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from_pretrained() lets you instantiate a model, configuration, and preprocessing class from a pretrained version either
provided by the library itself (the supported models can be found on the Model Hub) or
stored locally (or on a server) by the user.
save_pretrained() lets you save a model, configuration, and preprocessing class locally so that it can be reloaded using
from_pretrained().
push_to_hub() lets you share a model, configuration, and a preprocessing class to the Hub, so it is easily accessible to everyone.
|
Transformers Agents
Transformers Agents is an experimental API which is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change. |
Transformers version v4.29.0, building on the concept of tools and agents. You can play with in
this colab.
In short, it provides a natural language API on top of transformers: we define a set of curated tools and design an
agent to interpret natural language and to use these tools. It is extensible by design; we curated some relevant tools,
but we'll show you how the system can be extended easily to use any tool developed by the community.
Let's start with a few examples of what can be achieved with this new API. It is particularly powerful when it comes
to multimodal tasks, so let's take it for a spin to generate images and read text out loud.
py
agent.run("Caption the following image", image=image)
| Input | Output |
|-----------------------------------------------------------------------------------------------------------------------------|-----------------------------------|
| | A beaver is swimming in the water | |
py
agent.run("Read the following text out loud", text=text)
| Input | Output |
|-------------------------------------------------------------------------------------------------------------------------|----------------------------------------------|
| A beaver is swimming in the water | your browser does not support the audio element. |
py
agent.run(
"In the following `document`, where will the TRRF Scientific Advisory Council Meeting take place?",
document=document,
)
| Input | Output |
|-----------------------------------------------------------------------------------------------------------------------------|----------------|
| | ballroom foyer |
Quickstart
Before being able to use agent.run, you will need to instantiate an agent, which is a large language model (LLM).
We provide support for openAI models as well as opensource alternatives from BigCode and OpenAssistant. The openAI
models perform better (but require you to have an openAI API key, so cannot be used for free); Hugging Face is
providing free access to endpoints for BigCode and OpenAssistant models.
To start with, please install the agents extras in order to install all default dependencies. |
pip install transformers[agents]
To use openAI models, you instantiate an [OpenAiAgent] after installing the openai dependency:
pip install openai
from transformers import OpenAiAgent
agent = OpenAiAgent(model="text-davinci-003", api_key="")
To use BigCode or OpenAssistant, start by logging in to have access to the Inference API:
from huggingface_hub import login
login("")
Then, instantiate the agent |
from huggingface_hub import login
login("")
Then, instantiate the agent
from transformers import HfAgent
Starcoder
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
StarcoderBase
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoderbase")
OpenAssistant
agent = HfAgent(url_endpoint="https://api-inference.huggingface.co/models/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5") |
This is using the inference API that Hugging Face provides for free at the moment. If you have your own inference
endpoint for this model (or another one) you can replace the URL above with your URL endpoint. |
StarCoder and OpenAssistant are free to use and perform admirably well on simple tasks. However, the checkpoints
don't hold up when handling more complex prompts. If you're facing such an issue, we recommend trying out the OpenAI
model which, while sadly not open-source, performs better at this given time. |
You're now good to go! Let's dive into the two APIs that you now have at your disposal.
Single execution (run)
The single execution method is when using the [~Agent.run] method of the agent:
py
agent.run("Draw me a picture of rivers and lakes.") |
It automatically selects the tool (or tools) appropriate for the task you want to perform and runs them appropriately. It
can perform one or several tasks in the same instruction (though the more complex your instruction, the more likely
the agent is to fail).
py
agent.run("Draw me a picture of the sea then transform the picture to add an island") |
Every [~Agent.run] operation is independent, so you can run it several times in a row with different tasks.
Note that your agent is just a large-language model, so small variations in your prompt might yield completely
different results. It's important to explain as clearly as possible the task you want to perform. We go more in-depth
on how to write good prompts here.
If you'd like to keep a state across executions or to pass non-text objects to the agent, you can do so by specifying
variables that you would like the agent to use. For example, you could generate the first image of rivers and lakes,
and ask the model to update that picture to add an island by doing the following:
python
picture = agent.run("Generate a picture of rivers and lakes.")
updated_picture = agent.run("Transform the image in `picture` to add an island to it.", picture=picture) |
This can be helpful when the model is unable to understand your request and mixes tools. An example would be:
py
agent.run("Draw me the picture of a capybara swimming in the sea")
Here, the model could interpret in two ways:
- Have the text-to-image generate a capybara swimming in the sea
- Or, have the text-to-image generate capybara, then use the image-transformation tool to have it swim in the sea
In case you would like to force the first scenario, you could do so by passing it the prompt as an argument:
py
agent.run("Draw me a picture of the `prompt`", prompt="a capybara swimming in the sea") |
Chat-based execution (chat)
The agent also has a chat-based approach, using the [~Agent.chat] method:
py
agent.chat("Generate a picture of rivers and lakes")
py
agent.chat("Transform the picture so that there is a rock in there") |
This is an interesting approach when you want to keep the state across instructions. It's better for experimentation,
but will tend to be much better at single instructions rather than complex instructions (which the [~Agent.run]
method is better at handling).
This method can also take arguments if you would like to pass non-text types or specific prompts.
⚠️ Remote execution
For demonstration purposes and so that it could be used with all setups, we had created remote executors for several
of the default tools the agent has access for the release. These are created using
inference endpoints.
We have turned these off for now, but in order to see how to set up remote executors tools yourself,
we recommend reading the custom tool guide.
What's happening here? What are tools, and what are agents? |
Agents
The "agent" here is a large language model, and we're prompting it so that it has access to a specific set of tools.
LLMs are pretty good at generating small samples of code, so this API takes advantage of that by prompting the
LLM gives a small sample of code performing a task with a set of tools. This prompt is then completed by the
task you give your agent and the description of the tools you give it. This way it gets access to the doc of the
tools you are using, especially their expected inputs and outputs, and can generate the relevant code.
Tools
Tools are very simple: they're a single function, with a name, and a description. We then use these tools' descriptions
to prompt the agent. Through the prompt, we show the agent how it would leverage tools to perform what was
requested in the query.
This is using brand-new tools and not pipelines, because the agent writes better code with very atomic tools.
Pipelines are more refactored and often combine several tasks in one. Tools are meant to be focused on
one very simple task only.
Code-execution?!
This code is then executed with our small Python interpreter on the set of inputs passed along with your tools.
We hear you screaming "Arbitrary code execution!" in the back, but let us explain why that is not the case.
The only functions that can be called are the tools you provided and the print function, so you're already
limited in what can be executed. You should be safe if it's limited to Hugging Face tools.
Then, we don't allow any attribute lookup or imports (which shouldn't be needed anyway for passing along
inputs/outputs to a small set of functions) so all the most obvious attacks (and you'd need to prompt the LLM
to output them anyway) shouldn't be an issue. If you want to be on the super safe side, you can execute the
run() method with the additional argument return_code=True, in which case the agent will just return the code
to execute and you can decide whether to do it or not.
The execution will stop at any line trying to perform an illegal operation or if there is a regular Python error
with the code generated by the agent.
A curated set of tools
We identify a set of tools that can empower such agents. Here is an updated list of the tools we have integrated
in transformers: |
Document question answering: given a document (such as a PDF) in image format, answer a question on this document (Donut)
Text question answering: given a long text and a question, answer the question in the text (Flan-T5)
Unconditional image captioning: Caption the image! (BLIP)
Image question answering: given an image, answer a question on this image (VILT)
Image segmentation: given an image and a prompt, output the segmentation mask of that prompt (CLIPSeg)
Speech to text: given an audio recording of a person talking, transcribe the speech into text (Whisper)
Text to speech: convert text to speech (SpeechT5)
Zero-shot text classification: given a text and a list of labels, identify to which label the text corresponds the most (BART)
Text summarization: summarize a long text in one or a few sentences (BART)
Translation: translate the text into a given language (NLLB) |
These tools have an integration in transformers, and can be used manually as well, for example:
from transformers import load_tool
tool = load_tool("text-to-speech")
audio = tool("This is a text to speech tool") |
Custom tools
While we identify a curated set of tools, we strongly believe that the main value provided by this implementation is
the ability to quickly create and share custom tools.
By pushing the code of a tool to a Hugging Face Space or a model repository, you're then able to leverage the tool
directly with the agent. We've added a few
transformers-agnostic tools to the huggingface-tools organization: |
Text downloader: to download a text from a web URL
Text to image: generate an image according to a prompt, leveraging stable diffusion
Image transformation: modify an image given an initial image and a prompt, leveraging instruct pix2pix stable diffusion
Text to video: generate a small video according to a prompt, leveraging damo-vilab |
The text-to-image tool we have been using since the beginning is a remote tool that lives in
huggingface-tools/text-to-image! We will
continue releasing such tools on this and other organizations, to further supercharge this implementation.
The agents have by default access to tools that reside on huggingface-tools.
We explain how to you can write and share your tools as well as leverage any custom tool that resides on the Hub in following guide.
Code generation
So far we have shown how to use the agents to perform actions for you. However, the agent is only generating code
that we then execute using a very restricted Python interpreter. In case you would like to use the code generated in
a different setting, the agent can be prompted to return the code, along with tool definition and accurate imports.
For example, the following instruction
python
agent.run("Draw me a picture of rivers and lakes", return_code=True)
returns the following code
thon
from transformers import load_tool
image_generator = load_tool("huggingface-tools/text-to-image")
image = image_generator(prompt="rivers and lakes") |
that you can then modify and execute yourself. |
How to create a custom pipeline?
In this guide, we will see how to create a custom pipeline and share it on the Hub or add it to the
🤗 Transformers library.
First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes,
dictionaries or whatever seems to be the most likely desired input. Try to keep these inputs as pure Python as possible
as it makes compatibility easier (even through other languages via JSON). Those will be the inputs of the
pipeline (preprocess).
Then define the outputs. Same policy as the inputs. The simpler, the better. Those will be the outputs of
postprocess method.
Start by inheriting the base class Pipeline with the 4 methods needed to implement preprocess,
_forward, postprocess, and _sanitize_parameters.
thon
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 |
The structure of this breakdown is to support relatively seamless support for CPU/GPU, while supporting doing
pre/postprocessing on the CPU on different threads
preprocess will take the originally defined inputs, and turn them into something feedable to the model. It might
contain more information and is usually a Dict.
_forward is the implementation detail and is not meant to be called directly. forward is the preferred
called method as it contains safeguards to make sure everything is working on the expected device. If anything is
linked to a real model it belongs in the _forward method, anything else is in the preprocess/postprocess.
postprocess methods will take the output of _forward and turn it into the final output that was decided
earlier.
_sanitize_parameters exists to allow users to pass any parameters whenever they wish, be it at initialization
time pipeline(., maybe_arg=4) or at call time pipe = pipeline(); output = pipe(., maybe_arg=4).
The returns of _sanitize_parameters are the 3 dicts of kwargs that will be passed directly to preprocess,
_forward, and postprocess. Don't fill anything if the caller didn't call with any extra parameter. That
allows to keep the default arguments in the function definition which is always more "natural".
A classic example would be a top_k argument in the post processing in classification tasks.
thon |
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}] |
In order to achieve that, we'll update our postprocess method with a default parameter to 5. and edit
_sanitize_parameters to allow this new parameter.
thon
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 |
Try to keep the inputs/outputs very simple and ideally JSON-serializable as it makes the pipeline usage very easy
without requiring users to understand new kinds of objects. It's also relatively common to support many different types
of arguments for ease of use (audio files, which can be filenames, URLs or pure bytes)
Adding it to the list of supported tasks
To register your new-task to the list of supported tasks, you have to add it to the PIPELINE_REGISTRY:
thon
from transformers.pipelines import PIPELINE_REGISTRY
PIPELINE_REGISTRY.register_pipeline(
"new-task",
pipeline_class=MyPipeline,
pt_model=AutoModelForSequenceClassification,
) |
You can specify a default model if you want, in which case it should come with a specific revision (which can be the name of a branch or a commit hash, here we took "abcdef") as well as the type:
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
)
Share your pipeline on the Hub
To share your custom pipeline on the Hub, you just have to save the custom code of your Pipeline subclass in a
python file. For instance, let's say we want to use a custom pipeline for sentence pair classification like this: |
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} |
The implementation is framework agnostic, and will work for PyTorch and TensorFlow models. If we have saved this in
a file named pair_classification.py, we can then import it and register it like this: |
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,
) |
Once this is done, we can use it with a pretrained model. For instance sgugger/finetuned-bert-mrpc has been
fine-tuned on the MRPC dataset, which classifies pairs of sentences as paraphrases or not.
from transformers import pipeline
classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
Then we can share it on the Hub by using the save_pretrained method in a Repository: |
Then we can share it on the Hub by using the save_pretrained method in a Repository:
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() |
This will copy the file where you defined PairClassificationPipeline inside the folder "test-dynamic-pipeline",
along with saving the model and tokenizer of the pipeline, before pushing everything into the repository
{your_username}/test-dynamic-pipeline. After that, anyone can use it as long as they provide the option
trust_remote_code=True:
from transformers import pipeline
classifier = pipeline(model="{your_username}/test-dynamic-pipeline", trust_remote_code=True) |
Add the pipeline to 🤗 Transformers
If you want to contribute your pipeline to 🤗 Transformers, you will need to add a new module in the pipelines submodule
with the code of your pipeline, then add it to the list of tasks defined in pipelines/__init__.py.
Then you will need to add tests. Create a new file tests/test_pipelines_MY_PIPELINE.py with examples of the other tests.
The run_pipeline_test function will be very generic and run on small random models on every possible
architecture as defined by model_mapping and tf_model_mapping.
This is very important to test future compatibility, meaning if someone adds a new model for
XXXForQuestionAnswering then the pipeline test will attempt to run on it. Because the models are random it's
impossible to check for actual values, that's why there is a helper ANY that will simply attempt to match the
output of the pipeline TYPE.
You also need to implement 2 (ideally 4) tests. |
test_small_model_pt : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
and test the pipeline outputs. The results should be the same as test_small_model_tf.
test_small_model_tf : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
and test the pipeline outputs. The results should be the same as test_small_model_pt.
test_large_model_pt (optional): Tests the pipeline on a real pipeline where the results are supposed to
make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
sure there is no drift in future releases.
test_large_model_tf (optional): Tests the pipeline on a real pipeline where the results are supposed to
make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
sure there is no drift in future releases.
|
Multilingual models for inference
[[open-in-colab]]
There are several multilingual models in 🤗 Transformers, and their inference usage differs from monolingual models. Not all multilingual model usage is different though. Some models, like google-bert/bert-base-multilingual-uncased, can be used just like a monolingual model. This guide will show you how to use multilingual models whose usage differs for inference.
XLM
XLM has ten different checkpoints, only one of which is monolingual. The nine remaining model checkpoints can be split into two categories: the checkpoints that use language embeddings and those that don't.
XLM with language embeddings
The following XLM models use language embeddings to specify the language used at inference: |
FacebookAI/xlm-mlm-ende-1024 (Masked language modeling, English-German)
FacebookAI/xlm-mlm-enfr-1024 (Masked language modeling, English-French)
FacebookAI/xlm-mlm-enro-1024 (Masked language modeling, English-Romanian)
FacebookAI/xlm-mlm-xnli15-1024 (Masked language modeling, XNLI languages)
FacebookAI/xlm-mlm-tlm-xnli15-1024 (Masked language modeling + translation, XNLI languages)
FacebookAI/xlm-clm-enfr-1024 (Causal language modeling, English-French)
FacebookAI/xlm-clm-ende-1024 (Causal language modeling, English-German) |
Language embeddings are represented as a tensor of the same shape as the input_ids passed to the model. The values in these tensors depend on the language used and are identified by the tokenizer's lang2id and id2lang attributes.
In this example, load the FacebookAI/xlm-clm-enfr-1024 checkpoint (Causal language modeling, English-French): |
import torch
from transformers import XLMTokenizer, XLMWithLMHeadModel
tokenizer = XLMTokenizer.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
model = XLMWithLMHeadModel.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
The lang2id attribute of the tokenizer displays this model's languages and their ids:
print(tokenizer.lang2id)
{'en': 0, 'fr': 1}
Next, create an example input:
input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1 |
print(tokenizer.lang2id)
{'en': 0, 'fr': 1}
Next, create an example input:
input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
Set the language id as "en" and use it to define the language embedding. The language embedding is a tensor filled with 0 since that is the language id for English. This tensor should be the same size as input_ids. |
language_id = tokenizer.lang2id["en"] # 0
langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, , 0])
We reshape it to be of size (batch_size, sequence_length)
langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
Now you can pass the input_ids and language embedding to the model:
outputs = model(input_ids, langs=langs) |
Now you can pass the input_ids and language embedding to the model:
outputs = model(input_ids, langs=langs)
The run_generation.py script can generate text with language embeddings using the xlm-clm checkpoints.
XLM without language embeddings
The following XLM models do not require language embeddings during inference:
FacebookAI/xlm-mlm-17-1280 (Masked language modeling, 17 languages)
FacebookAI/xlm-mlm-100-1280 (Masked language modeling, 100 languages) |
FacebookAI/xlm-mlm-17-1280 (Masked language modeling, 17 languages)
FacebookAI/xlm-mlm-100-1280 (Masked language modeling, 100 languages)
These models are used for generic sentence representations, unlike the previous XLM checkpoints.
BERT
The following BERT models can be used for multilingual tasks: |
These models are used for generic sentence representations, unlike the previous XLM checkpoints.
BERT
The following BERT models can be used for multilingual tasks:
google-bert/bert-base-multilingual-uncased (Masked language modeling + Next sentence prediction, 102 languages)
google-bert/bert-base-multilingual-cased (Masked language modeling + Next sentence prediction, 104 languages) |
These models do not require language embeddings during inference. They should identify the language from the
context and infer accordingly.
XLM-RoBERTa
The following XLM-RoBERTa models can be used for multilingual tasks:
FacebookAI/xlm-roberta-base (Masked language modeling, 100 languages)
FacebookAI/xlm-roberta-large (Masked language modeling, 100 languages) |
FacebookAI/xlm-roberta-base (Masked language modeling, 100 languages)
FacebookAI/xlm-roberta-large (Masked language modeling, 100 languages)
XLM-RoBERTa was trained on 2.5TB of newly created and cleaned CommonCrawl data in 100 languages. It provides strong gains over previously released multilingual models like mBERT or XLM on downstream tasks like classification, sequence labeling, and question answering.
M2M100
The following M2M100 models can be used for multilingual translation: |
facebook/m2m100_418M (Translation)
facebook/m2m100_1.2B (Translation)
In this example, load the facebook/m2m100_418M checkpoint to translate from Chinese to English. You can set the source language in the tokenizer: |
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
chinese_text = "不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒."
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="zh")
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
Tokenize the text:
encoded_zh = tokenizer(chinese_text, return_tensors="pt") |
Tokenize the text:
encoded_zh = tokenizer(chinese_text, return_tensors="pt")
M2M100 forces the target language id as the first generated token to translate to the target language. Set the forced_bos_token_id to en in the generate method to translate to English: |
M2M100 forces the target language id as the first generated token to translate to the target language. Set the forced_bos_token_id to en in the generate method to translate to English:
generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.' |
MBart
The following MBart models can be used for multilingual translation:
facebook/mbart-large-50-one-to-many-mmt (One-to-many multilingual machine translation, 50 languages)
facebook/mbart-large-50-many-to-many-mmt (Many-to-many multilingual machine translation, 50 languages)
facebook/mbart-large-50-many-to-one-mmt (Many-to-one multilingual machine translation, 50 languages)
facebook/mbart-large-50 (Multilingual translation, 50 languages)
facebook/mbart-large-cc25 |
In this example, load the facebook/mbart-large-50-many-to-many-mmt checkpoint to translate Finnish to English. You can set the source language in the tokenizer: |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
fi_text = "Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia."
tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", src_lang="fi_FI")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
Tokenize the text: |
Tokenize the text:
encoded_en = tokenizer(en_text, return_tensors="pt")
MBart forces the target language id as the first generated token to translate to the target language. Set the forced_bos_token_id to en in the generate method to translate to English: |
MBart forces the target language id as the first generated token to translate to the target language. Set the forced_bos_token_id to en in the generate method to translate to English:
generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
"Don't interfere with the wizard's affairs, because they are subtle, will soon get angry." |
If you are using the facebook/mbart-large-50-many-to-one-mmt checkpoint, you don't need to force the target language id as the first generated token otherwise the usage is the same. |
Debugging
Training on multiple GPUs can be a tricky endeavor whether you're running into installation issues or communication problems between your GPUs. This debugging guide covers some issues you may run into and how to resolve them.
DeepSpeed CUDA installation
If you're using DeepSpeed, you've probably already installed it with the following command. |
pip install deepspeed
DeepSpeed compiles CUDA C++ code and it can be a potential source of errors when building PyTorch extensions that require CUDA. These errors depend on how CUDA is installed on your system, and this section focuses on PyTorch built with CUDA 10.2.
For any other installation issues, please open an issue with the DeepSpeed team. |
Non-identical CUDA toolkits
PyTorch comes with its own CUDA toolkit, but to use DeepSpeed with PyTorch, you need to have an identical version of CUDA installed system-wide. For example, if you installed PyTorch with cudatoolkit==10.2 in your Python environment, then you'll also need to have CUDA 10.2 installed system-wide. If you don't have CUDA installed system-wide, you should install it first.
The exact location may vary from system to system, but usr/local/cuda-10.2 is the most common location on many Unix systems. When CUDA is correctly setup and added to your PATH environment variable, you can find the installation location with the following command: |
which nvcc
Multiple CUDA toolkits
You may also have more than one CUDA toolkit installed system-wide. |
/usr/local/cuda-10.2
/usr/local/cuda-11.0
Typically, package installers set the paths to whatever the last version was installed. If the package build fails because it can't find the right CUDA version (despite it being installed system-wide already), then you need to configure the PATH and LD_LIBRARY_PATH environment variables to point to the correct path.
Take a look at the contents of these environment variables first: |
echo $PATH
echo $LD_LIBRARY_PATH
PATH lists the locations of the executables and LD_LIBRARY_PATH lists where to look for shared libraries. Earlier entries are prioritized over later ones, and : is used to separate multiple entries. To tell the build program where to find the specific CUDA toolkit you want, insert the correct path to list first. This command prepends rather than overwrites the existing values.
```bash
adjust the version and full path if needed
export PATH=/usr/local/cuda-10.2/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH |
In addition, you should also check the directories you assign actually exist. The lib64 sub-directory contains various CUDA .so objects (like libcudart.so) and while it is unlikely your system names them differently, you should check the actual names and change them accordingly.
Older CUDA versions
Sometimes, older CUDA versions may refuse to build with newer compilers. For example, if you have gcc-9 but CUDA wants gcc-7. Usually, installing the latest CUDA toolkit enables support for the newer compiler.
You could also install an older version of the compiler in addition to the one you're currently using (or it may already be installed but it's not used by default and the build system can't see it). To resolve this, you can create a symlink to give the build system visibility to the older compiler.
```bash
adapt the path to your system
sudo ln -s /usr/bin/gcc-7 /usr/local/cuda-10.2/bin/gcc
sudo ln -s /usr/bin/g++-7 /usr/local/cuda-10.2/bin/g++ |
Prebuild
If you're still having issues with installing DeepSpeed or if you're building DeepSpeed at run time, you can try to prebuild the DeepSpeed modules before installing them. To make a local build for DeepSpeed:
git clone https://github.com/microsoft/DeepSpeed/
cd DeepSpeed
rm -rf build
TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 pip install . \
--global-option="build_ext" --global-option="-j8" --no-cache -v \
--disable-pip-version-check 2>&1 | tee build.log |
To use NVMe offload, add the DS_BUILD_AIO=1 parameter to the build command and make sure you install the libaio-dev package system-wide.
Next, you'll have to specify your GPU's architecture by editing the TORCH_CUDA_ARCH_LIST variable (find a complete list of NVIDIA GPUs and their corresponding architectures on this page). To check the PyTorch version that corresponds to your architecture, run the following command: |
python -c "import torch; print(torch.cuda.get_arch_list())"
Find the architecture for a GPU with the following command:
CUDA_VISIBLE_DEVICES=0 python -c "import torch; print(torch.cuda.get_device_capability())"
To find the architecture for GPU 0: |
CUDA_VISIBLE_DEVICES=0 python -c "import torch; print(torch.cuda.get_device_capability())"
To find the architecture for GPU 0:
CUDA_VISIBLE_DEVICES=0 python -c "import torch; \
print(torch.cuda.get_device_properties(torch.device('cuda')))
"_CudaDeviceProperties(name='GeForce RTX 3090', major=8, minor=6, total_memory=24268MB, multi_processor_count=82)"
This means your GPU architecture is 8.6. |
If you get 8, 6, then you can set TORCH_CUDA_ARCH_LIST="8.6". For multiple GPUs with different architectures, list them like TORCH_CUDA_ARCH_LIST="6.1;8.6".
It is also possible to not specify TORCH_CUDA_ARCH_LIST and the build program automatically queries the GPU architecture of the build. However, it may or may not match the actual GPU on the target machine which is why it is better to explicitly specify the correct architecture.
For training on multiple machines with the same setup, you'll need to make a binary wheel: |
git clone https://github.com/microsoft/DeepSpeed/
cd DeepSpeed
rm -rf build
TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 \
python setup.py build_ext -j8 bdist_wheel
This command generates a binary wheel that'll look something like dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl. Now you can install this wheel locally or on another machine. |
pip install deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl
Multi-GPU Network Issues Debug
When training or inferencing with DistributedDataParallel and multiple GPU, if you run into issue of inter-communication between processes and/or nodes, you can use the following script to diagnose network issues.
wget https://raw.githubusercontent.com/huggingface/transformers/main/scripts/distributed/torch-distributed-gpu-test.py
For example to test how 2 GPUs interact do: |
python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
If both processes can talk to each and allocate GPU memory each will print an OK status.
For more GPUs or nodes adjust the arguments in the script.
You will find a lot more details inside the diagnostics script and even a recipe to how you could run it in a SLURM environment.
An additional level of debug is to add NCCL_DEBUG=INFO environment variable as follows: |
NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
This will dump a lot of NCCL-related debug information, which you can then search online if you find that some problems are reported. Or if you're not sure how to interpret the output you can share the log file in an Issue.
Underflow and Overflow Detection
This feature is currently available for PyTorch-only.
For multi-GPU training it requires DDP (torch.distributed.launch). |
This feature is currently available for PyTorch-only.
For multi-GPU training it requires DDP (torch.distributed.launch).
This feature can be used with any nn.Module-based model. |
For multi-GPU training it requires DDP (torch.distributed.launch).
This feature can be used with any nn.Module-based model.
If you start getting loss=NaN or the model inhibits some other abnormal behavior due to inf or nan in
activations or weights one needs to discover where the first underflow or overflow happens and what led to it. Luckily
you can accomplish that easily by activating a special module that will do the detection automatically.
If you're using [Trainer], you just need to add: |
--debug underflow_overflow
to the normal command line arguments, or pass debug="underflow_overflow" when creating the
[TrainingArguments] object.
If you're using your own training loop or another Trainer you can accomplish the same with:
thon
from transformers.debug_utils import DebugUnderflowOverflow
debug_overflow = DebugUnderflowOverflow(model) |
[~debug_utils.DebugUnderflowOverflow] inserts hooks into the model that immediately after each
forward call will test input and output variables and also the corresponding module's weights. As soon as inf or
nan is detected in at least one element of the activations or weights, the program will assert and print a report
like this (this was caught with google/mt5-small under fp16 mixed precision):
Detected inf/nan during batch_number=0
Last 21 forward frames:
abs min abs max metadata
encoder.block.1.layer.1.DenseReluDense.dropout Dropout
0.00e+00 2.57e+02 input[0]
0.00e+00 2.85e+02 output
[]
encoder.block.2.layer.0 T5LayerSelfAttention
6.78e-04 3.15e+03 input[0]
2.65e-04 3.42e+03 output[0]
None output[1]
2.25e-01 1.00e+04 output[2]
encoder.block.2.layer.1.layer_norm T5LayerNorm
8.69e-02 4.18e-01 weight
2.65e-04 3.42e+03 input[0]
1.79e-06 4.65e+00 output
encoder.block.2.layer.1.DenseReluDense.wi_0 Linear
2.17e-07 4.50e+00 weight
1.79e-06 4.65e+00 input[0]
2.68e-06 3.70e+01 output
encoder.block.2.layer.1.DenseReluDense.wi_1 Linear
8.08e-07 2.66e+01 weight
1.79e-06 4.65e+00 input[0]
1.27e-04 2.37e+02 output
encoder.block.2.layer.1.DenseReluDense.dropout Dropout
0.00e+00 8.76e+03 input[0]
0.00e+00 9.74e+03 output
encoder.block.2.layer.1.DenseReluDense.wo Linear
1.01e-06 6.44e+00 weight
0.00e+00 9.74e+03 input[0]
3.18e-04 6.27e+04 output
encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense
1.79e-06 4.65e+00 input[0]
3.18e-04 6.27e+04 output
encoder.block.2.layer.1.dropout Dropout
3.18e-04 6.27e+04 input[0]
0.00e+00 inf output
The example output has been trimmed in the middle for brevity.
The second column shows the value of the absolute largest element, so if you have a closer look at the last few frames,
the inputs and outputs were in the range of 1e4. So when this training was done under fp16 mixed precision the very
last step overflowed (since under fp16 the largest number before inf is 64e3). To avoid overflows under
fp16 the activations must remain way below 1e4, because 1e4 * 1e4 = 1e8 so any matrix multiplication with
large activations is going to lead to a numerical overflow condition.
At the very start of the trace you can discover at which batch number the problem occurred (here Detected inf/nan during batch_number=0 means the problem occurred on the first batch).
Each reported frame starts by declaring the fully qualified entry for the corresponding module this frame is reporting
for. If we look just at this frame:
encoder.block.2.layer.1.layer_norm T5LayerNorm
8.69e-02 4.18e-01 weight
2.65e-04 3.42e+03 input[0]
1.79e-06 4.65e+00 output
Here, encoder.block.2.layer.1.layer_norm indicates that it was a layer norm for the first layer, of the second
block of the encoder. And the specific calls of the forward is T5LayerNorm.
Let's look at the last few frames of that report:
Detected inf/nan during batch_number=0
Last 21 forward frames:
abs min abs max metadata
[]
encoder.block.2.layer.1.DenseReluDense.wi_0 Linear
2.17e-07 4.50e+00 weight
1.79e-06 4.65e+00 input[0]
2.68e-06 3.70e+01 output
encoder.block.2.layer.1.DenseReluDense.wi_1 Linear
8.08e-07 2.66e+01 weight
1.79e-06 4.65e+00 input[0]
1.27e-04 2.37e+02 output
encoder.block.2.layer.1.DenseReluDense.wo Linear
1.01e-06 6.44e+00 weight
0.00e+00 9.74e+03 input[0]
3.18e-04 6.27e+04 output
encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense
1.79e-06 4.65e+00 input[0]
3.18e-04 6.27e+04 output
encoder.block.2.layer.1.dropout Dropout
3.18e-04 6.27e+04 input[0]
0.00e+00 inf output
The last frame reports for Dropout.forward function with the first entry for the only input and the second for the
only output. You can see that it was called from an attribute dropout inside DenseReluDense class. We can see
that it happened during the first layer, of the 2nd block, during the very first batch. Finally, the absolute largest
input elements was 6.27e+04 and same for the output was inf.
You can see here, that T5DenseGatedGeluDense.forward resulted in output activations, whose absolute max value was
around 62.7K, which is very close to fp16's top limit of 64K. In the next frame we have Dropout which renormalizes
the weights, after it zeroed some of the elements, which pushes the absolute max value to more than 64K, and we get an
overflow (inf).
As you can see it's the previous frames that we need to look into when the numbers start going into very large for fp16
numbers.
Let's match the report to the code from models/t5/modeling_t5.py:
thon
class T5DenseGatedGeluDense(nn.Module):
def init(self, config):
super().init()
self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
self.gelu_act = ACT2FN["gelu_new"]
def forward(self, hidden_states):
hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
hidden_linear = self.wi_1(hidden_states)
hidden_states = hidden_gelu * hidden_linear
hidden_states = self.dropout(hidden_states)
hidden_states = self.wo(hidden_states)
return hidden_states |
Now it's easy to see the dropout call, and all the previous calls as well.
Since the detection is happening in a forward hook, these reports are printed immediately after each forward
returns.
Going back to the full report, to act on it and to fix the problem, we need to go a few frames up where the numbers
started to go up and most likely switch to the fp32 mode here, so that the numbers don't overflow when multiplied
or summed up. Of course, there might be other solutions. For example, we could turn off amp temporarily if it's
enabled, after moving the original forward into a helper wrapper, like so:
thon
def _forward(self, hidden_states):
hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
hidden_linear = self.wi_1(hidden_states)
hidden_states = hidden_gelu * hidden_linear
hidden_states = self.dropout(hidden_states)
hidden_states = self.wo(hidden_states)
return hidden_states
import torch
def forward(self, hidden_states):
if torch.is_autocast_enabled():
with torch.cuda.amp.autocast(enabled=False):
return self._forward(hidden_states)
else:
return self._forward(hidden_states) |
Since the automatic detector only reports on inputs and outputs of full frames, once you know where to look, you may
want to analyse the intermediary stages of any specific forward function as well. In such a case you can use the
detect_overflow helper function to inject the detector where you want it, for example:
thon
from debug_utils import detect_overflow
class T5LayerFF(nn.Module):
[]
def forward(self, hidden_states):
forwarded_states = self.layer_norm(hidden_states)
detect_overflow(forwarded_states, "after layer_norm")
forwarded_states = self.DenseReluDense(forwarded_states)
detect_overflow(forwarded_states, "after DenseReluDense")
return hidden_states + self.dropout(forwarded_states) |
You can see that we added 2 of these and now we track if inf or nan for forwarded_states was detected
somewhere in between.
Actually, the detector already reports these because each of the calls in the example above is a nn.Module, but
let's say if you had some local direct calculations this is how you'd do that.
Additionally, if you're instantiating the debugger in your own code, you can adjust the number of frames printed from
its default, e.g.:
thon
from transformers.debug_utils import DebugUnderflowOverflow
debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100) |
Specific batch absolute min and max value tracing
The same debugging class can be used for per-batch tracing with the underflow/overflow detection feature turned off.
Let's say you want to watch the absolute min and max values for all the ingredients of each forward call of a given
batch, and only do that for batches 1 and 3. Then you instantiate this class as:
python
debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3])
And now full batches 1 and 3 will be traced using the same format as the underflow/overflow detector does.
Batches are 0-indexed.
This is helpful if you know that the program starts misbehaving after a certain batch number, so you can fast-forward
right to that area. Here is a sample truncated output for such configuration: |
*** Starting batch number=1 ***
abs min abs max metadata
shared Embedding
1.01e-06 7.92e+02 weight
0.00e+00 2.47e+04 input[0]
5.36e-05 7.92e+02 output
[]
decoder.dropout Dropout
1.60e-07 2.27e+01 input[0]
0.00e+00 2.52e+01 output
decoder T5Stack
not a tensor output
lm_head Linear
1.01e-06 7.92e+02 weight
0.00e+00 1.11e+00 input[0]
6.06e-02 8.39e+01 output
T5ForConditionalGeneration
not a tensor output
*** Starting batch number=3 *** |
abs min abs max metadata
shared Embedding
1.01e-06 7.92e+02 weight
0.00e+00 2.78e+04 input[0]
5.36e-05 7.92e+02 output
[] |
Here you will get a huge number of frames dumped - as many as there were forward calls in your model, so it may or may
not what you want, but sometimes it can be easier to use for debugging purposes than a normal debugger. For example, if
a problem starts happening at batch number 150. So you can dump traces for batches 149 and 150 and compare where
numbers started to diverge.
You can also specify the batch number after which to stop the training, with:
python
debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3], abort_after_batch_num=3) |
Create a custom architecture
An AutoClass automatically infers the model architecture and downloads pretrained configuration and weights. Generally, we recommend using an AutoClass to produce checkpoint-agnostic code. But users who want more control over specific model parameters can create a custom 🤗 Transformers model from just a few base classes. This could be particularly useful for anyone who is interested in studying, training or experimenting with a 🤗 Transformers model. In this guide, dive deeper into creating a custom model without an AutoClass. Learn how to: |
Load and customize a model configuration.
Create a model architecture.
Create a slow and fast tokenizer for text.
Create an image processor for vision tasks.
Create a feature extractor for audio tasks.
Create a processor for multimodal tasks. |
Configuration
A configuration refers to a model's specific attributes. Each model configuration has different attributes; for instance, all NLP models have the hidden_size, num_attention_heads, num_hidden_layers and vocab_size attributes in common. These attributes specify the number of attention heads or hidden layers to construct a model with.
Get a closer look at DistilBERT by accessing [DistilBertConfig] to inspect it's attributes: |
from transformers import DistilBertConfig
config = DistilBertConfig()
print(config)
DistilBertConfig {
"activation": "gelu",
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"transformers_version": "4.16.2",
"vocab_size": 30522
} |
[DistilBertConfig] displays all the default attributes used to build a base [DistilBertModel]. All attributes are customizable, creating space for experimentation. For example, you can customize a default model to:
Try a different activation function with the activation parameter.
Use a higher dropout ratio for the attention probabilities with the attention_dropout parameter. |
my_config = DistilBertConfig(activation="relu", attention_dropout=0.4)
print(my_config)
DistilBertConfig {
"activation": "relu",
"attention_dropout": 0.4,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"transformers_version": "4.16.2",
"vocab_size": 30522
} |
Pretrained model attributes can be modified in the [~PretrainedConfig.from_pretrained] function:
my_config = DistilBertConfig.from_pretrained("distilbert/distilbert-base-uncased", activation="relu", attention_dropout=0.4)
Once you are satisfied with your model configuration, you can save it with [~PretrainedConfig.save_pretrained]. Your configuration file is stored as a JSON file in the specified save directory:
my_config.save_pretrained(save_directory="./your_model_save_path") |
my_config.save_pretrained(save_directory="./your_model_save_path")
To reuse the configuration file, load it with [~PretrainedConfig.from_pretrained]:
my_config = DistilBertConfig.from_pretrained("./your_model_save_path/config.json")
You can also save your configuration file as a dictionary or even just the difference between your custom configuration attributes and the default configuration attributes! See the configuration documentation for more details. |
Model
The next step is to create a model. The model - also loosely referred to as the architecture - defines what each layer is doing and what operations are happening. Attributes like num_hidden_layers from the configuration are used to define the architecture. Every model shares the base class [PreTrainedModel] and a few common methods like resizing input embeddings and pruning self-attention heads. In addition, all models are also either a torch.nn.Module, tf.keras.Model or flax.linen.Module subclass. This means models are compatible with each of their respective framework's usage. |
Load your custom configuration attributes into the model:
from transformers import DistilBertModel
my_config = DistilBertConfig.from_pretrained("./your_model_save_path/config.json")
model = DistilBertModel(my_config) |
This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.
Create a pretrained model with [~PreTrainedModel.from_pretrained]:
model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased") |
model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:
model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
Load your custom configuration attributes into the model: |
model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
Load your custom configuration attributes into the model:
from transformers import TFDistilBertModel
my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
tf_model = TFDistilBertModel(my_config) |
This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.
Create a pretrained model with [~TFPreTrainedModel.from_pretrained]:
tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased") |
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