paddlenlp.taskflow.task 源代码

# coding:utf-8
# Copyright (c) 2021  PaddlePaddle Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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import abc
import math
import os
from abc import abstractmethod
from multiprocessing import cpu_count

import paddle
from paddle.dataset.common import md5file

from ..utils.env import PPNLP_HOME
from ..utils.log import logger
from .utils import cut_chinese_sent, download_check, download_file, dygraph_mode_guard

[文档]class Task(metaclass=abc.ABCMeta): """ The meta classs of task in Taskflow. The meta class has the five abstract function, the subclass need to inherit from the meta class. Args: task(string): The name of task. model(string): The model name in the task. kwargs (dict, optional): Additional keyword arguments passed along to the specific task. """ def __init__(self, model, task, priority_path=None, **kwargs): self.model = model self.is_static_model = kwargs.get("is_static_model", False) self.task = task self.kwargs = kwargs self._priority_path = priority_path self._usage = "" # The dygraph model instance self._model = None # The static model instance self._input_spec = None self._config = None self._init_class = None self._custom_model = False self._param_updated = False self._num_threads = self.kwargs["num_threads"] if "num_threads" in self.kwargs else math.ceil(cpu_count() / 2) self._infer_precision = self.kwargs["precision"] if "precision" in self.kwargs else "fp32" # Default to use Paddle Inference self._predictor_type = "paddle-inference" # The root directory for storing Taskflow related files, default to ~/.paddlenlp. self._home_path = self.kwargs["home_path"] if "home_path" in self.kwargs else PPNLP_HOME self._task_flag = self.kwargs["task_flag"] if "task_flag" in self.kwargs else self.model self.from_hf_hub = kwargs.pop("from_hf_hub", False) # Add mode flag for onnx output path redirection self.export_type = None if "task_path" in self.kwargs: self._task_path = self.kwargs["task_path"] self._custom_model = True elif self._priority_path: self._task_path = os.path.join(self._home_path, "taskflow", self._priority_path) else: self._task_path = os.path.join(self._home_path, "taskflow", self.task, self.model) if self.is_static_model: self._static_model_name = self._get_static_model_name() if not self.from_hf_hub: download_check(self._task_flag) @abstractmethod def _construct_model(self, model): """ Construct the inference model for the predictor. """ @abstractmethod def _construct_tokenizer(self, model): """ Construct the tokenizer for the predictor. """ @abstractmethod def _preprocess(self, inputs, padding=True, add_special_tokens=True): """ Transform the raw text to the model inputs, two steps involved: 1) Transform the raw text to token ids. 2) Generate the other model inputs from the raw text and token ids. """ @abstractmethod def _run_model(self, inputs, **kwargs): """ Run the task model from the outputs of the `_tokenize` function. """ @abstractmethod def _postprocess(self, inputs): """ The model output is the logits and pros, this function will convert the model output to raw text. """ @abstractmethod def _construct_input_spec(self): """ Construct the input spec for the convert dygraph model to static model. """ def _get_static_model_name(self): names = [] for file_name in os.listdir(self._task_path): if ".pdmodel" in file_name: names.append(file_name[:-8]) if len(names) == 0: raise IOError(f"{self._task_path} should include '.pdmodel' file.") if len(names) > 1: logger.warning(f"{self._task_path} includes more than one '.pdmodel' file.") return names[0] def _check_task_files(self): """ Check files required by the task. """ for file_id, file_name in self.resource_files_names.items(): if self.task in ["information_extraction"]: dygraph_file = ["model_state.pdparams"] else: dygraph_file = ["model_state.pdparams", "config.json"] if self.is_static_model and file_name in dygraph_file: continue path = os.path.join(self._task_path, file_name) url = self.resource_files_urls[self.model][file_id][0] md5 = self.resource_files_urls[self.model][file_id][1] downloaded = True if not os.path.exists(path): downloaded = False else: if not self._custom_model: if os.path.exists(path): # Check whether the file is updated if not md5file(path) == md5: downloaded = False if file_id == "model_state": self._param_updated = True else: downloaded = False if not downloaded: download_file(self._task_path, file_name, url, md5) def _check_predictor_type(self): if paddle.get_device() == "cpu" and self._infer_precision == "fp16": logger.warning("The inference precision is change to 'fp32', 'fp16' inference only takes effect on gpu.") elif paddle.get_device().split(":", 1)[0] == "npu": if self._infer_precision == "fp16":"Inference on npu with fp16 precison") else: if self._infer_precision == "fp16": self._predictor_type = "onnxruntime" def _construct_ocr_engine(self, lang="ch", use_angle_cls=True): """ Construct the OCR engine """ try: from paddleocr import PaddleOCR except ImportError: raise ImportError("Please install the dependencies first, pip install paddleocr") use_gpu = False if paddle.get_device() == "cpu" else True self._ocr = PaddleOCR(use_angle_cls=use_angle_cls, show_log=False, use_gpu=use_gpu, lang=lang) def _construce_layout_analysis_engine(self): """ Construct the layout analysis engine """ try: from paddleocr import PPStructure except ImportError: raise ImportError("Please install the dependencies first, pip install paddleocr") self._layout_analysis_engine = PPStructure(table=False, ocr=True, show_log=False) def _prepare_static_mode(self): """ Construct the input data and predictor in the PaddlePaddele static mode. """ if paddle.get_device() == "cpu": self._config.disable_gpu() self._config.enable_mkldnn() if self._infer_precision == "int8": # EnableMKLDNN() only works when IR optimization is enabled. self._config.switch_ir_optim(True) self._config.enable_mkldnn_int8()">>> [InferBackend] INT8 inference on CPU ...")) elif paddle.get_device().split(":", 1)[0] == "npu": self._config.disable_gpu() self._config.enable_custom_device("npu", self.kwargs["device_id"]) else: if self._infer_precision == "int8": ">>> [InferBackend] It is a INT8 model which is not yet supported on gpu, use FP32 to inference here ..." ) self._config.enable_use_gpu(100, self.kwargs["device_id"]) # TODO(linjieccc): enable after fixed self._config.delete_pass("embedding_eltwise_layernorm_fuse_pass") self._config.delete_pass("fused_multi_transformer_encoder_pass") self._config.set_cpu_math_library_num_threads(self._num_threads) self._config.switch_use_feed_fetch_ops(False) self._config.disable_glog_info() self._config.enable_memory_optim() # TODO(linjieccc): some temporary settings and will be remove in future # after fixed if self.task in ["document_intelligence", "knowledge_mining", "zero_shot_text_classification"]: self._config.switch_ir_optim(False) if self.model == "uie-data-distill-gp": self._config.enable_memory_optim(False) self.predictor = paddle.inference.create_predictor(self._config) self.input_names = [name for name in self.predictor.get_input_names()] self.input_handles = [self.predictor.get_input_handle(name) for name in self.predictor.get_input_names()] self.output_handle = [self.predictor.get_output_handle(name) for name in self.predictor.get_output_names()] def _prepare_onnx_mode(self): try: import onnx import onnxruntime as ort import paddle2onnx from onnxconverter_common import float16 except ImportError: logger.warning( "The inference precision is change to 'fp32', please install the dependencies that required for 'fp16' inference, pip install onnxruntime-gpu onnx onnxconverter-common" ) if self.export_type is None: onnx_dir = os.path.join(self._task_path, "onnx") else: # Compatible multimodal model for saving image and text path onnx_dir = os.path.join(self._task_path, "onnx", self.export_type) if not os.path.exists(onnx_dir): os.makedirs(onnx_dir, exist_ok=True) float_onnx_file = os.path.join(onnx_dir, "model.onnx") if not os.path.exists(float_onnx_file) or self._param_updated: onnx_model = paddle2onnx.command.c_paddle_to_onnx( model_file=self._static_model_file, params_file=self._static_params_file, opset_version=13, enable_onnx_checker=True, ) with open(float_onnx_file, "wb") as f: f.write(onnx_model) fp16_model_file = os.path.join(onnx_dir, "fp16_model.onnx") if not os.path.exists(fp16_model_file) or self._param_updated: onnx_model = onnx.load_model(float_onnx_file) trans_model = float16.convert_float_to_float16(onnx_model, keep_io_types=True) onnx.save_model(trans_model, fp16_model_file) providers = [("CUDAExecutionProvider", {"device_id": self.kwargs["device_id"]})] sess_options = ort.SessionOptions() sess_options.intra_op_num_threads = self._num_threads sess_options.inter_op_num_threads = self._num_threads self.predictor = ort.InferenceSession(fp16_model_file, sess_options=sess_options, providers=providers) assert "CUDAExecutionProvider" in self.predictor.get_providers(), ( "The environment for GPU inference is not set properly. " "A possible cause is that you had installed both onnxruntime and onnxruntime-gpu. " "Please run the following commands to reinstall: \n " "1) pip uninstall -y onnxruntime onnxruntime-gpu \n 2) pip install onnxruntime-gpu" ) self.input_handler = [ for i in self.predictor.get_inputs()] def _get_inference_model(self): """ Return the inference program, inputs and outputs in static mode. """ if self._custom_model: param_path = os.path.join(self._task_path, "model_state.pdparams") if os.path.exists(param_path): cache_info_path = os.path.join(self._task_path, ".cache_info") md5 = md5file(param_path) self._param_updated = True if os.path.exists(cache_info_path) and open(cache_info_path).read()[:-8] == md5: self._param_updated = False elif self.task == "information_extraction" and self.model != "uie-data-distill-gp": # UIE related models are moved to paddlenlp.transformers after v2.4.5 # So we convert the parameter key names for compatibility # This check will be discard in future fp = open(cache_info_path, "w") fp.write(md5 + "taskflow") fp.close() model_state = paddle.load(param_path) prefix_map = {"UIE": "ernie", "UIEM": "ernie_m", "UIEX": "ernie_layout"} new_state_dict = {} for name, param in model_state.items(): if "ernie" in name: new_state_dict[name] = param elif "encoder.encoder" in name: trans_name = name.replace("encoder.encoder", prefix_map[self._init_class] + ".encoder") new_state_dict[trans_name] = param elif "encoder" in name: trans_name = name.replace("encoder", prefix_map[self._init_class]) new_state_dict[trans_name] = param else: new_state_dict[name] = param, param_path) else: fp = open(cache_info_path, "w") fp.write(md5 + "taskflow") fp.close() # When the user-provided model path is already a static model, skip to_static conversion if self.is_static_model: self.inference_model_path = os.path.join(self._task_path, self._static_model_name) if not os.path.exists(self.inference_model_path + ".pdmodel") or not os.path.exists( self.inference_model_path + ".pdiparams" ): raise IOError( f"{self._task_path} should include {self._static_model_name + '.pdmodel'} and {self._static_model_name + '.pdiparams'} while is_static_model is True" ) if self.paddle_quantize_model(self.inference_model_path): self._infer_precision = "int8" self._predictor_type = "paddle-inference" else: # Since 'self._task_path' is used to load the HF Hub path when 'from_hf_hub=True', we construct the static model path in a different way _base_path = ( self._task_path if not self.from_hf_hub else os.path.join(self._home_path, "taskflow", self.task, self._task_path) ) self.inference_model_path = os.path.join(_base_path, "static", "inference") if not os.path.exists(self.inference_model_path + ".pdiparams") or self._param_updated: with dygraph_mode_guard(): self._construct_model(self.model) self._construct_input_spec() self._convert_dygraph_to_static() self._static_model_file = self.inference_model_path + ".pdmodel" self._static_params_file = self.inference_model_path + ".pdiparams" if paddle.get_device().split(":", 1)[0] == "npu" and self._infer_precision == "fp16": # transform fp32 model tp fp16 model self._static_fp16_model_file = self.inference_model_path + "-fp16.pdmodel" self._static_fp16_params_file = self.inference_model_path + "-fp16.pdiparams" if not os.path.exists(self._static_fp16_model_file) and not os.path.exists(self._static_fp16_params_file):"Converting to the inference model from fp32 to fp16.") paddle.inference.convert_to_mixed_precision( os.path.join(self._static_model_file), os.path.join(self._static_params_file), os.path.join(self._static_fp16_model_file), os.path.join(self._static_fp16_params_file), backend=paddle.inference.PlaceType.CUSTOM, mixed_precision=paddle.inference.PrecisionType.Half, # Here, npu sigmoid will lead to OOM and cpu sigmoid don't support fp16. # So, we add sigmoid to black list temporarily. black_list={"sigmoid"}, ) "The inference model in fp16 precison save in the path:{}".format(self._static_fp16_model_file) ) self._static_model_file = self._static_fp16_model_file self._static_params_file = self._static_fp16_params_file if self._predictor_type == "paddle-inference": self._config = paddle.inference.Config(self._static_model_file, self._static_params_file) self._prepare_static_mode() else: self._prepare_onnx_mode() def _convert_dygraph_to_static(self): """ Convert the dygraph model to static model. """ assert ( self._model is not None ), "The dygraph model must be created before converting the dygraph model to static model." assert ( self._input_spec is not None ), "The input spec must be created before converting the dygraph model to static model.""Converting to the inference model cost a little time.") static_model = paddle.jit.to_static(self._model, input_spec=self._input_spec), self.inference_model_path)"The inference model save in the path:{}".format(self.inference_model_path)) def _check_input_text(self, inputs): """ Check whether the input text meet the requirement. """ inputs = inputs[0] if isinstance(inputs, str): if len(inputs) == 0: raise ValueError("Invalid inputs, input text should not be empty text, please check your input.") inputs = [inputs] elif isinstance(inputs, list): if not (isinstance(inputs[0], str) and len(inputs[0].strip()) > 0): raise TypeError( "Invalid inputs, input text should be list of str, and first element of list should not be empty text." ) else: raise TypeError( "Invalid inputs, input text should be str or list of str, but type of {} found!".format(type(inputs)) ) return inputs def _auto_splitter(self, input_texts, max_text_len, bbox_list=None, split_sentence=False): """ Split the raw texts automatically for model inference. Args: input_texts (List[str]): input raw texts. max_text_len (int): cutting length. bbox_list (List[float, float,float, float]): bbox for document input. split_sentence (bool): If True, sentence-level split will be performed. `split_sentence` will be set to False if bbox_list is not None since sentence-level split is not support for document. return: short_input_texts (List[str]): the short input texts for model inference. input_mapping (dict): mapping between raw text and short input texts. """ input_mapping = {} short_input_texts = [] cnt_org = 0 cnt_short = 0 with_bbox = False if bbox_list: with_bbox = True short_bbox_list = [] if split_sentence: logger.warning( "`split_sentence` will be set to False if bbox_list is not None since sentence-level split is not support for document." ) split_sentence = False for idx in range(len(input_texts)): if not split_sentence: sens = [input_texts[idx]] else: sens = cut_chinese_sent(input_texts[idx]) for sen in sens: lens = len(sen) if lens <= max_text_len: short_input_texts.append(sen) if with_bbox: short_bbox_list.append(bbox_list[idx]) input_mapping.setdefault(cnt_org, []).append(cnt_short) cnt_short += 1 else: temp_text_list = [sen[i : i + max_text_len] for i in range(0, lens, max_text_len)] short_input_texts.extend(temp_text_list) if with_bbox: if bbox_list[idx] is not None: temp_bbox_list = [ bbox_list[idx][i : i + max_text_len] for i in range(0, lens, max_text_len) ] short_bbox_list.extend(temp_bbox_list) else: short_bbox_list.extend([None for _ in range(len(temp_text_list))]) short_idx = cnt_short cnt_short += math.ceil(lens / max_text_len) temp_text_id = [short_idx + i for i in range(cnt_short - short_idx)] input_mapping.setdefault(cnt_org, []).extend(temp_text_id) cnt_org += 1 if with_bbox: return short_input_texts, short_bbox_list, input_mapping else: return short_input_texts, input_mapping def _auto_joiner(self, short_results, input_mapping, is_dict=False): """ Join the short results automatically and generate the final results to match with the user inputs. Args: short_results (List[dict] / List[List[str]] / List[str]): input raw texts. input_mapping (dict): cutting length. is_dict (bool): whether the element type is dict, default to False. return: short_input_texts (List[str]): the short input texts for model inference. """ concat_results = [] elem_type = {} if is_dict else [] for k, vs in input_mapping.items(): single_results = elem_type for v in vs: if len(single_results) == 0: single_results = short_results[v] elif isinstance(elem_type, list): single_results.extend(short_results[v]) elif isinstance(elem_type, dict): for sk in single_results.keys(): if isinstance(single_results[sk], str): single_results[sk] += short_results[v][sk] else: single_results[sk].extend(short_results[v][sk]) else: raise ValueError( "Invalid element type, the type of results " "for each element should be list of dict, " "but {} received.".format(type(single_results)) ) concat_results.append(single_results) return concat_results
[文档] def paddle_quantize_model(self, model_path): """ Determine whether it is an int8 model. """ model = paddle.jit.load(model_path) program = model.program() for block in program.blocks: for op in block.ops: if op.type.count("quantize"): return True return False
[文档] def help(self): """ Return the usage message of the current task. """ print("Examples:\n{}".format(self._usage))
def __call__(self, *args, **kwargs): inputs = self._preprocess(*args) outputs = self._run_model(inputs, **kwargs) results = self._postprocess(outputs) return results