qwerrwe / src /axolotl /core /trainer_builder.py
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# pylint: disable=too-many-lines
"""
Builder for the training args and trainer
"""
import abc
import importlib
import logging
import math
import sys
from abc import abstractmethod
from dataclasses import dataclass, field
from functools import wraps
from pathlib import Path
from typing import List, Optional, Type, Union
import torch
import transformers
from datasets import Dataset
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
from transformers import (
EarlyStoppingCallback,
Trainer,
TrainerCallback,
TrainingArguments,
)
from transformers.trainer_utils import seed_worker
from trl import DPOTrainer
from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
from axolotl.utils.callbacks import (
EvalFirstStepCallback,
GPUStatsCallback,
LossWatchDogCallback,
SaveAxolotlConfigtoMlflowCallback,
SaveAxolotlConfigtoWandBCallback,
SaveBetterTransformerModelCallback,
bench_eval_callback_factory,
log_prediction_callback_factory,
)
from axolotl.utils.collators import (
BatchSamplerDataCollatorForSeq2Seq,
DataCollatorForSeq2Seq,
MambaDataCollator,
V2BatchSamplerDataCollatorForSeq2Seq,
)
from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
from axolotl.utils.schedulers import (
get_cosine_schedule_with_min_lr,
get_cosine_schedule_with_quadratic_warmup,
)
try:
import torch._dynamo # pylint: disable=ungrouped-imports
except ImportError:
pass
LOG = logging.getLogger("axolotl.core.trainer_builder")
@dataclass
class AxolotlTrainingArguments(TrainingArguments):
"""
Extend the base TrainingArguments for axolotl helpers
"""
model_type: Optional[str] = field(
default=None, metadata={"help": "HF model configuration model_type."}
)
lr_quadratic_warmup: bool = field(
default=False,
metadata={"help": "Use quadratic warmup for cosine scheduling."},
)
pretraining: bool = field(
default=False,
metadata={
"help": "Indicates to trainer whether we are doing continued pretraining."
},
)
sample_packing: bool = field(
default=False,
metadata={"help": "Use sample packing for efficient training."},
)
eval_sample_packing: Optional[bool] = field(
default=None,
metadata={"help": "Use sample packing for efficient evals."},
)
sample_packing_efficiency: float = field(
default=1.0,
metadata={"help": "Sample packing efficiency for calculating batch length."},
)
max_seq_length: int = field(
default=2048,
metadata={"help": "The maximum sequence length the model can handle"},
)
sample_packing_seq_len_multiplier: int = field(
default=1,
metadata={"help": "the multiplier for the max len for packed sequences"},
)
relora_steps: Optional[int] = field(
default=None,
metadata={"help": "how often to reset for ReLoRA"},
)
relora_warmup_steps: Optional[int] = field(
default=None,
metadata={"help": "how many warmup steps to take after reset for ReLoRA"},
)
bench_split: Optional[str] = field(
default="eval", metadata={"help": "The benchmark split to run on"}
)
bench_dataset: Optional[str] = field(
default="pharaouk/dharma-1/dharma_1_mini.json",
metadata={
"help": "Benchmark dataset to use: options are `mmlu-zs`, `mmlu-fs`, or the full path to the dataset file"
},
)
do_bench_eval: Optional[bool] = field(
default=False, metadata={"help": "Whether to run the Benchmark evaluation."}
)
max_bench_samples: Optional[int] = field(
default=None,
metadata={
"help": "If set, only evaluates on `max_bench_samples` of the benchmark dataset."
},
)
bench_source_max_len: int = field(
default=2048, metadata={"help": "Maximum source sequence length for bench."}
)
dataloader_prefetch_factor: Optional[int] = field(
default=None,
metadata={"help": "prefetch_factor argument to the dataloader"},
)
cosine_min_lr_ratio: Optional[float] = field(
default=None,
metadata={"help": "Minimum learning rate is min_lr_ratio * learning_rate"},
)
class AxolotlTrainer(Trainer):
"""
Extend the base Trainer for axolotl helpers
"""
args = None # type: AxolotlTrainingArguments
tag_names = ["axolotl"]
def __init__(
self,
*_args,
num_epochs=1,
bench_data_collator=None,
eval_data_collator=None,
**kwargs
):
self.num_epochs = num_epochs
self.bench_data_collator = bench_data_collator
self.eval_data_collator = eval_data_collator
super().__init__(*_args, **kwargs)
self.train_data_collator = self.data_collator
def create_scheduler(
self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
):
"""
Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.
Args:
num_training_steps (int): The number of training steps to do.
optimizer (torch.optim.Optimizer): The training optimizer
"""
use_cosine_quadratic = (
self.args.lr_scheduler_type == "cosine"
and self.args.lr_quadratic_warmup is True
)
use_cosine_min_lr = (
self.args.lr_scheduler_type == "cosine"
and self.args.cosine_min_lr_ratio is not None
)
# fmt: off
if self.lr_scheduler is None: # type: ignore # pylint: disable=access-member-before-definition
# fmt: on
if use_cosine_quadratic:
if use_cosine_min_lr:
LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup( # pylint: disable=attribute-defined-outside-init
optimizer,
num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
num_training_steps=num_training_steps,
)
elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
self.lr_scheduler = get_cosine_schedule_with_min_lr( # pylint: disable=attribute-defined-outside-init
optimizer,
num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
num_training_steps=num_training_steps,
min_lr_ratio=self.args.cosine_min_lr_ratio,
)
else:
return super().create_scheduler(num_training_steps, optimizer)
else:
if use_cosine_quadratic:
LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
if use_cosine_min_lr:
LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
return self.lr_scheduler
def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
if self.args.sample_packing and not self.args.pretraining:
return MultipackBatchSampler(
RandomSampler(self.train_dataset),
self.args.train_batch_size,
drop_last=True,
batch_max_len=self._train_batch_size * self.args.max_seq_length,
lengths=get_dataset_lengths(self.train_dataset),
packing_efficiency_estimate=self.args.sample_packing_efficiency,
)
return super()._get_train_sampler()
def _get_eval_sampler(
self, eval_dataset: Dataset
) -> Optional[torch.utils.data.Sampler]:
if self.args.sample_packing and self.args.eval_sample_packing is not False:
return MultipackBatchSampler(
SequentialSampler(eval_dataset),
self.args.per_device_eval_batch_size,
drop_last=True,
batch_max_len=self.args.eval_batch_size * self.args.max_seq_length,
lengths=get_dataset_lengths(eval_dataset),
packing_efficiency_estimate=self.args.sample_packing_efficiency,
)
return super()._get_eval_sampler(eval_dataset)
def get_train_dataloader(self) -> DataLoader:
if self.args.sample_packing and not self.args.pretraining:
train_dataset = self.train_dataset
if "length" in train_dataset.features.keys():
train_dataset = train_dataset.remove_columns(["length"])
data_collator = self.data_collator
dataloader_params = {
"batch_size": self._train_batch_size,
"collate_fn": data_collator,
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
}
if self.args.dataloader_prefetch_factor:
dataloader_params[
"prefetch_factor"
] = self.args.dataloader_prefetch_factor
sampler = self._get_train_sampler()
if isinstance(sampler, BatchSampler):
dataloader_params["batch_sampler"] = sampler
del dataloader_params["batch_size"]
else:
dataloader_params["sampler"] = sampler
dataloader_params["drop_last"] = self.args.dataloader_drop_last
dataloader_params["worker_init_fn"] = seed_worker
self.accelerator.even_batches = False
return self.accelerator.prepare_data_loader(
DataLoader(train_dataset, **dataloader_params)
)
return super().get_train_dataloader()
def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
if self.args.sample_packing and self.args.eval_sample_packing is False:
self.data_collator = ( # pylint: disable=attribute-defined-outside-init
self.eval_data_collator
)
dataloader = super().get_eval_dataloader(eval_dataset)
self.data_collator = ( # pylint: disable=attribute-defined-outside-init
self.train_data_collator
)
return dataloader
if self.args.sample_packing and self.args.eval_sample_packing is not False:
eval_dataset = (
eval_dataset if eval_dataset is not None else self.eval_dataset
)
eval_sampler = self._get_eval_sampler(eval_dataset)
eval_dataset = eval_dataset.remove_columns(["length"])
data_collator = self.data_collator
dataloader_params = {
"batch_size": self.args.eval_batch_size,
"collate_fn": data_collator,
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
}
if self.args.dataloader_prefetch_factor:
dataloader_params[
"prefetch_factor"
] = self.args.dataloader_prefetch_factor
if isinstance(eval_sampler, BatchSampler):
dataloader_params["batch_sampler"] = eval_sampler
del dataloader_params["batch_size"]
else:
dataloader_params["sampler"] = eval_sampler
dataloader_params["drop_last"] = self.args.dataloader_drop_last
self.accelerator.even_batches = False
return self.accelerator.prepare_data_loader(
DataLoader(eval_dataset, **dataloader_params)
)
return super().get_eval_dataloader(eval_dataset)
def _get_bench_sampler(
self, bench_dataset: Dataset
) -> Optional[torch.utils.data.Sampler]:
if self.args.world_size <= 1:
return SequentialSampler(bench_dataset)
return None
def get_bench_dataloader(
self,
bench_dataset: Dataset,
) -> DataLoader:
dataloader_params = {
"batch_size": self.args.eval_batch_size,
"collate_fn": self.bench_data_collator,
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
}
if self.args.dataloader_prefetch_factor:
dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
if not isinstance(bench_dataset, torch.utils.data.IterableDataset):
dataloader_params["sampler"] = self._get_bench_sampler(bench_dataset)
dataloader_params["drop_last"] = self.args.dataloader_drop_last
return DataLoader(bench_dataset, **dataloader_params)
# return self.accelerator.prepare(DataLoader(bench_dataset, **dataloader_params))
def compute_loss(self, model, inputs, return_outputs=False):
# use one's weighted cross entropy loss calc
# if self.args.sample_packing:
# labels = inputs.pop("labels")
# outputs = model(**inputs)
# loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
# return (loss, outputs) if return_outputs else loss
return super().compute_loss(model, inputs, return_outputs=return_outputs)
def _sanitize_kwargs_for_tagging(self, tag_names, kwargs=None):
if isinstance(tag_names, str):
tag_names = [tag_names]
if kwargs is not None:
if "tags" not in kwargs:
kwargs["tags"] = tag_names
elif "tags" in kwargs and isinstance(kwargs["tags"], list):
kwargs["tags"].extend(tag_names)
elif "tags" in kwargs and isinstance(kwargs["tags"], str):
tag_names.append(kwargs["tags"])
kwargs["tags"] = tag_names
return kwargs
@wraps(Trainer.push_to_hub)
def push_to_hub(self, *args, **kwargs) -> str:
"""
Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
"""
kwargs = self._sanitize_kwargs_for_tagging(
tag_names=self.tag_names, kwargs=kwargs
)
return super().push_to_hub(*args, **kwargs)
class AxolotlMambaTrainer(AxolotlTrainer):
"""
Mamba specific trainer to handle loss calculation
"""
tag_names = ["axolotl", "mamba"]
def compute_loss(
self,
model,
inputs,
return_outputs=False, # pylint: disable=unused-argument
):
input_ids = inputs.pop("input_ids")
lm_logits = model(input_ids).logits
labels = input_ids.to(lm_logits.device)
shift_logits = lm_logits[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = torch.nn.CrossEntropyLoss()
lm_loss = loss_fct(
shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
)
return lm_loss
class OneCycleLRSchedulerTrainer(AxolotlTrainer):
"""
Trainer subclass that uses the OneCycleLR scheduler
"""
tag_names = ["axolotl", "onecycle"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.lr_scheduler = None
def create_scheduler(
self,
num_training_steps: int,
optimizer: Optional[torch.optim.Optimizer] = None,
):
optimizer = self.optimizer if optimizer is None else optimizer
num_warmup_steps = self.args.get_warmup_steps(num_training_steps)
pct_start = num_warmup_steps / num_training_steps
self.lr_scheduler = OneCycleLR(
optimizer,
max_lr=self.args.learning_rate,
total_steps=num_training_steps,
pct_start=pct_start,
div_factor=6,
)
return self.lr_scheduler
class ReLoRATrainer(AxolotlTrainer):
"""
Trainer subclass that uses the OneCycleLR scheduler
"""
tag_names = ["axolotl", "relora"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.lr_scheduler = None
def create_scheduler(
self,
num_training_steps: int,
optimizer: Optional[torch.optim.Optimizer] = None,
):
optimizer = self.optimizer if optimizer is None else optimizer
lr_scheduler = super().create_scheduler(num_training_steps, optimizer)
if self.args.relora_steps:
warmup_steps = (
self.args.relora_warmup_steps if self.args.relora_warmup_steps else 10
)
self.lr_scheduler = ReLoRAScheduler(
optimizer,
lr_scheduler,
self.args.relora_steps,
warmup_steps,
)
else:
self.lr_scheduler = lr_scheduler
return self.lr_scheduler
class TrainerBuilderBase(abc.ABC):
"""
Base class for trainer builder
"""
_train_dataset = None
_eval_dataset = None
_model_ref = None
_peft_config = None
def __init__(self, cfg, model, tokenizer):
self.cfg = cfg
self.model = model
self.tokenizer = tokenizer
@property
def model_ref(self):
return self._model_ref
@model_ref.setter
def model_ref(self, model):
self._model_ref = model
@property
def train_dataset(self):
return self._train_dataset
@train_dataset.setter
def train_dataset(self, dataset):
self._train_dataset = dataset
@property
def eval_dataset(self):
return self._eval_dataset
@eval_dataset.setter
def eval_dataset(self, dataset):
self._eval_dataset = dataset
@property
def peft_config(self):
return self._peft_config
@peft_config.setter
def peft_config(self, peft_config):
self._peft_config = peft_config
@abstractmethod
def build(self, total_num_steps):
pass
def get_callbacks(self) -> List[TrainerCallback]:
callbacks = []
if self.cfg.use_wandb:
callbacks.append(
SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
)
return callbacks
@abstractmethod
def get_post_trainer_create_callbacks(self, trainer):
"""
Callbacks added after the trainer is created, usually b/c these need access to the trainer
"""
def hook_pre_create_training_args(self, training_arguments_kwargs):
# TODO
return training_arguments_kwargs
def hook_post_create_training_args(self, training_arguments):
# TODO
return training_arguments
def hook_pre_create_trainer(self, trainer_kwargs, trainer_cls):
# TODO
return trainer_kwargs, trainer_cls
def hook_post_create_trainer(self, trainer):
# TODO
return trainer
class HFCausalTrainerBuilder(TrainerBuilderBase):
"""
Build the HuggingFace training args/trainer for Causal models
"""
def get_callbacks(self):
callbacks = super().get_callbacks()
callbacks.append(GPUStatsCallback(self.cfg))
callbacks.append(EvalFirstStepCallback())
if self.cfg.relora_steps:
callbacks.append(ReLoRACallback(self.cfg))
if (
hasattr(self.model, "use_bettertransformer")
and self.model.use_bettertransformer is True
):
callbacks.append(SaveBetterTransformerModelCallback())
if self.cfg.use_wandb:
callbacks.append(
SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
)
if self.cfg.use_mlflow:
callbacks.append(
SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
)
if self.cfg.loss_watchdog_threshold is not None:
callbacks.append(LossWatchDogCallback(self.cfg))
return callbacks
def get_post_trainer_create_callbacks(self, trainer):
callbacks = []
if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
LogPredictionCallback = log_prediction_callback_factory(
trainer, self.tokenizer
)
callbacks.append(LogPredictionCallback(self.cfg))
if self.cfg.do_bench_eval:
callbacks.append(bench_eval_callback_factory(trainer, self.tokenizer))
if self.cfg.early_stopping_patience:
early_stop_cb = EarlyStoppingCallback(
self.cfg.early_stopping_patience,
)
callbacks.append(early_stop_cb)
return callbacks
def _get_trainer_cls(self):
if self.cfg.lr_scheduler == "one_cycle" and (
self.cfg.fsdp or self.cfg.adapter == "qlora"
):
return OneCycleLRSchedulerTrainer
if self.cfg.relora_steps:
return ReLoRATrainer
if self.cfg.model_config_type == "mamba":
return AxolotlMambaTrainer
return AxolotlTrainer
def build(self, total_num_steps):
warmup_steps = None
if self.cfg.warmup_steps is not None:
warmup_steps = self.cfg.warmup_steps
elif self.cfg.warmup_ratio is not None:
warmup_steps = max(int(self.cfg.warmup_ratio * total_num_steps), 0)
else:
warmup_steps = min(int(0.03 * total_num_steps), 100)
logging_steps = (
self.cfg.logging_steps
if self.cfg.logging_steps is not None
else max(min(int(0.005 * total_num_steps), 10), 1)
)
training_arguments_kwargs = {}
if self.cfg.bf16 == "full":
training_arguments_kwargs["bf16_full_eval"] = True
else:
training_arguments_kwargs["bf16"] = self.cfg.bf16
training_arguments_kwargs["fp16"] = (
self.cfg.fp16 and not self.cfg.bf16
) or False
training_arguments_kwargs["tf32"] = self.cfg.tf32
training_arguments_kwargs["warmup_steps"] = warmup_steps
training_arguments_kwargs["logging_steps"] = logging_steps
if self.cfg.seed:
training_arguments_kwargs["seed"] = self.cfg.seed
if self.cfg.gradient_checkpointing:
training_arguments_kwargs[
"gradient_checkpointing"
] = self.cfg.gradient_checkpointing
if self.cfg.gradient_checkpointing_kwargs is not None:
training_arguments_kwargs[
"gradient_checkpointing_kwargs"
] = self.cfg.gradient_checkpointing_kwargs
else:
training_arguments_kwargs["gradient_checkpointing_kwargs"] = {
"use_reentrant": False
}
if self.cfg.fsdp:
training_arguments_kwargs["fsdp"] = self.cfg.fsdp
if self.cfg.fsdp_config:
training_arguments_kwargs["fsdp_config"] = dict(self.cfg.fsdp_config)
# deepspeed
if self.cfg.deepspeed:
training_arguments_kwargs["deepspeed"] = self.cfg.deepspeed
if self.cfg.lr_quadratic_warmup is not None:
training_arguments_kwargs[
"lr_quadratic_warmup"
] = self.cfg.lr_quadratic_warmup
if self.cfg.adam_beta1:
training_arguments_kwargs["adam_beta1"] = self.cfg.adam_beta1
if self.cfg.adam_beta2:
training_arguments_kwargs["adam_beta2"] = self.cfg.adam_beta2
if self.cfg.adam_epsilon:
training_arguments_kwargs["adam_epsilon"] = self.cfg.adam_epsilon
if self.cfg.max_grad_norm:
training_arguments_kwargs["max_grad_norm"] = self.cfg.max_grad_norm
if self.cfg.hub_model_id:
training_arguments_kwargs["hub_model_id"] = self.cfg.hub_model_id
training_arguments_kwargs["push_to_hub"] = True
training_arguments_kwargs["hub_private_repo"] = True
training_arguments_kwargs["hub_always_push"] = True
if self.cfg.hub_strategy:
training_arguments_kwargs["hub_strategy"] = self.cfg.hub_strategy
if self.cfg.save_safetensors is not None:
training_arguments_kwargs["save_safetensors"] = self.cfg.save_safetensors
if self.cfg.sample_packing_eff_est:
training_arguments_kwargs[
"sample_packing_efficiency"
] = self.cfg.sample_packing_eff_est
if self.cfg.dataloader_pin_memory is not None:
training_arguments_kwargs[
"dataloader_pin_memory"
] = self.cfg.dataloader_pin_memory
if self.cfg.dataloader_num_workers is not None:
training_arguments_kwargs[
"dataloader_num_workers"
] = self.cfg.dataloader_num_workers
if self.cfg.dataloader_prefetch_factor is not None:
training_arguments_kwargs[
"dataloader_prefetch_factor"
] = self.cfg.dataloader_prefetch_factor
if self.cfg.dataloader_drop_last is not None:
training_arguments_kwargs[
"dataloader_drop_last"
] = self.cfg.dataloader_drop_last
elif self.cfg.sample_packing and self.cfg.eval_sample_packing is False:
training_arguments_kwargs["dataloader_drop_last"] = True
if self.cfg.val_set_size == 0:
# no eval set, so don't eval
training_arguments_kwargs["evaluation_strategy"] = "no"
elif self.cfg.eval_steps:
training_arguments_kwargs["evaluation_strategy"] = "steps"
training_arguments_kwargs["eval_steps"] = self.cfg.eval_steps
elif self.cfg.evaluation_strategy:
training_arguments_kwargs[
"evaluation_strategy"
] = self.cfg.evaluation_strategy
else:
# we have an eval set, but no steps defined, default to use epoch
training_arguments_kwargs["evaluation_strategy"] = "epoch"
if self.cfg.save_steps:
training_arguments_kwargs["save_strategy"] = "steps"
training_arguments_kwargs["save_steps"] = self.cfg.save_steps
elif self.cfg.save_strategy:
training_arguments_kwargs["save_strategy"] = self.cfg.save_strategy
else:
# default to saving each epoch if not defined
training_arguments_kwargs["save_strategy"] = "epoch"
if self.cfg.do_bench_eval:
training_arguments_kwargs["do_bench_eval"] = self.cfg.do_bench_eval
if self.cfg.bench_dataset:
training_arguments_kwargs["bench_dataset"] = self.cfg.bench_dataset
if self.cfg.metric_for_best_model:
training_arguments_kwargs[
"metric_for_best_model"
] = self.cfg.metric_for_best_model
if self.cfg.greater_is_better:
training_arguments_kwargs["greater_is_better"] = self.cfg.greater_is_better
if self.cfg.torch_compile:
if torch.__version__ < "2.1.0": # pylint: disable=protected-access
LOG.warning("torch>=2.1.0 required for torch_compile to work properly")
elif torch._dynamo: # pylint: disable=protected-access
torch._dynamo.config.suppress_errors = ( # pylint: disable=protected-access
True
)
training_arguments_kwargs["torch_compile"] = self.cfg.torch_compile
if self.cfg.torch_compile_backend:
training_arguments_kwargs[
"torch_compile_backend"
] = self.cfg.torch_compile_backend
# DDP Config
if self.cfg.ddp_timeout:
training_arguments_kwargs["ddp_timeout"] = self.cfg.ddp_timeout
# see https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html
if self.cfg.ddp_bucket_cap_mb:
training_arguments_kwargs["ddp_bucket_cap_mb"] = self.cfg.ddp_bucket_cap_mb
if self.cfg.ddp_broadcast_buffers is not None:
training_arguments_kwargs[
"ddp_broadcast_buffers"
] = self.cfg.ddp_broadcast_buffers
# these are all the "standard" kwargs that are def used
training_arguments_kwargs["max_steps"] = (
total_num_steps if self.cfg.max_steps else -1
)
training_arguments_kwargs["max_seq_length"] = self.cfg.sequence_len
training_arguments_kwargs[
"per_device_train_batch_size"
] = self.cfg.micro_batch_size
if self.cfg.eval_batch_size:
training_arguments_kwargs[
"per_device_eval_batch_size"
] = self.cfg.eval_batch_size
training_arguments_kwargs[
"gradient_accumulation_steps"
] = self.cfg.gradient_accumulation_steps
training_arguments_kwargs[
"eval_accumulation_steps"
] = self.cfg.gradient_accumulation_steps
training_arguments_kwargs["num_train_epochs"] = self.cfg.num_epochs
training_arguments_kwargs["learning_rate"] = self.cfg.learning_rate
training_arguments_kwargs["output_dir"] = self.cfg.output_dir
training_arguments_kwargs["save_total_limit"] = (
self.cfg.save_total_limit if self.cfg.save_total_limit else 4
)
training_arguments_kwargs["load_best_model_at_end"] = (
(
self.cfg.load_best_model_at_end is not False
or self.cfg.early_stopping_patience
)
and self.cfg.val_set_size > 0
and self.cfg.save_steps
and self.cfg.eval_steps
and self.cfg.save_steps % self.cfg.eval_steps == 0
) or False
training_arguments_kwargs["ddp_find_unused_parameters"] = (
False if self.cfg.ddp else None
)
training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
report_to = None
if self.cfg.use_wandb:
report_to = "wandb"
if self.cfg.use_mlflow:
report_to = "mlflow"
training_arguments_kwargs["report_to"] = report_to
training_arguments_kwargs["run_name"] = (
self.cfg.wandb_name if self.cfg.use_wandb else None
)
training_arguments_kwargs["optim"] = (
self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
)
training_arguments_kwargs["lr_scheduler_type"] = (
self.cfg.lr_scheduler
if self.cfg.lr_scheduler
and self.cfg.lr_scheduler not in ("one_cycle", "log_sweep")
else "cosine"
)
training_arguments_kwargs["lr_scheduler_kwargs"] = (
self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
)
training_arguments_kwargs["cosine_min_lr_ratio"] = self.cfg.cosine_min_lr_ratio
training_arguments_kwargs["weight_decay"] = (
self.cfg.weight_decay if self.cfg.weight_decay is not None else 0.0
)
training_arguments_kwargs["sample_packing"] = (
self.cfg.sample_packing if self.cfg.sample_packing else False
)
training_arguments_kwargs["eval_sample_packing"] = (
self.cfg.sample_packing
if self.cfg.eval_sample_packing is not False
else False
)
training_arguments_kwargs[
"sample_packing_seq_len_multiplier"
] = self.cfg.micro_batch_size
training_arguments_kwargs["relora_steps"] = self.cfg.relora_steps
training_arguments_kwargs["relora_warmup_steps"] = self.cfg.relora_warmup_steps
training_arguments_kwargs = self.hook_pre_create_training_args(
training_arguments_kwargs
)
training_arguments_kwargs["model_type"] = self.cfg.model_config_type
training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
if self.cfg.neftune_noise_alpha is not None:
training_arguments_kwargs[
"neftune_noise_alpha"
] = self.cfg.neftune_noise_alpha
training_args = (
AxolotlTrainingArguments( # pylint: disable=unexpected-keyword-arg
**training_arguments_kwargs,
)
)
training_args = self.hook_post_create_training_args(training_args)
trainer_kwargs = {}
if self.cfg.optimizer == "adamw_anyprecision":
if Path(self.cfg.torchdistx_path).exists():
sys.path.append(self.cfg.torchdistx_path)
importlib.import_module("torchdistx")
data_collator_kwargs = {
"padding": True, # True/"longest" is the default
}
if self.cfg.pad_to_sequence_len:
data_collator_kwargs["pad_to_multiple_of"] = 64 * math.ceil(
self.cfg.sequence_len / 64
)
else:
# A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
# https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
data_collator_kwargs["pad_to_multiple_of"] = 64
trainer_cls = self._get_trainer_cls()
trainer_kwargs, trainer_cls = self.hook_pre_create_trainer(
trainer_kwargs, trainer_cls
)
trainer = trainer_cls(
model=self.model,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
args=training_args,
data_collator=self.build_collator(training_args, **data_collator_kwargs),
eval_data_collator=self.build_collator(
training_args, is_eval=True, **data_collator_kwargs
),
bench_data_collator=transformers.DataCollatorForSeq2Seq(
self.tokenizer,
return_tensors="pt",
**data_collator_kwargs,
),
callbacks=self.get_callbacks(),
num_epochs=self.cfg.num_epochs,
**trainer_kwargs,
)
trainer = self.hook_post_create_trainer(trainer)
for callback in self.get_post_trainer_create_callbacks(trainer):
trainer.add_callback(callback)
if self.cfg.deepspeed and self.cfg.sample_packing:
trainer.accelerator.state.deepspeed_plugin.deepspeed_config[
"train_micro_batch_size_per_gpu"
] = self.cfg.micro_batch_size
return trainer
def build_collator(
self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
):
if training_args.pretraining:
return None
if self.cfg.model_config_type == "mamba":
return MambaDataCollator(tokenizer=self.tokenizer)
use_batch_sampler_collator = False
if is_eval is False and training_args.sample_packing:
use_batch_sampler_collator = True
if is_eval and training_args.eval_sample_packing:
use_batch_sampler_collator = True
collator: Type[
Union[
V2BatchSamplerDataCollatorForSeq2Seq,
BatchSamplerDataCollatorForSeq2Seq,
DataCollatorForSeq2Seq,
]
]
if use_batch_sampler_collator:
if self.cfg.model_config_type in ["mixtral", "qwen2", "falcon", "phi"]:
collator = V2BatchSamplerDataCollatorForSeq2Seq
else:
collator = BatchSamplerDataCollatorForSeq2Seq
else:
collator = DataCollatorForSeq2Seq
return collator(
self.tokenizer,
return_tensors="pt",
**kwargs,
)
class HFDPOTrainerBuilder(TrainerBuilderBase):
"""
Trainer factory class for DPO Trainer
"""
def get_callbacks(self):
callbacks = super().get_callbacks()
return callbacks
def get_post_trainer_create_callbacks(self, trainer):
callbacks = []
return callbacks
def build_training_arguments(self, total_num_steps):
training_args_kwargs = {}
for arg in [
"adam_beta1",
"adam_beta2",
"adam_epsilon",
"dataloader_num_workers",
"dataloader_pin_memory",
]:
if hasattr(self.cfg, arg) and getattr(self.cfg, arg) is not None:
training_args_kwargs[arg] = getattr(self.cfg, arg)
if self.cfg.hub_model_id:
training_args_kwargs["hub_model_id"] = self.cfg.hub_model_id
training_args_kwargs["push_to_hub"] = True
training_args_kwargs["hub_private_repo"] = True
training_args_kwargs["hub_always_push"] = True
if self.cfg.hub_strategy:
training_args_kwargs["hub_strategy"] = self.cfg.hub_strategy
if self.cfg.save_safetensors is not None:
training_args_kwargs["save_safetensors"] = self.cfg.save_safetensors
if self.eval_dataset:
training_args_kwargs["evaluation_strategy"] = "steps"
training_args_kwargs["eval_steps"] = self.cfg.eval_steps
else:
training_args_kwargs["evaluation_strategy"] = "no"
if self.cfg.bf16 or self.cfg.bfloat16:
training_args_kwargs["bf16"] = True
training_args_kwargs["lr_scheduler_type"] = (
self.cfg.lr_scheduler if self.cfg.lr_scheduler else "cosine"
)
training_args_kwargs["lr_scheduler_kwargs"] = (
self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
)
if self.cfg.remove_unused_columns is not None:
training_args_kwargs[
"remove_unused_columns"
] = self.cfg.remove_unused_columns
else:
training_args_kwargs["remove_unused_columns"] = False
if self.cfg.dataloader_pin_memory is not None:
training_args_kwargs[
"dataloader_pin_memory"
] = self.cfg.dataloader_pin_memory
if self.cfg.dataloader_num_workers is not None:
training_args_kwargs[
"dataloader_num_workers"
] = self.cfg.dataloader_num_workers
if self.cfg.dataloader_prefetch_factor is not None:
training_args_kwargs[
"dataloader_prefetch_factor"
] = self.cfg.dataloader_prefetch_factor
if self.cfg.gradient_checkpointing:
training_args_kwargs[
"gradient_checkpointing"
] = self.cfg.gradient_checkpointing
if self.cfg.gradient_checkpointing_kwargs is not None:
training_args_kwargs[
"gradient_checkpointing_kwargs"
] = self.cfg.gradient_checkpointing_kwargs
else:
training_args_kwargs["gradient_checkpointing_kwargs"] = {
"use_reentrant": False
}
training_args = TrainingArguments(
per_device_train_batch_size=self.cfg.micro_batch_size,
max_steps=self.cfg.max_steps or total_num_steps,
gradient_accumulation_steps=self.cfg.gradient_accumulation_steps,
learning_rate=self.cfg.learning_rate,
save_strategy="steps",
save_steps=self.cfg.save_steps,
output_dir=self.cfg.output_dir,
warmup_steps=self.cfg.warmup_steps,
logging_first_step=True,
logging_steps=1,
optim=self.cfg.optimizer,
save_total_limit=self.cfg.save_total_limit or 5,
**training_args_kwargs,
)
return training_args
def build(self, total_num_steps):
training_args = self.build_training_arguments(total_num_steps)
dpo_trainer_kwargs = {}
if self.cfg.rl == "ipo":
dpo_trainer_kwargs["loss_type"] = "ipo"
if self.cfg.dpo_label_smoothing:
dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
elif self.cfg.rl == "kto_pair":
dpo_trainer_kwargs["loss_type"] = "kto_pair"
if self.eval_dataset:
dpo_trainer_kwargs["eval_dataset"] = self.eval_dataset
if self.cfg.adapter and self.peft_config:
dpo_trainer_kwargs["peft_config"] = self.peft_config
if self.cfg.precompute_ref_log_probs is not None:
dpo_trainer_kwargs[
"precompute_ref_log_probs"
] = self.cfg.precompute_ref_log_probs
dpo_trainer = DPOTrainer(
self.model,
self.model_ref,
args=training_args,
beta=self.cfg.dpo_beta or 0.1,
train_dataset=self.train_dataset,
tokenizer=self.tokenizer,
max_length=self.cfg.sequence_len,
max_target_length=None,
max_prompt_length=self.cfg.sequence_len,
generate_during_eval=True,
callbacks=self.get_callbacks(),
**dpo_trainer_kwargs,
)
dpo_trainer = self.hook_post_create_trainer(dpo_trainer)
for callback in self.get_post_trainer_create_callbacks(dpo_trainer):
dpo_trainer.add_callback(callback)
return dpo_trainer
class HFPPOTrainerBuilder(TrainerBuilderBase):
"""
HF Factory class for PPO Trainer
"""
def get_callbacks(self):
callbacks = []
return callbacks
def get_post_trainer_create_callbacks(self, trainer):
callbacks = []
return callbacks
def build(self, total_num_steps):
# build PPOConfig
pass