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