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"""
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 partial
from pathlib import Path
from typing import Optional
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, TrainingArguments
from transformers.trainer_utils import seed_worker
from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
from axolotl.utils.callbacks import (
EvalFirstStepCallback,
GPUStatsCallback,
LossWatchDogCallback,
SaveAxolotlConfigtoWandBCallback,
SaveBetterTransformerModelCallback,
bench_eval_callback_factory,
log_prediction_callback_factory,
)
from axolotl.utils.collators import (
BatchSamplerDataCollatorForSeq2Seq,
MambaDataCollator,
)
from axolotl.utils.samplers import MultipackBatchSampler
from axolotl.utils.schedulers import 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."},
)
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"},
)
class AxolotlTrainer(Trainer):
"""
Extend the base Trainer for axolotl helpers
"""
args = None # type: AxolotlTrainingArguments
def __init__(self, *args, num_epochs=1, bench_data_collator=None, **kwargs):
self.num_epochs = num_epochs
self.bench_data_collator = bench_data_collator
super().__init__(*args, **kwargs)
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
"""
# fmt: off
if self.lr_scheduler is None: # type: ignore # pylint: disable=access-member-before-definition
# fmt: on
if (
self.args.lr_scheduler_type == "cosine"
and self.args.lr_quadratic_warmup is True
):
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,
)
else:
return super().create_scheduler(num_training_steps, optimizer)
return self.lr_scheduler
def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
if self.args.sample_packing:
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=(
self.train_dataset.data.column("position_ids")
.to_pandas()
.apply(lambda x: x[-1] + 1)
.values
),
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=(
eval_dataset.data.column("position_ids")
.to_pandas()
.apply(lambda x: x[-1] + 1)
.values
),
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:
train_dataset = self.train_dataset
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 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)
class AxolotlMambaTrainer(AxolotlTrainer):
"""
Mamba specific trainer to handle loss calculation
"""
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
"""
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
"""
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
def __init__(self, cfg, model, tokenizer):
self.cfg = cfg
self.model = model
self.tokenizer = tokenizer
@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
@abstractmethod
def build(self, total_num_steps):
pass
@abstractmethod
def get_callbacks(self):
pass
@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
"""
class HFCausalTrainerBuilder(TrainerBuilderBase):
"""
Build the HuggingFace training args/trainer for Causal models
"""
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
def get_callbacks(self):
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.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.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
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.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
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
training_arguments_kwargs["report_to"] = "wandb" if self.cfg.use_wandb else None
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["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
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
if self.cfg.is_llama_derived_model and self.cfg.landmark_attention:
from axolotl.monkeypatch.llama_landmark_attn import (
add_mem_tokens,
get_mem_id,
set_model_mem_id,
)
set_model_mem_id(self.model, self.tokenizer)
LOG.info("Adding landmark attention tokens to dataset")
for dataset in [self.train_dataset, self.eval_dataset]:
dataset = dataset.map(
partial(
add_mem_tokens, mem_freq=50, mem_id=get_mem_id(self.tokenizer)
),
batched=False,
num_proc=32,
)
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(**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, **kwargs):
if self.cfg.model_config_type == "mamba":
return MambaDataCollator(tokenizer=self.tokenizer)
return BatchSamplerDataCollatorForSeq2Seq(
self.tokenizer,
return_tensors="pt",
**kwargs,
)
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