules FQNs of a base model to a tensor
parallel plan applied to the sub-module when `model.tensor_parallel` is called.
Common attributes (present in all subclasses):
- **vocab_size** (`int`) -- The number of tokens in the vocabulary, which is also the first dimension of the
embeddings matrix (this attribute may be missing for models that don't have a text modality like ViT).
- **hidden_size** (`int`) -- The hidden size of the model.
- **num_attention_heads** (`int`) -- The number of attention heads used in the multi-head attention layers of the
model.
- **num_hidden_layers** (`int`) -- The number of blocks in the model.
<Tip warning={true}>
Setting parameters for sequence generation in the model config is deprecated. For backward compatibility, loading
some of them will still be possible, but attempting to overwrite them will throw an exception -- you should set
them in a [~transformers.GenerationConfig]. Check the documentation of [~transformers.GenerationConfig] for more
information about the individual parameters.
</Tip>
Arg:
name_or_path (`str`, *optional*, defaults to `""`):
Store the string that was passed to [`PreTrainedModel.from_pretrained`] or
[`TFPreTrainedModel.from_pretrained`] as `pretrained_model_name_or_path` if the configuration was created
with such a method.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not the model should return all hidden-states.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not the model should returns all attentions.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not the model should return a [`~transformers.utils.ModelOutput`] instead of a plain tuple.
is_encoder_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as an encoder/decoder or not.
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as decoder or not (in which case it's used as an encoder).
cross_attention_hidden_size** (`bool`, *optional*):
The hidden size of the cross-attention layer in case the model is used as a decoder in an encoder-decoder
setting and the cross-attention hidden dimension differs from `self.config.hidden_size`.
add_cross_attention (`bool`, *optional*, defaults to `False`):
Whether cross-attention layers should be added to the model. Note, this option is only relevant for models
that can be used as decoder models within the [`EncoderDecoderModel`] class, which consists of all models
in `AUTO_MODELS_FOR_CAUSAL_LM`.
tie_encoder_decoder (`bool`, *optional*, defaults to `False`):
Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder
and decoder model to have the exact same parameter names.
prune_heads (`Dict[int, List[int]]`, *optional*, defaults to `{}`):
Pruned heads of the model. The keys are the selected layer indices and the associated values, the list of
heads to prune in said layer.
For instance `{1: [0, 2], 2: [2, 3]}` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
chunk_size_feed_forward (`int`, *optional*, defaults to `0`):
The chunk size of all feed forward layers in the residual attention blocks. A chunk size of `0` means that
the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes `n` <
sequence_length embeddings at a time. For more information on feed forward chunking, see [How does Feed
Forward Chunking work?](../glossary.html#feed-forward-chunking).
> Parameters for fine-tuning tasks
architectures (`List[str]`, *optional*):
Model architectures that can be used with the model pretrained weights.
finetuning_task (`str`, *optional*):
Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow
or PyTorch) checkpoint.
id2label (`Dict[int, str]`, *optional*):
A map from index (for instance prediction index, or target index) to label.
label2id (`Dict[str, int]`, *optional*): A map from label to index for the model.
num_labels (`int`, *optional*):
Number of labels to use in the last layer added to the model, typically for a classification task.
task_specific_params (`Dict[str, Any]`, *optional*):
Additional keyword arguments to store for the current task.
problem_type (`str`, *optional*):
Problem type for `XxxForSequenceClassification` models. Can be one of `"regression"`,
`"single_label_classification"` or `"multi_label_classification"`.
> Parameters linked to the tokenizer
tokenizer_class (`str`, *optional*):
The name of the associated tokenizer class to use (if none is set, will use the tokenizer associated to the
model by default).
prefix (`str`, *optional*):
A specific prompt that should be added at the beginning of each text before calling the model.
bos_token_id (`int`, *optional*): The id of the _beginning-of-stream_ token.
pad_token_id (`int`, *optional*): The id of the _padding_ token.
eos_token_id (`int`, *optional*): The id of the _end-of-stream_ token.
decoder_start_token_id (`int`, *optional*):
If an encoder-decoder model starts decoding with a different token than _bos_, the id of that token.
sep_token_id (`int`, *optional*): The id of the _separation_ token.
> PyTorch specific parameters
torchscript (`bool`, *optional*, defaults to `False`):
Whether or not the model should be used with Torchscript.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
model has a output word embedding layer.
torch_dtype (`str`, *optional*):
The `dtype` of the weights. This attribute can be used to initialize the model to a non-default `dtype`
(which is normally `float32`) and thus allow for optimal storage allocation. For example, if the saved
model is `float16`, ideally we want to load it back using the minimal amount of memory needed to load
`float16` weights. Since the config object is stored in plain text, this attribute contains just the
floating type string without the `torch.` prefix. For example, for `torch.float16` ``torch_dtype` is the
`"float16"` string.
This attribute is currently not being used during model loading time, but this may change in the future
versions. But we can already start preparing for the future by saving the dtype with save_pretrained.
> TensorFlow specific parameters
use_bfloat16 (`bool`, *optional*, defaults to `False`):
Whether or not the model should use BFloat16 scalars (only used by some TensorFlow models).
tf_legacy_loss (`bool`, *optional*, defaults to `False`):
Whether the model should use legacy TensorFlow losses. Legacy losses have variable output shapes and may
not be XLA-compatible. This option is here for backward compatibility and will be removed in Transformers
v5.
loss_type (`str`, *optional*):
The type of loss that the model should use. It should be in `LOSS_MAPPING`'s keys, otherwise the loss will
be automatically infered from the model architecture.
"""
model_type: str = ""
base_config_key: str = ""
sub_configs: Dict[str, "PretrainedConfig"] = {}
is_composition: bool = False
attribute_map: Dict[str, str] = {}
base_model_tp_plan: Optional[Dict[str, Any]] = None
_auto_class: Optional[str] = None
def __setattr__(self, key, value):
if key in super().__getattribute__("attribute_map"):
key = super().__getattribute__("attribute_map")[key]
super().__setattr__(key, value)
def __getattribute__(self, key):
if key != "attribute_map" and key in super().__getattribute__("attribute_map"):
key = super().__getattribute__("attribute_map")[key]
return super().__getattribute__(key)
def __init__(self, **kwargs):
# Attributes with defaults
self.return_dict = kwargs.pop("return_dict", True)
self.output_hidden_states = kwargs.pop("output_hidden_states", False)
self.output_attentions = kwargs.pop("output_attentions", False)
self.torchscript = kwargs.pop("torchscript", False) # Only used by PyTorch models
self.torch_dtype = kwargs.pop("torch_dtype", None) # Only used by PyTorch models
self.use_bfloat16 = kwargs.pop("use_bfloat16", False)
self.tf_legacy_loss = kwargs.pop("tf_legacy_loss", False) # Only used by TensorFlow models
self.pruned_heads = kwargs.pop("pruned_heads", {})
self.tie_word_embeddings = kwargs.pop(
"tie_word_embeddings", True
) # Whether input and output word embeddings should be tied for all MLM, LM and Seq2Seq models.
self.chunk_size_feed_forward = kwargs.pop("chunk_size_feed_forward", 0)
# Is decoder is used in encoder-decoder models to differentiate encoder from decoder
self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False)
self.is_decoder = kwargs.pop("is_decoder", False)
self.cross_attention_hidden_size = kwargs.pop("cross_attention_hidden_size", None)
self.add_cross_attention = kwargs.pop("add_cross_attention", False)
self.tie_encoder_decoder = kwargs.pop("tie_encoder_decoder", False)
# Retrocompatibility: Parameters for sequence generation. While we will keep the ability to load these
# parameters, saving them will be deprecated. In a distant future, we won't need to load them.
for parameter_name, default_value in self._get_global_generation_defaults().items():
setattr(self, parameter_name, kwargs.pop(parameter_name, default_value))
# Fine-tuning task arguments
self.architectures = kwargs.pop("architectures", None)
self.finetuning_task = kwargs.pop("finetuning_task", None)
self.id2label = kwargs.pop("id2label", None)
self.label2id = kwargs.pop("label2id", None)
if self.label2id is not None and not isinstance(self.label2id, dict):
raise ValueError("Argument label2id should be a dictionary.")
if self.id2label is not None:
if not isinstance(self.id2label, dict):
raise ValueError("Argument id2label should be a dictionary.")
num_labels = kwargs.pop("num_labels", None)
if num_labels is not None and len(self.id2label) != num_labels:
logger.warning(
f"You passed along `num_labels={num_labels}` with an incompatible id to label map: "
f"{self.id2label}. The number of labels wil be overwritten to {self.num_labels}."
)
self.id2label = {int(key): value for key, value in self.id2label.items()}
# Keys are always strings in JSON so convert ids to int here.
else:
self.num_labels = kwargs.pop("num_labels", 2)
if self.torch_dtype is not None and isinstance(self.torch_dtype, str):
# we will start using self.torch_dtype in v5, but to be consistent with
# from_pretrained's torch_dtype arg convert it to an actual torch.dtype object
if is_torch_available():
import torch
self.torch_dtype = getattr(torch, self.torch_dtype)
# Tokenizer arguments TODO: eventually tokenizer and models should share the same config
self.tokenizer_class = kwargs.pop("tokenizer_class", None)
self.prefix = kwargs.pop("prefix", None)
self.bos_token_id = kwargs.pop("bos_token_id", None)
self.pad_token_id = kwargs.pop("pad_token_id", None)
self.eos_token_id = kwargs.pop("eos_token_id", None)
self.sep_token_id = kwargs.pop("sep_token_id", None)
self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
# task specific arguments
self.task_specific_params = kwargs.pop("task_specific_params", None)
# regression / multi-label classification
self.problem_type = kwargs.pop("problem_type", None)
allowed_problem_types = ("regression", "single_label_classification", "multi_label_classification")
if self.problem_type is not None and self.problem_type not in allowed_problem_types:
raise ValueError(
f"The config parameter `problem_type` was not understood: received {self.problem_type} "
"but only 'regression', 'single_label_classification' and 'multi_label_classification' are valid."
)
# TPU arguments
if kwargs.pop("xla_device", None) is not None:
logger.warning(
"The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can "
"safely remove it from your `config.json` file."
)
# Name or path to the pretrained checkpoint
self._name_or_path = str(kwargs.pop("name_or_path", ""))
# Config hash
self._commit_hash = kwargs.pop("_commit_hash", None)
# Attention implementation to use, if relevant.
self._attn_implementation_internal = kwargs.pop("attn_implementation", None)
self._attn_implementation_autoset = False
# Drop the transformers version info
self.transformers_version = kwargs.pop("transformers_version", None)
# Deal with gradient checkpointing
if kwargs.get("gradient_checkpointing", False):
warnings.warn(
"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 "
"Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the "
"`Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`."
)
# Additional attributes without default values
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
@property
def name_or_path(self) -> str:
return getattr(self, "_name_or_path", None)
@name_or_path.setter
def name_or_path(self, value):
self._name_or_path = str(value) # Make sure that name_or_path is a string (for JSON encoding)
@property
def use_return_dict(self) -> bool:
"""
`bool`: Whether or not return [`~utils.ModelOutput`] instead of tuples.
"""
# If torchscript is set, force `return_dict=False` to avoid jit errors
return self.return_dict and not self.torchscript
@property
def num_labels(self) -> int:
"""
`int`: The number of labels for classification models.
"""
return len(self.id2label)
@num_labels.setter
def num_labels(self, num_labels: int):
if not hasattr(self, "id2label") or self.id2label is None or len(self.id2label) != num_labels:
self.id2label = {i: f"LABEL_{i}" for i in range(num_labels)}
self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))
@property
def _attn_implementation(self):
# This property is made private for now (as it cannot be changed and a PreTrainedModel.use_attn_implementation method needs to be implemented.)
if hasattr(self, "_attn_implementation_internal"):
if self._attn_implementation_internal is None:
# `config.attn_implementation` should never be None, for backward compatibility.
return "eager"
else:
return self._attn_implementation_internal
else:
return "eager"
@_attn_implementation.setter
def _attn_implementation(self, value):
self._attn_implementation_internal = value
def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
"""
Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
[`~PretrainedConfig.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).
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.
"""
self._set_token_in_kwargs(kwargs)
if os.path.isfile(save_directory):
raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
non_default_generation_parameters = self._get_non_default_generation_parameters()
if len(non_default_generation_parameters) > 0:
# TODO (joao): this should be an exception if the user has modified the loaded config. See #33886
warnings.warn(
"Some non-default generation parameters are set in the model config. These should go into either a) "
"`model.generation_config` (as opposed to `model.config`); OR b) a GenerationConfig file "
"(https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model)."
"This warning will become an exception in the future."
f"\nNon-default generation parameters: {str(non_default_generation_parameters)}",
UserWarning,
)
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)
# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
# loaded from the Hub.
if self._auto_class is not None:
custom_object_save(self, save_directory, config=self)
# If we save using the predefined names, we can load using `from_pretrained`
output_config_file = os.path.join(save_directory, CONFIG_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"),
)
@staticmethod
def _set_token_in_kwargs(kwargs, token=None):
"""Temporary method to deal with `token` and `use_auth_token`.
This method is to avoid apply the same changes in all model config classes that overwrite `from_pretrained`.
Need to clean up `use_auth_token` in a follow PR.
"""
# Some model config classes like CLIP define their own `from_pretrained` without the new argument `token` yet.
if token is None:
token = kwargs.pop("token", None)
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 token is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
token = use_auth_token
if token is not None:
kwargs["token"] = token
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
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,
) -> "PretrainedConfig":
r"""
Instantiate a [`PretrainedConfig`] (or a derived class) from a pretrained model configuration.
Args:
pretrained_model_name_or_path (`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
[`~PretrainedConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
- a path or url to a saved configuration JSON *file*, e.g., `./my_model_directory/configuration.json`.
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:
[`PretrainedConfig`]: The configuration object instantiated from this pretrained model.
Examples:
```python
# We can't instantiate directly the base class *PretrainedConfig* so let's show the examples on a
# derived class: BertConfig
config = BertConfig.from_pretrained(
"google-bert/bert-base-uncased"
) # Download configuration from huggingface.co and cache.
config = BertConfig.from_pretrained(
"./test/saved_model/"
) # E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')*
config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json")
config = BertConfig.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False)
assert config.output_attentions == True
config, unused_kwargs = BertConfig.from_pretrained(
"google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
)
assert config.output_attentions == True
assert unused_kwargs == {"foo": False}
```"""
kwargs["cache_dir"] = cache_dir
kwargs["force_download"] = force_download
kwargs["local_files_only"] = local_files_only
kwargs["revision"] = revision
cls._set_token_in_kwargs(kwargs, token)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
if cls.base_config_key and cls.base_config_key in config_dict:
config_dict = config_dict[cls.base_config_key]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
# sometimes the config has no `base_config_key` if the config is used in several composite models
# e.g. LlamaConfig. In that case we try to see if there is match in `model_type` before raising a warning
for k, v in config_dict.items():
if isinstance(v, dict) and v.get("model_type") == cls.model_type:
config_dict = v
# raise warning only if we still can't see a match in `model_type`
if config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
@classmethod
def get_config_dict(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
[`PretrainedConfig`] using `from_dict`.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`):
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
Returns:
`Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object.
"""
cls._set_token_in_kwargs(kwargs)
original_kwargs = copy.deepcopy(kwargs)
# Get config dict associated with the base config file
config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
if config_dict is None:
return {}, kwargs
if "_commit_hash" in config_dict:
original_kwargs["_commit_hash"] = config_dict["_commit_hash"]
# That config file may point us toward another config file to use.
if "configuration_files" in config_dict:
configuration_file = get_configuration_file(config_dict["configuration_files"])
config_dict, kwargs = cls._get_config_dict(
pretrained_model_name_or_path, _configuration_file=configuration_file, **original_kwargs
)
return config_dict, kwargs
@classmethod
def _get_config_dict(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", None)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None)
trust_remote_code = kwargs.pop("trust_remote_code", 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)
gguf_file = kwargs.get("gguf_file", None)
if trust_remote_code is True:
logger.warning(
"The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is"
" ignored."
)
user_agent = {"file_type": "config", "from_auto_class": from_auto_class}
if from_pipeline is not None:
user_agent["using_pipeline"] = from_pipeline
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
is_local = os.path.isdir(pretrained_model_name_or_path)
if os.path.isfile(os.path.join(subfolder, pretrained_model_name_or_path)):
# Special case when pretrained_model_name_or_path is a local file
resolved_config_file = pretrained_model_name_or_path
is_local = True
elif is_remote_url(pretrained_model_name_or_path):
configuration_file = pretrained_model_name_or_path if gguf_file is None else gguf_file
resolved_config_file = download_url(pretrained_model_name_or_path)
else:
configuration_file = kwargs.pop("_configuration_file", CONFIG_NAME) if gguf_file is None else gguf_file
try:
# Load from local folder or from cache or download from model Hub and cache
resolved_config_file = cached_file(
pretrained_model_name_or_path,
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,
)
if resolved_config_file is None:
return None, kwargs
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_or_path}'. 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_or_path}' is the correct path to a directory"
f" containing a {configuration_file} file"
)
try:
if gguf_file:
config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)["config"]
else:
# 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 "auto_map" in config_dict and not is_local:
config_dict["auto_map"] = add_model_info_to_auto_map(
config_dict["auto_map"], pretrained_model_name_or_path
)
if "custom_pipelines" in config_dict and not is_local:
config_dict["custom_pipelines"] = add_model_info_to_custom_pipelines(
config_dict["custom_pipelines"], pretrained_model_name_or_path
)
# timm models are not saved with the model_type in the config file
if "model_type" not in config_dict and is_timm_config_dict(config_dict):
config_dict["model_type"] = "timm_wrapper"
return config_dict, kwargs
@classmethod
def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "PretrainedConfig":
"""
Instantiates a [`PretrainedConfig`] from a Python dictionary of parameters.
Args:
config_dict (`Dict[str, Any]`):
Dictionary that will be used to instantiate the configuration object. Such a dictionary can be
retrieved from a pretrained checkpoint by leveraging the [`~PretrainedConfig.get_config_dict`] method.
kwargs (`Dict[str, Any]`):
Additional parameters from which to initialize the configuration object.
Returns:
[`PretrainedConfig`]: 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"]
# We remove it from kwargs so that it does not appear in `return_unused_kwargs`.
config_dict["attn_implementation"] = kwargs.pop("attn_implementation", None)
config = cls(**config_dict)
if hasattr(config, "pruned_heads"):
config.pruned_heads = {int(key): value for key, value in config.pruned_heads.items()}
# Update config with kwargs if needed
if "num_labels" in kwargs and "id2label" in kwargs:
num_labels = kwargs["num_labels"]
id2label = kwargs["id2label"] if kwargs["id2label"] is not None else []
if len(id2label) != num_labels:
raise ValueError(
f"You passed along `num_labels={num_labels }` with an incompatible id to label map: "
f"{kwargs['id2label']}. Since those arguments are inconsistent with each other, you should remove "
"one of them."
)
to_remove = []
for key, value in kwargs.items():
if hasattr(config, key):
current_attr = getattr(config, key)
# To authorize passing a custom subconfig as kwarg in models that have nested configs.
if isinstance(current_attr, PretrainedConfig) and isinstance(value, dict):
value = current_attr.__class__(**value)
setattr(config, key, value)
if key != "torch_dtype":
to_remove.append(key)
for key in to_remove:
kwargs.pop(key, None)
logger.info(f"Model config {config}")
if return_unused_kwargs:
return config, kwargs
else:
return config
@classmethod
def from_json_file(cls, json_file: Union[str, os.PathLike]) -> "PretrainedConfig":
"""
Instantiates a [`PretrainedConfig`] from the path to a JSON file of parameters.
Args:
json_file (`str` or `os.PathLike`):
Path to the JSON file containing the parameters.
Returns:
[`PretrainedConfig`]: The configuration object instantiated from that JSON file.
"""
config_dict = cls._dict_from_json_file(json_file)
return cls(**config_dict)
@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)
def __eq__(self, other):
return isinstance(other, PretrainedConfig) and (self.__dict__ == other.__dict__)
def __repr__(self):
return f"{self.__class__.__name__} {self.to_json_string()}"
def __iter__(self):
for attr in self.__dict__:
yield attr
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 = PretrainedConfig().to_dict()
# get class specific config dict
class_config_dict = self.__class__().to_dict() if not self.is_composition else {}
serializable_config_dict = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if (
isinstance(getattr(self, key, None), PretrainedConfig)
and key in class_config_dict
and isinstance(class_config_dict[key], dict)
):
# For nested configs we need to clean the diff recursively
diff = recursive_diff_dict(value, class_config_dict[key], config_obj=getattr(self, key, None))
if "model_type" in value:
# Needs to be set even if it's not in the diff
diff["model_type"] = value["model_type"]
if len(diff) > 0:
serializable_config_dict[key] = diff
elif (
key not in default_config_dict
or key == "transformers_version"
or value != default_config_dict[key]
or (key in class_config_dict and value != class_config_dict[key])
):
serializable_config_dict[key] = value
if hasattr(self, "quantization_config"):
serializable_config_dict["quantization_config"] = (
self.quantization_config.to_dict()
if not isinstance(self.quantization_config, dict)
else self.quantization_config
)
# pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
_ = serializable_config_dict.pop("_pre_quantization_dtype", None)
self.dict_torch_dtype_to_str(serializable_config_dict)
if "_attn_implementation_internal" in serializable_config_dict:
del serializable_config_dict["_attn_implementation_internal"]
# Do not serialize `base_model_tp_plan` for now
if "base_model_tp_plan" in serializable_config_dict:
del serializable_config_dict["base_model_tp_plan"]
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__)
if hasattr(self.__class__, "model_type"):
output["model_type"] = self.__class__.model_type
if "_auto_class" in output:
del output["_auto_class"]
if "_commit_hash" in output:
del output["_commit_hash"]
if "_attn_implementation_internal" in output:
del output["_attn_implementation_internal"]
# Do not serialize `base_model_tp_plan` for now
if "base_model_tp_plan" in output:
del output["base_model_tp_plan"]
# Transformers version when serializing the model
output["transformers_version"] = __version__
for key, value in output.items():
# Deal with nested configs like CLIP
if isinstance(value, PretrainedConfig):
value = value.to_dict()
del value["transformers_version"]
output[key] = value
if hasattr(self, "quantization_config"):
output["quantization_config"] = (
self.quantization_config.to_dict()
if not isinstance(self.quantization_config, dict)
else self.quantization_config
)
# pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
_ = output.pop("_pre_quantization_dtype", None)
self.dict_torch_dtype_to_str(output)
return output
def to_json_string(self, use_diff: bool = True) -> 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 `PretrainedConfig()`
is serialized to JSON string.
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()
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 `PretrainedConfig()`
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))
def update(self, config_dict: Dict[str, Any]):
"""
Updates attributes of this class with attributes from `config_dict`.
Args:
config_dict (`Dict[str, Any]`): Dictionary of attributes that should be updated for this class.
"""
for key, value in config_dict.items():
setattr(self, key, value)
def update_from_string(self, update_str: str):
"""
Updates attributes of this class with attributes from `update_str`.
The expected format is ints, floats and strings as is, and for booleans use `true` or `false`. For example:
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
The keys to change have to already exist in the config object.
Args:
update_str (`str`): String with attributes that should be updated for this class.
"""
d = dict(x.split("=") for x in update_str.split(","))
for k, v in d.items():
if not hasattr(self, k):
raise ValueError(f"key {k} isn't in the original config dict")
old_v = getattr(self, k)
if isinstance(old_v, bool):
if v.lower() in ["true", "1", "y", "yes"]:
v = True
elif v.lower() in ["false", "0", "n", "no"]:
v = False
else:
raise ValueError(f"can't derive true or false from {v} (key {k})")
elif isinstance(old_v, int):
v = int(v)
elif isinstance(old_v, float):
v = float(v)
elif not isinstance(old_v, str):
raise TypeError(
f"You can only update int, float, bool or string values in the config, got {v} for key {k}"
)
setattr(self, k, v)
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:
if isinstance(d["torch_dtype"], dict):
d["torch_dtype"] = {k: str(v).split(".")[-1] for k, v in d["torch_dtype"].items()}
elif 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)
@classmethod
def register_for_auto_class(cls, auto_class="AutoConfig"):
"""
Register this class with a given auto class. This should only be used for custom configurations as the ones in
the library are already mapped with `AutoConfig`.
<Tip warning={true}>
This API is experimental and may have some slight breaking changes in the next releases.
</Tip>
Args:
auto_class (`str` or `type`, *optional*, defaults to `"AutoConfig"`):
The auto class to register this new configuration with.
"""
if not isinstance(auto_class, str):
auto_class = auto_class.__name__
import transformers.models.auto as auto_module
if not hasattr(auto_module, auto_class):
raise ValueError(f"{auto_class} is not a valid auto class.")
cls._auto_class = auto_class
@staticmethod
def _get_global_generation_defaults() -> Dict[str, Any]:
return {
"max_length": 20,
"min_length": 0,
"do_sample": False,
"early_stopping": False,
"num_beams": 1,
"num_beam_groups": 1,
"diversity_penalty": 0.0,
"temperature": 1.0,
"top_k": 50,
"top_p": 1.0,
"typical_p": 1.0,
"repetition_penalty": 1.0,
"length_penalty": 1.0,
"no_repeat_ngram_size": 0,
"encoder_no_repeat_ngram_size": 0,
"bad_words_ids": None,
"num_return_sequences": 1,
"output_scores": False,
"return_dict_in_generate": False,
"forced_bos_token_id": None,
"forced_eos_token_id": None,
"remove_invalid_values": False,
"exponential_decay_length_penalty": None,
"suppress_tokens": None,
"begin_suppress_tokens": None,
}
def _get_non_default_generation_parameters(self) -> Dict[str, Any]:
"""
Gets the non-default generation parameters on the PretrainedConfig instance
"""
non_default_generation_parameters = {}
decoder_attribute_name = None
# Composite models don't have a default config, use their decoder config as a fallback for default values
# If no known pattern is matched, then `default_config = None` -> check against the global generation defaults
try:
default_config = self.__class__()
except ValueError:
decoder_config = self.get_text_config(decoder=True)
if decoder_config is not self:
default_config = decoder_config.__class__()
else:
default_config = None
# If it is a composite model, we want to check the subconfig that will be used for generation
self_decoder_config = self if decoder_attribute_name is None else getattr(self, decoder_attribute_name)
for parameter_name, default_global_value in self._get_global_generation_defaults().items():
if hasattr(self_decoder_config, parameter_name):
is_default_in_config = is_default_generation_value = None
parameter_value = getattr(self_decoder_config, parameter_name)
# Three cases in which is okay for the model config to hold generation config parameters:
# 1. The parameter is set to `None`, effectivelly delegating its value to the generation config
if parameter_value is None:
continue
# 2. If we have a default config, then the instance should hold the same generation defaults
if default_config is not None:
is_default_in_config = parameter_value == getattr(default_config, parameter_name)
# 3. if we don't have a default config, then the instance should hold the global generation defaults
else:
is_default_generation_value = parameter_value == default_global_value
is_non_default = (is_default_in_config is False) or (
is_default_in_config is None and is_default_generation_value is False
)
if is_non_default:
non_default_generation_parameters[parameter_name] = getattr(self_decoder_config, parameter_name)
return non_default_generation_parameters
def get_text_config(self, decoder=False) -> "PretrainedConfig":
"""
Returns the config that is meant to be used with text IO. On most models, it is the original config instance
itself. On specific composite models, it is under a set of valid names.
If `decoder` is set to `True`, then only search for decoder config names.
"""
decoder_possible_text_config_names = ("decoder", "generator", "text_config")
encoder_possible_text_config_names = ("text_encoder",)
if decoder:
possible_text_config_names = decoder_possible_text_config_names
else:
possible_text_config_names = encoder_possible_text_config_names + decoder_possible_text_config_names
valid_text_config_names = []
for text_config_name in possible_text_config_names:
if hasattr(self, text_config_name):
text_config = getattr(self, text_config_name, None)
if text_config is not None:
valid_text_config_names += [text_config_name]
if len(valid_text_config_names) > 1:
raise ValueError(
f"Multiple valid text configs were found in the model config: {valid_text_config_names}. In this "
"case, using `get_text_config()` would be ambiguous. Please specify the desied text config directly."
)
elif len(valid_text_config_names) == 1:
return getattr(self, valid_text_config_names[0])
return self
def get_configuration_file(configuration_files: List[str]) -> str:
"""
Get the configuration file to use for this version of transformers.
Args:
configuration_files (`List[str]`): The list of available configuration files.
Returns:
`str`: The configuration file to use.
"""
configuration_files_map = {}
for file_name in configuration_files:
search = _re_configuration_file.search(file_name)
if search is not None:
v = search.groups()[0]
configuration_files_map[v] = file_name
available_versions = sorted(configuration_files_map.keys())
# Defaults to FULL_CONFIGURATION_FILE and then try to look at some newer versions.
configuration_file = CONFIG_NAME
transformers_version = version.parse(__version__)
for v in available_versions:
if version.parse(v) <= transformers_version:
configuration_file = configuration_files_map[v]
else:
# No point going further since the versions are sorted.
break
return configuration_file
def recursive_diff_dict(dict_a, dict_b, config_obj=None):
"""
Helper function to recursively take the diff between two nested dictionaries. The resulting diff only contains the
values from `dict_a` that are different from values in `dict_b`.
"""
diff = {}
default = config_obj.__class__().to_dict() if config_obj is not None else {}
for key, value in dict_a.items():
obj_value = getattr(config_obj, str(key), None)
if isinstance(obj_value, PretrainedConfig) and key in dict_b and isinstance(dict_b[key], dict):
diff_value = recursive_diff_dict(value, dict_b[key], config_obj=obj_value)
if len(diff_value) > 0:
diff[key] = diff_value
elif key not in dict_b or value != dict_b[key] or key not in default or value != default[key]:
diff[key] = value
return diff
PretrainedConfig.push_to_hub = copy_func(PretrainedConfig.push_to_hub)
if PretrainedConfig.push_to_hub.__doc__ is not None:
PretrainedConfig.push_to_hub.__doc__ = PretrainedConfig.push_to_hub.__doc__.format(
object="config", object_class="AutoConfig", object_files="configuration file"
)
| transformers/src/transformers/configuration_utils.py/0 | {
"file_path": "transformers/src/transformers/configuration_utils.py",
"repo_id": "transformers",
"token_count": 23849
} |
# 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 json
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from ...models.bert.tokenization_bert import whitespace_tokenize
from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
from ...utils import is_tf_available, is_torch_available, logging
from .utils import DataProcessor
# Store the tokenizers which insert 2 separators tokens
MULTI_SEP_TOKENS_TOKENIZERS_SET = {"roberta", "camembert", "bart", "mpnet"}
if is_torch_available():
import torch
from torch.utils.data import TensorDataset
if is_tf_available():
import tensorflow as tf
logger = logging.get_logger(__name__)
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
for new_start in range(input_start, input_end + 1):
for new_end in range(input_end, new_start - 1, -1):
text_span = " ".join(doc_tokens[new_start : (new_end + 1)])
if text_span == tok_answer_text:
return (new_start, new_end)
return (input_start, input_end)
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
best_score = None
best_span_index = None
for span_index, doc_span in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
def _new_check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# if len(doc_spans) == 1:
# return True
best_score = None
best_span_index = None
for span_index, doc_span in enumerate(doc_spans):
end = doc_span["start"] + doc_span["length"] - 1
if position < doc_span["start"]:
continue
if position > end:
continue
num_left_context = position - doc_span["start"]
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span["length"]
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
def _is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False
def squad_convert_example_to_features(
example, max_seq_length, doc_stride, max_query_length, padding_strategy, is_training
):
features = []
if is_training and not example.is_impossible:
# Get start and end position
start_position = example.start_position
end_position = example.end_position
# If the answer cannot be found in the text, then skip this example.
actual_text = " ".join(example.doc_tokens[start_position : (end_position + 1)])
cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
if actual_text.find(cleaned_answer_text) == -1:
logger.warning(f"Could not find answer: '{actual_text}' vs. '{cleaned_answer_text}'")
return []
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for i, token in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
if tokenizer.__class__.__name__ in [
"RobertaTokenizer",
"LongformerTokenizer",
"BartTokenizer",
"RobertaTokenizerFast",
"LongformerTokenizerFast",
"BartTokenizerFast",
]:
sub_tokens = tokenizer.tokenize(token, add_prefix_space=True)
else:
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text
)
spans = []
truncated_query = tokenizer.encode(
example.question_text, add_special_tokens=False, truncation=True, max_length=max_query_length
)
# Tokenizers who insert 2 SEP tokens in-between <context> & <question> need to have special handling
# in the way they compute mask of added tokens.
tokenizer_type = type(tokenizer).__name__.replace("Tokenizer", "").lower()
sequence_added_tokens = (
tokenizer.model_max_length - tokenizer.max_len_single_sentence + 1
if tokenizer_type in MULTI_SEP_TOKENS_TOKENIZERS_SET
else tokenizer.model_max_length - tokenizer.max_len_single_sentence
)
sequence_pair_added_tokens = tokenizer.model_max_length - tokenizer.max_len_sentences_pair
span_doc_tokens = all_doc_tokens
while len(spans) * doc_stride < len(all_doc_tokens):
# Define the side we want to truncate / pad and the text/pair sorting
if tokenizer.padding_side == "right":
texts = truncated_query
pairs = span_doc_tokens
truncation = TruncationStrategy.ONLY_SECOND.value
else:
texts = span_doc_tokens
pairs = truncated_query
truncation = TruncationStrategy.ONLY_FIRST.value
encoded_dict = tokenizer.encode_plus( # TODO(thom) update this logic
texts,
pairs,
truncation=truncation,
padding=padding_strategy,
max_length=max_seq_length,
return_overflowing_tokens=True,
stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
return_token_type_ids=True,
)
paragraph_len = min(
len(all_doc_tokens) - len(spans) * doc_stride,
max_seq_length - len(truncated_query) - sequence_pair_added_tokens,
)
if tokenizer.pad_token_id in encoded_dict["input_ids"]:
if tokenizer.padding_side == "right":
non_padded_ids = encoded_dict["input_ids"][: encoded_dict["input_ids"].index(tokenizer.pad_token_id)]
else:
last_padding_id_position = (
len(encoded_dict["input_ids"]) - 1 - encoded_dict["input_ids"][::-1].index(tokenizer.pad_token_id)
)
non_padded_ids = encoded_dict["input_ids"][last_padding_id_position + 1 :]
else:
non_padded_ids = encoded_dict["input_ids"]
tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
token_to_orig_map = {}
for i in range(paragraph_len):
index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i
token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i]
encoded_dict["paragraph_len"] = paragraph_len
encoded_dict["tokens"] = tokens
encoded_dict["token_to_orig_map"] = token_to_orig_map
encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
encoded_dict["token_is_max_context"] = {}
encoded_dict["start"] = len(spans) * doc_stride
encoded_dict["length"] = paragraph_len
spans.append(encoded_dict)
if "overflowing_tokens" not in encoded_dict or (
"overflowing_tokens" in encoded_dict and len(encoded_dict["overflowing_tokens"]) == 0
):
break
span_doc_tokens = encoded_dict["overflowing_tokens"]
for doc_span_index in range(len(spans)):
for j in range(spans[doc_span_index]["paragraph_len"]):
is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
index = (
j
if tokenizer.padding_side == "left"
else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
)
spans[doc_span_index]["token_is_max_context"][index] = is_max_context
for span in spans:
# Identify the position of the CLS token
cls_index = span["input_ids"].index(tokenizer.cls_token_id)
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
# Original TF implementation also keep the classification token (set to 0)
p_mask = np.ones_like(span["token_type_ids"])
if tokenizer.padding_side == "right":
p_mask[len(truncated_query) + sequence_added_tokens :] = 0
else:
p_mask[-len(span["tokens"]) : -(len(truncated_query) + sequence_added_tokens)] = 0
pad_token_indices = np.where(np.atleast_1d(span["input_ids"] == tokenizer.pad_token_id))
special_token_indices = np.asarray(
tokenizer.get_special_tokens_mask(span["input_ids"], already_has_special_tokens=True)
).nonzero()
p_mask[pad_token_indices] = 1
p_mask[special_token_indices] = 1
# Set the cls index to 0: the CLS index can be used for impossible answers
p_mask[cls_index] = 0
span_is_impossible = example.is_impossible
start_position = 0
end_position = 0
if is_training and not span_is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = span["start"]
doc_end = span["start"] + span["length"] - 1
out_of_span = False
if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
start_position = cls_index
end_position = cls_index
span_is_impossible = True
else:
if tokenizer.padding_side == "left":
doc_offset = 0
else:
doc_offset = len(truncated_query) + sequence_added_tokens
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
features.append(
SquadFeatures(
span["input_ids"],
span["attention_mask"],
span["token_type_ids"],
cls_index,
p_mask.tolist(),
example_index=0, # Can not set unique_id and example_index here. They will be set after multiple processing.
unique_id=0,
paragraph_len=span["paragraph_len"],
token_is_max_context=span["token_is_max_context"],
tokens=span["tokens"],
token_to_orig_map=span["token_to_orig_map"],
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible,
qas_id=example.qas_id,
)
)
return features
def squad_convert_example_to_features_init(tokenizer_for_convert: PreTrainedTokenizerBase):
global tokenizer
tokenizer = tokenizer_for_convert
def squad_convert_examples_to_features(
examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
padding_strategy="max_length",
return_dataset=False,
threads=1,
tqdm_enabled=True,
):
"""
Converts a list of examples into a list of features that can be directly given as input to a model. It is
model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
Args:
examples: list of [`~data.processors.squad.SquadExample`]
tokenizer: an instance of a child of [`PreTrainedTokenizer`]
max_seq_length: The maximum sequence length of the inputs.
doc_stride: The stride used when the context is too large and is split across several features.
max_query_length: The maximum length of the query.
is_training: whether to create features for model evaluation or model training.
padding_strategy: Default to "max_length". Which padding strategy to use
return_dataset: Default False. Either 'pt' or 'tf'.
if 'pt': returns a torch.data.TensorDataset, if 'tf': returns a tf.data.Dataset
threads: multiple processing threads.
Returns:
list of [`~data.processors.squad.SquadFeatures`]
Example:
```python
processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=not evaluate,
)
```"""
# Defining helper methods
features = []
threads = min(threads, cpu_count())
with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:
annotate_ = partial(
squad_convert_example_to_features,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
padding_strategy=padding_strategy,
is_training=is_training,
)
features = list(
tqdm(
p.imap(annotate_, examples, chunksize=32),
total=len(examples),
desc="convert squad examples to features",
disable=not tqdm_enabled,
)
)
new_features = []
unique_id = 1000000000
example_index = 0
for example_features in tqdm(
features, total=len(features), desc="add example index and unique id", disable=not tqdm_enabled
):
if not example_features:
continue
for example_feature in example_features:
example_feature.example_index = example_index
example_feature.unique_id = unique_id
new_features.append(example_feature)
unique_id += 1
example_index += 1
features = new_features
del new_features
if return_dataset == "pt":
if not is_torch_available():
raise RuntimeError("PyTorch must be installed to return a PyTorch dataset.")
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_attention_masks = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
all_is_impossible = torch.tensor([f.is_impossible for f in features], dtype=torch.float)
if not is_training:
all_feature_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
dataset = TensorDataset(
all_input_ids, all_attention_masks, all_token_type_ids, all_feature_index, all_cls_index, all_p_mask
)
else:
all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
dataset = TensorDataset(
all_input_ids,
all_attention_masks,
all_token_type_ids,
all_start_positions,
all_end_positions,
all_cls_index,
all_p_mask,
all_is_impossible,
)
return features, dataset
elif return_dataset == "tf":
if not is_tf_available():
raise RuntimeError("TensorFlow must be installed to return a TensorFlow dataset.")
def gen():
for i, ex in enumerate(features):
if ex.token_type_ids is None:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"feature_index": i,
"qas_id": ex.qas_id,
},
{
"start_positions": ex.start_position,
"end_positions": ex.end_position,
"cls_index": ex.cls_index,
"p_mask": ex.p_mask,
"is_impossible": ex.is_impossible,
},
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
"feature_index": i,
"qas_id": ex.qas_id,
},
{
"start_positions": ex.start_position,
"end_positions": ex.end_position,
"cls_index": ex.cls_index,
"p_mask": ex.p_mask,
"is_impossible": ex.is_impossible,
},
)
# Why have we split the batch into a tuple? PyTorch just has a list of tensors.
if "token_type_ids" in tokenizer.model_input_names:
train_types = (
{
"input_ids": tf.int32,
"attention_mask": tf.int32,
"token_type_ids": tf.int32,
"feature_index": tf.int64,
"qas_id": tf.string,
},
{
"start_positions": tf.int64,
"end_positions": tf.int64,
"cls_index": tf.int64,
"p_mask": tf.int32,
"is_impossible": tf.int32,
},
)
train_shapes = (
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"token_type_ids": tf.TensorShape([None]),
"feature_index": tf.TensorShape([]),
"qas_id": tf.TensorShape([]),
},
{
"start_positions": tf.TensorShape([]),
"end_positions": tf.TensorShape([]),
"cls_index": tf.TensorShape([]),
"p_mask": tf.TensorShape([None]),
"is_impossible": tf.TensorShape([]),
},
)
else:
train_types = (
{"input_ids": tf.int32, "attention_mask": tf.int32, "feature_index": tf.int64, "qas_id": tf.string},
{
"start_positions": tf.int64,
"end_positions": tf.int64,
"cls_index": tf.int64,
"p_mask": tf.int32,
"is_impossible": tf.int32,
},
)
train_shapes = (
{
"input_ids": tf.TensorShape([None]),
"attention_mask": tf.TensorShape([None]),
"feature_index": tf.TensorShape([]),
"qas_id": tf.TensorShape([]),
},
{
"start_positions": tf.TensorShape([]),
"end_positions": tf.TensorShape([]),
"cls_index": tf.TensorShape([]),
"p_mask": tf.TensorShape([None]),
"is_impossible": tf.TensorShape([]),
},
)
return tf.data.Dataset.from_generator(gen, train_types, train_shapes)
else:
return features
class SquadProcessor(DataProcessor):
"""
Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
version 2.0 of SQuAD, respectively.
"""
train_file = None
dev_file = None
def _get_example_from_tensor_dict(self, tensor_dict, evaluate=False):
if not evaluate:
answer = tensor_dict["answers"]["text"][0].numpy().decode("utf-8")
answer_start = tensor_dict["answers"]["answer_start"][0].numpy()
answers = []
else:
answers = [
{"answer_start": start.numpy(), "text": text.numpy().decode("utf-8")}
for start, text in zip(tensor_dict["answers"]["answer_start"], tensor_dict["answers"]["text"])
]
answer = None
answer_start = None
return SquadExample(
qas_id=tensor_dict["id"].numpy().decode("utf-8"),
question_text=tensor_dict["question"].numpy().decode("utf-8"),
context_text=tensor_dict["context"].numpy().decode("utf-8"),
answer_text=answer,
start_position_character=answer_start,
title=tensor_dict["title"].numpy().decode("utf-8"),
answers=answers,
)
def get_examples_from_dataset(self, dataset, evaluate=False):
"""
Creates a list of [`~data.processors.squad.SquadExample`] using a TFDS dataset.
Args:
dataset: The tfds dataset loaded from *tensorflow_datasets.load("squad")*
evaluate: Boolean specifying if in evaluation mode or in training mode
Returns:
List of SquadExample
Examples:
```python
>>> import tensorflow_datasets as tfds
>>> dataset = tfds.load("squad")
>>> training_examples = get_examples_from_dataset(dataset, evaluate=False)
>>> evaluation_examples = get_examples_from_dataset(dataset, evaluate=True)
```"""
if evaluate:
dataset = dataset["validation"]
else:
dataset = dataset["train"]
examples = []
for tensor_dict in tqdm(dataset):
examples.append(self._get_example_from_tensor_dict(tensor_dict, evaluate=evaluate))
return examples
def get_train_examples(self, data_dir, filename=None):
"""
Returns the training examples from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the training file has a different name than the original one
which is `train-v1.1.json` and `train-v2.0.json` for squad versions 1.1 and 2.0 respectively.
"""
if data_dir is None:
data_dir = ""
if self.train_file is None:
raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
with open(
os.path.join(data_dir, self.train_file if filename is None else filename), "r", encoding="utf-8"
) as reader:
input_data = json.load(reader)["data"]
return self._create_examples(input_data, "train")
def get_dev_examples(self, data_dir, filename=None):
"""
Returns the evaluation example from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the evaluation file has a different name than the original one
which is `dev-v1.1.json` and `dev-v2.0.json` for squad versions 1.1 and 2.0 respectively.
"""
if data_dir is None:
data_dir = ""
if self.dev_file is None:
raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
with open(
os.path.join(data_dir, self.dev_file if filename is None else filename), "r", encoding="utf-8"
) as reader:
input_data = json.load(reader)["data"]
return self._create_examples(input_data, "dev")
def _create_examples(self, input_data, set_type):
is_training = set_type == "train"
examples = []
for entry in tqdm(input_data):
title = entry["title"]
for paragraph in entry["paragraphs"]:
context_text = paragraph["context"]
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position_character = None
answer_text = None
answers = []
is_impossible = qa.get("is_impossible", False)
if not is_impossible:
if is_training:
answer = qa["answers"][0]
answer_text = answer["text"]
start_position_character = answer["answer_start"]
else:
answers = qa["answers"]
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
context_text=context_text,
answer_text=answer_text,
start_position_character=start_position_character,
title=title,
is_impossible=is_impossible,
answers=answers,
)
examples.append(example)
return examples
class SquadV1Processor(SquadProcessor):
train_file = "train-v1.1.json"
dev_file = "dev-v1.1.json"
class SquadV2Processor(SquadProcessor):
train_file = "train-v2.0.json"
dev_file = "dev-v2.0.json"
class SquadExample:
"""
A single training/test example for the Squad dataset, as loaded from disk.
Args:
qas_id: The example's unique identifier
question_text: The question string
context_text: The context string
answer_text: The answer string
start_position_character: The character position of the start of the answer
title: The title of the example
answers: None by default, this is used during evaluation. Holds answers as well as their start positions.
is_impossible: False by default, set to True if the example has no possible answer.
"""
def __init__(
self,
qas_id,
question_text,
context_text,
answer_text,
start_position_character,
title,
answers=[],
is_impossible=False,
):
self.qas_id = qas_id
self.question_text = question_text
self.context_text = context_text
self.answer_text = answer_text
self.title = title
self.is_impossible = is_impossible
self.answers = answers
self.start_position, self.end_position = 0, 0
doc_tokens = []
char_to_word_offset = []
prev_is_whitespace = True
# Split on whitespace so that different tokens may be attributed to their original position.
for c in self.context_text:
if _is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
else:
doc_tokens[-1] += c
prev_is_whitespace = False
char_to_word_offset.append(len(doc_tokens) - 1)
self.doc_tokens = doc_tokens
self.char_to_word_offset = char_to_word_offset
# Start and end positions only has a value during evaluation.
if start_position_character is not None and not is_impossible:
self.start_position = char_to_word_offset[start_position_character]
self.end_position = char_to_word_offset[
min(start_position_character + len(answer_text) - 1, len(char_to_word_offset) - 1)
]
class SquadFeatures:
"""
Single squad example features to be fed to a model. Those features are model-specific and can be crafted from
[`~data.processors.squad.SquadExample`] using the
:method:*~transformers.data.processors.squad.squad_convert_examples_to_features* method.
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
token_type_ids: Segment token indices to indicate first and second portions of the inputs.
cls_index: the index of the CLS token.
p_mask: Mask identifying tokens that can be answers vs. tokens that cannot.
Mask with 1 for tokens than cannot be in the answer and 0 for token that can be in an answer
example_index: the index of the example
unique_id: The unique Feature identifier
paragraph_len: The length of the context
token_is_max_context:
List of booleans identifying which tokens have their maximum context in this feature object. If a token
does not have their maximum context in this feature object, it means that another feature object has more
information related to that token and should be prioritized over this feature for that token.
tokens: list of tokens corresponding to the input ids
token_to_orig_map: mapping between the tokens and the original text, needed in order to identify the answer.
start_position: start of the answer token index
end_position: end of the answer token index
encoding: optionally store the BatchEncoding with the fast-tokenizer alignment methods.
"""
def __init__(
self,
input_ids,
attention_mask,
token_type_ids,
cls_index,
p_mask,
example_index,
unique_id,
paragraph_len,
token_is_max_context,
tokens,
token_to_orig_map,
start_position,
end_position,
is_impossible,
qas_id: str = None,
encoding: BatchEncoding = None,
):
self.input_ids = input_ids
self.attention_mask = attention_mask
self.token_type_ids = token_type_ids
self.cls_index = cls_index
self.p_mask = p_mask
self.example_index = example_index
self.unique_id = unique_id
self.paragraph_len = paragraph_len
self.token_is_max_context = token_is_max_context
self.tokens = tokens
self.token_to_orig_map = token_to_orig_map
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
self.qas_id = qas_id
self.encoding = encoding
class SquadResult:
"""
Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
Args:
unique_id: The unique identifier corresponding to that example.
start_logits: The logits corresponding to the start of the answer
end_logits: The logits corresponding to the end of the answer
"""
def __init__(self, unique_id, start_logits, end_logits, start_top_index=None, end_top_index=None, cls_logits=None):
self.start_logits = start_logits
self.end_logits = end_logits
self.unique_id = unique_id
if start_top_index:
self.start_top_index = start_top_index
self.end_top_index = end_top_index
self.cls_logits = cls_logits
| transformers/src/transformers/data/processors/squad.py/0 | {
"file_path": "transformers/src/transformers/data/processors/squad.py",
"repo_id": "transformers",
"token_count": 15587
} |
# coding=utf-8
# Copyright 2021 The Google AI Flax Team Authors, and The HuggingFace Inc. team.
# Copyright (c) 2020, 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.
import copy
import inspect
import warnings
from functools import partial
from typing import Any, Dict, Optional, Union
import flax
import jax
import jax.numpy as jnp
import numpy as np
from jax import lax
from ..models.auto import (
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
)
from ..utils import ModelOutput, logging
from .configuration_utils import GenerationConfig
from .flax_logits_process import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxForceTokensLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxNoRepeatNGramLogitsProcessor,
FlaxSuppressTokensAtBeginLogitsProcessor,
FlaxSuppressTokensLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
logger = logging.get_logger(__name__)
@flax.struct.dataclass
class FlaxGreedySearchOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
"""
sequences: jnp.ndarray = None
@flax.struct.dataclass
class FlaxSampleOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using sampling.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
"""
sequences: jnp.ndarray = None
@flax.struct.dataclass
class FlaxBeamSearchOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
scores (`jnp.ndarray` of shape `(batch_size,)`):
The scores (log probabilities) of the generated sequences.
"""
sequences: jnp.ndarray = None
scores: jnp.ndarray = None
@flax.struct.dataclass
class GreedyState:
cur_len: jnp.ndarray
sequences: jnp.ndarray
running_token: jnp.ndarray
is_sent_finished: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray]
@flax.struct.dataclass
class SampleState:
cur_len: jnp.ndarray
sequences: jnp.ndarray
running_token: jnp.ndarray
is_sent_finished: jnp.ndarray
prng_key: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray]
@flax.struct.dataclass
class BeamSearchState:
cur_len: jnp.ndarray
running_sequences: jnp.ndarray
running_scores: jnp.ndarray
sequences: jnp.ndarray
scores: jnp.ndarray
is_sent_finished: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray]
class FlaxGenerationMixin:
"""
A class containing all functions for auto-regressive text generation, to be used as a mixin in
[`FlaxPreTrainedModel`].
The class exposes [`~generation.FlaxGenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.FlaxGenerationMixin._greedy_search`] if `num_beams=1` and
`do_sample=False`
- *multinomial sampling* by calling [`~generation.FlaxGenerationMixin._sample`] if `num_beams=1` and
`do_sample=True`
- *beam-search decoding* by calling [`~generation.FlaxGenerationMixin._beam_search`] if `num_beams>1` and
`do_sample=False`
You do not need to call any of the above methods directly. Pass custom parameter values to 'generate' instead. To
learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
"""
def prepare_inputs_for_generation(self, *args, **kwargs):
raise NotImplementedError(
"A model class needs to define a `prepare_inputs_for_generation` method in order to use `generate`."
)
@staticmethod
def _run_loop_in_debug(cond_fn, body_fn, init_state):
"""
Run generation in untraced mode. This should only be used for debugging purposes.
"""
state = init_state
while cond_fn(state):
state = body_fn(state)
return state
def _prepare_encoder_decoder_kwargs_for_generation(self, input_ids, params, model_kwargs):
encoder_kwargs = {
argument: value
for argument, value in model_kwargs.items()
if not (argument.startswith("decoder_") or argument.startswith("cross_attn"))
}
model_kwargs["encoder_outputs"] = self.encode(input_ids, params=params, return_dict=True, **encoder_kwargs)
return model_kwargs
def _prepare_decoder_input_ids_for_generation(
self,
batch_size: int,
decoder_start_token_id: int = None,
bos_token_id: int = None,
model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
) -> jnp.ndarray:
if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
# Only use this arg if not None, otherwise just remove from model_kwargs
decoder_input_ids = model_kwargs.pop("decoder_input_ids")
if decoder_input_ids is not None:
return decoder_input_ids
decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
return jnp.array(decoder_start_token_id, dtype="i4").reshape(1, -1).repeat(batch_size, axis=0)
def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
# retrieve decoder_start_token_id for encoder-decoder models
# fall back to bos_token_id if necessary
decoder_start_token_id = (
decoder_start_token_id
if decoder_start_token_id is not None
else self.generation_config.decoder_start_token_id
)
bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
if decoder_start_token_id is not None:
return decoder_start_token_id
elif (
hasattr(self.config, "decoder")
and hasattr(self.config.decoder, "decoder_start_token_id")
and self.config.decoder.decoder_start_token_id is not None
):
return self.config.decoder.decoder_start_token_id
elif bos_token_id is not None:
return bos_token_id
elif (
hasattr(self.config, "decoder")
and hasattr(self.config.decoder, "bos_token_id")
and self.config.decoder.bos_token_id is not None
):
return self.config.decoder.bos_token_id
raise ValueError(
"`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
)
@staticmethod
def _expand_to_num_beams(tensor, num_beams):
return jnp.broadcast_to(tensor[:, None], (tensor.shape[0], num_beams) + tensor.shape[1:])
def _adapt_logits_for_beam_search(self, logits):
"""
This function can be overwritten in the specific modeling_flax_<model-name>.py classes to allow for custom beam
search behavior. Note that the only model that overwrites this method is [`~transformes.FlaxMarianMTModel`].
"""
return logits
def _validate_model_class(self):
"""
Confirms that the model class is compatible with generation. If not, raises an exception that points to the
right class to use.
"""
if not self.can_generate():
generate_compatible_mappings = [
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
]
generate_compatible_classes = set()
for model_mapping in generate_compatible_mappings:
supported_models = model_mapping.get(type(self.config), default=None)
if supported_models is not None:
generate_compatible_classes.add(supported_models.__name__)
exception_message = (
f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
"it doesn't have a language model head."
)
if generate_compatible_classes:
exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
raise TypeError(exception_message)
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
"""Validates model kwargs for generation. Generate argument typos will also be caught here."""
unused_model_args = []
model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
# `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
# `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
if "kwargs" in model_args or "model_kwargs" in model_args:
model_args |= set(inspect.signature(self.__call__).parameters)
for key, value in model_kwargs.items():
if value is not None and key not in model_args:
unused_model_args.append(key)
if unused_model_args:
raise ValueError(
f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
" generate arguments will also show up in this list)"
)
def generate(
self,
input_ids: jnp.ndarray,
generation_config: Optional[GenerationConfig] = None,
prng_key: Optional[jnp.ndarray] = None,
trace: bool = True,
params: Optional[Dict[str, jnp.ndarray]] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
**kwargs,
):
r"""
Generates sequences of token ids for models with a language modeling head.
Parameters:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call. `**kwargs`
passed to generate matching the attributes of `generation_config` will override them. If
`generation_config` is not provided, the default will be used, which had the following loading
priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
default values, whose documentation should be checked to parameterize generation.
trace (`bool`, *optional*, defaults to `True`):
Whether to trace generation. Setting `trace=False` should only be used for debugging and will lead to a
considerably slower runtime.
params (`Dict[str, jnp.ndarray]`, *optional*):
Optionally the model parameters can be passed. Can be useful for parallelized generation.
logits_processor (`FlaxLogitsProcessorList `, *optional*):
Custom logits processors that complement the default logits processors built from arguments and
generation config. If a logit processor is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
kwargs (`Dict[str, Any]`, *optional*):
Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
Return:
[`~utils.ModelOutput`].
"""
# Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
self._validate_model_class()
# priority: `generation_config` argument > `model.generation_config` (the default generation config)
if generation_config is None:
# legacy: users may modify the model configuration to control generation. To trigger this legacy behavior,
# two conditions must be met
# 1) the generation config must have been created from the model config (`_from_model_config` field);
# 2) the generation config must have seen no modification since its creation (the hash is the same).
if self.generation_config._from_model_config and self.generation_config._original_object_hash == hash(
self.generation_config
):
new_generation_config = GenerationConfig.from_model_config(self.config)
if new_generation_config != self.generation_config:
warnings.warn(
"You have modified the pretrained model configuration to control generation. This is a"
" deprecated strategy to control generation and will be removed soon, in a future version."
" Please use and modify the model generation configuration (see"
" https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )"
)
self.generation_config = new_generation_config
generation_config = self.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
self._validate_model_kwargs(model_kwargs.copy())
logits_processor = logits_processor if logits_processor is not None else FlaxLogitsProcessorList()
# set init values
prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0)
if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
if model_kwargs.get("attention_mask") is None:
logger.warning(
"The attention mask and the pad token id were not set. As a consequence, you may observe "
"unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
)
eos_token_id = generation_config.eos_token_id
if isinstance(eos_token_id, list):
eos_token_id = eos_token_id[0]
generation_config.pad_token_id = eos_token_id
if generation_config.decoder_start_token_id is None and self.config.is_encoder_decoder:
raise ValueError("`decoder_start_token_id` has to be defined for encoder-decoder generation.")
# decoder-only models should use left-padding for generation (can't be checked with `trace=True`)
if not self.config.is_encoder_decoder and not trace:
if (
generation_config.pad_token_id is not None
and jnp.sum(input_ids[:, -1] == generation_config.pad_token_id) > 0
):
logger.warning(
"A decoder-only architecture is being used, but right-padding was detected! For correct "
"generation results, please set `padding_side='left'` when initializing the tokenizer."
)
batch_size = input_ids.shape[0]
if self.config.is_encoder_decoder:
# add encoder_outputs to model_kwargs
if model_kwargs.get("encoder_outputs") is None:
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, params, model_kwargs)
# prepare decoder_input_ids for generation
input_ids = self._prepare_decoder_input_ids_for_generation(
batch_size,
decoder_start_token_id=generation_config.decoder_start_token_id,
bos_token_id=generation_config.bos_token_id,
model_kwargs=model_kwargs,
)
# Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
# 20 is the default max_length of the generation config
warnings.warn(
f"Using the model-agnostic default `max_length` (={generation_config.max_length}) "
"to control the generation length. recommend setting `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif generation_config.max_new_tokens is not None:
if not has_default_max_length and generation_config.max_length is not None:
logger.warning(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
else: # by default let's always generate 10 new tokens
if generation_config.max_length == GenerationConfig().max_length:
generation_config.max_length = generation_config.max_length + input_ids_seq_length
max_position_embeddings = getattr(self.config, "max_position_embeddings", None)
if max_position_embeddings is not None:
generation_config.max_length = min(generation_config.max_length, max_position_embeddings)
if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
raise ValueError(
f"Unfeasable length constraints: the minimum length ({generation_config.min_length}) is larger than"
f" the maximum length ({generation_config.max_length})"
)
if input_ids_seq_length >= generation_config.max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
" increasing`max_new_tokens`."
)
logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
logits_processor=logits_processor,
)
if not generation_config.do_sample and generation_config.num_beams == 1:
return self._greedy_search(
input_ids,
generation_config.max_length,
generation_config.pad_token_id,
generation_config.eos_token_id,
logits_processor=logits_processor,
trace=trace,
params=params,
model_kwargs=model_kwargs,
)
elif generation_config.do_sample and generation_config.num_beams == 1:
logits_warper = self._get_logits_warper(generation_config=generation_config)
return self._sample(
input_ids,
generation_config.max_length,
generation_config.pad_token_id,
generation_config.eos_token_id,
prng_key,
logits_warper=logits_warper,
logits_processor=logits_processor,
trace=trace,
params=params,
model_kwargs=model_kwargs,
)
elif not generation_config.do_sample and generation_config.num_beams > 1:
# broadcast input_ids & encoder_outputs
input_ids = self._expand_to_num_beams(input_ids, num_beams=generation_config.num_beams)
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = self._expand_to_num_beams(
model_kwargs["encoder_outputs"]["last_hidden_state"], num_beams=generation_config.num_beams
)
for kwarg in ["attention_mask", "decoder_attention_mask"]:
if kwarg in model_kwargs:
model_kwargs[kwarg] = self._expand_to_num_beams(
model_kwargs[kwarg], num_beams=generation_config.num_beams
)
return self._beam_search(
input_ids,
generation_config.max_length,
generation_config.pad_token_id,
generation_config.eos_token_id,
length_penalty=generation_config.length_penalty,
early_stopping=generation_config.early_stopping,
logits_processor=logits_processor,
trace=trace,
params=params,
num_return_sequences=generation_config.num_return_sequences,
model_kwargs=model_kwargs,
)
else:
raise NotImplementedError("`Beam sampling is currently not implemented.")
def _get_logits_warper(self, generation_config: GenerationConfig) -> FlaxLogitsProcessorList:
"""
This class returns a [`FlaxLogitsProcessorList`] list object that contains all relevant [`FlaxLogitsWarper`]
instances used for multinomial sampling.
"""
warpers = FlaxLogitsProcessorList()
if generation_config.temperature is not None and generation_config.temperature != 1.0:
warpers.append(FlaxTemperatureLogitsWarper(generation_config.temperature))
if generation_config.top_k is not None and generation_config.top_k != 0:
warpers.append(FlaxTopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=1))
if generation_config.top_p is not None and generation_config.top_p < 1.0:
warpers.append(FlaxTopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=1))
return warpers
def _get_logits_processor(
self,
generation_config: GenerationConfig,
input_ids_seq_length: int,
logits_processor: Optional[FlaxLogitsProcessorList],
) -> FlaxLogitsProcessorList:
"""
This class returns a [`FlaxLogitsProcessorList`] list object that contains all relevant [`FlaxLogitsProcessor`]
instances used to modify the scores of the language model head.
"""
processors = FlaxLogitsProcessorList()
if (
generation_config.min_length is not None
and generation_config.eos_token_id is not None
and generation_config.min_length > -1
):
processors.append(
FlaxMinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id)
)
if generation_config.forced_bos_token_id is not None:
processors.append(FlaxForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id))
if generation_config.forced_eos_token_id is not None:
processors.append(
FlaxForcedEOSTokenLogitsProcessor(generation_config.max_length, generation_config.forced_eos_token_id)
)
if generation_config.suppress_tokens is not None:
processors.append(FlaxSuppressTokensLogitsProcessor(generation_config.suppress_tokens))
if generation_config.begin_suppress_tokens is not None:
begin_index = input_ids_seq_length
begin_index = (
begin_index
if (input_ids_seq_length > 1 or generation_config.forced_bos_token_id is None)
else begin_index + 1
)
if generation_config.forced_decoder_ids is not None and len(generation_config.forced_decoder_ids) > 0:
# generation starts after the last token that is forced
begin_index += generation_config.forced_decoder_ids[-1][0]
processors.append(
FlaxSuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index)
)
if generation_config.forced_decoder_ids is not None:
forced_decoder_ids = [
[input_ids_seq_length + i[0] - 1, i[1]] for i in generation_config.forced_decoder_ids
]
processors.append(FlaxForceTokensLogitsProcessor(forced_decoder_ids))
if generation_config.no_repeat_ngram_size is not None and generation_config.no_repeat_ngram_size > 0:
processors.append(FlaxNoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
processors = self._merge_criteria_processor_list(processors, logits_processor)
return processors
def _merge_criteria_processor_list(
self,
default_list: FlaxLogitsProcessorList,
custom_list: FlaxLogitsProcessorList,
) -> FlaxLogitsProcessorList:
if len(custom_list) == 0:
return default_list
for default in default_list:
for custom in custom_list:
if type(custom) is type(default):
object_type = "logits processor"
raise ValueError(
f"A custom {object_type} of type {type(custom)} with values {custom} has been passed to"
f" `generate`, but it has already been created with the values {default}. {default} has been"
" created by passing the corresponding arguments to generate or by the model's config default"
f" values. If you just want to change the default values of {object_type} consider passing"
f" them as arguments to `generate` instead of using a custom {object_type}."
)
default_list.extend(custom_list)
return default_list
def _greedy_search(
self,
input_ids: None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
trace: bool = True,
params: Optional[Dict[str, jnp.ndarray]] = None,
model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
):
# init values
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
batch_size, cur_len = input_ids.shape
eos_token_id = jnp.array(eos_token_id, dtype=jnp.int32 if eos_token_id is not None else None)
pad_token_id = jnp.array(pad_token_id, dtype=jnp.int32)
cur_len = jnp.array(cur_len)
# per batch-item holding current token in loop.
sequences = jnp.full((batch_size, max_length), pad_token_id, dtype=jnp.int32)
sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0))
# per batch-item state bit indicating if sentence has finished.
is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_)
# For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
# and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
model = self.decode if self.config.is_encoder_decoder else self
# initialize model specific kwargs
model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs)
# initialize state
state = GreedyState(
cur_len=cur_len,
sequences=sequences,
running_token=input_ids,
is_sent_finished=is_sent_finished,
model_kwargs=model_kwargs,
)
def greedy_search_cond_fn(state):
"""state termination condition fn."""
has_reached_max_length = state.cur_len == max_length
all_sequence_finished = jnp.all(state.is_sent_finished)
finish_generation = jnp.logical_or(has_reached_max_length, all_sequence_finished)
return ~finish_generation
def greedy_search_body_fn(state):
"""state update fn."""
model_outputs = model(state.running_token, params=params, **state.model_kwargs)
logits = model_outputs.logits[:, -1]
# apply min_length, ...
logits = logits_processor(state.sequences, logits, state.cur_len)
next_token = jnp.argmax(logits, axis=-1)
next_token = next_token * ~state.is_sent_finished + pad_token_id * state.is_sent_finished
next_is_sent_finished = state.is_sent_finished | (next_token == eos_token_id)
next_token = next_token[:, None]
next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len))
next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
return GreedyState(
cur_len=state.cur_len + 1,
sequences=next_sequences,
running_token=next_token,
is_sent_finished=next_is_sent_finished,
model_kwargs=next_model_kwargs,
)
# The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU
if input_ids.shape[1] > 1:
state = greedy_search_body_fn(state)
if not trace:
state = self._run_loop_in_debug(greedy_search_cond_fn, greedy_search_body_fn, state)
else:
state = lax.while_loop(greedy_search_cond_fn, greedy_search_body_fn, state)
return FlaxGreedySearchOutput(sequences=state.sequences)
def _sample(
self,
input_ids: None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
prng_key: Optional[jnp.ndarray] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
logits_warper: Optional[FlaxLogitsProcessorList] = None,
trace: bool = True,
params: Optional[Dict[str, jnp.ndarray]] = None,
model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
):
# init values
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0)
batch_size, cur_len = input_ids.shape
eos_token_id = jnp.array(eos_token_id, dtype=jnp.int32 if eos_token_id is not None else None)
pad_token_id = jnp.array(pad_token_id, dtype=jnp.int32)
cur_len = jnp.array(cur_len)
# per batch-item holding current token in loop.
sequences = jnp.full((batch_size, max_length), pad_token_id, dtype=jnp.int32)
sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0))
# per batch-item state bit indicating if sentence has finished.
is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_)
# For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
# and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
model = self.decode if self.config.is_encoder_decoder else self
# initialize model specific kwargs
model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs)
# initialize state
state = SampleState(
cur_len=cur_len,
sequences=sequences,
running_token=input_ids,
is_sent_finished=is_sent_finished,
prng_key=prng_key,
model_kwargs=model_kwargs,
)
def sample_search_cond_fn(state):
"""state termination condition fn."""
has_reached_max_length = state.cur_len == max_length
all_sequence_finished = jnp.all(state.is_sent_finished)
finish_generation = jnp.logical_or(has_reached_max_length, all_sequence_finished)
return ~finish_generation
def sample_search_body_fn(state):
"""state update fn."""
prng_key, prng_key_next = jax.random.split(state.prng_key)
model_outputs = model(state.running_token, params=params, **state.model_kwargs)
logits = model_outputs.logits[:, -1]
# apply min_length, ...
logits = logits_processor(state.sequences, logits, state.cur_len)
# apply top_p, top_k, temperature
logits = logits_warper(logits, logits, state.cur_len)
next_token = jax.random.categorical(prng_key, logits, axis=-1)
next_token = next_token * ~state.is_sent_finished + pad_token_id * state.is_sent_finished
next_is_sent_finished = state.is_sent_finished | (next_token == eos_token_id)
next_token = next_token[:, None]
next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len))
next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
return SampleState(
cur_len=state.cur_len + 1,
sequences=next_sequences,
running_token=next_token,
is_sent_finished=next_is_sent_finished,
model_kwargs=next_model_kwargs,
prng_key=prng_key_next,
)
# The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU
if input_ids.shape[1] > 1:
state = sample_search_body_fn(state)
if not trace:
state = self._run_loop_in_debug(sample_search_cond_fn, sample_search_body_fn, state)
else:
state = lax.while_loop(sample_search_cond_fn, sample_search_body_fn, state)
return FlaxSampleOutput(sequences=state.sequences)
def _beam_search(
self,
input_ids: None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
length_penalty: Optional[float] = None,
early_stopping: Optional[Union[bool, str]] = None,
logits_processor: Optional[FlaxLogitsProcessorList] = None,
trace: bool = True,
params: Optional[Dict[str, jnp.ndarray]] = None,
num_return_sequences: Optional[int] = None,
model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
):
"""
This beam search function is heavily inspired by Flax's official example:
https://github.com/google/flax/blob/main/examples/wmt/decode.py
"""
def flatten_beam_dim(tensor):
"""Flattens the first two dimensions of a non-scalar array."""
# ignore scalars (e.g. cache index)
if tensor.ndim == 0:
return tensor
return tensor.reshape((tensor.shape[0] * tensor.shape[1],) + tensor.shape[2:])
def unflatten_beam_dim(tensor, batch_size, num_beams):
"""Unflattens the first, flat batch*beam dimension of a non-scalar array."""
# ignore scalars (e.g. cache index)
if tensor.ndim == 0:
return tensor
return tensor.reshape((batch_size, num_beams) + tensor.shape[1:])
def gather_beams(nested, beam_indices, batch_size, new_num_beams):
"""
Gathers the beam slices indexed by beam_indices into new beam array.
"""
batch_indices = jnp.reshape(
jnp.arange(batch_size * new_num_beams) // new_num_beams, (batch_size, new_num_beams)
)
def gather_fn(tensor):
# ignore scalars (e.g. cache index)
if tensor.ndim == 0:
return tensor
else:
return tensor[batch_indices, beam_indices]
return jax.tree_util.tree_map(gather_fn, nested)
# init values
max_length = max_length if max_length is not None else self.generation_config.max_length
pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
length_penalty = length_penalty if length_penalty is not None else self.generation_config.length_penalty
early_stopping = early_stopping if early_stopping is not None else self.generation_config.early_stopping
num_return_sequences = (
num_return_sequences if num_return_sequences is not None else self.generation_config.num_return_sequences
)
batch_size, num_beams, cur_len = input_ids.shape
eos_token_id = jnp.array(eos_token_id, dtype=jnp.int32 if eos_token_id is not None else None)
pad_token_id = jnp.array(pad_token_id, dtype=jnp.int32)
cur_len = jnp.array(cur_len)
# record the prompt length of decoder
decoder_prompt_len = input_ids.shape[-1]
# per batch,beam-item holding current token in loop.
sequences = jnp.full((batch_size, num_beams, max_length), pad_token_id, dtype=jnp.int32)
running_sequences = jnp.full((batch_size, num_beams, max_length), pad_token_id, dtype=jnp.int32)
running_sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0, 0))
# per batch,beam-item state bit indicating if sentence has finished.
is_sent_finished = jnp.zeros((batch_size, num_beams), dtype=jnp.bool_)
# per batch,beam-item score, logprobs
running_scores = jnp.tile(jnp.array([0.0] + [np.array(-1.0e7)] * (num_beams - 1)), [batch_size, 1])
scores = jnp.ones((batch_size, num_beams)) * np.array(-1.0e7)
# For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
# and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
model = self.decode if self.config.is_encoder_decoder else self
# flatten beam dim
if "encoder_outputs" in model_kwargs:
model_kwargs["encoder_outputs"]["last_hidden_state"] = flatten_beam_dim(
model_kwargs["encoder_outputs"]["last_hidden_state"]
)
for kwarg in ["attention_mask", "decoder_attention_mask"]:
if kwarg in model_kwargs:
model_kwargs[kwarg] = flatten_beam_dim(model_kwargs[kwarg])
# initialize model specific kwargs
model_kwargs = self.prepare_inputs_for_generation(flatten_beam_dim(input_ids), max_length, **model_kwargs)
# initialize state
state = BeamSearchState(
cur_len=cur_len,
running_sequences=running_sequences,
running_scores=running_scores,
sequences=sequences,
scores=scores,
is_sent_finished=is_sent_finished,
model_kwargs=model_kwargs,
)
def beam_search_cond_fn(state):
"""beam search state termination condition fn."""
# 1. is less than max length?
not_max_length_yet = state.cur_len < max_length
# 2. can the new beams still improve?
# early_stopping == False -> apply heuristic = always get the best score from `cur_len`. See the discussion
# below for more details.
# https://github.com/huggingface/transformers/pull/20901#issuecomment-1369845565
# early_stopping == "never" -> compute the best score from max_length or cur_len, depending on the sign of
# length_penalty. Positive length_penalty favors longer sequences, thus we use max_length there.
if early_stopping == "never" and length_penalty > 0.0:
best_running_score = state.running_scores[:, :1] / (
(max_length - decoder_prompt_len) ** length_penalty
)
else:
best_running_score = state.running_scores[:, :1] / (
(state.cur_len - decoder_prompt_len) ** length_penalty
)
worst_finished_score = jnp.where(
state.is_sent_finished, jnp.min(state.scores, axis=1, keepdims=True), np.array(-1.0e7)
)
improvement_still_possible = jnp.any(best_running_score > worst_finished_score)
# 3. is there still a beam that has not finished?
still_open_beam = ~(jnp.all(state.is_sent_finished) & (early_stopping is True))
return not_max_length_yet & still_open_beam & improvement_still_possible
def beam_search_body_fn(state, input_ids_length=1):
"""beam search state update fn."""
# 1. Forward current tokens
# Collect the current position slice along length to feed the fast
# autoregressive decoder model. Flatten the beam dimension into batch
# dimension for feeding into the model.
# unflatten beam dimension
# Unflatten beam dimension in attention cache arrays
input_token = flatten_beam_dim(
lax.dynamic_slice(
state.running_sequences,
(0, 0, state.cur_len - input_ids_length),
(batch_size, num_beams, input_ids_length),
)
)
model_outputs = model(input_token, params=params, **state.model_kwargs)
logits = unflatten_beam_dim(model_outputs.logits[:, -1], batch_size, num_beams)
cache = jax.tree_util.tree_map(
lambda tensor: unflatten_beam_dim(tensor, batch_size, num_beams), model_outputs.past_key_values
)
# adapt logits for FlaxMarianMTModel
logits = self._adapt_logits_for_beam_search(logits)
# 2. Compute log probs
# get log probabilities from logits,
# process logits with processors (*e.g.* min_length, ...), and
# add new logprobs to existing running logprobs scores.
log_probs = jax.nn.log_softmax(logits)
log_probs = logits_processor(
flatten_beam_dim(state.running_sequences), flatten_beam_dim(log_probs), state.cur_len
)
log_probs = unflatten_beam_dim(log_probs, batch_size, num_beams)
log_probs = log_probs + jnp.expand_dims(state.running_scores, axis=2)
vocab_size = log_probs.shape[2]
log_probs = log_probs.reshape((batch_size, num_beams * vocab_size))
# 3. Retrieve top-K
# Each item in batch has num_beams * vocab_size candidate sequences.
# For each item, get the top 2*k candidates with the highest log-
# probabilities. We gather the top 2*K beams here so that even if the best
# K sequences reach EOS simultaneously, we have another K sequences
# remaining to continue the live beam search.
# Gather the top 2*K scores from _all_ beams.
# Gather 2*k top beams.
# Recover the beam index by floor division.
# Recover token id by modulo division and expand Id array for broadcasting.
# Update sequences for the 2*K top-k new sequences.
beams_to_keep = 2 * num_beams
topk_log_probs, topk_indices = lax.top_k(log_probs, k=beams_to_keep)
topk_beam_indices = topk_indices // vocab_size
topk_running_sequences = gather_beams(
state.running_sequences, topk_beam_indices, batch_size, beams_to_keep
)
topk_ids = jnp.expand_dims(topk_indices % vocab_size, axis=2)
topk_sequences = lax.dynamic_update_slice(topk_running_sequences, topk_ids, (0, 0, state.cur_len))
# 4. Check which sequences have ended
# Update current sequences:
# Did any of these sequences reach an end marker?
# To prevent these just finished sequences from being added to the current sequences
# set of active beam search sequences, set their log probs to a very large
# negative value.
did_topk_just_finished = topk_sequences[:, :, state.cur_len] == eos_token_id
running_topk_log_probs = topk_log_probs + did_topk_just_finished * np.array(-1.0e7)
# 5. Get running sequences scores for next
# Determine the top k beam indices (from top 2*k beams) from log probs
# and gather top k beams (from top 2*k beams).
next_topk_indices = lax.top_k(running_topk_log_probs, k=num_beams)[1]
next_running_sequences, next_running_scores = gather_beams(
[topk_sequences, running_topk_log_probs], next_topk_indices, batch_size, num_beams
)
# 6. Process topk logits
# Further process log probs:
# - add length penalty
# - make sure no scores can be added anymore if beam is full
# - make sure still running sequences cannot be chosen as finalized beam
topk_log_probs = topk_log_probs / ((state.cur_len + 1 - decoder_prompt_len) ** length_penalty)
beams_in_batch_are_full = jnp.broadcast_to(
state.is_sent_finished.all(axis=-1, keepdims=True), did_topk_just_finished.shape
) & (early_stopping is True)
add_penalty = ~did_topk_just_finished | beams_in_batch_are_full
topk_log_probs += add_penalty * np.array(-1.0e7)
# 7. Get scores, sequences, is sentence finished for next.
# Combine sequences, scores, and flags along the beam dimension and compare
# new finished sequence scores to existing finished scores and select the
# best from the new set of beams
merged_sequences = jnp.concatenate([state.sequences, topk_sequences], axis=1)
merged_scores = jnp.concatenate([state.scores, topk_log_probs], axis=1)
merged_is_sent_finished = jnp.concatenate([state.is_sent_finished, did_topk_just_finished], axis=1)
topk_merged_indices = lax.top_k(merged_scores, k=num_beams)[1]
next_sequences, next_scores, next_is_sent_finished = gather_beams(
[merged_sequences, merged_scores, merged_is_sent_finished], topk_merged_indices, batch_size, num_beams
)
# 8. Update model kwargs.
# Determine the top k beam indices from the original set of all beams.
# With these, gather the top k beam-associated caches.
next_running_indices = gather_beams(topk_beam_indices, next_topk_indices, batch_size, num_beams)
next_cache = gather_beams(cache, next_running_indices, batch_size, num_beams)
model_outputs["past_key_values"] = jax.tree_util.tree_map(lambda x: flatten_beam_dim(x), next_cache)
next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
return BeamSearchState(
cur_len=state.cur_len + 1,
running_scores=next_running_scores,
running_sequences=next_running_sequences,
scores=next_scores,
sequences=next_sequences,
is_sent_finished=next_is_sent_finished,
model_kwargs=next_model_kwargs,
)
# Always run first iteration outside of `lax.while_loop` to avoid calling `beam_search_cond_fn`
# when `state.cur_len` equals `decoder_prompt_len`. This also helps to comply with TPU when
# the very first prompt has sequence length > 1.
state = partial(beam_search_body_fn, input_ids_length=input_ids.shape[-1])(state)
if not trace:
state = self._run_loop_in_debug(beam_search_cond_fn, beam_search_body_fn, state)
else:
state = lax.while_loop(beam_search_cond_fn, beam_search_body_fn, state)
# Account for the edge-case where there are no finished sequences for a
# particular batch item. If so, return running sequences for that batch item.
none_finished = jnp.any(state.is_sent_finished, axis=1)
sequences = jnp.where(none_finished[:, None, None], state.sequences, state.running_sequences)
scores = jnp.where(none_finished[:, None], state.scores, state.running_scores)
# Take best beams for each batch (the score is sorted in descending order)
sequences = flatten_beam_dim(sequences[:, :num_return_sequences, :])
scores = flatten_beam_dim(scores[:, :num_return_sequences])
return FlaxBeamSearchOutput(sequences=sequences, scores=scores)
| transformers/src/transformers/generation/flax_utils.py/0 | {
"file_path": "transformers/src/transformers/generation/flax_utils.py",
"repo_id": "transformers",
"token_count": 21986
} |
# Copyright 2024 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.
"AQLM (Additive Quantization of Language Model) integration file"
from ..utils import ACCELERATE_MIN_VERSION, is_accelerate_available, is_aqlm_available, is_torch_available
if is_torch_available():
import torch.nn as nn
def replace_with_aqlm_linear(
model,
quantization_config=None,
linear_weights_not_to_quantize=None,
current_key_name=None,
has_been_replaced=False,
):
"""
Public method that recursively replaces the Linear layers of the given model with AQLM quantized layers.
`accelerate` is needed to use this method. Returns the converted model and a boolean that indicates if the
conversion has been successfull or not.
Args:
model (`torch.nn.Module`):
The model to convert, can be any `torch.nn.Module` instance.
quantization_config (`AqlmConfig`):
The quantization config object that contains the quantization parameters.
linear_weights_not_to_quantize (`list[str]`, *optional*):
A list of nn.Linear weights to not convert. If a parameter path is in the list (e.g. `lm_head.weight`), the corresponding module will not be
converted.
current_key_name (`list`, *optional*):
A list that contains the current key name. This is used for recursion and should not be passed by the user.
has_been_replaced (`bool`, *optional*):
A boolean that indicates if the conversion has been successful or not. This is used for recursion and
should not be passed by the user.
"""
if not is_aqlm_available():
raise ValueError("AQLM is not available. Please install it with `pip install aqlm[cpu,gpu]`")
if not is_accelerate_available():
raise ValueError(
f"AQLM requires Accelerate to be installed: `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`"
)
if linear_weights_not_to_quantize is None:
linear_weights_not_to_quantize = []
from accelerate import init_empty_weights
from aqlm import QuantizedLinear
for name, module in model.named_children():
if current_key_name is None:
current_key_name = []
current_key_name.append(name)
if isinstance(module, nn.Linear):
# Check if the current key is not in the `linear_weights_not_to_quantize`
if ".".join(current_key_name) + ".weight" not in linear_weights_not_to_quantize:
with init_empty_weights():
in_features = module.in_features
out_features = module.out_features
model._modules[name] = QuantizedLinear(
in_features,
out_features,
bias=module.bias is not None,
in_group_size=quantization_config.in_group_size,
out_group_size=quantization_config.out_group_size,
num_codebooks=quantization_config.num_codebooks,
nbits_per_codebook=quantization_config.nbits_per_codebook,
)
has_been_replaced = True
# Store the module class in case we need to transpose the weight later
model._modules[name].source_cls = type(module)
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(False)
if len(list(module.children())) > 0:
_, has_been_replaced = replace_with_aqlm_linear(
module,
quantization_config=quantization_config,
linear_weights_not_to_quantize=linear_weights_not_to_quantize,
current_key_name=current_key_name,
has_been_replaced=has_been_replaced,
)
# Remove the last key for recursion
current_key_name.pop(-1)
return model, has_been_replaced
| transformers/src/transformers/integrations/aqlm.py/0 | {
"file_path": "transformers/src/transformers/integrations/aqlm.py",
"repo_id": "transformers",
"token_count": 1868
} |
# 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.
import importlib
import inspect
import warnings
from typing import Any, Dict, List, Optional, Union
from packaging import version
from ..utils import (
check_peft_version,
find_adapter_config_file,
is_accelerate_available,
is_peft_available,
is_torch_available,
logging,
)
if is_torch_available():
import torch
if is_accelerate_available():
from accelerate import dispatch_model
from accelerate.utils import get_balanced_memory, infer_auto_device_map
# Minimum PEFT version supported for the integration
MIN_PEFT_VERSION = "0.5.0"
logger = logging.get_logger(__name__)
class PeftAdapterMixin:
"""
A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them on a transformer-based model, check out the documentation of PEFT
library: https://huggingface.co/docs/peft/index
Currently supported PEFT methods are all non-prefix tuning methods. Below is the list of supported PEFT methods
that anyone can load, train and run with this mixin class:
- Low Rank Adapters (LoRA): https://huggingface.co/docs/peft/conceptual_guides/lora
- IA3: https://huggingface.co/docs/peft/conceptual_guides/ia3
- AdaLora: https://arxiv.org/abs/2303.10512
Other PEFT models such as prompt tuning, prompt learning are out of scope as these adapters are not "injectable"
into a torch module. For using these methods, please refer to the usage guide of PEFT library.
With this mixin, if the correct PEFT version is installed, it is possible to:
- Load an adapter stored on a local path or in a remote Hub repository, and inject it in the model
- Attach new adapters in the model and train them with Trainer or by your own.
- Attach multiple adapters and iteratively activate / deactivate them
- Activate / deactivate all adapters from the model.
- Get the `state_dict` of the active adapter.
"""
_hf_peft_config_loaded = False
def load_adapter(
self,
peft_model_id: Optional[str] = None,
adapter_name: Optional[str] = None,
revision: Optional[str] = None,
token: Optional[str] = None,
device_map: Optional[str] = "auto",
max_memory: Optional[str] = None,
offload_folder: Optional[str] = None,
offload_index: Optional[int] = None,
peft_config: Dict[str, Any] = None,
adapter_state_dict: Optional[Dict[str, "torch.Tensor"]] = None,
low_cpu_mem_usage: bool = False,
is_trainable: bool = False,
adapter_kwargs: Optional[Dict[str, Any]] = None,
) -> None:
"""
Load adapter weights from file or remote Hub folder. If you are not familiar with adapters and PEFT methods, we
invite you to read more about them on PEFT official documentation: https://huggingface.co/docs/peft
Requires peft as a backend to load the adapter weights.
Args:
peft_model_id (`str`, *optional*):
The identifier of the model to look for on the Hub, or a local path to the saved adapter config file
and adapter weights.
adapter_name (`str`, *optional*):
The adapter name to use. If not set, will use the default adapter.
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>
token (`str`, `optional`):
Whether to use authentication token to load the remote folder. Useful to load private repositories
that are on HuggingFace Hub. You might need to call `huggingface-cli login` and paste your tokens to
cache it.
device_map (`str` or `Dict[str, Union[int, str, torch.device]]` or `int` or `torch.device`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be refined to each
parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
same device. If we only pass the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank
like `1`) on which the model will be allocated, the device map will map the entire model to this
device. Passing `device_map = 0` means put the whole model on GPU 0.
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, *optional*):
A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
GPU and the available CPU RAM if unset.
offload_folder (`str` or `os.PathLike`, `optional`):
If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
offload_index (`int`, `optional`):
`offload_index` argument to be passed to `accelerate.dispatch_model` method.
peft_config (`Dict[str, Any]`, *optional*):
The configuration of the adapter to add, supported adapters are non-prefix tuning and adaption prompts
methods. This argument is used in case users directly pass PEFT state dicts
adapter_state_dict (`Dict[str, torch.Tensor]`, *optional*):
The state dict of the adapter to load. This argument is used in case users directly pass PEFT state
dicts
low_cpu_mem_usage (`bool`, *optional*, defaults to `False`):
Reduce memory usage while loading the PEFT adapter. This should also speed up the loading process.
Requires PEFT version 0.13.0 or higher.
is_trainable (`bool`, *optional*, defaults to `False`):
Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be
used for inference.
adapter_kwargs (`Dict[str, Any]`, *optional*):
Additional keyword arguments passed along to the `from_pretrained` method of the adapter config and
`find_adapter_config_file` method.
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
# peft only supports low_cpu_mem_usage starting from v0.13.0
peft_load_kwargs = {}
if low_cpu_mem_usage:
min_version_lcmu = "0.13.0"
if version.parse(importlib.metadata.version("peft")) >= version.parse(min_version_lcmu):
peft_load_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
else:
raise ValueError(
"The version of PEFT you are using does not support `low_cpu_mem_usage` yet, "
f"please install PEFT >= {min_version_lcmu}."
)
adapter_name = adapter_name if adapter_name is not None else "default"
if adapter_kwargs is None:
adapter_kwargs = {}
from peft import PeftConfig, inject_adapter_in_model, load_peft_weights
from peft.utils import set_peft_model_state_dict
if self._hf_peft_config_loaded and adapter_name in self.peft_config:
raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
if peft_model_id is None and (adapter_state_dict is None and peft_config is None):
raise ValueError(
"You should either pass a `peft_model_id` or a `peft_config` and `adapter_state_dict` to load an adapter."
)
if "device" not in adapter_kwargs:
device = self.device if not hasattr(self, "hf_device_map") else list(self.hf_device_map.values())[0]
else:
device = adapter_kwargs.pop("device")
# To avoid PEFT errors later on with safetensors.
if isinstance(device, torch.device):
device = str(device)
# We keep `revision` in the signature for backward compatibility
if revision is not None and "revision" not in adapter_kwargs:
adapter_kwargs["revision"] = revision
elif revision is not None and "revision" in adapter_kwargs and revision != adapter_kwargs["revision"]:
logger.error(
"You passed a `revision` argument both in `adapter_kwargs` and as a standalone argument. "
"The one in `adapter_kwargs` will be used."
)
# Override token with adapter_kwargs' token
if "token" in adapter_kwargs:
token = adapter_kwargs.pop("token")
if peft_config is None:
adapter_config_file = find_adapter_config_file(
peft_model_id,
token=token,
**adapter_kwargs,
)
if adapter_config_file is None:
raise ValueError(
f"adapter model file not found in {peft_model_id}. Make sure you are passing the correct path to the "
"adapter model."
)
peft_config = PeftConfig.from_pretrained(
peft_model_id,
token=token,
**adapter_kwargs,
)
peft_config.inference_mode = not is_trainable
# Create and add fresh new adapters into the model.
inject_adapter_in_model(peft_config, self, adapter_name, **peft_load_kwargs)
if not self._hf_peft_config_loaded:
self._hf_peft_config_loaded = True
if peft_model_id is not None:
adapter_state_dict = load_peft_weights(peft_model_id, token=token, device=device, **adapter_kwargs)
# We need to pre-process the state dict to remove unneeded prefixes - for backward compatibility
processed_adapter_state_dict = {}
prefix = "base_model.model."
for key, value in adapter_state_dict.items():
if key.startswith(prefix):
new_key = key[len(prefix) :]
else:
new_key = key
processed_adapter_state_dict[new_key] = value
# Load state dict
incompatible_keys = set_peft_model_state_dict(
self, processed_adapter_state_dict, adapter_name, **peft_load_kwargs
)
if incompatible_keys is not None:
err_msg = ""
origin_name = peft_model_id if peft_model_id is not None else "state_dict"
# Check for unexpected keys.
if hasattr(incompatible_keys, "unexpected_keys") and len(incompatible_keys.unexpected_keys) > 0:
err_msg = (
f"Loading adapter weights from {origin_name} led to unexpected keys not found in the model: "
f"{', '.join(incompatible_keys.unexpected_keys)}. "
)
# Check for missing keys.
missing_keys = getattr(incompatible_keys, "missing_keys", None)
if missing_keys:
# Filter missing keys specific to the current adapter, as missing base model keys are expected.
lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
if lora_missing_keys:
err_msg += (
f"Loading adapter weights from {origin_name} led to missing keys in the model: "
f"{', '.join(lora_missing_keys)}"
)
if err_msg:
logger.warning(err_msg)
if peft_config.inference_mode:
self.eval()
# Re-dispatch model and hooks in case the model is offloaded to CPU / Disk.
if (
(getattr(self, "hf_device_map", None) is not None)
and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
and len(self.peft_config) == 1
):
self._dispatch_accelerate_model(
device_map=device_map,
max_memory=max_memory,
offload_folder=offload_folder,
offload_index=offload_index,
)
def add_adapter(self, adapter_config, adapter_name: Optional[str] = None) -> None:
r"""
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
official documentation: https://huggingface.co/docs/peft
Adds a fresh new adapter to the current model for training purpose. If no adapter name is passed, a default
name is assigned to the adapter to follow the convention of PEFT library (in PEFT we use "default" as the
default adapter name).
Args:
adapter_config (`~peft.PeftConfig`):
The configuration of the adapter to add, supported adapters are non-prefix tuning and adaption prompts
methods
adapter_name (`str`, *optional*, defaults to `"default"`):
The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
from peft import PeftConfig, inject_adapter_in_model
adapter_name = adapter_name or "default"
if not self._hf_peft_config_loaded:
self._hf_peft_config_loaded = True
elif adapter_name in self.peft_config:
raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
if not isinstance(adapter_config, PeftConfig):
raise TypeError(f"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead.")
# Retrieve the name or path of the model, one could also use self.config._name_or_path
# but to be consistent with what we do in PEFT: https://github.com/huggingface/peft/blob/6e783780ca9df3a623992cc4d1d665001232eae0/src/peft/mapping.py#L100
adapter_config.base_model_name_or_path = self.__dict__.get("name_or_path", None)
inject_adapter_in_model(adapter_config, self, adapter_name)
self.set_adapter(adapter_name)
def set_adapter(self, adapter_name: Union[List[str], str]) -> None:
"""
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
official documentation: https://huggingface.co/docs/peft
Sets a specific adapter by forcing the model to use a that adapter and disable the other adapters.
Args:
adapter_name (`Union[List[str], str]`):
The name of the adapter to set. Can be also a list of strings to set multiple adapters.
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
elif isinstance(adapter_name, list):
missing = set(adapter_name) - set(self.peft_config)
if len(missing) > 0:
raise ValueError(
f"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s)."
f" current loaded adapters are: {list(self.peft_config.keys())}"
)
elif adapter_name not in self.peft_config:
raise ValueError(
f"Adapter with name {adapter_name} not found. Please pass the correct adapter name among {list(self.peft_config.keys())}"
)
from peft.tuners.tuners_utils import BaseTunerLayer
from peft.utils import ModulesToSaveWrapper
_adapters_has_been_set = False
for _, module in self.named_modules():
if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)):
# For backward compatbility with previous PEFT versions
if hasattr(module, "set_adapter"):
module.set_adapter(adapter_name)
else:
module.active_adapter = adapter_name
_adapters_has_been_set = True
if not _adapters_has_been_set:
raise ValueError(
"Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters."
)
def disable_adapters(self) -> None:
r"""
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
official documentation: https://huggingface.co/docs/peft
Disable all adapters that are attached to the model. This leads to inferring with the base model only.
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
from peft.tuners.tuners_utils import BaseTunerLayer
from peft.utils import ModulesToSaveWrapper
for _, module in self.named_modules():
if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)):
# The recent version of PEFT need to call `enable_adapters` instead
if hasattr(module, "enable_adapters"):
module.enable_adapters(enabled=False)
else:
module.disable_adapters = True
def enable_adapters(self) -> None:
"""
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
official documentation: https://huggingface.co/docs/peft
Enable adapters that are attached to the model.
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
from peft.tuners.tuners_utils import BaseTunerLayer
for _, module in self.named_modules():
if isinstance(module, BaseTunerLayer):
# The recent version of PEFT need to call `enable_adapters` instead
if hasattr(module, "enable_adapters"):
module.enable_adapters(enabled=True)
else:
module.disable_adapters = False
def active_adapters(self) -> List[str]:
"""
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
official documentation: https://huggingface.co/docs/peft
Gets the current active adapters of the model. In case of multi-adapter inference (combining multiple adapters
for inference) returns the list of all active adapters so that users can deal with them accordingly.
For previous PEFT versions (that does not support multi-adapter inference), `module.active_adapter` will return
a single string.
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not is_peft_available():
raise ImportError("PEFT is not available. Please install PEFT to use this function: `pip install peft`.")
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
from peft.tuners.tuners_utils import BaseTunerLayer
for _, module in self.named_modules():
if isinstance(module, BaseTunerLayer):
active_adapters = module.active_adapter
break
# For previous PEFT versions
if isinstance(active_adapters, str):
active_adapters = [active_adapters]
return active_adapters
def active_adapter(self) -> str:
warnings.warn(
"The `active_adapter` method is deprecated and will be removed in a future version.", FutureWarning
)
return self.active_adapters()[0]
def get_adapter_state_dict(self, adapter_name: Optional[str] = None) -> dict:
"""
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
official documentation: https://huggingface.co/docs/peft
Gets the adapter state dict that should only contain the weights tensors of the specified adapter_name adapter.
If no adapter_name is passed, the active adapter is used.
Args:
adapter_name (`str`, *optional*):
The name of the adapter to get the state dict from. If no name is passed, the active adapter is used.
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
from peft import get_peft_model_state_dict
if adapter_name is None:
adapter_name = self.active_adapters()[0]
adapter_state_dict = get_peft_model_state_dict(self, adapter_name=adapter_name)
return adapter_state_dict
def _dispatch_accelerate_model(
self,
device_map: str,
max_memory: Optional[int] = None,
offload_folder: Optional[str] = None,
offload_index: Optional[int] = None,
) -> None:
"""
Optional re-dispatch the model and attach new hooks to the model in case the model has been loaded with
accelerate (i.e. with `device_map=xxx`)
Args:
device_map (`str` or `Dict[str, Union[int, str, torch.device]]` or `int` or `torch.device`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be refined to each
parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
same device. If we only pass the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank
like `1`) on which the model will be allocated, the device map will map the entire model to this
device. Passing `device_map = 0` means put the whole model on GPU 0.
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, *optional*):
A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
GPU and the available CPU RAM if unset.
offload_folder (`str` or `os.PathLike`, *optional*):
If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
offload_index (`int`, *optional*):
The offload_index argument to be passed to `accelerate.dispatch_model` method.
"""
dispatch_model_kwargs = {}
# Safety checker for previous `accelerate` versions
# `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
if "offload_index" in inspect.signature(dispatch_model).parameters:
dispatch_model_kwargs["offload_index"] = offload_index
no_split_module_classes = self._no_split_modules
if device_map != "sequential":
max_memory = get_balanced_memory(
self,
max_memory=max_memory,
no_split_module_classes=no_split_module_classes,
low_zero=(device_map == "balanced_low_0"),
)
if isinstance(device_map, str):
device_map = infer_auto_device_map(
self, max_memory=max_memory, no_split_module_classes=no_split_module_classes
)
dispatch_model(
self,
device_map=device_map,
offload_dir=offload_folder,
**dispatch_model_kwargs,
)
def delete_adapter(self, adapter_names: Union[List[str], str]) -> None:
"""
Delete an adapter's LoRA layers from the underlying model.
Args:
adapter_names (`Union[List[str], str]`):
The name(s) of the adapter(s) to delete.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
)
pipeline.delete_adapters("cinematic")
```
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
from peft.tuners.tuners_utils import BaseTunerLayer
if isinstance(adapter_names, str):
adapter_names = [adapter_names]
# Check that all adapter names are present in the config
missing_adapters = [name for name in adapter_names if name not in self.peft_config]
if missing_adapters:
raise ValueError(
f"The following adapter(s) are not present and cannot be deleted: {', '.join(missing_adapters)}"
)
for adapter_name in adapter_names:
for module in self.modules():
if isinstance(module, BaseTunerLayer):
if hasattr(module, "delete_adapter"):
module.delete_adapter(adapter_name)
else:
raise ValueError(
"The version of PEFT you are using is not compatible, please use a version that is greater than 0.6.1"
)
# For transformers integration - we need to pop the adapter from the config
if getattr(self, "_hf_peft_config_loaded", False) and hasattr(self, "peft_config"):
self.peft_config.pop(adapter_name, None)
# In case all adapters are deleted, we need to delete the config
# and make sure to set the flag to False
if len(self.peft_config) == 0:
del self.peft_config
self._hf_peft_config_loaded = False
| transformers/src/transformers/integrations/peft.py/0 | {
"file_path": "transformers/src/transformers/integrations/peft.py",
"repo_id": "transformers",
"token_count": 11440
} |
#include <stdio.h>
#include <assert.h>
#include "ATen/ATen.h"
#define MIN_VALUE (-1e38)
typedef at::BFloat16 bf16;
__global__ void kernel_forward_bf16(
const int B, const int T, const int C, const float *__restrict__ const _w, const bf16 *__restrict__ const _u,
const bf16 *__restrict__ const _k, const bf16 *__restrict__ const _v, bf16 *__restrict__ const _y
) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int _b = idx / C;
const int _c = idx % C;
const int _offset = _b * T * C + _c;
float u = float(_u[_c]);
float w = _w[_c];
const bf16 *__restrict__ const k = _k + _offset;
const bf16 *__restrict__ const v = _v + _offset;
bf16 *__restrict__ const y = _y + _offset;
// aa and bb are running sums divided by exp(pp) (to avoid overflow)
float aa = 0, bb = 0, pp = MIN_VALUE;
for (int i = 0; i < T; i++) {
const int ii = i * C;
const float kk = float(k[ii]);
const float vv = float(v[ii]);
float ww = u + kk;
float p = max(pp, ww);
float e1 = exp(pp - p);
float e2 = exp(ww - p);
y[ii] = bf16((e1 * aa + e2 * vv) / (e1 * bb + e2));
ww = w + pp;
p = max(ww, kk);
e1 = exp(ww - p);
e2 = exp(kk - p);
aa = e1 * aa + e2 * vv;
bb = e1 * bb + e2;
pp = p;
}
}
__global__ void kernel_forward_with_state_bf16(
const int B, const int T, const int C, const float *__restrict__ const _w, const bf16 *__restrict__ const _u,
const bf16 *__restrict__ const _k, const bf16 *__restrict__ const _v, bf16 *__restrict__ const _y,
float *__restrict__ const _s
) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int _b = idx / C;
const int _c = idx % C;
const int _offset_s = _b * C * 3 + _c * 3;
const int _offset = _b * T * C + _c;
float u = float(_u[_c]);
float w = _w[_c];
const bf16 *__restrict__ const k = _k + _offset;
const bf16 *__restrict__ const v = _v + _offset;
bf16 *__restrict__ const y = _y + _offset;
float *__restrict__ const s = _s + _offset_s;
// aa and bb are running sums divided by exp(pp) (to avoid overflow)
float aa = s[0], bb = s[1], pp = s[2];
for (int i = 0; i < T; i++) {
const int ii = i * C;
const float kk = float(k[ii]);
const float vv = float(v[ii]);
float ww = u + kk;
float p = max(pp, ww);
float e1 = exp(pp - p);
float e2 = exp(ww - p);
y[ii] = bf16(e1 * aa + e2 * vv) / (e1 * bb + e2);
ww = w + pp;
p = max(ww, kk);
e1 = exp(ww - p);
e2 = exp(kk - p);
aa = e1 * aa + e2 * vv;
bb = e1 * bb + e2;
pp = p;
}
s[0] = aa;
s[1] = bb;
s[2] = pp;
}
__global__ void kernel_backward_bf16(
const int B, const int T, const int C, const float *__restrict__ const _w, const bf16 *__restrict__ const _u,
const bf16 *__restrict__ const _k, const bf16 *__restrict__ const _v, const bf16 *__restrict__ const _y,
const bf16 *__restrict__ const _gy, bf16 *__restrict__ const _gw, bf16 *__restrict__ const _gu,
bf16 *__restrict__ const _gk, bf16 *__restrict__ const _gv
) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int _b = idx / C;
const int _c = idx % C;
const int _offset = _b * T * C + _c;
float u = float(_u[_c]);
float w = _w[_c];
const bf16 *__restrict__ const k = _k + _offset;
const bf16 *__restrict__ const v = _v + _offset;
const bf16 *__restrict__ const y = _y + _offset;
const bf16 *__restrict__ const gy = _gy + _offset;
bf16 *__restrict__ const gk = _gk + _offset;
bf16 *__restrict__ const gv = _gv + _offset;
float q[Tmax], r[Tmax];
float gw = 0, gu = 0, aa = 0, bb = 0, ga = 0, gb = 0, pp = MIN_VALUE;
for (int i = 0; i < T; i++) {
const int ii = i * C;
const float kk = float(k[ii]);
const float vv = float(v[ii]);
const float yy = float(y[ii]);
float ww = u + kk;
float p = max(pp, ww);
float e1 = exp(pp - p);
float e2 = exp(ww - p);
const float qq = float(gy[ii]) / (e1 * bb + e2);
gw += (ga - gb * yy) * e1 * qq;
gu += (vv - yy) * e2 * qq;
q[i] = qq;
r[i] = ww - p;
ww = w + pp;
p = max(ww, kk);
e1 = exp(ww - p);
e2 = exp(kk - p);
ga = e1 * (aa + ga);
gb = e1 * (bb + gb);
aa = e1 * aa + e2 * vv;
bb = e1 * bb + e2;
pp = p;
}
const int _offsetBC = _b * C + _c;
_gw[_offsetBC] = bf16(gw * _w[_c]); // multiply by w because of w -> -exp(w) in python forward()
_gu[_offsetBC] = bf16(gu);
aa = 0, bb = 0, pp = MIN_VALUE;
for (int i = T - 1; i >= 0; i--) {
const int ii = i * C;
const float kk = float(k[ii]);
const float vv = float(v[ii]);
const float yy = float(y[ii]);
const float qq = q[i];
const float rr = r[i];
float e1 = qq * exp(rr);
float e2 = exp(kk + pp);
gk[ii] = bf16(e1 * (vv - yy) + e2 * (aa * vv + bb));
gv[ii] = bf16(e1 + e2 * aa);
const float ww = w + pp;
const float www = rr - u - kk;
const float p = max(ww, www);
e1 = exp(ww - p);
e2 = qq * exp(www - p);
aa = e1 * aa + e2;
bb = e1 * bb - e2 * yy;
pp = p;
}
}
void cuda_forward_bf16(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y) {
dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
assert(B * C % threadsPerBlock.x == 0);
dim3 numBlocks(B * C / threadsPerBlock.x);
kernel_forward_bf16<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y);
}
void cuda_forward_with_state_bf16(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y, float *s) {
dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
assert(B * C % threadsPerBlock.x == 0);
dim3 numBlocks(B * C / threadsPerBlock.x);
kernel_forward_with_state_bf16<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, s);
}
void cuda_backward_bf16(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y, bf16 *gy, bf16 *gw, bf16 *gu, bf16 *gk, bf16 *gv) {
dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
assert(B * C % threadsPerBlock.x == 0);
dim3 numBlocks(B * C / threadsPerBlock.x);
kernel_backward_bf16<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, gy, gw, gu, gk, gv);
}
| transformers/src/transformers/kernels/rwkv/wkv_cuda_bf16.cu/0 | {
"file_path": "transformers/src/transformers/kernels/rwkv/wkv_cuda_bf16.cu",
"repo_id": "transformers",
"token_count": 3302
} |
# 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.
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
from .utils.import_utils import is_torchdynamo_compiling
@dataclass
class AttentionMaskConverter:
"""
A utility attention mask class that allows one to:
- Create a causal 4d mask
- Create a causal 4d mask with slided window
- Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
key_value_length) that can be multiplied with attention scores
Examples:
```python
>>> import torch
>>> from transformers.modeling_attn_mask_utils import AttentionMaskConverter
>>> converter = AttentionMaskConverter(True)
>>> converter.to_4d(torch.tensor([[0, 0, 0, 1, 1]]), 5, key_value_length=5, dtype=torch.float32)
tensor([[[[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
[-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, -3.4028e+38],
[-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, 0.0000e+00]]]])
```
Parameters:
is_causal (`bool`):
Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
sliding_window (`int`, *optional*):
Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
"""
is_causal: bool
sliding_window: int
def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
self.is_causal = is_causal
self.sliding_window = sliding_window
if self.sliding_window is not None and self.sliding_window <= 0:
raise ValueError(
f"Make sure that when passing `sliding_window` that its value is a strictly positive integer, not `{self.sliding_window}`"
)
def to_causal_4d(
self,
batch_size: int,
query_length: int,
key_value_length: int,
dtype: torch.dtype,
device: Union[torch.device, "str"] = "cpu",
) -> Optional[torch.Tensor]:
"""
Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
bias to upper right hand triangular matrix (causal mask).
"""
if not self.is_causal:
raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")
# If shape is not cached, create a new causal mask and cache it
input_shape = (batch_size, query_length)
past_key_values_length = key_value_length - query_length
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
causal_4d_mask = None
if input_shape[-1] > 1 or self.sliding_window is not None:
causal_4d_mask = self._make_causal_mask(
input_shape,
dtype,
device=device,
past_key_values_length=past_key_values_length,
sliding_window=self.sliding_window,
)
return causal_4d_mask
def to_4d(
self,
attention_mask_2d: torch.Tensor,
query_length: int,
dtype: torch.dtype,
key_value_length: Optional[int] = None,
) -> torch.Tensor:
"""
Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
causal, a causal mask will be added.
"""
input_shape = (attention_mask_2d.shape[0], query_length)
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
causal_4d_mask = None
if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
if key_value_length is None:
raise ValueError(
"This attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask."
)
past_key_values_length = key_value_length - query_length
causal_4d_mask = self._make_causal_mask(
input_shape,
dtype,
device=attention_mask_2d.device,
past_key_values_length=past_key_values_length,
sliding_window=self.sliding_window,
)
elif self.sliding_window is not None:
raise NotImplementedError("Sliding window is currently only implemented for causal masking")
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
attention_mask_2d.device
)
if causal_4d_mask is not None:
expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min)
# expanded_attn_mask + causal_4d_mask can cause some overflow
expanded_4d_mask = expanded_attn_mask
return expanded_4d_mask
@staticmethod
def _make_causal_mask(
input_ids_shape: torch.Size,
dtype: torch.dtype,
device: torch.device,
past_key_values_length: int = 0,
sliding_window: Optional[int] = None,
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
# add lower triangular sliding window mask if necessary
if sliding_window is not None:
diagonal = past_key_values_length - sliding_window - 1
context_mask = torch.tril(torch.ones_like(mask, dtype=torch.bool), diagonal=diagonal)
# Recent changes in PyTorch prevent mutations on tensors converted with aten::_to_copy
# See https://github.com/pytorch/pytorch/issues/127571
if is_torchdynamo_compiling():
mask = mask.clone()
mask.masked_fill_(context_mask, torch.finfo(dtype).min)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
@staticmethod
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
@staticmethod
def _unmask_unattended(
expanded_mask: torch.FloatTensor,
min_dtype: float,
):
# fmt: off
"""
Attend to all tokens in masked rows from the expanded attention mask, for example the relevant first rows when
using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
Details: https://github.com/pytorch/pytorch/issues/110213
`expanded_mask` is [bsz, num_masks, tgt_seq_len, src_seq_len] or [bsz, tgt_seq_len, src_seq_len].
`attention_mask` is [bsz, src_seq_len].
The dimension num_masks of `expanded_mask` is most often 1, but it can also be the number of heads in the case of alibi attention bias.
For example, if `expanded_mask` is (e.g. here left-padding case)
```
[[[[0, 0, 0],
[0, 0, 0],
[0, 0, 1]]],
[[[1, 0, 0],
[1, 1, 0],
[1, 1, 1]]],
[[[0, 0, 0],
[0, 1, 0],
[0, 1, 1]]]]
```
then the modified `expanded_mask` will be
```
[[[[1, 1, 1], <-- modified
[1, 1, 1], <-- modified
[0, 0, 1]]],
[[[1, 0, 0],
[1, 1, 0],
[1, 1, 1]]],
[[[1, 1, 1], <-- modified
[0, 1, 0],
[0, 1, 1]]]]
```
"""
# fmt: on
if expanded_mask.dtype == torch.bool:
raise ValueError(
"AttentionMaskConverter._unmask_unattended expects a float `expanded_mask`, got a BoolTensor."
)
return expanded_mask.mul(~torch.all(expanded_mask == min_dtype, dim=-1, keepdim=True))
@staticmethod
def _ignore_causal_mask_sdpa(
attention_mask: Optional[torch.Tensor],
inputs_embeds: torch.Tensor,
past_key_values_length: int,
sliding_window: Optional[int] = None,
is_training: bool = False,
) -> bool:
"""
Detects whether the optional user-specified attention_mask & the automatically created causal mask can be
ignored in case PyTorch's SDPA is used, rather relying on SDPA's `is_causal` argument.
In case no token is masked in the `attention_mask` argument, if `query_length == 1` or
`key_value_length == query_length`, we rather rely on SDPA `is_causal` argument to use causal/non-causal masks,
allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is
passed).
"""
_, query_length = inputs_embeds.shape[0], inputs_embeds.shape[1]
key_value_length = query_length + past_key_values_length
is_tracing = torch.jit.is_tracing() or isinstance(inputs_embeds, torch.fx.Proxy) or is_torchdynamo_compiling()
ignore_causal_mask = False
if attention_mask is None:
# TODO: When tracing with TorchDynamo with fullgraph=True, the model is recompiled depending on the input
# shape, thus SDPA's `is_causal` argument is rightfully updated
# (see https://gist.github.com/fxmarty/1313f39037fc1c112508989628c57363). However, when using
# `torch.export` or `torch.onnx.dynamo_export`, we must pass an example input, and `is_causal` behavior is
# hard-coded. If a user exports a model with q_len > 1, the exported model will hard-code `is_causal=True`
# which is in general wrong (see https://github.com/pytorch/pytorch/issues/108108).
# Thus, we only set `ignore_causal_mask = True` if the model is set to training.
#
# Besides, jit.trace can not handle the `q_len > 1` condition for `is_causal`
# ("TypeError: scaled_dot_product_attention(): argument 'is_causal' must be bool, not Tensor").
if (
(is_training or not is_tracing)
and (query_length == 1 or key_value_length == query_length)
and (sliding_window is None or key_value_length < sliding_window)
):
ignore_causal_mask = True
elif sliding_window is None or key_value_length < sliding_window:
if len(attention_mask.shape) == 4:
return False
elif not is_tracing and torch.all(attention_mask == 1):
if query_length == 1 or key_value_length == query_length:
# For query_length == 1, causal attention and bi-directional attention are the same.
ignore_causal_mask = True
# Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore
# the attention mask, as SDPA causal mask generation may be wrong. We will set `is_causal=False` in
# SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
# Reference: https://github.com/pytorch/pytorch/issues/108108
# TODO: maybe revisit this with https://github.com/pytorch/pytorch/pull/114823 in PyTorch 2.3.
return ignore_causal_mask
def _prepare_4d_causal_attention_mask(
attention_mask: Optional[torch.Tensor],
input_shape: Union[torch.Size, Tuple, List],
inputs_embeds: torch.Tensor,
past_key_values_length: int,
sliding_window: Optional[int] = None,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`
Args:
attention_mask (`torch.Tensor` or `None`):
A 2D attention mask of shape `(batch_size, key_value_length)`
input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
The input shape should be a tuple that defines `(batch_size, query_length)`.
inputs_embeds (`torch.Tensor`):
The embedded inputs as a torch Tensor.
past_key_values_length (`int`):
The length of the key value cache.
sliding_window (`int`, *optional*):
If the model uses windowed attention, a sliding window should be passed.
"""
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
key_value_length = input_shape[-1] + past_key_values_length
# 4d mask is passed through the layers
if attention_mask is not None and len(attention_mask.shape) == 2:
attention_mask = attn_mask_converter.to_4d(
attention_mask, input_shape[-1], key_value_length=key_value_length, dtype=inputs_embeds.dtype
)
elif attention_mask is not None and len(attention_mask.shape) == 4:
expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
if tuple(attention_mask.shape) != expected_shape:
raise ValueError(
f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
)
else:
# if the 4D mask has correct shape - invert it and fill with negative infinity
inverted_mask = 1.0 - attention_mask
attention_mask = inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min
)
else:
attention_mask = attn_mask_converter.to_causal_4d(
input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
)
return attention_mask
# Adapted from _prepare_4d_causal_attention_mask
def _prepare_4d_causal_attention_mask_for_sdpa(
attention_mask: Optional[torch.Tensor],
input_shape: Union[torch.Size, Tuple, List],
inputs_embeds: torch.Tensor,
past_key_values_length: int,
sliding_window: Optional[int] = None,
):
"""
Prepares the correct `attn_mask` argument to be used by `torch.nn.functional.scaled_dot_product_attention`.
In case no token is masked in the `attention_mask` argument, we simply set it to `None` for the cases `query_length == 1` and
`key_value_length == query_length`, and rely instead on SDPA `is_causal` argument to use causal/non-causal masks,
allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
"""
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
key_value_length = input_shape[-1] + past_key_values_length
# torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
# used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
# TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
is_tracing = torch.jit.is_tracing() or isinstance(inputs_embeds, torch.fx.Proxy) or is_torchdynamo_compiling()
ignore_causal_mask = AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
sliding_window=sliding_window,
)
if ignore_causal_mask:
expanded_4d_mask = None
elif attention_mask is None:
expanded_4d_mask = attn_mask_converter.to_causal_4d(
input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
)
else:
if attention_mask.dim() == 4:
expanded_4d_mask = attention_mask
else:
expanded_4d_mask = attn_mask_converter.to_4d(
attention_mask,
input_shape[-1],
dtype=inputs_embeds.dtype,
key_value_length=key_value_length,
)
# Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
if not is_tracing and expanded_4d_mask.device.type == "cuda":
expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
expanded_4d_mask, min_dtype=torch.finfo(inputs_embeds.dtype).min
)
return expanded_4d_mask
def _prepare_4d_attention_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`
Args:
mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)`
dtype (`torch.dtype`):
The torch dtype the created mask shall have.
tgt_len (`int`):
The target length or query length the created mask shall have.
"""
return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
def _prepare_4d_attention_mask_for_sdpa(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`
Args:
mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)`
dtype (`torch.dtype`):
The torch dtype the created mask shall have.
tgt_len (`int`):
The target length or query length the created mask shall have.
"""
_, key_value_length = mask.shape
tgt_len = tgt_len if tgt_len is not None else key_value_length
is_tracing = torch.jit.is_tracing() or isinstance(mask, torch.fx.Proxy) or is_torchdynamo_compiling()
# torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture data-dependent controlflows.
if not is_tracing and torch.all(mask == 1):
return None
else:
return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
def _create_4d_causal_attention_mask(
input_shape: Union[torch.Size, Tuple, List],
dtype: torch.dtype,
device: torch.device,
past_key_values_length: int = 0,
sliding_window: Optional[int] = None,
) -> Optional[torch.Tensor]:
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)`
Args:
input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
The input shape should be a tuple that defines `(batch_size, query_length)`.
dtype (`torch.dtype`):
The torch dtype the created mask shall have.
device (`int`):
The torch device the created mask shall have.
sliding_window (`int`, *optional*):
If the model uses windowed attention, a sliding window should be passed.
"""
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
key_value_length = past_key_values_length + input_shape[-1]
attention_mask = attn_mask_converter.to_causal_4d(
input_shape[0], input_shape[-1], key_value_length, dtype=dtype, device=device
)
return attention_mask
| transformers/src/transformers/modeling_attn_mask_utils.py/0 | {
"file_path": "transformers/src/transformers/modeling_attn_mask_utils.py",
"repo_id": "transformers",
"token_count": 9169
} |
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and 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.
"""PyTorch ALBERT model."""
import math
import os
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPooling,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import (
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
is_torch_greater_or_equal_than_2_2,
prune_linear_layer,
)
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_albert import AlbertConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "albert/albert-base-v2"
_CONFIG_FOR_DOC = "AlbertConfig"
def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
print(name)
for name, array in zip(names, arrays):
original_name = name
# If saved from the TF HUB module
name = name.replace("module/", "")
# Renaming and simplifying
name = name.replace("ffn_1", "ffn")
name = name.replace("bert/", "albert/")
name = name.replace("attention_1", "attention")
name = name.replace("transform/", "")
name = name.replace("LayerNorm_1", "full_layer_layer_norm")
name = name.replace("LayerNorm", "attention/LayerNorm")
name = name.replace("transformer/", "")
# The feed forward layer had an 'intermediate' step which has been abstracted away
name = name.replace("intermediate/dense/", "")
name = name.replace("ffn/intermediate/output/dense/", "ffn_output/")
# ALBERT attention was split between self and output which have been abstracted away
name = name.replace("/output/", "/")
name = name.replace("/self/", "/")
# The pooler is a linear layer
name = name.replace("pooler/dense", "pooler")
# The classifier was simplified to predictions from cls/predictions
name = name.replace("cls/predictions", "predictions")
name = name.replace("predictions/attention", "predictions")
# Naming was changed to be more explicit
name = name.replace("embeddings/attention", "embeddings")
name = name.replace("inner_group_", "albert_layers/")
name = name.replace("group_", "albert_layer_groups/")
# Classifier
if len(name.split("/")) == 1 and ("output_bias" in name or "output_weights" in name):
name = "classifier/" + name
# No ALBERT model currently handles the next sentence prediction task
if "seq_relationship" in name:
name = name.replace("seq_relationship/output_", "sop_classifier/classifier/")
name = name.replace("weights", "weight")
name = name.split("/")
# Ignore the gradients applied by the LAMB/ADAM optimizers.
if (
"adam_m" in name
or "adam_v" in name
or "AdamWeightDecayOptimizer" in name
or "AdamWeightDecayOptimizer_1" in name
or "global_step" in name
):
logger.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scope_names = re.split(r"_(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "squad":
pointer = getattr(pointer, "classifier")
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
if pointer.shape != array.shape:
raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except ValueError as e:
e.args += (pointer.shape, array.shape)
raise
print(f"Initialize PyTorch weight {name} from {original_name}")
pointer.data = torch.from_numpy(array)
return model
class AlbertEmbeddings(nn.Module):
"""
Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config: AlbertConfig):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
)
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.forward
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values_length: int = 0,
) -> torch.Tensor:
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class AlbertAttention(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads}"
)
self.num_attention_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.attention_head_size = config.hidden_size // config.num_attention_heads
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.attention_dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.output_dropout = nn.Dropout(config.hidden_dropout_prob)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pruned_heads = set()
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention.transpose_for_scores
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def prune_heads(self, heads: List[int]) -> None:
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.num_attention_heads, self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.query = prune_linear_layer(self.query, index)
self.key = prune_linear_layer(self.key, index)
self.value = prune_linear_layer(self.value, index)
self.dense = prune_linear_layer(self.dense, index, dim=1)
# Update hyper params and store pruned heads
self.num_attention_heads = self.num_attention_heads - len(heads)
self.all_head_size = self.attention_head_size * self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.attention_dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.transpose(2, 1).flatten(2)
projected_context_layer = self.dense(context_layer)
projected_context_layer_dropout = self.output_dropout(projected_context_layer)
layernormed_context_layer = self.LayerNorm(hidden_states + projected_context_layer_dropout)
return (layernormed_context_layer, attention_probs) if output_attentions else (layernormed_context_layer,)
class AlbertSdpaAttention(AlbertAttention):
def __init__(self, config):
super().__init__(config)
self.dropout_prob = config.attention_probs_dropout_prob
self.require_contiguous_qkv = not is_torch_greater_or_equal_than_2_2
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
if self.position_embedding_type != "absolute" or output_attentions or head_mask is not None:
logger.warning(
"AlbertSdpaAttention is used but `torch.nn.functional.scaled_dot_product_attention` does not support "
"non-absolute `position_embedding_type` or `output_attentions=True` or `head_mask`. Falling back to "
"the eager attention implementation, but specifying the eager 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, attention_mask, head_mask, output_attentions)
batch_size, seq_len, _ = hidden_states.size()
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom
# attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0.
# Reference: https://github.com/pytorch/pytorch/issues/112577
if self.require_contiguous_qkv and query_layer.device.type == "cuda" and attention_mask is not None:
query_layer = query_layer.contiguous()
key_layer = key_layer.contiguous()
value_layer = value_layer.contiguous()
attention_output = torch.nn.functional.scaled_dot_product_attention(
query=query_layer,
key=key_layer,
value=value_layer,
attn_mask=attention_mask,
dropout_p=self.dropout_prob if self.training else 0.0,
is_causal=False,
)
attention_output = attention_output.transpose(1, 2)
attention_output = attention_output.reshape(batch_size, seq_len, self.all_head_size)
projected_context_layer = self.dense(attention_output)
projected_context_layer_dropout = self.output_dropout(projected_context_layer)
layernormed_context_layer = self.LayerNorm(hidden_states + projected_context_layer_dropout)
return (layernormed_context_layer,)
ALBERT_ATTENTION_CLASSES = {
"eager": AlbertAttention,
"sdpa": AlbertSdpaAttention,
}
class AlbertLayer(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.config = config
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.attention = ALBERT_ATTENTION_CLASSES[config._attn_implementation](config)
self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
self.activation = ACT2FN[config.hidden_act]
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
attention_output = self.attention(hidden_states, attention_mask, head_mask, output_attentions)
ffn_output = apply_chunking_to_forward(
self.ff_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output[0],
)
hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0])
return (hidden_states,) + attention_output[1:] # add attentions if we output them
def ff_chunk(self, attention_output: torch.Tensor) -> torch.Tensor:
ffn_output = self.ffn(attention_output)
ffn_output = self.activation(ffn_output)
ffn_output = self.ffn_output(ffn_output)
return ffn_output
class AlbertLayerGroup(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)])
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
layer_hidden_states = ()
layer_attentions = ()
for layer_index, albert_layer in enumerate(self.albert_layers):
layer_output = albert_layer(hidden_states, attention_mask, head_mask[layer_index], output_attentions)
hidden_states = layer_output[0]
if output_attentions:
layer_attentions = layer_attentions + (layer_output[1],)
if output_hidden_states:
layer_hidden_states = layer_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if output_hidden_states:
outputs = outputs + (layer_hidden_states,)
if output_attentions:
outputs = outputs + (layer_attentions,)
return outputs # last-layer hidden state, (layer hidden states), (layer attentions)
class AlbertTransformer(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.config = config
self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)])
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[BaseModelOutput, Tuple]:
hidden_states = self.embedding_hidden_mapping_in(hidden_states)
all_hidden_states = (hidden_states,) if output_hidden_states else None
all_attentions = () if output_attentions else None
head_mask = [None] * self.config.num_hidden_layers if head_mask is None else head_mask
for i in range(self.config.num_hidden_layers):
# Number of layers in a hidden group
layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups)
# Index of the hidden group
group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
layer_group_output = self.albert_layer_groups[group_idx](
hidden_states,
attention_mask,
head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group],
output_attentions,
output_hidden_states,
)
hidden_states = layer_group_output[0]
if output_attentions:
all_attentions = all_attentions + layer_group_output[-1]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
class AlbertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = AlbertConfig
load_tf_weights = load_tf_weights_in_albert
base_model_prefix = "albert"
_supports_sdpa = True
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@dataclass
class AlbertForPreTrainingOutput(ModelOutput):
"""
Output type of [`AlbertForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
sop_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(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.
"""
loss: Optional[torch.FloatTensor] = None
prediction_logits: torch.FloatTensor = None
sop_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
ALBERT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Args:
config ([`AlbertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
ALBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ALBERT Model transformer outputting raw hidden-states without any specific head on top.",
ALBERT_START_DOCSTRING,
)
class AlbertModel(AlbertPreTrainedModel):
config_class = AlbertConfig
base_model_prefix = "albert"
def __init__(self, config: AlbertConfig, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
self.embeddings = AlbertEmbeddings(config)
self.encoder = AlbertTransformer(config)
if add_pooling_layer:
self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
self.pooler_activation = nn.Tanh()
else:
self.pooler = None
self.pooler_activation = None
self.attn_implementation = config._attn_implementation
self.position_embedding_type = config.position_embedding_type
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.embeddings.word_embeddings
def set_input_embeddings(self, value: nn.Embedding) -> None:
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} ALBERT has
a different architecture in that its layers are shared across groups, which then has inner groups. If an ALBERT
model has 12 hidden layers and 2 hidden groups, with two inner groups, there is a total of 4 different layers.
These layers are flattened: the indices [0,1] correspond to the two inner groups of the first hidden layer,
while [2,3] correspond to the two inner groups of the second hidden layer.
Any layer with in index other than [0,1,2,3] will result in an error. See base class PreTrainedModel for more
information about head pruning
"""
for layer, heads in heads_to_prune.items():
group_idx = int(layer / self.config.inner_group_num)
inner_group_idx = int(layer - group_idx * self.config.inner_group_num)
self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[BaseModelOutputWithPooling, Tuple]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
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")
batch_size, seq_length = input_shape
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 token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
embedding_output = self.embeddings(
input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
)
use_sdpa_attention_mask = (
self.attn_implementation == "sdpa"
and self.position_embedding_type == "absolute"
and head_mask is None
and not output_attentions
)
if use_sdpa_attention_mask:
extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
attention_mask, embedding_output.dtype, tgt_len=seq_length
)
else:
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
encoder_outputs = self.encoder(
embedding_output,
extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"""
Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
`sentence order prediction (classification)` head.
""",
ALBERT_START_DOCSTRING,
)
class AlbertForPreTraining(AlbertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.albert = AlbertModel(config)
self.predictions = AlbertMLMHead(config)
self.sop_classifier = AlbertSOPHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self) -> nn.Linear:
return self.predictions.decoder
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
self.predictions.decoder = new_embeddings
def get_input_embeddings(self) -> nn.Embedding:
return self.albert.embeddings.word_embeddings
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=AlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
sentence_order_label: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[AlbertForPreTrainingOutput, Tuple]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
sentence_order_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring) Indices should be in `[0, 1]`. `0` indicates original order (sequence A, then
sequence B), `1` indicates switched order (sequence B, then sequence A).
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, AlbertForPreTraining
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("albert/albert-base-v2")
>>> model = AlbertForPreTraining.from_pretrained("albert/albert-base-v2")
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)
>>> # Batch size 1
>>> outputs = model(input_ids)
>>> prediction_logits = outputs.prediction_logits
>>> sop_logits = outputs.sop_logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.albert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output, pooled_output = outputs[:2]
prediction_scores = self.predictions(sequence_output)
sop_scores = self.sop_classifier(pooled_output)
total_loss = None
if labels is not None and sentence_order_label is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
sentence_order_loss = loss_fct(sop_scores.view(-1, 2), sentence_order_label.view(-1))
total_loss = masked_lm_loss + sentence_order_loss
if not return_dict:
output = (prediction_scores, sop_scores) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return AlbertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
sop_logits=sop_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class AlbertMLMHead(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.dense = nn.Linear(config.hidden_size, config.embedding_size)
self.decoder = nn.Linear(config.embedding_size, config.vocab_size)
self.activation = ACT2FN[config.hidden_act]
self.decoder.bias = self.bias
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
hidden_states = self.decoder(hidden_states)
prediction_scores = hidden_states
return prediction_scores
def _tie_weights(self) -> None:
# For accelerate compatibility and to not break backward compatibility
if self.decoder.bias.device.type == "meta":
self.decoder.bias = self.bias
else:
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
self.bias = self.decoder.bias
class AlbertSOPHead(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
dropout_pooled_output = self.dropout(pooled_output)
logits = self.classifier(dropout_pooled_output)
return logits
@add_start_docstrings(
"Albert Model with a `language modeling` head on top.",
ALBERT_START_DOCSTRING,
)
class AlbertForMaskedLM(AlbertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.albert = AlbertModel(config, add_pooling_layer=False)
self.predictions = AlbertMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self) -> nn.Linear:
return self.predictions.decoder
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
self.predictions.decoder = new_embeddings
self.predictions.bias = new_embeddings.bias
def get_input_embeddings(self) -> nn.Embedding:
return self.albert.embeddings.word_embeddings
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[MaskedLMOutput, Tuple]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
Example:
```python
>>> import torch
>>> from transformers import AutoTokenizer, AlbertForMaskedLM
>>> tokenizer = AutoTokenizer.from_pretrained("albert/albert-base-v2")
>>> model = AlbertForMaskedLM.from_pretrained("albert/albert-base-v2")
>>> # add mask_token
>>> inputs = tokenizer("The capital of [MASK] is Paris.", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # retrieve index of [MASK]
>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
>>> tokenizer.decode(predicted_token_id)
'france'
```
```python
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
>>> outputs = model(**inputs, labels=labels)
>>> round(outputs.loss.item(), 2)
0.81
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_outputs = outputs[0]
prediction_scores = self.predictions(sequence_outputs)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
ALBERT_START_DOCSTRING,
)
class AlbertForSequenceClassification(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.albert = AlbertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="textattack/albert-base-v2-imdb",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'LABEL_1'",
expected_loss=0.12,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[SequenceClassifierOutput, Tuple]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Albert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
ALBERT_START_DOCSTRING,
)
class AlbertForTokenClassification(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.albert = AlbertModel(config, add_pooling_layer=False)
classifier_dropout_prob = (
config.classifier_dropout_prob
if config.classifier_dropout_prob is not None
else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[TokenClassifierOutput, Tuple]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.albert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
ALBERT_START_DOCSTRING,
)
class AlbertForQuestionAnswering(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.albert = AlbertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="twmkn9/albert-base-v2-squad2",
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target_start_index=12,
qa_target_end_index=13,
expected_output="'a nice puppet'",
expected_loss=7.36,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[AlbertForPreTrainingOutput, Tuple]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits: torch.Tensor = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
Albert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
ALBERT_START_DOCSTRING,
)
class AlbertForMultipleChoice(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.albert = AlbertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[AlbertForPreTrainingOutput, Tuple]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where *num_choices* is the size of the second dimension of the input tensors. (see
*input_ids* above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.albert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits: torch.Tensor = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = [
"load_tf_weights_in_albert",
"AlbertPreTrainedModel",
"AlbertModel",
"AlbertForPreTraining",
"AlbertForMaskedLM",
"AlbertForSequenceClassification",
"AlbertForTokenClassification",
"AlbertForQuestionAnswering",
"AlbertForMultipleChoice",
]
| transformers/src/transformers/models/albert/modeling_albert.py/0 | {
"file_path": "transformers/src/transformers/models/albert/modeling_albert.py",
"repo_id": "transformers",
"token_count": 27212
} |
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