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import sys
import os
from typing import Optional
import torch
from torch.utils.data import DataLoader
import transformers
from transformers.trainer import *
from .ffd_sampler import FFDDistributedBatchSampler
from .utils import ExtendedFSDPOption, enable_low_gpu_full_post_state_dict_hook
class FSDPTrainer(transformers.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[Union[Dataset, Dict[str, Dataset]]] = None,
tokenizer: Optional[PreTrainedTokenizerBase] = None,
model_init: Optional[Callable[[], PreTrainedModel]] = None,
compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
callbacks: Optional[List[TrainerCallback]] = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
):
if args is None:
output_dir = "tmp_trainer"
logger.info(f"No `TrainingArguments` passed, using `output_dir={output_dir}`.")
args = TrainingArguments(output_dir=output_dir)
self.args = args
# Seed must be set before instantiating the model when using model
enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)
self.hp_name = None
self.deepspeed = None
self.is_in_train = False
self.create_accelerator_and_postprocess()
# memory metrics - must set up as early as possible
self._memory_tracker = TrainerMemoryTracker(self.args.skip_memory_metrics)
self._memory_tracker.start()
# set the correct log level depending on the node
log_level = args.get_process_log_level()
logging.set_verbosity(log_level)
# force device and distributed setup init explicitly
args._setup_devices
if model is None:
if model_init is not None:
self.model_init = model_init
model = self.call_model_init()
else:
raise RuntimeError("`Trainer` requires either a `model` or `model_init` argument")
else:
if model_init is not None:
warnings.warn(
"`Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will"
" overwrite your model when calling the `train` method. This will become a fatal error in the next"
" release.",
FutureWarning,
)
self.model_init = model_init
if model.__class__.__name__ in MODEL_MAPPING_NAMES:
raise ValueError(
f"The model you have picked ({model.__class__.__name__}) cannot be used as is for training: it only "
"computes hidden states and does not accept any labels. You should choose a model with a head "
"suitable for your task like any of the `AutoModelForXxx` listed at "
"https://huggingface.co/docs/transformers/model_doc/auto."
)
if hasattr(model, "is_parallelizable") and model.is_parallelizable and model.model_parallel:
self.is_model_parallel = True
else:
self.is_model_parallel = False
if getattr(model, "hf_device_map", None) is not None:
devices = [device for device in set(model.hf_device_map.values()) if device not in ["cpu", "disk"]]
if len(devices) > 1:
self.is_model_parallel = True
else:
self.is_model_parallel = self.args.device != torch.device(devices[0])
# warn users
logger.info(
"You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set"
" to `True` to avoid any unexpected behavior such as device placement mismatching."
)
# At this stage the model is already loaded
if getattr(model, "is_quantized", False):
if getattr(model, "_is_quantized_training_enabled", False):
logger.info(
"The model is loaded in 8-bit precision. To train this model you need to add additional modules"
" inside the model such as adapters using `peft` library and freeze the model weights. Please"
" check "
" the examples in https://github.com/huggingface/peft for more details."
)
else:
raise ValueError(
"The model you want to train is loaded in 8-bit precision. if you want to fine-tune an 8-bit"
" model, please make sure that you have installed `bitsandbytes>=0.37.0`. "
)
# Setup Sharded DDP training
self.sharded_ddp = None
if len(args.sharded_ddp) > 0:
if self.is_deepspeed_enabled:
raise ValueError(
"Using --sharded_ddp xxx together with --deepspeed is not possible, deactivate one of those flags."
)
if len(args.fsdp) > 0:
raise ValueError(
"Using --sharded_ddp xxx together with --fsdp is not possible, deactivate one of those flags."
)
if args.parallel_mode != ParallelMode.DISTRIBUTED:
raise ValueError("Using sharded DDP only works in distributed training.")
elif not is_fairscale_available():
raise ImportError("Sharded DDP training requires fairscale: `pip install fairscale`.")
elif ShardedDDPOption.SIMPLE not in args.sharded_ddp and FullyShardedDDP is None:
raise ImportError(
"Sharded DDP in a mode other than simple training requires fairscale version >= 0.3, found "
f"{fairscale.__version__}. Upgrade your fairscale library: `pip install --upgrade fairscale`."
)
elif ShardedDDPOption.SIMPLE in args.sharded_ddp:
self.sharded_ddp = ShardedDDPOption.SIMPLE
elif ShardedDDPOption.ZERO_DP_2 in args.sharded_ddp:
self.sharded_ddp = ShardedDDPOption.ZERO_DP_2
elif ShardedDDPOption.ZERO_DP_3 in args.sharded_ddp:
self.sharded_ddp = ShardedDDPOption.ZERO_DP_3
self.fsdp = None
if len(args.fsdp) > 0:
if self.is_deepspeed_enabled:
raise ValueError(
"Using --fsdp xxx together with --deepspeed is not possible, deactivate one of those flags."
)
if not args.fsdp_config["xla"] and args.parallel_mode != ParallelMode.DISTRIBUTED:
raise ValueError("Using fsdp only works in distributed training.")
# dep_version_check("torch>=1.12.0")
# Would have to update setup.py with torch>=1.12.0
# which isn't ideally given that it will force people not using FSDP to also use torch>=1.12.0
# below is the current alternative.
if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.12.0"):
raise ValueError("FSDP requires PyTorch >= 1.12.0")
from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch, ShardingStrategy
if ExtendedFSDPOption.FULL_SHARD in args.fsdp:
self.fsdp = ShardingStrategy.FULL_SHARD
elif ExtendedFSDPOption.SHARD_GRAD_OP in args.fsdp:
self.fsdp = ShardingStrategy.SHARD_GRAD_OP
elif ExtendedFSDPOption.NO_SHARD in args.fsdp:
self.fsdp = ShardingStrategy.NO_SHARD
# extention starts here
elif ExtendedFSDPOption.HYBRID_SHARD in args.fsdp:
self.fsdp = ShardingStrategy.HYBRID_SHARD
elif ExtendedFSDPOption._HYBRID_SHARD_ZERO2 in args.fsdp:
self.fsdp = ShardingStrategy._HYBRID_SHARD_ZERO2
# extention ends here
self.backward_prefetch = BackwardPrefetch.BACKWARD_PRE
# modification starts here
if self.args.fsdp_config.get("fsdp_backward_prefetch", "") == "backward_post":
self.backward_prefetch = BackwardPrefetch.BACKWARD_POST
# modification ends here
self.forward_prefetch = False
# modification starts here
if self.args.fsdp_config.get("forward_prefetch", False):
# modification ends here
self.forward_prefetch = True
self.limit_all_gathers = False
if self.args.fsdp_config.get("limit_all_gathers", False):
self.limit_all_gathers = True
# one place to sort out whether to place the model on device or not
# postpone switching model to cuda when:
# 1. MP - since we are trying to fit a much bigger than 1 gpu model
# 2. fp16-enabled DeepSpeed loads the model in half the size and it doesn't need .to() anyway,
# and we only use deepspeed for training at the moment
# 3. full bf16 or fp16 eval - since the model needs to be cast to the right dtype first
# 4. Sharded DDP - same as MP
# 5. FSDP - same as MP
self.place_model_on_device = args.place_model_on_device
if (
self.is_model_parallel
or self.is_deepspeed_enabled
or ((args.fp16_full_eval or args.bf16_full_eval) and not args.do_train)
or (self.sharded_ddp in [ShardedDDPOption.ZERO_DP_2, ShardedDDPOption.ZERO_DP_3])
or (self.fsdp is not None)
or self.is_fsdp_enabled
):
self.place_model_on_device = False
default_collator = default_data_collator if tokenizer is None else DataCollatorWithPadding(tokenizer)
self.data_collator = data_collator if data_collator is not None else default_collator
self.train_dataset = train_dataset
self.eval_dataset = eval_dataset
self.tokenizer = tokenizer
# Quantized models doesn't support `.to` operation.
if self.place_model_on_device and not getattr(model, "is_quantized", False):
self._move_model_to_device(model, args.device)
# Force n_gpu to 1 to avoid DataParallel as MP will manage the GPUs
if self.is_model_parallel:
self.args._n_gpu = 1
# later use `self.model is self.model_wrapped` to check if it's wrapped or not
self.model_wrapped = model
self.model = model
self.compute_metrics = compute_metrics
self.preprocess_logits_for_metrics = preprocess_logits_for_metrics
self.optimizer, self.lr_scheduler = optimizers
if model_init is not None and (self.optimizer is not None or self.lr_scheduler is not None):
raise RuntimeError(
"Passing a `model_init` is incompatible with providing the `optimizers` argument. "
"You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
)
if is_torch_tpu_available() and self.optimizer is not None:
for param in self.model.parameters():
model_device = param.device
break
for param_group in self.optimizer.param_groups:
if len(param_group["params"]) > 0:
optimizer_device = param_group["params"][0].device
break
if model_device != optimizer_device:
raise ValueError(
"The model and the optimizer parameters are not on the same device, which probably means you"
" created an optimizer around your model **before** putting on the device and passing it to the"
" `Trainer`. Make sure the lines `import torch_xla.core.xla_model as xm` and"
" `model.to(xm.xla_device())` is performed before the optimizer creation in your script."
)
if ((self.sharded_ddp is not None) or self.is_deepspeed_enabled or (self.fsdp is not None)) and (
self.optimizer is not None or self.lr_scheduler is not None
):
raise RuntimeError(
"Passing `optimizers` is not allowed if Fairscale, Deepspeed or PyTorch FSDP is enabled."
"You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
)
default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to)
callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks
self.callback_handler = CallbackHandler(
callbacks, self.model, self.tokenizer, self.optimizer, self.lr_scheduler
)
self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)
# Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
self._loggers_initialized = False
# Create clone of distant repo and output directory if needed
if self.args.push_to_hub:
self.init_git_repo(at_init=True)
# In case of pull, we need to make sure every process has the latest.
if is_torch_tpu_available():
xm.rendezvous("init git repo")
elif args.parallel_mode == ParallelMode.DISTRIBUTED:
dist.barrier()
if self.args.should_save:
os.makedirs(self.args.output_dir, exist_ok=True)
if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
raise ValueError("The `data_collator` should be a simple callable (function, class with `__call__`).")
if args.max_steps > 0:
logger.info("max_steps is given, it will override any value given in num_train_epochs")
if train_dataset is not None and not has_length(train_dataset) and args.max_steps <= 0:
raise ValueError(
"The train_dataset does not implement __len__, max_steps has to be specified. "
"The number of steps needs to be known in advance for the learning rate scheduler."
)
if (
train_dataset is not None
and isinstance(train_dataset, torch.utils.data.IterableDataset)
and args.group_by_length
):
raise ValueError("the `--group_by_length` option is only available for `Dataset`, not `IterableDataset")
self._signature_columns = None
# Mixed precision setup
self.use_apex = False
self.use_cuda_amp = False
self.use_cpu_amp = False
# Mixed precision setup for SageMaker Model Parallel
if is_sagemaker_mp_enabled():
# BF16 + model parallelism in SageMaker: currently not supported, raise an error
if args.bf16:
raise ValueError("SageMaker Model Parallelism does not support BF16 yet. Please use FP16 instead ")
if IS_SAGEMAKER_MP_POST_1_10:
# When there's mismatch between SMP config and trainer argument, use SMP config as truth
if args.fp16 != smp.state.cfg.fp16:
logger.warning(
f"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16},"
f"but FP16 provided in trainer argument is {args.fp16},"
f"setting to {smp.state.cfg.fp16}"
)
args.fp16 = smp.state.cfg.fp16
else:
# smp < 1.10 does not support fp16 in trainer.
if hasattr(smp.state.cfg, "fp16"):
logger.warning(
f"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16}, "
"but SageMaker Model Parallelism < 1.10 does not support FP16 in trainer."
)
if (args.fp16 or args.bf16) and self.sharded_ddp is not None:
if args.half_precision_backend == "auto":
if args.device == torch.device("cpu"):
if args.fp16:
raise ValueError("Tried to use `fp16` but it is not supported on cpu")
else:
args.half_precision_backend = "cpu_amp"
else:
args.half_precision_backend = "cuda_amp"
logger.info(f"Using {args.half_precision_backend} half precision backend")
self.do_grad_scaling = False
if (args.fp16 or args.bf16) and not (self.is_deepspeed_enabled or is_sagemaker_mp_enabled()):
# deepspeed and SageMaker Model Parallel manage their own half precision
if self.sharded_ddp is not None:
if args.half_precision_backend == "cuda_amp":
self.use_cuda_amp = True
self.amp_dtype = torch.float16 if args.fp16 else torch.bfloat16
# bf16 does not need grad scaling
self.do_grad_scaling = self.amp_dtype == torch.float16
if self.do_grad_scaling:
if self.sharded_ddp is not None:
self.scaler = ShardedGradScaler()
elif self.fsdp is not None:
from torch.distributed.fsdp.sharded_grad_scaler import (
ShardedGradScaler as FSDPShardedGradScaler,
)
self.scaler = FSDPShardedGradScaler()
elif is_torch_tpu_available():
from torch_xla.amp import GradScaler
self.scaler = GradScaler()
else:
self.scaler = torch.cuda.amp.GradScaler()
elif args.half_precision_backend == "cpu_amp":
self.use_cpu_amp = True
self.amp_dtype = torch.bfloat16
elif args.half_precision_backend == "apex":
if not is_apex_available():
raise ImportError(
"Using FP16 with APEX but APEX is not installed, please refer to"
" https://www.github.com/nvidia/apex."
)
self.use_apex = True
# FP16 + model parallelism in SageMaker: gradient clipping does not work for now so we raise a helpful error.
if (
is_sagemaker_mp_enabled()
and self.use_cuda_amp
and args.max_grad_norm is not None
and args.max_grad_norm > 0
):
raise ValueError(
"SageMaker Model Parallelism in mixed precision mode does not support gradient clipping yet. Pass "
"along 'max_grad_norm': 0 in your hyperparameters."
)
# Label smoothing
if self.args.label_smoothing_factor != 0:
self.label_smoother = LabelSmoother(epsilon=self.args.label_smoothing_factor)
else:
self.label_smoother = None
self.state = TrainerState(
is_local_process_zero=self.is_local_process_zero(),
is_world_process_zero=self.is_world_process_zero(),
)
self.control = TrainerControl()
# Internal variable to count flos in each process, will be accumulated in `self.state.total_flos` then
# returned to 0 every time flos need to be logged
self.current_flos = 0
self.hp_search_backend = None
self.use_tune_checkpoints = False
default_label_names = find_labels(self.model.__class__)
self.label_names = default_label_names if self.args.label_names is None else self.args.label_names
self.can_return_loss = can_return_loss(self.model.__class__)
self.control = self.callback_handler.on_init_end(self.args, self.state, self.control)
# Internal variables to help with automatic batch size reduction
self._train_batch_size = args.train_batch_size
self._created_lr_scheduler = False
# very last
self._memory_tracker.stop_and_update_metrics()
# torch.compile
if args.torch_compile and not is_torch_compile_available():
raise RuntimeError("Using torch.compile requires PyTorch 2.0 or higher.")
# finally applying `low_gpu_full_post_state_dict_hook`` for fsdp `state_dict`
enable_low_gpu_full_post_state_dict_hook()
def _wrap_model(self, model, training=True, dataloader=None):
if self.args.use_ipex:
dtype = torch.bfloat16 if self.use_cpu_amp else torch.float32
model = self.ipex_optimize_model(model, training, dtype=dtype)
if is_sagemaker_mp_enabled():
# Wrapping the base model twice in a DistributedModel will raise an error.
if isinstance(self.model_wrapped, smp.model.DistributedModel):
return self.model_wrapped
return smp.DistributedModel(model, backward_passes_per_step=self.args.gradient_accumulation_steps)
# train/eval could be run multiple-times - if already wrapped, don't re-wrap it again
if unwrap_model(model) is not model:
return model
# Mixed precision training with apex (torch < 1.6)
if self.use_apex and training:
model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)
# Multi-gpu training (should be after apex fp16 initialization) / 8bit models does not support DDP
if self.args.n_gpu > 1 and not getattr(model, "is_loaded_in_8bit", False):
model = nn.DataParallel(model)
if self.args.jit_mode_eval:
start_time = time.time()
model = self.torch_jit_model_eval(model, dataloader, training)
self.jit_compilation_time = round(time.time() - start_time, 4)
# Note: in torch.distributed mode, there's no point in wrapping the model
# inside a DistributedDataParallel as we'll be under `no_grad` anyways.
if not training:
return model
# Distributed training (should be after apex fp16 initialization)
if self.sharded_ddp is not None:
# Sharded DDP!
if self.sharded_ddp == ShardedDDPOption.SIMPLE:
model = ShardedDDP(model, self.optimizer)
else:
mixed_precision = self.args.fp16 or self.args.bf16
cpu_offload = ShardedDDPOption.OFFLOAD in self.args.sharded_ddp
zero_3 = self.sharded_ddp == ShardedDDPOption.ZERO_DP_3
# XXX: Breaking the self.model convention but I see no way around it for now.
if ShardedDDPOption.AUTO_WRAP in self.args.sharded_ddp:
model = auto_wrap(model)
self.model = model = FullyShardedDDP(
model,
mixed_precision=mixed_precision,
reshard_after_forward=zero_3,
cpu_offload=cpu_offload,
).to(self.args.device)
# Distributed training using PyTorch FSDP
elif self.fsdp is not None:
# fix starts here
if not self.args.fsdp_config["xla"]:
# PyTorch FSDP!
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, MixedPrecision
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy
import torch.distributed.fsdp._traversal_utils as traversal_utils
if FSDPOption.OFFLOAD in self.args.fsdp:
cpu_offload = CPUOffload(offload_params=True)
else:
cpu_offload = CPUOffload(offload_params=False)
auto_wrap_policy = None
if FSDPOption.AUTO_WRAP in self.args.fsdp:
if self.args.fsdp_config["fsdp_min_num_params"] > 0:
auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config["fsdp_min_num_params"]
)
elif self.args.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None:
transformer_cls_to_wrap = set()
for layer_class in self.args.fsdp_config["fsdp_transformer_layer_cls_to_wrap"]:
transformer_cls = get_module_class_from_name(model, layer_class)
if transformer_cls is None:
raise Exception("Could not find the transformer layer class to wrap in the model.")
else:
transformer_cls_to_wrap.add(transformer_cls)
auto_wrap_policy = functools.partial(
transformer_auto_wrap_policy,
# Transformer layer class to wrap
transformer_layer_cls=transformer_cls_to_wrap,
)
mixed_precision_policy = None
dtype = None
if self.args.fp16:
dtype = torch.float16
elif self.args.bf16:
dtype = torch.bfloat16
if dtype is not None:
mixed_precision_policy = MixedPrecision(param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype)
if type(model) != FSDP:
# XXX: Breaking the self.model convention but I see no way around it for now.
signature = inspect.signature(FSDP.__init__).parameters.keys()
kwargs = {}
for arg in ["limit_all_gathers", "forward_prefetch", "backward_prefetch"]:
if arg in signature:
kwargs[arg] = getattr(self, arg)
self.model = model = FSDP(
model,
sharding_strategy=self.fsdp,
cpu_offload=cpu_offload,
auto_wrap_policy=auto_wrap_policy,
mixed_precision=mixed_precision_policy,
device_id=self.args.device,
**kwargs,
)
for submodule in traversal_utils._get_fsdp_states(model):
print(submodule._state_dict_type, submodule._state_dict_config)
break
# fix ends here
else:
try:
from torch_xla.distributed.fsdp import XlaFullyShardedDataParallel as FSDP
from torch_xla.distributed.fsdp import checkpoint_module
from torch_xla.distributed.fsdp.wrap import (
size_based_auto_wrap_policy,
transformer_auto_wrap_policy,
)
except ImportError:
raise ImportError("Missing XLA FSDP related module; please make sure to use torch-xla >= 2.0.")
auto_wrap_policy = None
auto_wrapper_callable = None
if self.args.fsdp_config["fsdp_min_num_params"] > 0:
auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config["fsdp_min_num_params"]
)
elif self.args.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None:
transformer_cls_to_wrap = set()
for layer_class in self.args.fsdp_config["fsdp_transformer_layer_cls_to_wrap"]:
transformer_cls = get_module_class_from_name(model, layer_class)
if transformer_cls is None:
raise Exception("Could not find the transformer layer class to wrap in the model.")
else:
transformer_cls_to_wrap.add(transformer_cls)
auto_wrap_policy = functools.partial(
transformer_auto_wrap_policy,
# Transformer layer class to wrap
transformer_layer_cls=transformer_cls_to_wrap,
)
fsdp_kwargs = self.args.xla_fsdp_config
if self.args.fsdp_config["xla_fsdp_grad_ckpt"]:
# Apply gradient checkpointing to auto-wrapped sub-modules if specified
def auto_wrapper_callable(m, *args, **kwargs):
return FSDP(checkpoint_module(m), *args, **kwargs)
# Wrap the base model with an outer FSDP wrapper
self.model = model = FSDP(
model,
auto_wrap_policy=auto_wrap_policy,
auto_wrapper_callable=auto_wrapper_callable,
**fsdp_kwargs,
)
# Patch `xm.optimizer_step` should not reduce gradients in this case,
# as FSDP does not need gradient reduction over sharded parameters.
def patched_optimizer_step(optimizer, barrier=False, optimizer_args={}):
loss = optimizer.step(**optimizer_args)
if barrier:
xm.mark_step()
return loss
xm.optimizer_step = patched_optimizer_step
elif is_sagemaker_dp_enabled():
model = nn.parallel.DistributedDataParallel(
model, device_ids=[int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"))]
)
elif self.args.parallel_mode == ParallelMode.DISTRIBUTED:
if is_torch_neuroncore_available():
return model
kwargs = {}
if self.args.ddp_find_unused_parameters is not None:
kwargs["find_unused_parameters"] = self.args.ddp_find_unused_parameters
elif isinstance(model, PreTrainedModel):
# find_unused_parameters breaks checkpointing as per
# https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021
kwargs["find_unused_parameters"] = not model.is_gradient_checkpointing
else:
kwargs["find_unused_parameters"] = True
if self.args.ddp_bucket_cap_mb is not None:
kwargs["bucket_cap_mb"] = self.args.ddp_bucket_cap_mb
if self.args.ddp_broadcast_buffers is not None:
kwargs["broadcast_buffers"] = self.args.ddp_broadcast_buffers
self.accelerator.ddp_handler = DistributedDataParallelKwargs(**kwargs)
return model
def get_batch_sampler(self, dataset=None):
if self.args.use_ffd_sampler and self.args.group_by_length and self.args.world_size > 1:
dataset = dataset if dataset is not None else self.train_dataset
try:
batch_max_len = self.args.per_device_train_batch_size * unwrap_model(self.model).model_avg_context
except:
# raise RuntimeError("group_by_length in distributed training requires model has attr `model_max_context`")
batch_max_len = self.args.per_device_train_batch_size * self.args.model_avg_context
model_input_name = self.tokenizer.model_input_names[0] if self.tokenizer is not None else None
lengths = LengthGroupedSampler(
batch_size=-1, # we just want to know about the lengths of the dataset so no need to pass `batch_size`
dataset=dataset,
model_input_name=model_input_name
).lengths
seed = self.args.data_seed if self.args.data_seed is not None else self.args.seed
batch_sampler = FFDDistributedBatchSampler(
batch_max_length=batch_max_len,
lengths=np.array(lengths),
seed=seed
)
return batch_sampler
return None
def get_train_dataloader(self) -> DataLoader:
if self.args.use_ffd_sampler and self.args.group_by_length and self.args.world_size > 1:
if self.train_dataset is None:
raise ValueError("Trainer: training requires a train_dataset.")
train_dataset = self.train_dataset
data_collator = self.data_collator
if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
train_dataset = self._remove_unused_columns(train_dataset, description="training")
else:
data_collator = self._get_collator_with_removed_columns(data_collator, description="training")
batch_sampler = self.get_batch_sampler(train_dataset)
dataloader = DataLoader(
train_dataset,
batch_sampler=batch_sampler,
drop_last=self.args.dataloader_drop_last,
collate_fn=data_collator
)
# return self.accelerator.prepare(dataloader)
return dataloader
return super().get_train_dataloader()
def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):
"""
Will save the model, so you can reload it using `from_pretrained()`.
Will only save from the main process.
"""
if output_dir is None:
output_dir = self.args.output_dir
if is_torch_tpu_available():
self._save_tpu(output_dir)
elif is_sagemaker_mp_enabled():
# Calling the state_dict needs to be done on the wrapped model and on all processes.
os.makedirs(output_dir, exist_ok=True)
state_dict = self.model_wrapped.state_dict()
if self.args.should_save:
self._save(output_dir, state_dict=state_dict)
if IS_SAGEMAKER_MP_POST_1_10:
# 'user_content.pt' indicates model state_dict saved with smp >= 1.10
Path(os.path.join(output_dir, "user_content.pt")).touch()
elif (
ShardedDDPOption.ZERO_DP_2 in self.args.sharded_ddp
or ShardedDDPOption.ZERO_DP_3 in self.args.sharded_ddp
or self.fsdp is not None
or self.is_fsdp_enabled
):
state_dict = self.model.state_dict()
if self.args.should_save:
self._save(output_dir, state_dict=state_dict)
# modification starts here
if self.is_fsdp_enabled and self.args.save_with_fsdp:
save_fsdp_model(self.accelerator.state.fsdp_plugin, self.accelerator, self.model, output_dir)
# modification ends here
elif self.is_deepspeed_enabled:
# this takes care of everything as long as we aren't under zero3
if version.parse(accelerate_version) <= version.parse("0.20.3"):
raise ValueError("Install Accelerate from main branch")
try:
state_dict = self.accelerator.get_state_dict(self.deepspeed)
if self.args.should_save:
self._save(output_dir, state_dict=state_dict)
except ValueError:
logger.warning(
" stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead, use"
" zero_to_fp32.py to recover weights"
)
self.model_wrapped.save_checkpoint(output_dir)
elif self.args.should_save:
self._save(output_dir)
# Push to the Hub when `save_model` is called by the user.
if self.args.push_to_hub and not _internal_call:
self.push_to_hub(commit_message="Model save")
def _save_checkpoint(self, model, trial, metrics=None):
# In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we
# want to save except FullyShardedDDP.
# assert unwrap_model(model) is self.model, "internal model should be a reference to self.model"
# Save model checkpoint
checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
if self.hp_search_backend is None and trial is None:
self.store_flos()
run_dir = self._get_output_dir(trial=trial)
output_dir = os.path.join(run_dir, checkpoint_folder)
self.save_model(output_dir, _internal_call=True)
if self.is_deepspeed_enabled:
# under zero3 model file itself doesn't get saved since it's bogus! Unless deepspeed
# config `stage3_gather_16bit_weights_on_model_save` is True
self.model_wrapped.save_checkpoint(output_dir)
# Save optimizer and scheduler
if self.sharded_ddp == ShardedDDPOption.SIMPLE:
self.optimizer.consolidate_state_dict()
if self.fsdp or self.is_fsdp_enabled:
if self.is_fsdp_enabled:
# modification starts here
if self.args.save_with_fsdp:
save_fsdp_optimizer(
self.accelerator.state.fsdp_plugin, self.accelerator, self.optimizer, self.model, output_dir
)
# modification ends here
else:
# FSDP has a different interface for saving optimizer states.
# Needs to be called on all ranks to gather all states.
# full_optim_state_dict will be deprecated after Pytorch 2.2!
full_osd = self.model.__class__.full_optim_state_dict(self.model, self.optimizer)
if is_torch_tpu_available():
xm.rendezvous("saving_optimizer_states")
xm.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
with warnings.catch_warnings(record=True) as caught_warnings:
xm.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
reissue_pt_warnings(caught_warnings)
elif is_sagemaker_mp_enabled():
opt_state_dict = self.optimizer.local_state_dict(gather_if_shard=False)
smp.barrier()
if smp.rdp_rank() == 0 or smp.state.cfg.shard_optimizer_state:
smp.save(
opt_state_dict,
os.path.join(output_dir, OPTIMIZER_NAME),
partial=True,
v3=smp.state.cfg.shard_optimizer_state,
)
if self.args.should_save:
with warnings.catch_warnings(record=True) as caught_warnings:
torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
reissue_pt_warnings(caught_warnings)
if self.do_grad_scaling:
torch.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))
elif self.args.should_save and not self.is_deepspeed_enabled:
# deepspeed.save_checkpoint above saves model/optim/sched
if self.fsdp and not self.is_fsdp_enabled:
torch.save(full_osd, os.path.join(output_dir, OPTIMIZER_NAME))
else:
torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
with warnings.catch_warnings(record=True) as caught_warnings:
torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
reissue_pt_warnings(caught_warnings)
if self.do_grad_scaling:
torch.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))
# Determine the new best metric / best model checkpoint
if metrics is not None and self.args.metric_for_best_model is not None:
metric_to_check = self.args.metric_for_best_model
if not metric_to_check.startswith("eval_"):
metric_to_check = f"eval_{metric_to_check}"
metric_value = metrics[metric_to_check]
operator = np.greater if self.args.greater_is_better else np.less
if (
self.state.best_metric is None
or self.state.best_model_checkpoint is None
or operator(metric_value, self.state.best_metric)
):
self.state.best_metric = metric_value
self.state.best_model_checkpoint = output_dir
# Save the Trainer state
if self.args.should_save:
self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
# Save RNG state in non-distributed training
rng_states = {
"python": random.getstate(),
"numpy": np.random.get_state(),
"cpu": torch.random.get_rng_state(),
}
if torch.cuda.is_available():
if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
# In non distributed, we save the global CUDA RNG state (will take care of DataParallel)
rng_states["cuda"] = torch.cuda.random.get_rng_state_all()
else:
rng_states["cuda"] = torch.cuda.random.get_rng_state()
if is_torch_tpu_available():
rng_states["xla"] = xm.get_rng_state()
# A process can arrive here before the process 0 has a chance to save the model, in which case output_dir may
# not yet exist.
os.makedirs(output_dir, exist_ok=True)
if self.args.world_size <= 1:
torch.save(rng_states, os.path.join(output_dir, "rng_state.pth"))
else:
torch.save(rng_states, os.path.join(output_dir, f"rng_state_{self.args.process_index}.pth"))
if self.args.push_to_hub:
self._push_from_checkpoint(output_dir)
# Maybe delete some older checkpoints.
if self.args.should_save:
self._rotate_checkpoints(use_mtime=True, output_dir=run_dir)
def _load_optimizer_and_scheduler(self, checkpoint):
"""If optimizer and scheduler states exist, load them."""
if checkpoint is None:
return
if self.is_deepspeed_enabled:
# deepspeed loads optimizer/lr_scheduler together with the model in deepspeed_init
return
checkpoint_file_exists = (
glob.glob(os.path.join(checkpoint, OPTIMIZER_NAME) + "_*")
if is_sagemaker_mp_enabled()
else os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME))
)
if checkpoint_file_exists and os.path.isfile(os.path.join(checkpoint, SCHEDULER_NAME)):
# Load in optimizer and scheduler states
if is_torch_tpu_available():
# On TPU we have to take some extra precautions to properly load the states on the right device.
optimizer_state = torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location="cpu")
with warnings.catch_warnings(record=True) as caught_warnings:
lr_scheduler_state = torch.load(os.path.join(checkpoint, SCHEDULER_NAME), map_location="cpu")
reissue_pt_warnings(caught_warnings)
xm.send_cpu_data_to_device(optimizer_state, self.args.device)
xm.send_cpu_data_to_device(lr_scheduler_state, self.args.device)
self.optimizer.load_state_dict(optimizer_state)
self.lr_scheduler.load_state_dict(lr_scheduler_state)
else:
if is_sagemaker_mp_enabled():
if os.path.isfile(os.path.join(checkpoint, "user_content.pt")):
# Optimizer checkpoint was saved with smp >= 1.10
def opt_load_hook(mod, opt):
opt.load_state_dict(smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True))
else:
# Optimizer checkpoint was saved with smp < 1.10
def opt_load_hook(mod, opt):
if IS_SAGEMAKER_MP_POST_1_10:
opt.load_state_dict(
smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True, back_compat=True)
)
else:
opt.load_state_dict(smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True))
self.model_wrapped.register_post_step_hook(opt_load_hook)
else:
# We use the CPU when training on one GPU to avoid OOM for GPU RAM when training big models.
# In distributed training however, we load directly on each GPU and risk the GPU OOM as it's more
# likely to get OOM on CPU (since we load num_gpu times the optimizer state
map_location = self.args.device if self.args.world_size > 1 else "cpu"
if self.fsdp or self.is_fsdp_enabled:
# modification starts here
if self.is_fsdp_enabled and self.args.save_with_fsdp:
load_fsdp_optimizer(
self.accelerator.state.fsdp_plugin,
self.accelerator,
self.optimizer,
self.model,
checkpoint,
)
elif not self.is_fsdp_enabled:
full_osd = None
# In FSDP, we need to load the full optimizer state dict on rank 0 and then shard it
if self.args.process_index == 0:
full_osd = torch.load(os.path.join(checkpoint, OPTIMIZER_NAME))
# call scatter_full_optim_state_dict on all ranks
sharded_osd = self.model.__class__.scatter_full_optim_state_dict(full_osd, self.model)
self.optimizer.load_state_dict(sharded_osd)
else:
self.optimizer.load_state_dict(
torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location=map_location)
)
# modification ends here
else:
self.optimizer.load_state_dict(
torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location=map_location)
)
with warnings.catch_warnings(record=True) as caught_warnings:
self.lr_scheduler.load_state_dict(torch.load(os.path.join(checkpoint, SCHEDULER_NAME)))
reissue_pt_warnings(caught_warnings)
if self.do_grad_scaling and os.path.isfile(os.path.join(checkpoint, SCALER_NAME)):
self.scaler.load_state_dict(torch.load(os.path.join(checkpoint, SCALER_NAME)))