1191 lines
40 KiB
Python
1191 lines
40 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Doc utilities: Utilities related to documentation
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"""
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import functools
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import re
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import types
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def add_start_docstrings(*docstr):
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def docstring_decorator(fn):
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fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
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return fn
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return docstring_decorator
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def add_start_docstrings_to_model_forward(*docstr):
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def docstring_decorator(fn):
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docstring = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
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class_name = f"[`{fn.__qualname__.split('.')[0]}`]"
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intro = f" The {class_name} forward method, overrides the `__call__` special method."
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note = r"""
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<Tip>
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Although the recipe for forward pass needs to be defined within this function, one should call the [`Module`]
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instance afterwards instead of this since the former takes care of running the pre and post processing steps while
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the latter silently ignores them.
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</Tip>
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"""
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fn.__doc__ = intro + note + docstring
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return fn
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return docstring_decorator
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def add_end_docstrings(*docstr):
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def docstring_decorator(fn):
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fn.__doc__ = (fn.__doc__ if fn.__doc__ is not None else "") + "".join(docstr)
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return fn
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return docstring_decorator
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PT_RETURN_INTRODUCTION = r"""
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Returns:
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[`{full_output_type}`] or `tuple(torch.FloatTensor)`: A [`{full_output_type}`] or a tuple of
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`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
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elements depending on the configuration ([`{config_class}`]) and inputs.
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"""
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TF_RETURN_INTRODUCTION = r"""
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Returns:
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[`{full_output_type}`] or `tuple(tf.Tensor)`: A [`{full_output_type}`] or a tuple of `tf.Tensor` (if
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`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
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configuration ([`{config_class}`]) and inputs.
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"""
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def _get_indent(t):
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"""Returns the indentation in the first line of t"""
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search = re.search(r"^(\s*)\S", t)
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return "" if search is None else search.groups()[0]
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def _convert_output_args_doc(output_args_doc):
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"""Convert output_args_doc to display properly."""
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# Split output_arg_doc in blocks argument/description
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indent = _get_indent(output_args_doc)
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blocks = []
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current_block = ""
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for line in output_args_doc.split("\n"):
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# If the indent is the same as the beginning, the line is the name of new arg.
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if _get_indent(line) == indent:
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if len(current_block) > 0:
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blocks.append(current_block[:-1])
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current_block = f"{line}\n"
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else:
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# Otherwise it's part of the description of the current arg.
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# We need to remove 2 spaces to the indentation.
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current_block += f"{line[2:]}\n"
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blocks.append(current_block[:-1])
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# Format each block for proper rendering
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for i in range(len(blocks)):
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blocks[i] = re.sub(r"^(\s+)(\S+)(\s+)", r"\1- **\2**\3", blocks[i])
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blocks[i] = re.sub(r":\s*\n\s*(\S)", r" -- \1", blocks[i])
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return "\n".join(blocks)
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def _prepare_output_docstrings(output_type, config_class, min_indent=None):
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"""
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Prepares the return part of the docstring using `output_type`.
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"""
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output_docstring = output_type.__doc__
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# Remove the head of the docstring to keep the list of args only
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lines = output_docstring.split("\n")
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i = 0
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while i < len(lines) and re.search(r"^\s*(Args|Parameters):\s*$", lines[i]) is None:
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i += 1
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if i < len(lines):
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params_docstring = "\n".join(lines[(i + 1) :])
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params_docstring = _convert_output_args_doc(params_docstring)
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else:
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raise ValueError(
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f"No `Args` or `Parameters` section is found in the docstring of `{output_type.__name__}`. Make sure it has "
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"docstring and contain either `Args` or `Parameters`."
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)
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# Add the return introduction
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full_output_type = f"{output_type.__module__}.{output_type.__name__}"
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intro = TF_RETURN_INTRODUCTION if output_type.__name__.startswith("TF") else PT_RETURN_INTRODUCTION
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intro = intro.format(full_output_type=full_output_type, config_class=config_class)
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result = intro + params_docstring
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# Apply minimum indent if necessary
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if min_indent is not None:
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lines = result.split("\n")
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# Find the indent of the first nonempty line
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i = 0
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while len(lines[i]) == 0:
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i += 1
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indent = len(_get_indent(lines[i]))
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# If too small, add indentation to all nonempty lines
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if indent < min_indent:
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to_add = " " * (min_indent - indent)
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lines = [(f"{to_add}{line}" if len(line) > 0 else line) for line in lines]
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result = "\n".join(lines)
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return result
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FAKE_MODEL_DISCLAIMER = """
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<Tip warning={true}>
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This example uses a random model as the real ones are all very big. To get proper results, you should use
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{real_checkpoint} instead of {fake_checkpoint}. If you get out-of-memory when loading that checkpoint, you can try
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adding `device_map="auto"` in the `from_pretrained` call.
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</Tip>
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"""
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PT_TOKEN_CLASSIFICATION_SAMPLE = r"""
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Example:
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```python
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>>> from transformers import AutoTokenizer, {model_class}
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>>> import torch
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>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> inputs = tokenizer(
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... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
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... )
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>>> with torch.no_grad():
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... logits = model(**inputs).logits
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>>> predicted_token_class_ids = logits.argmax(-1)
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>>> # Note that tokens are classified rather then input words which means that
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>>> # there might be more predicted token classes than words.
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>>> # Multiple token classes might account for the same word
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>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
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>>> predicted_tokens_classes
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{expected_output}
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>>> labels = predicted_token_class_ids
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>>> loss = model(**inputs, labels=labels).loss
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>>> round(loss.item(), 2)
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{expected_loss}
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```
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"""
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PT_QUESTION_ANSWERING_SAMPLE = r"""
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Example:
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```python
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>>> from transformers import AutoTokenizer, {model_class}
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>>> import torch
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>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
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>>> inputs = tokenizer(question, text, return_tensors="pt")
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>>> with torch.no_grad():
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... outputs = model(**inputs)
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>>> answer_start_index = outputs.start_logits.argmax()
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>>> answer_end_index = outputs.end_logits.argmax()
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>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
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>>> tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
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{expected_output}
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>>> # target is "nice puppet"
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>>> target_start_index = torch.tensor([{qa_target_start_index}])
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>>> target_end_index = torch.tensor([{qa_target_end_index}])
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>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
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>>> loss = outputs.loss
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>>> round(loss.item(), 2)
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{expected_loss}
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```
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"""
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PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
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Example of single-label classification:
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```python
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>>> import torch
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>>> from transformers import AutoTokenizer, {model_class}
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>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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>>> with torch.no_grad():
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... logits = model(**inputs).logits
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>>> predicted_class_id = logits.argmax().item()
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>>> model.config.id2label[predicted_class_id]
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{expected_output}
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>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
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>>> num_labels = len(model.config.id2label)
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>>> model = {model_class}.from_pretrained("{checkpoint}", num_labels=num_labels)
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>>> labels = torch.tensor([1])
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>>> loss = model(**inputs, labels=labels).loss
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>>> round(loss.item(), 2)
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{expected_loss}
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```
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Example of multi-label classification:
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```python
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>>> import torch
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>>> from transformers import AutoTokenizer, {model_class}
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>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}", problem_type="multi_label_classification")
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>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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>>> with torch.no_grad():
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... logits = model(**inputs).logits
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>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
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>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
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>>> num_labels = len(model.config.id2label)
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>>> model = {model_class}.from_pretrained(
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... "{checkpoint}", num_labels=num_labels, problem_type="multi_label_classification"
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... )
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>>> labels = torch.sum(
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... torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
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... ).to(torch.float)
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>>> loss = model(**inputs, labels=labels).loss
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```
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"""
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PT_MASKED_LM_SAMPLE = r"""
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Example:
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```python
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>>> from transformers import AutoTokenizer, {model_class}
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>>> import torch
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>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")
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>>> with torch.no_grad():
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... logits = model(**inputs).logits
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>>> # retrieve index of {mask}
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>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
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>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
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>>> tokenizer.decode(predicted_token_id)
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{expected_output}
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>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
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>>> # mask labels of non-{mask} tokens
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>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
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>>> outputs = model(**inputs, labels=labels)
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>>> round(outputs.loss.item(), 2)
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{expected_loss}
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```
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"""
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PT_BASE_MODEL_SAMPLE = r"""
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Example:
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```python
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>>> from transformers import AutoTokenizer, {model_class}
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>>> import torch
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>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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>>> outputs = model(**inputs)
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>>> last_hidden_states = outputs.last_hidden_state
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```
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"""
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PT_MULTIPLE_CHOICE_SAMPLE = r"""
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Example:
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```python
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>>> from transformers import AutoTokenizer, {model_class}
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>>> import torch
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>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
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>>> choice0 = "It is eaten with a fork and a knife."
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>>> choice1 = "It is eaten while held in the hand."
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>>> labels = torch.tensor(0).unsqueeze(0) # choice0 is correct (according to Wikipedia ;)), batch size 1
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>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
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>>> outputs = model(**{{k: v.unsqueeze(0) for k, v in encoding.items()}}, labels=labels) # batch size is 1
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>>> # the linear classifier still needs to be trained
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>>> loss = outputs.loss
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>>> logits = outputs.logits
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```
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"""
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PT_CAUSAL_LM_SAMPLE = r"""
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Example:
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```python
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>>> import torch
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>>> from transformers import AutoTokenizer, {model_class}
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>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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>>> outputs = model(**inputs, labels=inputs["input_ids"])
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>>> loss = outputs.loss
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>>> logits = outputs.logits
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```
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"""
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PT_SPEECH_BASE_MODEL_SAMPLE = r"""
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Example:
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```python
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>>> from transformers import AutoProcessor, {model_class}
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>>> import torch
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>>> from datasets import load_dataset
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>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
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>>> dataset = dataset.sort("id")
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>>> sampling_rate = dataset.features["audio"].sampling_rate
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>>> processor = AutoProcessor.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> # audio file is decoded on the fly
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>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
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>>> with torch.no_grad():
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... outputs = model(**inputs)
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>>> last_hidden_states = outputs.last_hidden_state
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>>> list(last_hidden_states.shape)
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{expected_output}
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```
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"""
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PT_SPEECH_CTC_SAMPLE = r"""
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Example:
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```python
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>>> from transformers import AutoProcessor, {model_class}
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>>> from datasets import load_dataset
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>>> import torch
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>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
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>>> dataset = dataset.sort("id")
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>>> sampling_rate = dataset.features["audio"].sampling_rate
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>>> processor = AutoProcessor.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> # audio file is decoded on the fly
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>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
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>>> with torch.no_grad():
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... logits = model(**inputs).logits
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>>> predicted_ids = torch.argmax(logits, dim=-1)
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>>> # transcribe speech
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>>> transcription = processor.batch_decode(predicted_ids)
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>>> transcription[0]
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{expected_output}
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>>> inputs["labels"] = processor(text=dataset[0]["text"], return_tensors="pt").input_ids
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>>> # compute loss
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>>> loss = model(**inputs).loss
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>>> round(loss.item(), 2)
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{expected_loss}
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```
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"""
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PT_SPEECH_SEQ_CLASS_SAMPLE = r"""
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Example:
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```python
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>>> from transformers import AutoFeatureExtractor, {model_class}
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>>> from datasets import load_dataset
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>>> import torch
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>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
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>>> dataset = dataset.sort("id")
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>>> sampling_rate = dataset.features["audio"].sampling_rate
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>>> feature_extractor = AutoFeatureExtractor.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> # audio file is decoded on the fly
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>>> inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
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>>> with torch.no_grad():
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... logits = model(**inputs).logits
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>>> predicted_class_ids = torch.argmax(logits, dim=-1).item()
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>>> predicted_label = model.config.id2label[predicted_class_ids]
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>>> predicted_label
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{expected_output}
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>>> # compute loss - target_label is e.g. "down"
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>>> target_label = model.config.id2label[0]
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>>> inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
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>>> loss = model(**inputs).loss
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>>> round(loss.item(), 2)
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{expected_loss}
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```
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"""
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PT_SPEECH_FRAME_CLASS_SAMPLE = r"""
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Example:
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```python
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>>> from transformers import AutoFeatureExtractor, {model_class}
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>>> from datasets import load_dataset
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>>> import torch
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>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
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>>> dataset = dataset.sort("id")
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>>> sampling_rate = dataset.features["audio"].sampling_rate
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>>> feature_extractor = AutoFeatureExtractor.from_pretrained("{checkpoint}")
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>>> model = {model_class}.from_pretrained("{checkpoint}")
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>>> # audio file is decoded on the fly
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>>> inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=sampling_rate)
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>>> with torch.no_grad():
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... logits = model(**inputs).logits
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>>> probabilities = torch.sigmoid(logits[0])
|
|
>>> # labels is a one-hot array of shape (num_frames, num_speakers)
|
|
>>> labels = (probabilities > 0.5).long()
|
|
>>> labels[0].tolist()
|
|
{expected_output}
|
|
```
|
|
"""
|
|
|
|
|
|
PT_SPEECH_XVECTOR_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoFeatureExtractor, {model_class}
|
|
>>> 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
|
|
|
|
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> # audio file is decoded on the fly
|
|
>>> inputs = feature_extractor(
|
|
... [d["array"] for d in dataset[:2]["audio"]], sampling_rate=sampling_rate, return_tensors="pt", padding=True
|
|
... )
|
|
>>> with torch.no_grad():
|
|
... embeddings = model(**inputs).embeddings
|
|
|
|
>>> embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu()
|
|
|
|
>>> # the resulting embeddings can be used for cosine similarity-based retrieval
|
|
>>> cosine_sim = torch.nn.CosineSimilarity(dim=-1)
|
|
>>> similarity = cosine_sim(embeddings[0], embeddings[1])
|
|
>>> threshold = 0.7 # the optimal threshold is dataset-dependent
|
|
>>> if similarity < threshold:
|
|
... print("Speakers are not the same!")
|
|
>>> round(similarity.item(), 2)
|
|
{expected_output}
|
|
```
|
|
"""
|
|
|
|
PT_VISION_BASE_MODEL_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, {model_class}
|
|
>>> import torch
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> dataset = load_dataset("huggingface/cats-image")
|
|
>>> image = dataset["test"]["image"][0]
|
|
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = image_processor(image, return_tensors="pt")
|
|
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**inputs)
|
|
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
>>> list(last_hidden_states.shape)
|
|
{expected_output}
|
|
```
|
|
"""
|
|
|
|
PT_VISION_SEQ_CLASS_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, {model_class}
|
|
>>> import torch
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> dataset = load_dataset("huggingface/cats-image")
|
|
>>> image = dataset["test"]["image"][0]
|
|
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = image_processor(image, return_tensors="pt")
|
|
|
|
>>> with torch.no_grad():
|
|
... logits = model(**inputs).logits
|
|
|
|
>>> # model predicts one of the 1000 ImageNet classes
|
|
>>> predicted_label = logits.argmax(-1).item()
|
|
>>> print(model.config.id2label[predicted_label])
|
|
{expected_output}
|
|
```
|
|
"""
|
|
|
|
|
|
PT_SAMPLE_DOCSTRINGS = {
|
|
"SequenceClassification": PT_SEQUENCE_CLASSIFICATION_SAMPLE,
|
|
"QuestionAnswering": PT_QUESTION_ANSWERING_SAMPLE,
|
|
"TokenClassification": PT_TOKEN_CLASSIFICATION_SAMPLE,
|
|
"MultipleChoice": PT_MULTIPLE_CHOICE_SAMPLE,
|
|
"MaskedLM": PT_MASKED_LM_SAMPLE,
|
|
"LMHead": PT_CAUSAL_LM_SAMPLE,
|
|
"BaseModel": PT_BASE_MODEL_SAMPLE,
|
|
"SpeechBaseModel": PT_SPEECH_BASE_MODEL_SAMPLE,
|
|
"CTC": PT_SPEECH_CTC_SAMPLE,
|
|
"AudioClassification": PT_SPEECH_SEQ_CLASS_SAMPLE,
|
|
"AudioFrameClassification": PT_SPEECH_FRAME_CLASS_SAMPLE,
|
|
"AudioXVector": PT_SPEECH_XVECTOR_SAMPLE,
|
|
"VisionBaseModel": PT_VISION_BASE_MODEL_SAMPLE,
|
|
"ImageClassification": PT_VISION_SEQ_CLASS_SAMPLE,
|
|
}
|
|
|
|
|
|
TF_TOKEN_CLASSIFICATION_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
>>> import tensorflow as tf
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer(
|
|
... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
|
|
... )
|
|
|
|
>>> logits = model(**inputs).logits
|
|
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)
|
|
|
|
>>> # Note that tokens are classified rather then input words which means that
|
|
>>> # there might be more predicted token classes than words.
|
|
>>> # Multiple token classes might account for the same word
|
|
>>> predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
|
|
>>> predicted_tokens_classes
|
|
{expected_output}
|
|
```
|
|
|
|
```python
|
|
>>> labels = predicted_token_class_ids
|
|
>>> loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)
|
|
>>> round(float(loss), 2)
|
|
{expected_loss}
|
|
```
|
|
"""
|
|
|
|
TF_QUESTION_ANSWERING_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
>>> import tensorflow as tf
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
|
|
|
>>> inputs = tokenizer(question, text, return_tensors="tf")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
|
|
>>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
|
|
|
|
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
|
|
>>> tokenizer.decode(predict_answer_tokens)
|
|
{expected_output}
|
|
```
|
|
|
|
```python
|
|
>>> # target is "nice puppet"
|
|
>>> target_start_index = tf.constant([{qa_target_start_index}])
|
|
>>> target_end_index = tf.constant([{qa_target_end_index}])
|
|
|
|
>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
|
|
>>> loss = tf.math.reduce_mean(outputs.loss)
|
|
>>> round(float(loss), 2)
|
|
{expected_loss}
|
|
```
|
|
"""
|
|
|
|
TF_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
>>> import tensorflow as tf
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
|
|
|
|
>>> logits = model(**inputs).logits
|
|
|
|
>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
|
|
>>> model.config.id2label[predicted_class_id]
|
|
{expected_output}
|
|
```
|
|
|
|
```python
|
|
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
|
|
>>> num_labels = len(model.config.id2label)
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}", num_labels=num_labels)
|
|
|
|
>>> labels = tf.constant(1)
|
|
>>> loss = model(**inputs, labels=labels).loss
|
|
>>> round(float(loss), 2)
|
|
{expected_loss}
|
|
```
|
|
"""
|
|
|
|
TF_MASKED_LM_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
>>> import tensorflow as tf
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="tf")
|
|
>>> logits = model(**inputs).logits
|
|
|
|
>>> # retrieve index of {mask}
|
|
>>> mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[0])
|
|
>>> selected_logits = tf.gather_nd(logits[0], indices=mask_token_index)
|
|
|
|
>>> predicted_token_id = tf.math.argmax(selected_logits, axis=-1)
|
|
>>> tokenizer.decode(predicted_token_id)
|
|
{expected_output}
|
|
```
|
|
|
|
```python
|
|
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
|
|
>>> # mask labels of non-{mask} tokens
|
|
>>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
|
|
|
|
>>> outputs = model(**inputs, labels=labels)
|
|
>>> round(float(outputs.loss), 2)
|
|
{expected_loss}
|
|
```
|
|
"""
|
|
|
|
TF_BASE_MODEL_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
>>> import tensorflow as tf
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
|
|
>>> outputs = model(inputs)
|
|
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
```
|
|
"""
|
|
|
|
TF_MULTIPLE_CHOICE_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
>>> import tensorflow as tf
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
|
|
>>> choice0 = "It is eaten with a fork and a knife."
|
|
>>> choice1 = "It is eaten while held in the hand."
|
|
|
|
>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
|
|
>>> inputs = {{k: tf.expand_dims(v, 0) for k, v in encoding.items()}}
|
|
>>> outputs = model(inputs) # batch size is 1
|
|
|
|
>>> # the linear classifier still needs to be trained
|
|
>>> logits = outputs.logits
|
|
```
|
|
"""
|
|
|
|
TF_CAUSAL_LM_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
>>> import tensorflow as tf
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
|
|
>>> outputs = model(inputs)
|
|
>>> logits = outputs.logits
|
|
```
|
|
"""
|
|
|
|
TF_SPEECH_BASE_MODEL_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, {model_class}
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
|
|
>>> dataset = dataset.sort("id")
|
|
>>> sampling_rate = dataset.features["audio"].sampling_rate
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> # audio file is decoded on the fly
|
|
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="tf")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
>>> list(last_hidden_states.shape)
|
|
{expected_output}
|
|
```
|
|
"""
|
|
|
|
TF_SPEECH_CTC_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, {model_class}
|
|
>>> from datasets import load_dataset
|
|
>>> import tensorflow as tf
|
|
|
|
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
|
|
>>> dataset = dataset.sort("id")
|
|
>>> sampling_rate = dataset.features["audio"].sampling_rate
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> # audio file is decoded on the fly
|
|
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="tf")
|
|
>>> logits = model(**inputs).logits
|
|
>>> predicted_ids = tf.math.argmax(logits, axis=-1)
|
|
|
|
>>> # transcribe speech
|
|
>>> transcription = processor.batch_decode(predicted_ids)
|
|
>>> transcription[0]
|
|
{expected_output}
|
|
```
|
|
|
|
```python
|
|
>>> inputs["labels"] = processor(text=dataset[0]["text"], return_tensors="tf").input_ids
|
|
|
|
>>> # compute loss
|
|
>>> loss = model(**inputs).loss
|
|
>>> round(float(loss), 2)
|
|
{expected_loss}
|
|
```
|
|
"""
|
|
|
|
TF_VISION_BASE_MODEL_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, {model_class}
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> dataset = load_dataset("huggingface/cats-image")
|
|
>>> image = dataset["test"]["image"][0]
|
|
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = image_processor(image, return_tensors="tf")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
>>> list(last_hidden_states.shape)
|
|
{expected_output}
|
|
```
|
|
"""
|
|
|
|
TF_VISION_SEQ_CLASS_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, {model_class}
|
|
>>> import tensorflow as tf
|
|
>>> from datasets import load_dataset
|
|
|
|
>>> dataset = load_dataset("huggingface/cats-image")
|
|
>>> image = dataset["test"]["image"][0]
|
|
|
|
>>> image_processor = AutoImageProcessor.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = image_processor(image, return_tensors="tf")
|
|
>>> logits = model(**inputs).logits
|
|
|
|
>>> # model predicts one of the 1000 ImageNet classes
|
|
>>> predicted_label = int(tf.math.argmax(logits, axis=-1))
|
|
>>> print(model.config.id2label[predicted_label])
|
|
{expected_output}
|
|
```
|
|
"""
|
|
|
|
TF_SAMPLE_DOCSTRINGS = {
|
|
"SequenceClassification": TF_SEQUENCE_CLASSIFICATION_SAMPLE,
|
|
"QuestionAnswering": TF_QUESTION_ANSWERING_SAMPLE,
|
|
"TokenClassification": TF_TOKEN_CLASSIFICATION_SAMPLE,
|
|
"MultipleChoice": TF_MULTIPLE_CHOICE_SAMPLE,
|
|
"MaskedLM": TF_MASKED_LM_SAMPLE,
|
|
"LMHead": TF_CAUSAL_LM_SAMPLE,
|
|
"BaseModel": TF_BASE_MODEL_SAMPLE,
|
|
"SpeechBaseModel": TF_SPEECH_BASE_MODEL_SAMPLE,
|
|
"CTC": TF_SPEECH_CTC_SAMPLE,
|
|
"VisionBaseModel": TF_VISION_BASE_MODEL_SAMPLE,
|
|
"ImageClassification": TF_VISION_SEQ_CLASS_SAMPLE,
|
|
}
|
|
|
|
|
|
FLAX_TOKEN_CLASSIFICATION_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> logits = outputs.logits
|
|
```
|
|
"""
|
|
|
|
FLAX_QUESTION_ANSWERING_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
|
>>> inputs = tokenizer(question, text, return_tensors="jax")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> start_scores = outputs.start_logits
|
|
>>> end_scores = outputs.end_logits
|
|
```
|
|
"""
|
|
|
|
FLAX_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> logits = outputs.logits
|
|
```
|
|
"""
|
|
|
|
FLAX_MASKED_LM_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="jax")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> logits = outputs.logits
|
|
```
|
|
"""
|
|
|
|
FLAX_BASE_MODEL_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> last_hidden_states = outputs.last_hidden_state
|
|
```
|
|
"""
|
|
|
|
FLAX_MULTIPLE_CHOICE_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
|
|
>>> choice0 = "It is eaten with a fork and a knife."
|
|
>>> choice1 = "It is eaten while held in the hand."
|
|
|
|
>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="jax", padding=True)
|
|
>>> outputs = model(**{{k: v[None, :] for k, v in encoding.items()}})
|
|
|
|
>>> logits = outputs.logits
|
|
```
|
|
"""
|
|
|
|
FLAX_CAUSAL_LM_SAMPLE = r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, {model_class}
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("{checkpoint}")
|
|
>>> model = {model_class}.from_pretrained("{checkpoint}")
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> # retrieve logts for next token
|
|
>>> next_token_logits = outputs.logits[:, -1]
|
|
```
|
|
"""
|
|
|
|
FLAX_SAMPLE_DOCSTRINGS = {
|
|
"SequenceClassification": FLAX_SEQUENCE_CLASSIFICATION_SAMPLE,
|
|
"QuestionAnswering": FLAX_QUESTION_ANSWERING_SAMPLE,
|
|
"TokenClassification": FLAX_TOKEN_CLASSIFICATION_SAMPLE,
|
|
"MultipleChoice": FLAX_MULTIPLE_CHOICE_SAMPLE,
|
|
"MaskedLM": FLAX_MASKED_LM_SAMPLE,
|
|
"BaseModel": FLAX_BASE_MODEL_SAMPLE,
|
|
"LMHead": FLAX_CAUSAL_LM_SAMPLE,
|
|
}
|
|
|
|
|
|
def filter_outputs_from_example(docstring, **kwargs):
|
|
"""
|
|
Removes the lines testing an output with the doctest syntax in a code sample when it's set to `None`.
|
|
"""
|
|
for key, value in kwargs.items():
|
|
if value is not None:
|
|
continue
|
|
|
|
doc_key = "{" + key + "}"
|
|
docstring = re.sub(rf"\n([^\n]+)\n\s+{doc_key}\n", "\n", docstring)
|
|
|
|
return docstring
|
|
|
|
|
|
def add_code_sample_docstrings(
|
|
*docstr,
|
|
processor_class=None,
|
|
checkpoint=None,
|
|
output_type=None,
|
|
config_class=None,
|
|
mask="[MASK]",
|
|
qa_target_start_index=14,
|
|
qa_target_end_index=15,
|
|
model_cls=None,
|
|
modality=None,
|
|
expected_output=None,
|
|
expected_loss=None,
|
|
real_checkpoint=None,
|
|
revision=None,
|
|
):
|
|
def docstring_decorator(fn):
|
|
# model_class defaults to function's class if not specified otherwise
|
|
model_class = fn.__qualname__.split(".")[0] if model_cls is None else model_cls
|
|
|
|
if model_class[:2] == "TF":
|
|
sample_docstrings = TF_SAMPLE_DOCSTRINGS
|
|
elif model_class[:4] == "Flax":
|
|
sample_docstrings = FLAX_SAMPLE_DOCSTRINGS
|
|
else:
|
|
sample_docstrings = PT_SAMPLE_DOCSTRINGS
|
|
|
|
# putting all kwargs for docstrings in a dict to be used
|
|
# with the `.format(**doc_kwargs)`. Note that string might
|
|
# be formatted with non-existing keys, which is fine.
|
|
doc_kwargs = {
|
|
"model_class": model_class,
|
|
"processor_class": processor_class,
|
|
"checkpoint": checkpoint,
|
|
"mask": mask,
|
|
"qa_target_start_index": qa_target_start_index,
|
|
"qa_target_end_index": qa_target_end_index,
|
|
"expected_output": expected_output,
|
|
"expected_loss": expected_loss,
|
|
"real_checkpoint": real_checkpoint,
|
|
"fake_checkpoint": checkpoint,
|
|
"true": "{true}", # For <Tip warning={true}> syntax that conflicts with formatting.
|
|
}
|
|
|
|
if ("SequenceClassification" in model_class or "AudioClassification" in model_class) and modality == "audio":
|
|
code_sample = sample_docstrings["AudioClassification"]
|
|
elif "SequenceClassification" in model_class:
|
|
code_sample = sample_docstrings["SequenceClassification"]
|
|
elif "QuestionAnswering" in model_class:
|
|
code_sample = sample_docstrings["QuestionAnswering"]
|
|
elif "TokenClassification" in model_class:
|
|
code_sample = sample_docstrings["TokenClassification"]
|
|
elif "MultipleChoice" in model_class:
|
|
code_sample = sample_docstrings["MultipleChoice"]
|
|
elif "MaskedLM" in model_class or model_class in ["FlaubertWithLMHeadModel", "XLMWithLMHeadModel"]:
|
|
code_sample = sample_docstrings["MaskedLM"]
|
|
elif "LMHead" in model_class or "CausalLM" in model_class:
|
|
code_sample = sample_docstrings["LMHead"]
|
|
elif "CTC" in model_class:
|
|
code_sample = sample_docstrings["CTC"]
|
|
elif "AudioFrameClassification" in model_class:
|
|
code_sample = sample_docstrings["AudioFrameClassification"]
|
|
elif "XVector" in model_class and modality == "audio":
|
|
code_sample = sample_docstrings["AudioXVector"]
|
|
elif "Model" in model_class and modality == "audio":
|
|
code_sample = sample_docstrings["SpeechBaseModel"]
|
|
elif "Model" in model_class and modality == "vision":
|
|
code_sample = sample_docstrings["VisionBaseModel"]
|
|
elif "Model" in model_class or "Encoder" in model_class:
|
|
code_sample = sample_docstrings["BaseModel"]
|
|
elif "ImageClassification" in model_class:
|
|
code_sample = sample_docstrings["ImageClassification"]
|
|
else:
|
|
raise ValueError(f"Docstring can't be built for model {model_class}")
|
|
|
|
code_sample = filter_outputs_from_example(
|
|
code_sample, expected_output=expected_output, expected_loss=expected_loss
|
|
)
|
|
if real_checkpoint is not None:
|
|
code_sample = FAKE_MODEL_DISCLAIMER + code_sample
|
|
func_doc = (fn.__doc__ or "") + "".join(docstr)
|
|
output_doc = "" if output_type is None else _prepare_output_docstrings(output_type, config_class)
|
|
built_doc = code_sample.format(**doc_kwargs)
|
|
if revision is not None:
|
|
if re.match(r"^refs/pr/\\d+", revision):
|
|
raise ValueError(
|
|
f"The provided revision '{revision}' is incorrect. It should point to"
|
|
" a pull request reference on the hub like 'refs/pr/6'"
|
|
)
|
|
built_doc = built_doc.replace(
|
|
f'from_pretrained("{checkpoint}")', f'from_pretrained("{checkpoint}", revision="{revision}")'
|
|
)
|
|
fn.__doc__ = func_doc + output_doc + built_doc
|
|
return fn
|
|
|
|
return docstring_decorator
|
|
|
|
|
|
def replace_return_docstrings(output_type=None, config_class=None):
|
|
def docstring_decorator(fn):
|
|
func_doc = fn.__doc__
|
|
lines = func_doc.split("\n")
|
|
i = 0
|
|
while i < len(lines) and re.search(r"^\s*Returns?:\s*$", lines[i]) is None:
|
|
i += 1
|
|
if i < len(lines):
|
|
indent = len(_get_indent(lines[i]))
|
|
lines[i] = _prepare_output_docstrings(output_type, config_class, min_indent=indent)
|
|
func_doc = "\n".join(lines)
|
|
else:
|
|
raise ValueError(
|
|
f"The function {fn} should have an empty 'Return:' or 'Returns:' in its docstring as placeholder, "
|
|
f"current docstring is:\n{func_doc}"
|
|
)
|
|
fn.__doc__ = func_doc
|
|
return fn
|
|
|
|
return docstring_decorator
|
|
|
|
|
|
def copy_func(f):
|
|
"""Returns a copy of a function f."""
|
|
# Based on http://stackoverflow.com/a/6528148/190597 (Glenn Maynard)
|
|
g = types.FunctionType(f.__code__, f.__globals__, name=f.__name__, argdefs=f.__defaults__, closure=f.__closure__)
|
|
g = functools.update_wrapper(g, f)
|
|
g.__kwdefaults__ = f.__kwdefaults__
|
|
return g
|