# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections import csv import importlib import json import os import pickle import sys import traceback import types import warnings from abc import ABC, abstractmethod from collections import UserDict from contextlib import contextmanager from os.path import abspath, exists from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union from ..dynamic_module_utils import custom_object_save from ..feature_extraction_utils import PreTrainedFeatureExtractor from ..image_processing_utils import BaseImageProcessor from ..modelcard import ModelCard from ..models.auto.configuration_auto import AutoConfig from ..tokenization_utils import PreTrainedTokenizer from ..utils import ModelOutput, add_end_docstrings, infer_framework, is_tf_available, is_torch_available, logging GenericTensor = Union[List["GenericTensor"], "torch.Tensor", "tf.Tensor"] if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import TFAutoModel if is_torch_available(): import torch from torch.utils.data import DataLoader, Dataset from ..models.auto.modeling_auto import AutoModel # Re-export for backward compatibility from .pt_utils import KeyDataset else: Dataset = None KeyDataset = None if TYPE_CHECKING: from ..modeling_tf_utils import TFPreTrainedModel from ..modeling_utils import PreTrainedModel logger = logging.get_logger(__name__) def no_collate_fn(items): if len(items) != 1: raise ValueError("This collate_fn is meant to be used with batch_size=1") return items[0] def _pad(items, key, padding_value, padding_side): batch_size = len(items) if isinstance(items[0][key], torch.Tensor): # Others include `attention_mask` etc... shape = items[0][key].shape dim = len(shape) if key in ["pixel_values", "image"]: # This is probable image so padding shouldn't be necessary # B, C, H, W return torch.cat([item[key] for item in items], dim=0) elif dim == 4 and key == "input_features": # this is probably a mel spectrogram batched return torch.cat([item[key] for item in items], dim=0) max_length = max(item[key].shape[1] for item in items) min_length = min(item[key].shape[1] for item in items) dtype = items[0][key].dtype if dim == 2: if max_length == min_length: # Bypass for `ImageGPT` which doesn't provide a padding value, yet # we can consistently pad since the size should be matching return torch.cat([item[key] for item in items], dim=0) tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value elif dim == 3: tensor = torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype) + padding_value elif dim == 4: tensor = torch.zeros((batch_size, max_length, shape[-2], shape[-1]), dtype=dtype) + padding_value for i, item in enumerate(items): if dim == 2: if padding_side == "left": tensor[i, -len(item[key][0]) :] = item[key][0].clone() else: tensor[i, : len(item[key][0])] = item[key][0].clone() elif dim == 3: if padding_side == "left": tensor[i, -len(item[key][0]) :, :] = item[key][0].clone() else: tensor[i, : len(item[key][0]), :] = item[key][0].clone() elif dim == 4: if padding_side == "left": tensor[i, -len(item[key][0]) :, :, :] = item[key][0].clone() else: tensor[i, : len(item[key][0]), :, :] = item[key][0].clone() return tensor else: return [item[key] for item in items] def pad_collate_fn(tokenizer, feature_extractor): # Tokenizer t_padding_side = None # Feature extractor f_padding_side = None if tokenizer is None and feature_extractor is None: raise ValueError("Pipeline without tokenizer or feature_extractor cannot do batching") if tokenizer is not None: if tokenizer.pad_token_id is None: raise ValueError( "Pipeline with tokenizer without pad_token cannot do batching. You can try to set it with " "`pipe.tokenizer.pad_token_id = model.config.eos_token_id`." ) else: t_padding_value = tokenizer.pad_token_id t_padding_side = tokenizer.padding_side if feature_extractor is not None: # Feature extractor can be images, where no padding is expected f_padding_value = getattr(feature_extractor, "padding_value", None) f_padding_side = getattr(feature_extractor, "padding_side", None) if t_padding_side is not None and f_padding_side is not None and t_padding_side != f_padding_side: raise ValueError( f"The feature extractor, and tokenizer don't agree on padding side {t_padding_side} != {f_padding_side}" ) padding_side = "right" if t_padding_side is not None: padding_side = t_padding_side if f_padding_side is not None: padding_side = f_padding_side def inner(items): keys = set(items[0].keys()) for item in items: if set(item.keys()) != keys: raise ValueError( f"The elements of the batch contain different keys. Cannot batch them ({set(item.keys())} !=" f" {keys})" ) # input_values, input_pixels, input_ids, ... padded = {} for key in keys: if key in {"input_ids"}: # ImageGPT uses a feature extractor if tokenizer is None and feature_extractor is not None: _padding_value = f_padding_value else: _padding_value = t_padding_value elif key in {"input_values", "pixel_values", "input_features"}: _padding_value = f_padding_value elif key in {"p_mask", "special_tokens_mask"}: _padding_value = 1 elif key in {"attention_mask", "token_type_ids"}: _padding_value = 0 else: # This is likely another random key maybe even user provided _padding_value = 0 padded[key] = _pad(items, key, _padding_value, padding_side) return padded return inner def infer_framework_load_model( model, config: AutoConfig, model_classes: Optional[Dict[str, Tuple[type]]] = None, task: Optional[str] = None, framework: Optional[str] = None, **model_kwargs, ): """ Select framework (TensorFlow or PyTorch) to use from the `model` passed. Returns a tuple (framework, model). If `model` is instantiated, this function will just infer the framework from the model class. Otherwise `model` is actually a checkpoint name and this method will try to instantiate it using `model_classes`. Since we don't want to instantiate the model twice, this model is returned for use by the pipeline. If both frameworks are installed and available for `model`, PyTorch is selected. Args: model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel`]): The model to infer the framework from. If `str`, a checkpoint name. The model to infer the framewrok from. config ([`AutoConfig`]): The config associated with the model to help using the correct class model_classes (dictionary `str` to `type`, *optional*): A mapping framework to class. task (`str`): The task defining which pipeline will be returned. model_kwargs: Additional dictionary of keyword arguments passed along to the model's `from_pretrained(..., **model_kwargs)` function. Returns: `Tuple`: A tuple framework, model. """ if not is_tf_available() and not is_torch_available(): raise RuntimeError( "At least one of TensorFlow 2.0 or PyTorch should be installed. " "To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ " "To install PyTorch, read the instructions at https://pytorch.org/." ) if isinstance(model, str): model_kwargs["_from_pipeline"] = task class_tuple = () look_pt = is_torch_available() and framework in {"pt", None} look_tf = is_tf_available() and framework in {"tf", None} if model_classes: if look_pt: class_tuple = class_tuple + model_classes.get("pt", (AutoModel,)) if look_tf: class_tuple = class_tuple + model_classes.get("tf", (TFAutoModel,)) if config.architectures: classes = [] for architecture in config.architectures: transformers_module = importlib.import_module("transformers") if look_pt: _class = getattr(transformers_module, architecture, None) if _class is not None: classes.append(_class) if look_tf: _class = getattr(transformers_module, f"TF{architecture}", None) if _class is not None: classes.append(_class) class_tuple = class_tuple + tuple(classes) if len(class_tuple) == 0: raise ValueError(f"Pipeline cannot infer suitable model classes from {model}") all_traceback = {} for model_class in class_tuple: kwargs = model_kwargs.copy() if framework == "pt" and model.endswith(".h5"): kwargs["from_tf"] = True logger.warning( "Model might be a TensorFlow model (ending with `.h5`) but TensorFlow is not available. " "Trying to load the model with PyTorch." ) elif framework == "tf" and model.endswith(".bin"): kwargs["from_pt"] = True logger.warning( "Model might be a PyTorch model (ending with `.bin`) but PyTorch is not available. " "Trying to load the model with Tensorflow." ) try: model = model_class.from_pretrained(model, **kwargs) if hasattr(model, "eval"): model = model.eval() # Stop loading on the first successful load. break except (OSError, ValueError): all_traceback[model_class.__name__] = traceback.format_exc() continue if isinstance(model, str): error = "" for class_name, trace in all_traceback.items(): error += f"while loading with {class_name}, an error is thrown:\n{trace}\n" raise ValueError( f"Could not load model {model} with any of the following classes: {class_tuple}. See the original errors:\n\n{error}\n" ) if framework is None: framework = infer_framework(model.__class__) return framework, model def infer_framework_from_model( model, model_classes: Optional[Dict[str, Tuple[type]]] = None, task: Optional[str] = None, framework: Optional[str] = None, **model_kwargs, ): """ Select framework (TensorFlow or PyTorch) to use from the `model` passed. Returns a tuple (framework, model). If `model` is instantiated, this function will just infer the framework from the model class. Otherwise `model` is actually a checkpoint name and this method will try to instantiate it using `model_classes`. Since we don't want to instantiate the model twice, this model is returned for use by the pipeline. If both frameworks are installed and available for `model`, PyTorch is selected. Args: model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel`]): The model to infer the framework from. If `str`, a checkpoint name. The model to infer the framewrok from. model_classes (dictionary `str` to `type`, *optional*): A mapping framework to class. task (`str`): The task defining which pipeline will be returned. model_kwargs: Additional dictionary of keyword arguments passed along to the model's `from_pretrained(..., **model_kwargs)` function. Returns: `Tuple`: A tuple framework, model. """ if isinstance(model, str): config = AutoConfig.from_pretrained(model, _from_pipeline=task, **model_kwargs) else: config = model.config return infer_framework_load_model( model, config, model_classes=model_classes, _from_pipeline=task, task=task, framework=framework, **model_kwargs ) def get_framework(model, revision: Optional[str] = None): """ Select framework (TensorFlow or PyTorch) to use. Args: model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel`]): If both frameworks are installed, picks the one corresponding to the model passed (either a model class or the model name). If no specific model is provided, defaults to using PyTorch. """ warnings.warn( "`get_framework` is deprecated and will be removed in v5, use `infer_framework_from_model` instead.", FutureWarning, ) if not is_tf_available() and not is_torch_available(): raise RuntimeError( "At least one of TensorFlow 2.0 or PyTorch should be installed. " "To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ " "To install PyTorch, read the instructions at https://pytorch.org/." ) if isinstance(model, str): if is_torch_available() and not is_tf_available(): model = AutoModel.from_pretrained(model, revision=revision) elif is_tf_available() and not is_torch_available(): model = TFAutoModel.from_pretrained(model, revision=revision) else: try: model = AutoModel.from_pretrained(model, revision=revision) except OSError: model = TFAutoModel.from_pretrained(model, revision=revision) framework = infer_framework(model.__class__) return framework def get_default_model_and_revision( targeted_task: Dict, framework: Optional[str], task_options: Optional[Any] ) -> Union[str, Tuple[str, str]]: """ Select a default model to use for a given task. Defaults to pytorch if ambiguous. Args: targeted_task (`Dict` ): Dictionary representing the given task, that should contain default models framework (`str`, None) "pt", "tf" or None, representing a specific framework if it was specified, or None if we don't know yet. task_options (`Any`, None) Any further value required by the task to get fully specified, for instance (SRC, TGT) languages for translation task. Returns `str` The model string representing the default model for this pipeline """ if is_torch_available() and not is_tf_available(): framework = "pt" elif is_tf_available() and not is_torch_available(): framework = "tf" defaults = targeted_task["default"] if task_options: if task_options not in defaults: raise ValueError(f"The task does not provide any default models for options {task_options}") default_models = defaults[task_options]["model"] elif "model" in defaults: default_models = targeted_task["default"]["model"] else: # XXX This error message needs to be updated to be more generic if more tasks are going to become # parametrized raise ValueError('The task defaults can\'t be correctly selected. You probably meant "translation_XX_to_YY"') if framework is None: framework = "pt" return default_models[framework] class PipelineException(Exception): """ Raised by a [`Pipeline`] when handling __call__. Args: task (`str`): The task of the pipeline. model (`str`): The model used by the pipeline. reason (`str`): The error message to display. """ def __init__(self, task: str, model: str, reason: str): super().__init__(reason) self.task = task self.model = model class ArgumentHandler(ABC): """ Base interface for handling arguments for each [`~pipelines.Pipeline`]. """ @abstractmethod def __call__(self, *args, **kwargs): raise NotImplementedError() class PipelineDataFormat: """ Base class for all the pipeline supported data format both for reading and writing. Supported data formats currently includes: - JSON - CSV - stdin/stdout (pipe) `PipelineDataFormat` also includes some utilities to work with multi-columns like mapping from datasets columns to pipelines keyword arguments through the `dataset_kwarg_1=dataset_column_1` format. Args: output_path (`str`): Where to save the outgoing data. input_path (`str`): Where to look for the input data. column (`str`): The column to read. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the `output_path`. """ SUPPORTED_FORMATS = ["json", "csv", "pipe"] def __init__( self, output_path: Optional[str], input_path: Optional[str], column: Optional[str], overwrite: bool = False, ): self.output_path = output_path self.input_path = input_path self.column = column.split(",") if column is not None else [""] self.is_multi_columns = len(self.column) > 1 if self.is_multi_columns: self.column = [tuple(c.split("=")) if "=" in c else (c, c) for c in self.column] if output_path is not None and not overwrite: if exists(abspath(self.output_path)): raise OSError(f"{self.output_path} already exists on disk") if input_path is not None: if not exists(abspath(self.input_path)): raise OSError(f"{self.input_path} doesnt exist on disk") @abstractmethod def __iter__(self): raise NotImplementedError() @abstractmethod def save(self, data: Union[dict, List[dict]]): """ Save the provided data object with the representation for the current [`~pipelines.PipelineDataFormat`]. Args: data (`dict` or list of `dict`): The data to store. """ raise NotImplementedError() def save_binary(self, data: Union[dict, List[dict]]) -> str: """ Save the provided data object as a pickle-formatted binary data on the disk. Args: data (`dict` or list of `dict`): The data to store. Returns: `str`: Path where the data has been saved. """ path, _ = os.path.splitext(self.output_path) binary_path = os.path.extsep.join((path, "pickle")) with open(binary_path, "wb+") as f_output: pickle.dump(data, f_output) return binary_path @staticmethod def from_str( format: str, output_path: Optional[str], input_path: Optional[str], column: Optional[str], overwrite=False, ) -> "PipelineDataFormat": """ Creates an instance of the right subclass of [`~pipelines.PipelineDataFormat`] depending on `format`. Args: format (`str`): The format of the desired pipeline. Acceptable values are `"json"`, `"csv"` or `"pipe"`. output_path (`str`, *optional*): Where to save the outgoing data. input_path (`str`, *optional*): Where to look for the input data. column (`str`, *optional*): The column to read. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the `output_path`. Returns: [`~pipelines.PipelineDataFormat`]: The proper data format. """ if format == "json": return JsonPipelineDataFormat(output_path, input_path, column, overwrite=overwrite) elif format == "csv": return CsvPipelineDataFormat(output_path, input_path, column, overwrite=overwrite) elif format == "pipe": return PipedPipelineDataFormat(output_path, input_path, column, overwrite=overwrite) else: raise KeyError(f"Unknown reader {format} (Available reader are json/csv/pipe)") class CsvPipelineDataFormat(PipelineDataFormat): """ Support for pipelines using CSV data format. Args: output_path (`str`): Where to save the outgoing data. input_path (`str`): Where to look for the input data. column (`str`): The column to read. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the `output_path`. """ def __init__( self, output_path: Optional[str], input_path: Optional[str], column: Optional[str], overwrite=False, ): super().__init__(output_path, input_path, column, overwrite=overwrite) def __iter__(self): with open(self.input_path, "r") as f: reader = csv.DictReader(f) for row in reader: if self.is_multi_columns: yield {k: row[c] for k, c in self.column} else: yield row[self.column[0]] def save(self, data: List[dict]): """ Save the provided data object with the representation for the current [`~pipelines.PipelineDataFormat`]. Args: data (`List[dict]`): The data to store. """ with open(self.output_path, "w") as f: if len(data) > 0: writer = csv.DictWriter(f, list(data[0].keys())) writer.writeheader() writer.writerows(data) class JsonPipelineDataFormat(PipelineDataFormat): """ Support for pipelines using JSON file format. Args: output_path (`str`): Where to save the outgoing data. input_path (`str`): Where to look for the input data. column (`str`): The column to read. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the `output_path`. """ def __init__( self, output_path: Optional[str], input_path: Optional[str], column: Optional[str], overwrite=False, ): super().__init__(output_path, input_path, column, overwrite=overwrite) with open(input_path, "r") as f: self._entries = json.load(f) def __iter__(self): for entry in self._entries: if self.is_multi_columns: yield {k: entry[c] for k, c in self.column} else: yield entry[self.column[0]] def save(self, data: dict): """ Save the provided data object in a json file. Args: data (`dict`): The data to store. """ with open(self.output_path, "w") as f: json.dump(data, f) class PipedPipelineDataFormat(PipelineDataFormat): """ Read data from piped input to the python process. For multi columns data, columns should separated by \t If columns are provided, then the output will be a dictionary with {column_x: value_x} Args: output_path (`str`): Where to save the outgoing data. input_path (`str`): Where to look for the input data. column (`str`): The column to read. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the `output_path`. """ def __iter__(self): for line in sys.stdin: # Split for multi-columns if "\t" in line: line = line.split("\t") if self.column: # Dictionary to map arguments yield {kwargs: l for (kwargs, _), l in zip(self.column, line)} else: yield tuple(line) # No dictionary to map arguments else: yield line def save(self, data: dict): """ Print the data. Args: data (`dict`): The data to store. """ print(data) def save_binary(self, data: Union[dict, List[dict]]) -> str: if self.output_path is None: raise KeyError( "When using piped input on pipeline outputting large object requires an output file path. " "Please provide such output path through --output argument." ) return super().save_binary(data) class _ScikitCompat(ABC): """ Interface layer for the Scikit and Keras compatibility. """ @abstractmethod def transform(self, X): raise NotImplementedError() @abstractmethod def predict(self, X): raise NotImplementedError() PIPELINE_INIT_ARGS = r""" Arguments: model ([`PreTrainedModel`] or [`TFPreTrainedModel`]): The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow. tokenizer ([`PreTrainedTokenizer`]): The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from [`PreTrainedTokenizer`]. modelcard (`str` or [`ModelCard`], *optional*): Model card attributed to the model for this pipeline. framework (`str`, *optional*): The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be installed. If no framework is specified, will default to the one currently installed. If no framework is specified and both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is provided. task (`str`, defaults to `""`): A task-identifier for the pipeline. num_workers (`int`, *optional*, defaults to 8): When the pipeline will use *DataLoader* (when passing a dataset, on GPU for a Pytorch model), the number of workers to be used. batch_size (`int`, *optional*, defaults to 1): When the pipeline will use *DataLoader* (when passing a dataset, on GPU for a Pytorch model), the size of the batch to use, for inference this is not always beneficial, please read [Batching with pipelines](https://huggingface.co./transformers/main_classes/pipelines.html#pipeline-batching) . args_parser ([`~pipelines.ArgumentHandler`], *optional*): Reference to the object in charge of parsing supplied pipeline parameters. device (`int`, *optional*, defaults to -1): Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on the associated CUDA device id. You can pass native `torch.device` or a `str` too. binary_output (`bool`, *optional*, defaults to `False`): Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text. """ if is_torch_available(): from transformers.pipelines.pt_utils import ( PipelineChunkIterator, PipelineDataset, PipelineIterator, PipelinePackIterator, ) @add_end_docstrings(PIPELINE_INIT_ARGS) class Pipeline(_ScikitCompat): """ The Pipeline class is the class from which all pipelines inherit. Refer to this class for methods shared across different pipelines. Base class implementing pipelined operations. Pipeline workflow is defined as a sequence of the following operations: Input -> Tokenization -> Model Inference -> Post-Processing (task dependent) -> Output Pipeline supports running on CPU or GPU through the device argument (see below). Some pipeline, like for instance [`FeatureExtractionPipeline`] (`'feature-extraction'`) output large tensor object as nested-lists. In order to avoid dumping such large structure as textual data we provide the `binary_output` constructor argument. If set to `True`, the output will be stored in the pickle format. """ default_input_names = None def __init__( self, model: Union["PreTrainedModel", "TFPreTrainedModel"], tokenizer: Optional[PreTrainedTokenizer] = None, feature_extractor: Optional[PreTrainedFeatureExtractor] = None, image_processor: Optional[BaseImageProcessor] = None, modelcard: Optional[ModelCard] = None, framework: Optional[str] = None, task: str = "", args_parser: ArgumentHandler = None, device: Union[int, "torch.device"] = None, torch_dtype: Optional[Union[str, "torch.dtype"]] = None, binary_output: bool = False, **kwargs, ): if framework is None: framework, model = infer_framework_load_model(model, config=model.config) self.task = task self.model = model self.tokenizer = tokenizer self.feature_extractor = feature_extractor self.image_processor = image_processor self.modelcard = modelcard self.framework = framework # `accelerate` device map hf_device_map = getattr(self.model, "hf_device_map", None) if hf_device_map is not None and device is not None: raise ValueError( "The model has been loaded with `accelerate` and therefore cannot be moved to a specific device. Please " "discard the `device` argument when creating your pipeline object." ) # We shouldn't call `model.to()` for models loaded with accelerate if self.framework == "pt" and device is not None and not (isinstance(device, int) and device < 0): self.model.to(device) if device is None: if hf_device_map is not None: # Take the first device used by `accelerate`. device = next(iter(hf_device_map.values())) else: device = -1 if is_torch_available() and self.framework == "pt": if isinstance(device, torch.device): self.device = device elif isinstance(device, str): self.device = torch.device(device) elif device < 0: self.device = torch.device("cpu") else: self.device = torch.device(f"cuda:{device}") else: self.device = device if device is not None else -1 self.torch_dtype = torch_dtype self.binary_output = binary_output # Update config and generation_config with task specific parameters task_specific_params = self.model.config.task_specific_params if task_specific_params is not None and task in task_specific_params: self.model.config.update(task_specific_params.get(task)) if self.model.can_generate(): self.model.generation_config.update(**task_specific_params.get(task)) self.call_count = 0 self._batch_size = kwargs.pop("batch_size", None) self._num_workers = kwargs.pop("num_workers", None) self._preprocess_params, self._forward_params, self._postprocess_params = self._sanitize_parameters(**kwargs) if self.image_processor is None and self.feature_extractor is not None: if isinstance(self.feature_extractor, BaseImageProcessor): # Backward compatible change, if users called # ImageSegmentationPipeline(.., feature_extractor=MyFeatureExtractor()) # then we should keep working self.image_processor = self.feature_extractor def save_pretrained(self, save_directory: str, safe_serialization: bool = False): """ Save the pipeline's model and tokenizer. Args: save_directory (`str`): A path to the directory where to saved. It will be created if it doesn't exist. safe_serialization (`str`): Whether to save the model using `safetensors` or the traditional way for PyTorch or Tensorflow """ if os.path.isfile(save_directory): logger.error(f"Provided path ({save_directory}) should be a directory, not a file") return os.makedirs(save_directory, exist_ok=True) if hasattr(self, "_registered_impl"): # Add info to the config pipeline_info = self._registered_impl.copy() custom_pipelines = {} for task, info in pipeline_info.items(): if info["impl"] != self.__class__: continue info = info.copy() module_name = info["impl"].__module__ last_module = module_name.split(".")[-1] # Change classes into their names/full names info["impl"] = f"{last_module}.{info['impl'].__name__}" info["pt"] = tuple(c.__name__ for c in info["pt"]) info["tf"] = tuple(c.__name__ for c in info["tf"]) custom_pipelines[task] = info self.model.config.custom_pipelines = custom_pipelines # Save the pipeline custom code custom_object_save(self, save_directory) self.model.save_pretrained(save_directory, safe_serialization=safe_serialization) if self.tokenizer is not None: self.tokenizer.save_pretrained(save_directory) if self.feature_extractor is not None: self.feature_extractor.save_pretrained(save_directory) if self.image_processor is not None: self.image_processor.save_pretrained(save_directory) if self.modelcard is not None: self.modelcard.save_pretrained(save_directory) def transform(self, X): """ Scikit / Keras interface to transformers' pipelines. This method will forward to __call__(). """ return self(X) def predict(self, X): """ Scikit / Keras interface to transformers' pipelines. This method will forward to __call__(). """ return self(X) @contextmanager def device_placement(self): """ Context Manager allowing tensor allocation on the user-specified device in framework agnostic way. Returns: Context manager Examples: ```python # Explicitly ask for tensor allocation on CUDA device :0 pipe = pipeline(..., device=0) with pipe.device_placement(): # Every framework specific tensor allocation will be done on the request device output = pipe(...) ```""" if self.framework == "tf": with tf.device("/CPU:0" if self.device == -1 else f"/device:GPU:{self.device}"): yield else: if self.device.type == "cuda": with torch.cuda.device(self.device): yield else: yield def ensure_tensor_on_device(self, **inputs): """ Ensure PyTorch tensors are on the specified device. Args: inputs (keyword arguments that should be `torch.Tensor`, the rest is ignored): The tensors to place on `self.device`. Recursive on lists **only**. Return: `Dict[str, torch.Tensor]`: The same as `inputs` but on the proper device. """ return self._ensure_tensor_on_device(inputs, self.device) def _ensure_tensor_on_device(self, inputs, device): if isinstance(inputs, ModelOutput): return ModelOutput( {name: self._ensure_tensor_on_device(tensor, device) for name, tensor in inputs.items()} ) elif isinstance(inputs, dict): return {name: self._ensure_tensor_on_device(tensor, device) for name, tensor in inputs.items()} elif isinstance(inputs, UserDict): return UserDict({name: self._ensure_tensor_on_device(tensor, device) for name, tensor in inputs.items()}) elif isinstance(inputs, list): return [self._ensure_tensor_on_device(item, device) for item in inputs] elif isinstance(inputs, tuple): return tuple([self._ensure_tensor_on_device(item, device) for item in inputs]) elif isinstance(inputs, torch.Tensor): if device == torch.device("cpu") and inputs.dtype in {torch.float16, torch.bfloat16}: inputs = inputs.float() return inputs.to(device) else: return inputs def check_model_type(self, supported_models: Union[List[str], dict]): """ Check if the model class is in supported by the pipeline. Args: supported_models (`List[str]` or `dict`): The list of models supported by the pipeline, or a dictionary with model class values. """ if not isinstance(supported_models, list): # Create from a model mapping supported_models_names = [] for _, model_name in supported_models.items(): # Mapping can now contain tuples of models for the same configuration. if isinstance(model_name, tuple): supported_models_names.extend(list(model_name)) else: supported_models_names.append(model_name) if hasattr(supported_models, "_model_mapping"): for _, model in supported_models._model_mapping._extra_content.items(): if isinstance(model_name, tuple): supported_models_names.extend([m.__name__ for m in model]) else: supported_models_names.append(model.__name__) supported_models = supported_models_names if self.model.__class__.__name__ not in supported_models: logger.error( f"The model '{self.model.__class__.__name__}' is not supported for {self.task}. Supported models are" f" {supported_models}." ) @abstractmethod def _sanitize_parameters(self, **pipeline_parameters): """ _sanitize_parameters will be called with any excessive named arguments from either `__init__` or `__call__` methods. It should return 3 dictionnaries of the resolved parameters used by the various `preprocess`, `forward` and `postprocess` methods. Do not fill dictionnaries if the caller didn't specify a kwargs. This let's you keep defaults in function signatures, which is more "natural". It is not meant to be called directly, it will be automatically called and the final parameters resolved by `__init__` and `__call__` """ raise NotImplementedError("_sanitize_parameters not implemented") @abstractmethod def preprocess(self, input_: Any, **preprocess_parameters: Dict) -> Dict[str, GenericTensor]: """ Preprocess will take the `input_` of a specific pipeline and return a dictionary of everything necessary for `_forward` to run properly. It should contain at least one tensor, but might have arbitrary other items. """ raise NotImplementedError("preprocess not implemented") @abstractmethod def _forward(self, input_tensors: Dict[str, GenericTensor], **forward_parameters: Dict) -> ModelOutput: """ _forward will receive the prepared dictionary from `preprocess` and run it on the model. This method might involve the GPU or the CPU and should be agnostic to it. Isolating this function is the reason for `preprocess` and `postprocess` to exist, so that the hot path, this method generally can run as fast as possible. It is not meant to be called directly, `forward` is preferred. It is basically the same but contains additional code surrounding `_forward` making sure tensors and models are on the same device, disabling the training part of the code (leading to faster inference). """ raise NotImplementedError("_forward not implemented") @abstractmethod def postprocess(self, model_outputs: ModelOutput, **postprocess_parameters: Dict) -> Any: """ Postprocess will receive the raw outputs of the `_forward` method, generally tensors, and reformat them into something more friendly. Generally it will output a list or a dict or results (containing just strings and numbers). """ raise NotImplementedError("postprocess not implemented") def get_inference_context(self): return torch.no_grad def forward(self, model_inputs, **forward_params): with self.device_placement(): if self.framework == "tf": model_inputs["training"] = False model_outputs = self._forward(model_inputs, **forward_params) elif self.framework == "pt": inference_context = self.get_inference_context() with inference_context(): model_inputs = self._ensure_tensor_on_device(model_inputs, device=self.device) model_outputs = self._forward(model_inputs, **forward_params) model_outputs = self._ensure_tensor_on_device(model_outputs, device=torch.device("cpu")) else: raise ValueError(f"Framework {self.framework} is not supported") return model_outputs def get_iterator( self, inputs, num_workers: int, batch_size: int, preprocess_params, forward_params, postprocess_params ): if isinstance(inputs, collections.abc.Sized): dataset = PipelineDataset(inputs, self.preprocess, preprocess_params) else: if num_workers > 1: logger.warning( "For iterable dataset using num_workers>1 is likely to result" " in errors since everything is iterable, setting `num_workers=1`" " to guarantee correctness." ) num_workers = 1 dataset = PipelineIterator(inputs, self.preprocess, preprocess_params) if "TOKENIZERS_PARALLELISM" not in os.environ: logger.info("Disabling tokenizer parallelism, we're using DataLoader multithreading already") os.environ["TOKENIZERS_PARALLELISM"] = "false" # TODO hack by collating feature_extractor and image_processor feature_extractor = self.feature_extractor if self.feature_extractor is not None else self.image_processor collate_fn = no_collate_fn if batch_size == 1 else pad_collate_fn(self.tokenizer, feature_extractor) dataloader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, collate_fn=collate_fn) model_iterator = PipelineIterator(dataloader, self.forward, forward_params, loader_batch_size=batch_size) final_iterator = PipelineIterator(model_iterator, self.postprocess, postprocess_params) return final_iterator def __call__(self, inputs, *args, num_workers=None, batch_size=None, **kwargs): if args: logger.warning(f"Ignoring args : {args}") if num_workers is None: if self._num_workers is None: num_workers = 0 else: num_workers = self._num_workers if batch_size is None: if self._batch_size is None: batch_size = 1 else: batch_size = self._batch_size preprocess_params, forward_params, postprocess_params = self._sanitize_parameters(**kwargs) # Fuse __init__ params and __call__ params without modifying the __init__ ones. preprocess_params = {**self._preprocess_params, **preprocess_params} forward_params = {**self._forward_params, **forward_params} postprocess_params = {**self._postprocess_params, **postprocess_params} self.call_count += 1 if self.call_count > 10 and self.framework == "pt" and self.device.type == "cuda": warnings.warn( "You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a" " dataset", UserWarning, ) is_dataset = Dataset is not None and isinstance(inputs, Dataset) is_generator = isinstance(inputs, types.GeneratorType) is_list = isinstance(inputs, list) is_iterable = is_dataset or is_generator or is_list # TODO make the get_iterator work also for `tf` (and `flax`). can_use_iterator = self.framework == "pt" and (is_dataset or is_generator or is_list) if is_list: if can_use_iterator: final_iterator = self.get_iterator( inputs, num_workers, batch_size, preprocess_params, forward_params, postprocess_params ) outputs = list(final_iterator) return outputs else: return self.run_multi(inputs, preprocess_params, forward_params, postprocess_params) elif can_use_iterator: return self.get_iterator( inputs, num_workers, batch_size, preprocess_params, forward_params, postprocess_params ) elif is_iterable: return self.iterate(inputs, preprocess_params, forward_params, postprocess_params) elif self.framework == "pt" and isinstance(self, ChunkPipeline): return next( iter( self.get_iterator( [inputs], num_workers, batch_size, preprocess_params, forward_params, postprocess_params ) ) ) else: return self.run_single(inputs, preprocess_params, forward_params, postprocess_params) def run_multi(self, inputs, preprocess_params, forward_params, postprocess_params): return [self.run_single(item, preprocess_params, forward_params, postprocess_params) for item in inputs] def run_single(self, inputs, preprocess_params, forward_params, postprocess_params): model_inputs = self.preprocess(inputs, **preprocess_params) model_outputs = self.forward(model_inputs, **forward_params) outputs = self.postprocess(model_outputs, **postprocess_params) return outputs def iterate(self, inputs, preprocess_params, forward_params, postprocess_params): # This function should become `get_iterator` again, this is a temporary # easy solution. for input_ in inputs: yield self.run_single(input_, preprocess_params, forward_params, postprocess_params) class ChunkPipeline(Pipeline): def run_single(self, inputs, preprocess_params, forward_params, postprocess_params): all_outputs = [] for model_inputs in self.preprocess(inputs, **preprocess_params): model_outputs = self.forward(model_inputs, **forward_params) all_outputs.append(model_outputs) outputs = self.postprocess(all_outputs, **postprocess_params) return outputs def get_iterator( self, inputs, num_workers: int, batch_size: int, preprocess_params, forward_params, postprocess_params ): if "TOKENIZERS_PARALLELISM" not in os.environ: logger.info("Disabling tokenizer parallelism, we're using DataLoader multithreading already") os.environ["TOKENIZERS_PARALLELISM"] = "false" if num_workers > 1: logger.warning( "For ChunkPipeline using num_workers>0 is likely to result in errors since everything is iterable," " setting `num_workers=1` to guarantee correctness." ) num_workers = 1 dataset = PipelineChunkIterator(inputs, self.preprocess, preprocess_params) # TODO hack by collating feature_extractor and image_processor feature_extractor = self.feature_extractor if self.feature_extractor is not None else self.image_processor collate_fn = no_collate_fn if batch_size == 1 else pad_collate_fn(self.tokenizer, feature_extractor) dataloader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, collate_fn=collate_fn) model_iterator = PipelinePackIterator(dataloader, self.forward, forward_params, loader_batch_size=batch_size) final_iterator = PipelineIterator(model_iterator, self.postprocess, postprocess_params) return final_iterator class PipelineRegistry: def __init__(self, supported_tasks: Dict[str, Any], task_aliases: Dict[str, str]) -> None: self.supported_tasks = supported_tasks self.task_aliases = task_aliases def get_supported_tasks(self) -> List[str]: supported_task = list(self.supported_tasks.keys()) + list(self.task_aliases.keys()) supported_task.sort() return supported_task def check_task(self, task: str) -> Tuple[str, Dict, Any]: if task in self.task_aliases: task = self.task_aliases[task] if task in self.supported_tasks: targeted_task = self.supported_tasks[task] return task, targeted_task, None if task.startswith("translation"): tokens = task.split("_") if len(tokens) == 4 and tokens[0] == "translation" and tokens[2] == "to": targeted_task = self.supported_tasks["translation"] task = "translation" return task, targeted_task, (tokens[1], tokens[3]) raise KeyError(f"Invalid translation task {task}, use 'translation_XX_to_YY' format") raise KeyError( f"Unknown task {task}, available tasks are {self.get_supported_tasks() + ['translation_XX_to_YY']}" ) def register_pipeline( self, task: str, pipeline_class: type, pt_model: Optional[Union[type, Tuple[type]]] = None, tf_model: Optional[Union[type, Tuple[type]]] = None, default: Optional[Dict] = None, type: Optional[str] = None, ) -> None: if task in self.supported_tasks: logger.warning(f"{task} is already registered. Overwriting pipeline for task {task}...") if pt_model is None: pt_model = () elif not isinstance(pt_model, tuple): pt_model = (pt_model,) if tf_model is None: tf_model = () elif not isinstance(tf_model, tuple): tf_model = (tf_model,) task_impl = {"impl": pipeline_class, "pt": pt_model, "tf": tf_model} if default is not None: if "model" not in default and ("pt" in default or "tf" in default): default = {"model": default} task_impl["default"] = default if type is not None: task_impl["type"] = type self.supported_tasks[task] = task_impl pipeline_class._registered_impl = {task: task_impl} def to_dict(self): return self.supported_tasks