seq2seq_trainer.py

# Copyright 2020 The HuggingFace Team. 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, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
import os

from transformers import PreTrainedModel, Trainer, logging, TrainerCallback
from transformers.file_utils import is_torch_tpu_available
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode


if is_fairscale_available():
    from fairscale.optim import OSS


logger = logging.get_logger(__name__)

arg_to_scheduler = {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    "constant": get_constant_schedule,
    "constant_w_warmup": get_constant_schedule_with_warmup,
}

class UploaderCallback(TrainerCallback):
    def __init__(self, gcp, output_dir, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.gcp = gcp
        self.output_dir = output_dir 
    
    def on_train_end(self, args, state, control, **kwargs):
        print('INFO: Training done. Copying traning files to GCP.')
        os.system('gsutil -m cp -r "{}" "{}"'.format(self.output_dir ,self.gcp ))

class Seq2SeqTrainer(Trainer):
    def __init__(self, config=None, data_args=None, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if config is None:
            assert isinstance(
                self.model, PreTrainedModel
            ), f"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is {self.model.__class__}"
            self.config = self.model.config
        else:
            self.config = config

        self.data_args = data_args
        self.vocab_size = self.config.tgt_vocab_size if isinstance(self.config, FSMTConfig) else self.config.vocab_size

        if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
            assert (
                self.config.pad_token_id is not None
            ), "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss calculation or doing label smoothing."

        if self.config.pad_token_id is None and self.config.eos_token_id is not None:
            logger.warn(
                f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for padding.."
            )

        if self.args.label_smoothing == 0:
            self.loss_fn = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
        else:
            # dynamically import label_smoothed_nll_loss
            from utils import label_smoothed_nll_loss

            self.loss_fn = label_smoothed_nll_loss

    def create_optimizer_and_scheduler(self, num_training_steps: int):
        """
        Setup the optimizer and the learning rate scheduler.
        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass.
        """
        if self.optimizer is None:
            no_decay = ["bias", "LayerNorm.weight"]
            optimizer_grouped_parameters = [
                {
                    "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
                    "weight_decay": self.args.weight_decay,
                },
                {
                    "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
                    "weight_decay": 0.0,
                },
            ]
            optimizer_cls = Adafactor if self.args.adafactor else AdamW
            if self.args.adafactor:
                optimizer_cls = Adafactor
                optimizer_kwargs = {"scale_parameter": False, "relative_step": False}
            else:
                optimizer_cls = AdamW
                optimizer_kwargs = {
                    "betas": (self.args.adam_beta1, self.args.adam_beta2),
                    "eps": self.args.adam_epsilon,
                }
            optimizer_kwargs["lr"] = self.args.learning_rate
            if self.sharded_dpp:
                self.optimizer = OSS(
                    params=optimizer_grouped_parameters,
                    optim=optimizer_cls,
                    **optimizer_kwargs,
                )
            else:
                self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)

        if self.lr_scheduler is None:
            self.lr_scheduler = self._get_lr_scheduler(num_training_steps)
        else:  # ignoring --lr_scheduler
            logger.warn("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.")

    def _get_lr_scheduler(self, num_training_steps):
        schedule_func = arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
            scheduler = schedule_func(self.optimizer)
        elif self.args.lr_scheduler == "constant_w_warmup":
            scheduler = schedule_func(self.optimizer, num_warmup_steps=self.args.warmup_steps)
        else:
            scheduler = schedule_func(
                self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps
            )
        return scheduler

    def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
        if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
            return None
        elif is_torch_tpu_available():
            return get_tpu_sampler(self.train_dataset)
        else:
            if self.args.sortish_sampler:
                self.train_dataset.make_sortish_sampler(
                    self.args.per_device_train_batch_size,
                    distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),
                )

            return (
                RandomSampler(self.train_dataset)
                if self.args.local_rank == -1
                else DistributedSampler(self.train_dataset)
            )

    def _compute_loss(self, model, inputs, labels):
        if self.args.label_smoothing == 0:
            if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                # force training to ignore pad token
                logits = model(**inputs, use_cache=False)[0]
                loss = self.loss_fn(logits.view(-1, logits.shape[-1]), labels.view(-1))
            else:
                # compute usual loss via models
                loss, logits = model(**inputs, labels=labels, use_cache=False)[:2]
        else:
            # compute label smoothed loss
            logits = model(**inputs, use_cache=False)[0]
            lprobs = torch.nn.functional.log_softmax(logits, dim=-1)
            loss, _ = self.loss_fn(lprobs, labels, self.args.label_smoothing, ignore_index=self.config.pad_token_id)
        return loss, logits

    def compute_loss(self, model, inputs):
        labels = inputs.pop("labels")
        loss, _ = self._compute_loss(model, inputs, labels)
        return loss

    def prediction_step(
        self,
        model: nn.Module,
        inputs: Dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[List[str]] = None,
    ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform an evaluation step on :obj:`model` using obj:`inputs`.
        Subclass and override to inject custom behavior.
        Args:
            model (:obj:`nn.Module`):
                The model to evaluate.
            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.
                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument :obj:`labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (:obj:`bool`):
                Whether or not to return the loss only.
        Return:
            Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
            A tuple with the loss, logits and labels (each being optional).
        """
        inputs = self._prepare_inputs(inputs)

        gen_kwargs = {
            "max_length": self.data_args.val_max_target_length
            if self.data_args is not None
            else self.config.max_length,
            "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
        }

        if self.args.predict_with_generate and not self.args.prediction_loss_only:
            generated_tokens = self.model.generate(
                inputs["input_ids"],
                attention_mask=inputs["attention_mask"],
                **gen_kwargs,
            )
            # in case the batch is shorter than max length, the output should be padded
            if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])

        labels = inputs.pop("labels")
        with torch.no_grad():
            # compute loss on predict data
            loss, logits = self._compute_loss(model, inputs, labels)

        loss = loss.mean().detach()
        if self.args.prediction_loss_only:
            return (loss, None, None)

        logits = generated_tokens if self.args.predict_with_generate else logits

        if labels.shape[-1] < gen_kwargs["max_length"]:
            labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])

        return (loss, logits, labels)

    def _pad_tensors_to_max_len(self, tensor, max_length):
        # If PAD token is not defined at least EOS token has to be defined
        pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

        if pad_token_id is None:
            raise ValueError(
                f"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be padded to `max_length`={max_length}"
            )

        padded_tensor = pad_token_id * torch.ones(
            (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device
        )
        padded_tensor[:, : tensor.shape[-1]] = tensor
        return padded_tensor