213 lines
8.5 KiB
Python
213 lines
8.5 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team All rights reserved.
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# Copyright 2021 NVIDIA Corporation. 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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A subclass of `Trainer` specific to Question-Answering tasks
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"""
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import logging
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import os
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import quant_trainer
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import torch
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from torch.utils.data import DataLoader
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from transformers import Trainer, is_torch_tpu_available
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from transformers.trainer_utils import PredictionOutput
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logger = logging.getLogger(__name__)
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if is_torch_tpu_available(check_device=False):
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import torch_xla.core.xla_model as xm
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import torch_xla.debug.metrics as met
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class QuestionAnsweringTrainer(Trainer):
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def __init__(self, *args, eval_examples=None, post_process_function=None, quant_trainer_args=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.eval_examples = eval_examples
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self.post_process_function = post_process_function
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self.quant_trainer_args = quant_trainer_args
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self.calib_num = 128 # default number of calibration samples
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def get_calib_dataloader(self, calib_dataset=None):
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"""
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Returns the calibration dataloader :class:`~torch.utils.data.DataLoader`.
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Args:
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calib_dataset (:obj:`torch.utils.data.Dataset`, `optional`)
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"""
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if calib_dataset is None and self.calib_dataset is None:
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raise ValueError("Trainer: calibration requires an calib_dataset.")
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calib_dataset = calib_dataset if calib_dataset is not None else self.calib_dataset
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calib_dataset = self._remove_unused_columns(calib_dataset, description="Calibration")
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return DataLoader(
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calib_dataset,
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batch_size=self.args.eval_batch_size,
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collate_fn=self.data_collator,
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drop_last=self.args.dataloader_drop_last,
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num_workers=self.args.dataloader_num_workers,
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pin_memory=self.args.dataloader_pin_memory,
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shuffle=True,
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)
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def calibrate(self, calib_dataset=None):
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calib_dataset = self.train_dataset if calib_dataset is None else calib_dataset
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calib_dataloader = self.get_calib_dataloader(calib_dataset)
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model = self.model
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quant_trainer.configure_model(model, self.quant_trainer_args, calib=True)
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model.eval()
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quant_trainer.enable_calibration(model)
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logger.info("***** Running calibration *****")
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logger.info(f" Num examples = {self.calib_num}")
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logger.info(f" Batch size = {calib_dataloader.batch_size}")
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for step, inputs in enumerate(calib_dataloader):
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# Prediction step
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loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only=True)
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if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
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break
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quant_trainer.finish_calibration(model, self.quant_trainer_args)
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self.model = model
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def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str = "eval"):
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eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset
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eval_dataloader = self.get_eval_dataloader(eval_dataset)
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eval_examples = self.eval_examples if eval_examples is None else eval_examples
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# Temporarily disable metric computation, we will do it in the loop here.
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compute_metrics = self.compute_metrics
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self.compute_metrics = None
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eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
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try:
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output = eval_loop(
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eval_dataloader,
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description="Evaluation",
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# No point gathering the predictions if there are no metrics, otherwise we defer to
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# self.args.prediction_loss_only
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prediction_loss_only=True if compute_metrics is None else None,
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ignore_keys=ignore_keys,
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)
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finally:
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self.compute_metrics = compute_metrics
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if self.post_process_function is not None and self.compute_metrics is not None:
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eval_preds = self.post_process_function(eval_examples, eval_dataset, output.predictions)
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metrics = self.compute_metrics(eval_preds)
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# Prefix all keys with metric_key_prefix + '_'
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for key in list(metrics.keys()):
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if not key.startswith(f"{metric_key_prefix}_"):
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metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
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self.log(metrics)
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else:
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metrics = {}
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if self.args.tpu_metrics_debug or self.args.debug:
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# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
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xm.master_print(met.metrics_report())
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self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics)
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return metrics
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def predict(self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test"):
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predict_dataloader = self.get_test_dataloader(predict_dataset)
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# Temporarily disable metric computation, we will do it in the loop here.
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compute_metrics = self.compute_metrics
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self.compute_metrics = None
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eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
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try:
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output = eval_loop(
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predict_dataloader,
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description="Prediction",
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# No point gathering the predictions if there are no metrics, otherwise we defer to
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# self.args.prediction_loss_only
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prediction_loss_only=True if compute_metrics is None else None,
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ignore_keys=ignore_keys,
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)
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finally:
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self.compute_metrics = compute_metrics
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if self.post_process_function is None or self.compute_metrics is None:
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return output
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predictions = self.post_process_function(predict_examples, predict_dataset, output.predictions, "predict")
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metrics = self.compute_metrics(predictions)
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# Prefix all keys with metric_key_prefix + '_'
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for key in list(metrics.keys()):
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if not key.startswith(f"{metric_key_prefix}_"):
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metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
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return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=metrics)
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def save_onnx(self, output_dir="./"):
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eval_dataset = self.eval_dataset
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eval_dataloader = self.get_eval_dataloader(eval_dataset)
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batch = next(iter(eval_dataloader))
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# saving device - to make it consistent
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# convert to tuple
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input_tuple = tuple(v.to(device) for k, v in batch.items())
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logger.info("Converting model to be onnx compatible")
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from pytorch_quantization.nn import TensorQuantizer
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TensorQuantizer.use_fb_fake_quant = True
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model = self.model.to(device)
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model.eval()
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model.float()
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model_to_save = model.module if hasattr(model, "module") else model
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quant_trainer.configure_model(model_to_save, self.quant_trainer_args)
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output_model_file = os.path.join(output_dir, "model.onnx")
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logger.info(f"exporting model to {output_model_file}")
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axes = {0: "batch_size", 1: "seq_len"}
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torch.onnx.export(
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model_to_save,
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input_tuple,
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output_model_file,
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export_params=True,
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opset_version=13,
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do_constant_folding=True,
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input_names=["input_ids", "attention_mask", "token_type_ids"],
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output_names=["output_start_logits", "output_end_logits"],
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dynamic_axes={
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"input_ids": axes,
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"attention_mask": axes,
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"token_type_ids": axes,
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"output_start_logits": axes,
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"output_end_logits": axes,
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},
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verbose=True,
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)
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logger.info("onnx export finished")
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