wehub-resource-sync
240 lines
8.9 KiB
Python
240 lines
8.9 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. 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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import time
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from typing import Any, Dict, List, Optional, Tuple, Union
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import numpy as np
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import paddle
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import paddle.nn as nn
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from paddle.optimizer.lr import LambdaDecay
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from rouge import Rouge
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from sklearn.metrics import accuracy_score
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from paddlenlp.metrics import BLEU
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from paddlenlp.trainer import PrinterCallback, ProgressCallback, Trainer
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from paddlenlp.trainer.integrations import TrainerCallback
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from paddlenlp.utils.log import logger
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class AverageStatistical(object):
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def __init__(self):
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self.reset()
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def reset(self):
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self.total_cnt = 0
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self.time = 0
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def record(self, val, cnt=1):
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self.time += val
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self.total_cnt += cnt
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def get_average(self):
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if self.total_cnt == 0:
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return 0
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return self.time / self.total_cnt
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def get_average_per_sec(self):
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if self.time == 0.0:
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return 0.0
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return float(self.total_cnt) / self.time
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def get_total_cnt(self):
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return self.total_cnt
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def get_total_time(self):
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return self.time
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class BenchmarkCallback(TrainerCallback):
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def __init__(self, benchmark=True, profiler_options=None):
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self.benchmark = benchmark
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self.profiler_options = profiler_options
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def on_train_begin(self, args, state, control, **kwargs):
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assert args.gradient_accumulation_steps == 1 and not args.do_eval and not args.do_predict
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if self.benchmark:
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self.reader_cost_avg = AverageStatistical()
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def on_epoch_begin(self, args, state, control, **kwargs):
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if self.benchmark:
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self.epoch_start = time.time()
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self.batch_start = time.time()
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def on_step_begin(self, args, state, control, **kwargs):
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if self.benchmark:
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self.reader_cost_avg.record(time.time() - self.batch_start)
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def on_step_end(self, args, state, control, **kwargs):
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if self.benchmark:
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self.batch_start = time.time()
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if control.should_log:
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self.maybe_log_save_evaluate_start = time.time()
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def on_log(self, args, state, control, logs=None, **kwargs):
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if self.benchmark:
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if logs is not None and "interval_steps_per_second" in logs:
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self.batch_start = self.batch_start + (time.time() - self.maybe_log_save_evaluate_start)
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ips = logs["interval_steps_per_second"] * args.train_batch_size
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avg_batch_cost = 1 / logs["interval_steps_per_second"]
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max_mem_reserved_msg = ""
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max_mem_allocated_msg = ""
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if paddle.device.is_compiled_with_cuda():
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max_mem_reserved_msg = (
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f"max_mem_reserved: {paddle.device.cuda.max_memory_reserved() // (1024 ** 2)} MB,"
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)
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max_mem_allocated_msg = (
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f"max_mem_allocated: {paddle.device.cuda.max_memory_allocated() // (1024 ** 2)} MB"
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)
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logger.info(
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"global step %d / %d, loss: %f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, "
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"avg_samples: %.5f, ips: %.5f sample/sec, %s %s"
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% (
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state.global_step,
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state.max_steps,
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logs["loss"],
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self.reader_cost_avg.get_average(),
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avg_batch_cost,
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args.train_batch_size,
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ips,
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max_mem_reserved_msg,
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max_mem_allocated_msg,
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)
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)
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self.reader_cost_avg.reset()
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def on_epoch_end(self, args, state, control, **kwargs):
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if self.benchmark:
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train_epoch_cost = time.time() - self.epoch_start
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logger.info("train epoch: %d, epoch_cost: %.5f s" % (state.epoch, train_epoch_cost))
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class LlamaTrainer(Trainer):
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def __init__(self, do_generation: bool, **kwargs):
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super().__init__(**kwargs)
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self.add_callback(BenchmarkCallback(benchmark=True, profiler_options=self.args.profiler_options))
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if self.args.disable_tqdm:
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self.pop_callback(PrinterCallback)
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else:
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self.pop_callback(ProgressCallback)
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self.do_generation = do_generation
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def prediction_step(
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self,
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model: nn.Layer,
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inputs: Dict[str, Union[paddle.Tensor, Any]],
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prediction_loss_only: bool,
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ignore_keys: Optional[List[str]] = None,
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) -> Tuple[Optional[paddle.Tensor], Optional[paddle.Tensor], Optional[paddle.Tensor]]:
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if prediction_loss_only:
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return super().prediction_step(model, inputs, prediction_loss_only, ignore_keys)
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elif not self.do_generation:
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loss, logits, labels = super().prediction_step(model, inputs, prediction_loss_only, ignore_keys)
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# argmax here to avoid gather all logits, which is too memory-consuming.
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# keepdim in order to maintain the same shape as logits
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return (loss, logits.argmax(axis=-1, keepdim=True), labels)
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model.eval()
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preds = model.generate(
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input_ids=inputs["input_ids"],
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attention_mask=inputs["attention_mask"],
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max_length=self.args.tgt_length,
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min_length=0,
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use_cache=True,
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temperature=1.0,
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top_k=1,
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top_p=1.0,
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repetition_penalty=1.0,
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decode_strategy="sampling",
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)[0]
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all_labels = []
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for label in inputs["labels"].numpy():
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label = [x for x in label[label != self.tokenizer.pad_token_id]]
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all_labels.append(label)
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max_label_length = max([len(x) for x in all_labels])
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for index, labels in enumerate(all_labels):
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all_labels[index] = labels + [-100] * (max_label_length - len(labels))
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return (None, paddle.to_tensor(preds), paddle.to_tensor(all_labels))
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def create_scheduler(self, num_training_steps: int):
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num_warmup_steps = (
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self.args.warmup_steps if self.args.warmup_steps > 0 else self.args.warmup_ratio * num_training_steps
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)
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def lr_lambda(current_step: int):
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if current_step < num_warmup_steps:
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return float(current_step) / float(max(1, num_warmup_steps))
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else:
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decay_step_ratio = (current_step - num_warmup_steps) / (num_training_steps - num_warmup_steps)
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return 1.0 - (1.0 - self.args.lr_decay_ratio) * decay_step_ratio
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if self.lr_scheduler is None:
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self.lr_scheduler = LambdaDecay(self.args.learning_rate, lr_lambda, last_epoch=-1)
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return self.lr_scheduler
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def log(self, logs: Dict[str, float], **kwargs) -> None:
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if "loss" in logs:
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logs["ppl"] = np.exp(logs["loss"])
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if "eval_loss" in logs:
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logs["eval_ppl"] = np.exp(logs["eval_loss"])
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super(LlamaTrainer, self).log(logs, **kwargs)
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def compute_metrics(preds, targets):
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assert len(preds) == len(targets), (
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"The length of pred_responses should be equal to the length of "
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"target_responses. But received {} and {}.".format(len(preds), len(targets))
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)
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rouge = Rouge()
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bleu4 = BLEU(n_size=4)
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scores = []
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for pred, target in zip(preds, targets):
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try:
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score = rouge.get_scores(" ".join(pred), " ".join(target))
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scores.append([score[0]["rouge-1"]["f"], score[0]["rouge-2"]["f"], score[0]["rouge-l"]["f"]])
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except ValueError:
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scores.append([0, 0, 0])
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bleu4.add_inst(pred, [target])
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rouge1 = np.mean([i[0] for i in scores])
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rouge2 = np.mean([i[1] for i in scores])
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rougel = np.mean([i[2] for i in scores])
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rouge1 = round(rouge1, 4)
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rouge2 = round(rouge2, 4)
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rougel = round(rougel, 4)
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bleu4 = round(bleu4.score(), 4)
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return dict(
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rouge1=rouge1,
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rouge2=rouge2,
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rougel=rougel,
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bleu4=bleu4,
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)
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def compute_metrics_not_do_generation(eval_preds):
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flattened_preds = np.array(eval_preds.predictions).flatten()
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flattened_labels = np.array(eval_preds.label_ids).flatten()
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filtered_preds = flattened_preds[flattened_labels != -100]
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filtered_labels = flattened_labels[flattened_labels != -100]
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accuracy = accuracy_score(y_true=filtered_labels, y_pred=filtered_preds)
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return {
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"accuracy": accuracy,
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}
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