paddlenlp.taskflow.task 源代码

# coding:utf-8
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import os
import abc
import math
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 download_check, static_mode_guard, dygraph_mode_guard, download_file, cut_chinese_sent


[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.task = task self.kwargs = kwargs self._priority_path = priority_path self._usage = "" # The dygraph model instantce self._model = None # The static model instantce self._input_spec = None self._config = 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 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) 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): """ 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 _check_task_files(self): """ Check files required by the task. """ for file_id, file_name in self.resource_files_names.items(): 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." ) else: if self._infer_precision == 'fp16': try: import onnx import onnxruntime as ort import paddle2onnx from onnxconverter_common import float16 self._predictor_type = 'onnxruntime' except: 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" ) 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() else: self._config.enable_use_gpu(100, self.kwargs['device_id']) # TODO(linjieccc): enable embedding_eltwise_layernorm_fuse_pass after fixed self._config.delete_pass("embedding_eltwise_layernorm_fuse_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() if self.task in ["document_question_answering", "knowledge_mining"]: self._config.switch_ir_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): import onnx import onnxruntime as ort import paddle2onnx from onnxconverter_common import float16 onnx_dir = os.path.join(self._task_path, 'onnx') if not os.path.exists(onnx_dir): os.mkdir(onnx_dir) float_onnx_file = os.path.join(onnx_dir, 'model.onnx') if not os.path.exists(float_onnx_file): 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): 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(), f"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" def _get_inference_model(self): """ Return the inference program, inputs and outputs in static mode. """ inference_model_path = os.path.join(self._task_path, "static", "inference") if not os.path.exists(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 = inference_model_path + ".pdmodel" self._static_params_file = inference_model_path + ".pdiparams" 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.' logger.info("Converting to the inference model cost a little time.") static_model = paddle.jit.to_static(self._model, input_spec=self._input_spec) save_path = os.path.join(self._task_path, "static", "inference") paddle.jit.save(static_model, save_path) logger.info("The inference model save in the path:{}".format(save_path)) def _check_input_text(self, inputs): 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." .format(type(inputs))) 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." .format(type(inputs[0]))) 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, 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. split_sentence (bool): If True, sentence-level split will be performed. 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 for text in input_texts: if not split_sentence: sens = [text] else: sens = cut_chinese_sent(text) for sen in sens: lens = len(sen) if lens <= max_text_len: short_input_texts.append(sen) if cnt_org not in input_mapping.keys(): input_mapping[cnt_org] = [cnt_short] else: input_mapping[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) 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) ] if cnt_org not in input_mapping.keys(): input_mapping[cnt_org] = temp_text_id else: input_mapping[cnt_org].extend(temp_text_id) cnt_org += 1 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 help(self): """ Return the usage message of the current task. """ print("Examples:\n{}".format(self._usage))
def __call__(self, *args): inputs = self._preprocess(*args) outputs = self._run_model(inputs) results = self._postprocess(outputs) return results