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# Copyright 2023 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.
import warnings
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
from datasets import Dataset
from torch.utils.data import DataLoader
from transformers import (
DataCollator,
DataCollatorForLanguageModeling,
DataCollatorForSeq2Seq,
PreTrainedModel,
PreTrainedTokenizerBase,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import EvalLoopOutput
from ..core import PPODecorators
from ..import_utils import is_peft_available
if is_peft_available():
from peft import PeftModel
class IterativeSFTTrainer(Trainer):
"""
The IterativeSFTTrainer can be used to finetune models with methods that requires some steps between optimization.
Attributes:
**model** (`PreTrainedModel`) -- Model to be optimized, either an 'AutoModelForCausalLM' or an 'AutoModelForSeq2SeqLM'.
Check the documentation of `PreTrainedModel` for more details.
**args** (`transformers.TrainingArguments`): -- The arguments to use for training.
**tokenizer** (`PreTrainedTokenizerBase`) -- Tokenizer to be used for encoding the
data. Check the documentation of `transformers.PreTrainedTokenizer` and
`transformers.PreTrainedTokenizerFast` for more details.
**optimizers** (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): -- The optimizer and scheduler to use for training.
**data_collator** (Union[DataCollatorForLanguageModeling, DataCollatorForSeq2Seq], *optional*) -- Data collator to be used for training and
passed along the dataloader.
**eval_dataset** (`datasets.Dataset`): The dataset to use for evaluation.
**max_length** (`int`, defaults to `None`): -- The maximum length of the input.
**truncation_mode** (`str`, defaults to `keep_end`): -- The truncation mode to use, either `keep_end` or `keep_start`.
**preprocess_logits_for_metrics** (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): -- The function to use to preprocess the logits before computing the metrics.
**compute_metrics** (`Callable[[EvalPrediction], Dict]`, *optional*): -- The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to metric values.
**optimize_device_cache ** (`bool`, *optional*, defaults to `False`) -- Optimize CUDA cache for slightly more memory-efficient training.
"""
def __init__(
self,
model: PreTrainedModel = None,
args: TrainingArguments = None,
tokenizer: PreTrainedTokenizerBase = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (
None,
None,
),
data_collator: Optional[DataCollator] = None,
eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
max_length: Optional[int] = None,
truncation_mode: Optional[str] = "keep_end",
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
compute_metrics: Optional[Callable[[EvalLoopOutput], Dict]] = None,
optimize_device_cache: Optional[bool] = False,
):
# Step 0: check positional arguments validity
if not isinstance(tokenizer, (PreTrainedTokenizerBase)):
raise ValueError(f"tokenizer must be a PreTrainedTokenizerBase like a PreTrainedTokenizer or a PreTrainedTokenizerFast, got {type(tokenizer)}")
if not isinstance(model, PreTrainedModel):
raise ValueError(f"model must be a PreTrainedModel, got {type(model)}")
if not model.can_generate():
warnings.warn(f"The current model class {type(model)} is not compatible with `.generate()`" "Please make sure that this is intended.")
if optimizers[1] is None and args.max_steps == -1:
raise ValueError("When no scheduler is provided, you need to set the total number of training steps to perform `max_steps`")
self.is_encoder_decoder = getattr(model.config, "is_encoder_decoder", False)
self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)
self.tokenizer = tokenizer
if data_collator is None:
if self.is_encoder_decoder:
warnings.warn("No data collator is provided. Using 'DataCollatorForSeq2Seq' with" "'labels_pad_token_id' set to '-100' and 'pad_to_multiple_of' set to 8.")
self.data_collator = DataCollatorForSeq2Seq(tokenizer, label_pad_token_id=-100, pad_to_multiple_of=8)
else:
warnings.warn("No data collator is provided. Using 'DataCollatorForLanguageModeling'")
self.data_collator = DataCollatorForLanguageModeling(self.tokenizer, mlm=False)
else:
self.data_collator = data_collator
self.max_length = max_length
self.truncation_mode = truncation_mode
self.optimize_device_cache = optimize_device_cache
super().__init__(
model=model,
args=args,
data_collator=self.data_collator,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
optimizers=optimizers,
preprocess_logits_for_metrics=preprocess_logits_for_metrics,
)
self.create_optimizer_and_scheduler(self.args.max_steps)
# prepare model, optimizer and lr_scheduler
self.model, self.optimizer, self.lr_scheduler = self.accelerator.prepare(self.model, self.optimizer, self.lr_scheduler)
self.tokenizer.truncation_side = "left" if self.truncation_mode == "keep_end" else "right"
if not hasattr(self, "accelerator"):
raise AttributeError("Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`.")
PPODecorators.optimize_device_cache = self.optimize_device_cache
def prepare_model_inputs(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, labels: torch.Tensor):
if attention_mask is None:
attention_mask = [torch.ones_like(ids) for ids in input_ids]
if self.is_encoder_decoder:
input_data = self.data_collator([{"input_ids": ids, "attention_mask": att, "labels": lab} for ids, att, lab in zip(input_ids, attention_mask, labels)]).to(self.model.device)
input_data.pop("decoder_input_ids", None) # This is directly computed inside the model
input_data["labels"][input_data["labels"] == self.tokenizer.pad_token_id] = -100
else:
input_data = self.data_collator([{"input_ids": ids, "attention_mask": att} for ids, att in zip(input_ids, attention_mask)]).to(self.model.device)
# truncate in case the user has provided input_ids, attention_mask and labels
if self.max_length is not None:
if self.truncation_mode == "keep_start":
input_data = {k: v[: self.max_length] for k, v in input_data.items()}
elif self.truncation_mode == "keep_end":
input_data = {k: v[-self.max_length :] for k, v in input_data.items()}
else:
raise ValueError(f"Unknown truncation mode: {self.truncation_mode}")
return input_data
@staticmethod
def _step_safety_checker(
input_ids: List[torch.LongTensor],
attention_mask: List[torch.LongTensor],
labels: List[torch.LongTensor],
texts: List[str],
texts_labels: List[str],
):
"""
Check if the input data is valid for training.
Args:
input_ids (List[`torch.LongTensor`]):
List of tensors containing the input_ids
attention_mask (List[`torch.LongTensor`]):
List of tensors containing the attention_mask
labels (List[`torch.FloatTensor`]):
List of tensors containing the labels
texts (List[`str`]):
List of string containing the text input.
texts_labels (List[`str`]):
List of string containing the text labels.
Returns:
`tuple`: The input data.
"""
if texts is None:
if attention_mask is None:
for name, tensor_list in zip(["input_ids", "labels"], [input_ids, labels]):
if not isinstance(tensor_list, list):
raise ValueError(f"{name} must be a list of tensors - got {type(tensor_list)}")
if not isinstance(tensor_list[0], torch.Tensor):
raise ValueError(f"Elements in {name} must be tensors - got {type(tensor_list[0])}")
else:
for name, tensor_list in zip(["input_ids", "attention_mask", "labels"], [input_ids, attention_mask, labels]):
if not isinstance(tensor_list, list):
raise ValueError(f"{name} must be a list of tensors - got {type(tensor_list)}")
if not isinstance(tensor_list[0], torch.Tensor):
raise ValueError(f"Elements in {name} must be tensors - got {type(tensor_list[0])}")
else:
if not isinstance(texts, list):
raise ValueError(f"'text' must be a list of strings - got {type(texts)}")
if not isinstance(texts[0], str):
raise ValueError(f"Elements in 'text' must be strings - got {type(texts[0])}")
if texts_labels is not None:
if not isinstance(texts_labels, list):
raise ValueError(f"'text_labels' must be a list of strings - got {type(texts_labels)}")
if not isinstance(texts_labels[0], str):
raise ValueError(f"Elements in 'text_labels' must be strings - got {type(texts_labels[0])}")
return input_ids, attention_mask, labels, texts, texts_labels
@PPODecorators.empty_device_cache()
def step(
self,
input_ids: Optional[List[torch.LongTensor]] = None,
attention_mask: Optional[List[torch.LongTensor]] = None,
labels: Optional[List[torch.LongTensor]] = None,
texts: Optional[List[str]] = None,
texts_labels: Optional[List[str]] = None,
):
"""
Run an optimisation step given a list of input_ids, attention_mask, and labels or a list of text and text_labels.
Args:
input_ids (List[`torch.LongTensor`]):
List of tensors containing the input_ids (if not provided, text will be used)
attention_mask (List[`torch.LongTensor`], , *optional*):
List of tensors containing the attention_mask
labels (List[`torch.FloatTensor`], *optional*):
List of tensors containing the labels (if set to None, will default to input_ids)
texts (List[`str`], *optional*):
List of strings containing the text input (if not provided, input_ids will directly be used)
texts_labels (List[`str`], *optional*):
List of strings containing the text labels (if set to None, will default to text)
Returns:
`dict[str, Any]`: A summary of the training statistics
"""
self.model.train()
if self.state.global_step == 0:
self.tr_loss = torch.tensor(0.0).to(self.args.device)
self._globalstep_last_logged = self.state.global_step
if input_ids is None and texts is None:
raise ValueError("Step should include `input_ids` or `texts` as keyword arguments.")
elif input_ids is not None and texts is not None:
warnings.warn("Both 'input_ids' and 'texts' are provided. 'input_ids' will be overwritten using inputs provided by the 'texts' keyword argument.")
if labels is None and texts_labels is None and self.is_encoder_decoder:
raise ValueError("No 'labels' or 'text_labels' are provided. When using an encoder-decoder architecture, 'labels' or 'text_labels' must be passed.")
input_ids, attention_mask, labels, texts, texts_labels = self._step_safety_checker(input_ids, attention_mask, labels, texts, texts_labels)
if texts is not None:
model_inputs = self.tokenizer(texts, max_length=self.max_length, truncation=True, padding=True, return_tensors="pt")
input_ids, attention_mask = model_inputs["input_ids"], model_inputs["attention_mask"]
if texts_labels is not None:
labels = self.tokenizer(texts, max_length=self.max_length, truncation=True, padding=True, return_tensors="pt")["input_ids"]
if labels is None:
warnings.warn("No labels are provided. Setting labels to input_ids")
labels = input_ids
model_inputs = self.prepare_model_inputs(input_ids, attention_mask, labels)
model_inputs_names = list(model_inputs.keys())
batch_dict = {}
batch_dict.update(model_inputs)
def collator(data):
return_dict = dict()
for key in data[0]:
if key in ["input_ids", "attention_mask", "labels"]:
return_dict[key] = torch.stack([d[key] for d in data]).to(self.model.device)
return return_dict
batch_data = Dataset.from_dict(batch_dict)
batch_data.set_format("torch")
step_dataloader = DataLoader(
batch_data,
batch_size=self.args.per_device_train_batch_size,
shuffle=True,
collate_fn=collator,
)
for _, batch in enumerate(step_dataloader):
with self.accelerator.accumulate(self.model):
model_inputs = {k: batch[k] for k in model_inputs_names}
loss = self.compute_loss(self.model, model_inputs)
if self.args.n_gpu > 1:
loss = loss.mean()
tr_loss_step = loss.detach()
self.accelerator.backward(loss)
if self.accelerator.sync_gradients and self.args.max_grad_norm is not None:
self.accelerator.clip_grad_norm_(
self.model.parameters(),
self.args.max_grad_norm,
)
self.optimizer.step()
self.optimizer.zero_grad()
if self.lr_scheduler is not None:
self.lr_scheduler.step()
self.state.global_step += 1
# update stats etc
self.tr_loss += tr_loss_step
self._maybe_log_save_evaluate()
def _maybe_log_save_evaluate(self):
# check if eval is required
if self.args.eval_steps is not None:
if self.state.global_step % self.args.eval_steps == 0 and self.state.global_step != 0:
self.evaluate(self.eval_dataset)
# check if logging is required
if self.args.logging_steps is not None:
if self.state.global_step % self.args.logging_steps == 0 and self.state.global_step != 0:
logs: Dict[str, float] = {}
tr_loss_scalar = self._nested_gather(self.tr_loss).mean().item()
# reset tr_loss to zero
self.tr_loss -= self.tr_loss
logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
logs["learning_rate"] = self._get_learning_rate()
self._globalstep_last_logged = self.state.global_step
self.log(logs)
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