# 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. # 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. from typing import Any, Dict, List, Union import numpy as np from paddle.static import InputSpec from scipy.special import expit as np_sigmoid from scipy.special import softmax as np_softmax from ..prompt import PromptDataCollatorWithPadding, UTCTemplate from ..transformers import UTC, AutoTokenizer from .task import Task from .utils import static_mode_guard usage = r""" from paddlenlp import Taskflow schema = ['这是一条差评', '这是一条好评'] text_cls = Taskflow("zero_shot_text_classification", schema=schema) text_cls('房间干净明亮，非常不错') ''' [{'predictions': [{'label': '这是一条好评', 'score': 0.9695149765679986}], 'text_a': '房间干净明亮，非常不错'}] ''' """ class ZeroShotTextClassificationTask(Task): """ Zero-shot Universal Text Classification Task. Args: task (string): The name of task. model (string): The model_name in the task. schema (list): List of candidate labels. kwargs (dict, optional): Additional keyword arguments passed along to the specific task. """ resource_files_names = { "model_state": "model_state.pdparams", "config": "config.json", "vocab_file": "vocab.txt", "special_tokens_map": "special_tokens_map.json", "tokenizer_config": "tokenizer_config.json", } resource_files_urls = { "utc-xbase": { "model_state": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-xbase/model_state.pdparams", "e751c3a78d4caff923759c0d0547bfe6", ], "config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-xbase/config.json", "4c2b035c71ff226a14236171a1a202a4", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-xbase/vocab.txt", "97eb0ec5a5890c8190e10e251af2e133", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-xbase/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-xbase/tokenizer_config.json", "be86466f6769fde498690269d099ea7c", ], }, "utc-base": { "model_state": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-base/model_state.pdparams", "72089351c6fb02bcf8f270fe0cc508e9", ], "config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-base/config.json", "79aa9a69286604436937b03f429f4d34", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-base/vocab.txt", "97eb0ec5a5890c8190e10e251af2e133", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-base/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-base/tokenizer_config.json", "be86466f6769fde498690269d099ea7c", ], }, "utc-medium": { "model_state": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-medium/model_state.pdparams", "2802c766a8b880aad910dd5a7db809ae", ], "config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-medium/config.json", "2899cd7c8590dcdc4223e4b1262e2f4e", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-medium/vocab.txt", "97eb0ec5a5890c8190e10e251af2e133", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-medium/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-medium/tokenizer_config.json", "be86466f6769fde498690269d099ea7c", ], }, "utc-micro": { "model_state": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-micro/model_state.pdparams", "d9ebdfce9a8c6ebda43630ed18b07c58", ], "config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-micro/config.json", "8c8da9337e09e0c3962196987dca18bd", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-micro/vocab.txt", "97eb0ec5a5890c8190e10e251af2e133", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-micro/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-micro/tokenizer_config.json", "be86466f6769fde498690269d099ea7c", ], }, "utc-mini": { "model_state": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-mini/model_state.pdparams", "848a2870cd51bfc22174a2a38884085c", ], "config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-mini/config.json", "933b8ebfcf995b1f965764ac426a2ffa", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-mini/vocab.txt", "97eb0ec5a5890c8190e10e251af2e133", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-mini/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-mini/tokenizer_config.json", "be86466f6769fde498690269d099ea7c", ], }, "utc-nano": { "model_state": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-nano/model_state.pdparams", "2bd31212d989619148eda3afebc7354d", ], "config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-nano/config.json", "02fe311fdcc127e56ff0975038cc4d65", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-nano/vocab.txt", "97eb0ec5a5890c8190e10e251af2e133", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-nano/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-nano/tokenizer_config.json", "be86466f6769fde498690269d099ea7c", ], }, "utc-pico": { "model_state": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-pico/model_state.pdparams", "f7068d63ad2930de7ac850d475052946", ], "config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-pico/config.json", "c0c7412cdd070edb5a1ce70c7fc68ad3", ], "vocab_file": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-pico/vocab.txt", "97eb0ec5a5890c8190e10e251af2e133", ], "special_tokens_map": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-pico/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://paddlenlp.bj.bcebos.com/taskflow/zero_shot_text_classification/utc-pico/tokenizer_config.json", "be86466f6769fde498690269d099ea7c", ], }, "utc-large": { "model_state": [ "https://bj.bcebos.com/paddlenlp/taskflow/zero_shot_text_classification/utc-large/model_state.pdparams", "71eb9a732c743a513b84ca048dc4945b", ], "config": [ "https://bj.bcebos.com/paddlenlp/taskflow/zero_shot_text_classification/utc-large/config.json", "9496be2cc99f7e6adf29280320274142", ], "vocab_file": [ "https://bj.bcebos.com/paddlenlp/taskflow/zero_text_classification/utc-large/vocab.txt", "afc01b5680a53525df5afd7518b42b48", ], "special_tokens_map": [ "https://bj.bcebos.com/paddlenlp/taskflow/zero_text_classification/utc-large/special_tokens_map.json", "2458e2131219fc1f84a6e4843ae07008", ], "tokenizer_config": [ "https://bj.bcebos.com/paddlenlp/taskflow/zero_text_classification/utc-large/tokenizer_config.json", "dcb0f3257830c0eb1f2de47f2d86f89a", ], }, "__internal_testing__/tiny-random-utc": { "model_state": [ "https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-utc/model_state.pdparams", "d303b59447be690530c35c73f8fd03cd", ], "config": [ "https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-utc/config.json", "3420a6638a7c73c6239eb1d7ca1bc5fe", ], "vocab_file": [ "https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-utc/vocab.txt", "97eb0ec5a5890c8190e10e251af2e133", ], "special_tokens_map": [ "https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-utc/special_tokens_map.json", "8b3fb1023167bb4ab9d70708eb05f6ec", ], "tokenizer_config": [ "https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-utc/tokenizer_config.json", "258fc552c15cec90046066ca122899e2", ], }, } def __init__(self, task: str, model: str, schema: list = None, **kwargs): super().__init__(task=task, model=model, **kwargs) self._static_mode = False self._set_utc_schema(schema) self._max_seq_len = kwargs.get("max_seq_len", 512) self._batch_size = kwargs.get("batch_size", 1) self._pred_threshold = kwargs.get("pred_threshold", 0.5) self._num_workers = kwargs.get("num_workers", 0) self._single_label = kwargs.get("single_label", False) self._check_task_files() self._construct_tokenizer() self._check_predictor_type() if self._static_mode: self._get_inference_model() else: self._construct_model(model) def _set_utc_schema(self, schema): if schema is None: self._choices = None elif isinstance(schema, list): self._choices = schema elif isinstance(schema, dict) and len(schema) == 1: for key in schema: self._choices = schema[key] else: raise ValueError(f"Invalid schema: {schema}.") def set_schema(self, schema): self._set_utc_schema(schema) def _construct_input_spec(self): """ Construct the input spec for the convert dygraph model to static model. """ self._input_spec = [ InputSpec(shape=[None, None], dtype="int64", name="input_ids"), InputSpec(shape=[None, None], dtype="int64", name="token_type_ids"), InputSpec(shape=[None, None], dtype="int64", name="position_ids"), InputSpec(shape=[None, None], dtype="float32", name="attention_mask"), InputSpec(shape=[None, None], dtype="int64", name="omask_positions"), InputSpec(shape=[None], dtype="int64", name="cls_positions"), ] def _construct_model(self, model): """ Construct the inference model for the predictor. """ model_instance = UTC.from_pretrained(self._task_path, from_hf_hub=self.from_hf_hub) self._model = model_instance self._model.eval() def _construct_tokenizer(self): """ Construct the tokenizer for the predictor. """ self._tokenizer = AutoTokenizer.from_pretrained(self._task_path, from_hf_hub=self.from_hf_hub) if self._static_mode: self._collator = PromptDataCollatorWithPadding(self._tokenizer, return_tensors="np") else: self._collator = PromptDataCollatorWithPadding(self._tokenizer, return_tensors="pd") self._template = UTCTemplate(self._tokenizer, self._max_seq_len) def _check_input_text(self, inputs): inputs = inputs[0] if isinstance(inputs, str) or isinstance(inputs, dict): inputs = [inputs] if isinstance(inputs, list): input_list = [] for example in inputs: data = {"text_a": "", "text_b": "", "choices": self._choices} if isinstance(example, dict): for k in example: if k in data: data[k] = example[k] elif isinstance(example, str): data["text_a"] = example data["text_b"] = "" elif isinstance(example, list): for x in example: if not isinstance(x, str): raise ValueError("Invalid inputs, input text should be strings.") data["text_a"] = example[0] data["text_b"] = "".join(example[1:]) if len(example) > 1 else "" else: raise ValueError( "Invalid inputs, the input should be {'text_a': a, 'text_b': b}, a text or a list of text." ) if len(data["text_a"]) < 1 and len(data["text_b"]) < 1: raise ValueError("Invalid inputs, input `text_a` and `text_b` are both missing or empty.") if not isinstance(data["choices"], list) or len(data["choices"]) < 2: raise ValueError("Invalid inputs, label candidates should be a list with length >= 2.") input_list.append(data) else: raise TypeError("Invalid input format!") return input_list def _preprocess(self, inputs: Union[str, List[str]]) -> Dict[str, Any]: """ 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. """ inputs = self._check_input_text(inputs) # Get the config from the kwargs tokenized_inputs = [self._template(i) for i in inputs] batches = [ tokenized_inputs[idx : idx + self._batch_size] for idx in range(0, len(tokenized_inputs), self._batch_size) ] inputs = [inputs[idx : idx + self._batch_size] for idx in range(0, len(inputs), self._batch_size)] outputs = {} outputs["text"] = inputs outputs["batches"] = [self._collator(batch) for batch in batches] return outputs def _run_model(self, inputs: Dict[str, Any]) -> Dict[str, Any]: outputs = {} outputs["text"] = inputs["text"] outputs["batch_logits"] = [] dtype_dict = { "input_ids": "int64", "token_type_ids": "int64", "position_ids": "int64", "attention_mask": "float32", "omask_positions": "int64", "cls_positions": "int64", } if self._static_mode: with static_mode_guard(): for batch in inputs["batches"]: if self._predictor_type == "paddle-inference": for i, input_name in enumerate(self.input_names): self.input_handles[i].copy_from_cpu(batch[input_name].astype(dtype_dict[input_name])) self.predictor.run() logits = self.output_handle[0].copy_to_cpu().tolist() else: input_dict = {} for input_name in dtype_dict: input_dict[input_name] = batch[input_name].astype(dtype_dict[input_name]) logits = self.predictor.run(None, input_dict)[0].tolist() outputs["batch_logits"].append(logits) else: for batch in inputs["batches"]: if batch["soft_token_ids"] is not None: del batch["soft_token_ids"] logits = self._model(**batch) outputs["batch_logits"].append(np.array(logits)) return outputs def _postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """ This function converts the model logits output to class score and predictions """ outputs = [] for batch_text, batch_logits in zip(inputs["text"], inputs["batch_logits"]): for text, logits in zip(batch_text, batch_logits): output = {} if len(text["text_a"]) > 0: output["text_a"] = text["text_a"] if len(text["text_b"]) > 0: output["text_b"] = text["text_b"] if self._single_label: score = np_softmax(logits, axis=-1) label = np.argmax(logits, axis=-1) output["predictions"] = [{"label": text["choices"][label], "score": score[label]}] else: scores = np_sigmoid(logits) output["predictions"] = [] if scores.ndim == 2: scores = scores[0] for i, class_score in enumerate(scores): if class_score > self._pred_threshold: output["predictions"].append({"label": text["choices"][i], "score": class_score}) outputs.append(output) return outputs