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Zero
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import os
import re
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data.dataset import Dataset
from transformers import PreTrainedModel, Seq2SeqTrainer, Trainer, __version__
from transformers.configuration_utils import PretrainedConfig
from transformers.data.data_collator import DataCollator
from transformers.modeling_utils import unwrap_model
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
from transformers.trainer_utils import EvalPrediction
from transformers.training_args import TrainingArguments
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, is_sagemaker_mp_enabled, logging
from .composition import AdapterCompositionBlock, Fuse
if is_sagemaker_mp_enabled():
import smdistributed.modelparallel.torch as smp
logger = logging.get_logger(__name__)
class AdapterTrainer(Trainer):
def __init__(
self,
model: Union[PreTrainedModel, nn.Module] = None,
args: TrainingArguments = None,
data_collator: Optional[DataCollator] = None,
train_dataset: Optional[Dataset] = None,
eval_dataset: Optional[Dataset] = None,
tokenizer: Optional[PreTrainedTokenizerBase] = None,
model_init: Callable[[], PreTrainedModel] = None,
compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
callbacks: Optional[List[TrainerCallback]] = None,
adapter_names: Optional[List[List[str]]] = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
preprocess_logits_for_metrics: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = None,
):
if model is not None:
model_quantized = getattr(model, "is_quantized", False)
model.is_quantized = False
super().__init__(
model,
args,
data_collator,
train_dataset,
eval_dataset,
tokenizer=tokenizer,
model_init=model_init,
compute_metrics=compute_metrics,
callbacks=[AdapterTrainerCallback(self)] + callbacks if callbacks else [AdapterTrainerCallback(self)],
optimizers=optimizers,
preprocess_logits_for_metrics=preprocess_logits_for_metrics,
)
if model is not None:
model.is_quantized = model_quantized
if adapter_names is not None:
self.model.set_active_adapters(adapter_names)
# Set the defaults for loading/ saving model & adapters
if isinstance(self.model, PreTrainedModel):
model_frozen = getattr(self.model.base_model, "model_frozen", False)
else:
model_frozen = False
if model_frozen and self.model.active_adapters:
# Check if training AdapterFusion
self.train_adapter_fusion = (
isinstance(self.model.active_adapters, Fuse)
or isinstance(self.model.active_adapters, AdapterCompositionBlock)
and any([isinstance(child, Fuse) for child in self.model.active_adapters.children])
)
if self.model.active_adapters is None:
raise ValueError(
"Expected a model with an active adapter setup."
"If you want to fully finetune the model use the Trainer class."
)
if (self.label_names is None or len(self.label_names) < 1) and self.model.active_head is not None:
all_label_names = set()
for head in self.model._active_heads:
all_label_names |= set(self.model.heads[head].get_label_names())
self.label_names = list(all_label_names)
def create_optimizer(self):
"""
Setup the optimizer.
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 `optimizers`, or subclass and override this method in a subclass.
"""
opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
if self.optimizer is None:
decay_parameters = self.get_decay_parameter_names(opt_model)
if hasattr(self.model, "config") and hasattr(self.model.config, "adapters"):
match_str = r"adapter_fusion_layer\..*\.value"
decay_parameters = [name for name in decay_parameters if not re.match(match_str, name)]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
],
"weight_decay": self.args.weight_decay,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
],
"weight_decay": 0.0,
},
]
optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)
self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
if is_sagemaker_mp_enabled():
self.optimizer = smp.DistributedOptimizer(self.optimizer)
return self.optimizer
def _save(self, output_dir: Optional[str] = None, state_dict=None):
# If we are executing this function, we are the process zero, so we don't check for that.
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving model checkpoint to {output_dir}")
# Save a trained model and configuration using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
if not isinstance(self.model, PreTrainedModel):
if isinstance(unwrap_model(self.model), PreTrainedModel):
if state_dict is None:
state_dict = self.model.state_dict()
unwrap_model(self.model).save_pretrained(output_dir, state_dict=state_dict)
else:
logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
if state_dict is None:
state_dict = self.model.state_dict()
torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
else:
self.model.save_all_adapters(output_dir)
if self.train_adapter_fusion:
self.model.save_all_adapter_fusions(output_dir)
if hasattr(self.model, "heads"):
self.model.save_all_heads(output_dir)
if self.tokenizer is not None:
self.tokenizer.save_pretrained(output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
def _load_from_checkpoint(self, resume_from_checkpoint):
args = self.args
if os.path.isfile(os.path.join(resume_from_checkpoint, WEIGHTS_NAME)):
logger.info(f"Loading model from {resume_from_checkpoint}).")
if os.path.isfile(os.path.join(resume_from_checkpoint, CONFIG_NAME)):
config = PretrainedConfig.from_json_file(os.path.join(resume_from_checkpoint, CONFIG_NAME))
checkpoint_version = config.transformers_version
if checkpoint_version is not None and checkpoint_version != __version__:
logger.warn(
f"You are resuming training from a checkpoint trained with {checkpoint_version} of "
f"Transformers but your current version is {__version__}. This is not recommended and could "
"yield to errors or unwanted behaviors."
)
if args.deepspeed:
# will be resumed in deepspeed_init
pass
else:
adapter_loaded = False
if os.path.isdir(resume_from_checkpoint):
adapter_loaded = self._load_adapters(resume_from_checkpoint)
self._load_adapter_fusions(resume_from_checkpoint)
# Save all heads for a model with heads
if hasattr(self.model, "heads"):
self._load_heads(resume_from_checkpoint)
if not adapter_loaded:
raise Exception("Can't find a valid checkpoint at {}".format(resume_from_checkpoint))
def _load_adapters(self, resume_from_checkpoint):
adapter_loaded = False
for file_name in os.listdir(resume_from_checkpoint):
if os.path.isdir(os.path.join(resume_from_checkpoint, file_name)):
if "," not in file_name and "adapter_config.json" in os.listdir(
os.path.join(resume_from_checkpoint, file_name)
):
self.model.load_adapter(os.path.join(os.path.join(resume_from_checkpoint, file_name)))
adapter_loaded = True
return adapter_loaded
def _load_adapter_fusions(self, resume_from_checkpoint):
for file_name in os.listdir(resume_from_checkpoint):
if os.path.isdir(os.path.join(resume_from_checkpoint, file_name)):
if "," in file_name:
self.model.load_adapter_fusion(os.path.join(resume_from_checkpoint, file_name))
def _load_heads(self, resume_from_checkpoint):
for file_name in os.listdir(resume_from_checkpoint):
if os.path.isdir(os.path.join(resume_from_checkpoint, file_name)):
if "," not in file_name and "head_config.json" in os.listdir(
os.path.join(resume_from_checkpoint, file_name)
):
self.model.load_head(os.path.join(resume_from_checkpoint, file_name))
def _load_best_model(self):
model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
logger.info(
f"Loading best adapter(s) from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
)
# attempt to re-load all adapters from checkpoint
for adapter in model.adapters_config.adapters:
adapter_dir = os.path.join(self.state.best_model_checkpoint, adapter)
if os.path.exists(adapter_dir):
model.load_adapter(adapter_dir)
model.adapter_to(adapter, device=self.args.device)
if self.train_adapter_fusion:
logger.info(
f"Loading best adapter fusion(s) from {self.state.best_model_checkpoint} (score:"
f" {self.state.best_metric})."
)
# attempt to re-load all adapter fusions from checkpoint
for fusion in model.adapters_config.fusions:
fusion_dir = os.path.join(self.state.best_model_checkpoint, fusion)
if os.path.exists(fusion_dir):
model.load_adapter_fusion(fusion_dir)
model.adapter_fusion_to(fusion, device=self.args.device)
class AdapterTrainerCallback(TrainerCallback):
def __init__(self, trainer):
super().__init__()
self.trainer = trainer
def on_train_begin(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
model = kwargs.pop("model")
model_frozen = getattr(model.base_model, "model_frozen", False)
if not model_frozen:
raise ValueError(
"The pre-trained model weights are not frozen. For training adapters, please call the train_adapter()"
" method"
)
def on_step_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
# apply adapter fusion weight regularization on the value matrix
model = kwargs.pop("model")
if self.trainer.train_adapter_fusion:
fusion_reg_loss = model.base_model.get_fusion_regularization_loss()
if fusion_reg_loss is not None:
fusion_reg_loss.backward()
class Seq2SeqAdapterTrainer(AdapterTrainer, Seq2SeqTrainer):
pass
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