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|---|---|---|
if self.optimizer is None:
decay_parameters = self.get_decay_parameter_names(opt_model)
optimizer_grouped_parameters = [
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
# e.g. for GaLore optimizer.
if "params" in optimizer_kwargs:
optimizer_grouped_parameters = optimizer_kwargs.pop("params")
# Overwrite `model` in case it's created by `get_optimizer_cls_and_... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
skipped = 0
for module in opt_model.modules():
if isinstance(module, nn.Embedding):
skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def get_learning_rates(self):
"""
Returns the learning rate of each parameter from self.optimizer.
"""
if self.optimizer is None:
raise ValueError("Trainer optimizer is None, please make sure you have setup the optimizer before.")
return [group["lr"] for group in self... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Args:
param (`str` or `torch.nn.parameter.Parameter`, *optional*):
The parameter for which optimizer group needs to be returned.
"""
if self.optimizer is None:
raise ValueError("Trainer optimizer is None, please make sure you have setup the optimizer before.")
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# parse args.optim_args
optim_args = {}
if args.optim_args:
for mapping in args.optim_args.replace(" ", "").split(","):
key, value = mapping.split("=")
optim_args[key] = value
optimizer_kwargs = {"lr": args.learning_rate}
adam_kwargs = {
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_cls = AdamW
optimizer_kwargs.update(adam_kwargs)
if args.optim == OptimizerNames.ADAMW_TORCH_FUSED:
optimizer_kwargs.update({"fused": True})
elif args.optim == OptimizerNames.ADAMW_TORCH_XLA:
try:
from torch_xla.amp.syncfree import Ad... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_cls = NpuFusedAdamW
optimizer_kwargs.update(adam_kwargs)
except ImportError:
raise ValueError("Trainer failed to import FusedAdamW from torch_npu.")
elif args.optim == OptimizerNames.ADAMW_APEX_FUSED:
try:
from apex.optimizers imp... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_cls = FusedAdam
optimizer_kwargs.update(adam_kwargs)
except ImportError:
raise ValueError("Trainer tried to instantiate apex FusedAdam but apex is not installed!")
elif args.optim in [
OptimizerNames.ADAMW_BNB,
OptimizerNames.ADAMW_8B... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
is_paged = False
optim_bits = 32
optimizer_cls = None
additional_optim_kwargs = adam_kwargs
if "paged" in args.optim:
is_paged = True
if "8bit" in args.optim:
optim_bits = 8
if "adam" ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
importlib.metadata.version("bitsandbytes")
) < version.parse("0.44.0"):
raise ValueError(
"The AdEMAMix optimizer is not supported by your current version of `bitsandbytes`. "
"Please install `bitsandbytes` >= 0.44.0."
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
from bitsandbytes.optim import AdEMAMix
optimizer_cls = AdEMAMix
additional_optim_kwargs = {
"betas": (
float(optim_args.get("beta1", args.adam_beta1)),
float(optim_args.get("beta2", args.adam_beta2)... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
bnb_kwargs = {"optim_bits": optim_bits}
if "rmsprop" not in args.optim:
bnb_kwargs["is_paged"] = is_paged
optimizer_kwargs.update(additional_optim_kwargs)
optimizer_kwargs.update(bnb_kwargs)
except ImportError:
raise ValueE... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_cls = AnyPrecisionAdamW
optimizer_kwargs.update(adam_kwargs) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# TODO Change dtypes back to M=FP32, Var = BF16, Kahan = False once they can be cast together in torchdistx.
optimizer_kwargs.update(
{
"use_kahan_summation": strtobool(optim_args.get("use_kahan_summation", "False")),
"momentum_dtype": ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_cls = torch.optim.Adagrad
elif args.optim == OptimizerNames.RMSPROP:
optimizer_cls = torch.optim.RMSprop
elif args.optim in [
OptimizerNames.GALORE_ADAMW,
OptimizerNames.GALORE_ADAMW_8BIT,
OptimizerNames.GALORE_ADAFACTOR,
OptimizerNam... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
is_layerwise = args.optim.lower().endswith("layerwise")
if is_layerwise and args.parallel_mode == ParallelMode.DISTRIBUTED:
raise NotImplementedError("Layer-wise GaLore does not support DDP at this time")
optimizer_mapping = {
OptimizerNames.GALORE_ADAMW: GaLoreA... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if not isinstance(args.optim_target_modules, (list, str)):
raise ValueError(
f"`optim_target_modules` has to be a list of strings, a string corresponding to a regex, or a specific module or 'all-linear', you passed {args.optim_target_modules}"
)
if model ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
galore_params = []
galore_params_names = []
for module_name, module in model.named_modules():
target_module_exists, is_regex = check_target_module_exists(
args.optim_target_modules, module_name, return_is_regex=True
)
if not is... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if len(galore_params) == 0:
raise ValueError(
f"None of the target modules were found! ({args.optim_target_modules}). Please make sure to pass a valid `target_modules`."
)
non_galore_params = [p for n, p in model.named_parameters() if n not in galore_para... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if is_layerwise:
# For layer-wise optimizers, the optimization step is done through post accumulation
# gradient hooks. The trick is to first attach these hooks to the model parameters then
# create a dummy optimizer that will perform no-ops in the Trainer.
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_dict = {}
for param in non_galore_params:
param_groups = [{"params": [param]}]
optimizer_dict[param] = optimizer_cls(param_groups, **optimizer_kwargs)
for param in galore_params:
param_groups = [{"params": [param], **g... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if args.optim == OptimizerNames.GALORE_ADAFACTOR:
optimizer_kwargs.update({"scale_parameter": False, "relative_step": False})
elif args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]:
if not is_lomo_available():
raise ImportError(
"You need... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_kwargs.update({"model": model})
elif args.optim == OptimizerNames.GROKADAMW:
if not is_grokadamw_available():
raise ValueError("Please install grokadamw with `pip install grokadamw`")
from grokadamw import GrokAdamW | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_cls = GrokAdamW
optimizer_kwargs.update(
{
"alpha_init": float(optim_args.get("alpha_init", 0.98)),
"lamb": float(optim_args.get("lamb", 2.0)),
"gamma": float(optim_args.get("gamma", 0.1)),
"grokking_si... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if version.parse(importlib.metadata.version("torch")) <= version.parse("2.4"):
raise ImportError(
"You need to have `torch>2.4` in order to use torch 4-bit optimizers. "
"Install it with `pip install --upgrade torch` it is available on pipy. Otherwise, you need to... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_cls = AdamW4bit
optimizer_kwargs.update(adam_kwargs)
elif args.optim in [
OptimizerNames.SCHEDULE_FREE_ADAMW,
OptimizerNames.SCHEDULE_FREE_SGD,
]:
if not is_schedulefree_available():
raise ImportError(
"You nee... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
additional_optim_kwargs = {}
if args.optim == OptimizerNames.SCHEDULE_FREE_ADAMW:
optimizer_cls = AdamWScheduleFree
additional_optim_kwargs = adam_kwargs
elif args.optim == OptimizerNames.SCHEDULE_FREE_SGD:
optimizer_cls = SGDScheduleFree
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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 ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def num_examples(self, dataloader: DataLoader) -> int:
"""
Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When
dataloader.dataset does not exist or has no length, estimates as best it can
"""
try:
dataset = dataloader.d... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
@staticmethod
def num_tokens(train_dl: DataLoader, max_steps: Optional[int] = None) -> int:
"""
Helper to get number of tokens in a [`~torch.utils.data.DataLoader`] by enumerating dataloader.
"""
train_tokens = 0
try:
for batch in train_dl:
tokens ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.hp_search_backend is None or trial is None:
return
if self.hp_search_backend == HPSearchBackend.OPTUNA:
params = self.hp_space(trial)
elif self.hp_search_backend == HPSearchBackend.RAY:
params = trial
params.pop("wandb", None)
elif self.hp_... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
for key, value in params.items():
if not hasattr(self.args, key):
logger.warning(
f"Trying to set {key} in the hyperparameter search but there is no corresponding field in"
" `TrainingArguments`."
)
continue
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
setattr(self.args, key, value)
if self.hp_search_backend == HPSearchBackend.OPTUNA:
logger.info(f"Trial: {trial.params}")
if self.hp_search_backend == HPSearchBackend.SIGOPT:
logger.info(f"SigOpt Assignments: {trial.assignments}")
if self.hp_search_backend == HPSearchBack... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.args.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.args.deepspeed)
self.args.hf_deepspeed_config.trainer_config_process(self.args)
self.args.deepspeed_plugin = DeepSpeedPlugin(hf_ds_config=self.args.hf_deepspeed_config)
# From 1.0 on, we need to fully wipe the DS plugin w... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if hasattr(trial, "study") and not trial.study._is_multi_objective():
trial.report(self.objective, step)
if trial.should_prune():
self.callback_handler.on_train_end(self.args, self.state, self.control)
raise optuna.TrialPruned()
elif self.h... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _tune_save_checkpoint(self, checkpoint_dir: str):
output_dir = os.path.join(checkpoint_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
self.save_model(output_dir, _internal_call=True)
if self.args.should_save:
# Update the `TrainerControl` state to where we are currentl... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def call_model_init(self, trial=None):
model_init_argcount = number_of_arguments(self.model_init)
if model_init_argcount == 0:
model = self.model_init()
elif model_init_argcount == 1:
model = self.model_init(trial)
else:
raise RuntimeError("model_init ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def torch_jit_model_eval(self, model, dataloader, training=False):
if not training:
if dataloader is None:
logger.warning("failed to use PyTorch jit mode due to current dataloader is none.")
return model
example_batch = next(iter(dataloader))
e... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if isinstance(example_batch, dict):
jit_model = torch.jit.trace(jit_model, example_kwarg_inputs=example_batch, strict=False)
else:
jit_model = torch.jit.trace(
jit_model,
examp... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
jit_model(**example_batch)
jit_model(**example_batch)
model = jit_model
self.use_cpu_amp = False
except (RuntimeError, TypeError, ValueError, NameError, IndexError) as e:
logger.warning(f"failed to use PyTorch jit mode due to: {e}.") | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
return model
def ipex_optimize_model(self, model, training=False, dtype=torch.float32):
if not is_ipex_available():
raise ImportError(
"Using IPEX but IPEX is not installed or IPEX's version does not match current PyTorch, please refer"
" to https://github.com/in... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if not training:
model.eval()
dtype = torch.bfloat16 if not self.is_in_train and self.args.bf16_full_eval else dtype
# conv_bn_folding is disabled as it fails in symbolic tracing, resulting in ipex warnings
model = ipex.optimize(model, dtype=dtype, level="O1", conv_bn_fol... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
has_warning = False
warning_str = "Warning: The following arguments do not match the ones in the `trainer_state.json` within the checkpoint directory: "
for arg_attr, state_attr in attributes_map.items():
arg_value = getattr(training_args, arg_attr, None)
state_value = getattr(tr... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if train_bs_args != train_bs_state:
warning_str += f"\n\tper_device_train_batch_size: {train_bs_args} (from args) != {train_bs_state} (from trainer_state.json)"
has_warning = True
if has_warning:
logger.warning_once(warning_str)
def _wrap_model(self, model, training=Tru... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# train/eval could be run multiple-times - if already wrapped, don't re-wrap it again
if self.accelerator.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.initiali... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# 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)
# Distributed training using PyTo... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.is_fsdp_xla_v2_enabled:
from torch_xla.experimental.spmd_fully_sharded_data_parallel import (
SpmdFullyShardedDataParallel as FSDPv2,
)
except ImportError:
raise ImportError("Missing XLA FSDP related module; please make ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.fsdp_config["min_num_params"] > 0:
auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config["min_num_params"]
)
elif fsdp_transformer_layer_cls_to_wrap is not None:
transformer_cls... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Wrap the base model with an outer FSDP wrapper
if self.is_fsdp_xla_v2_enabled:
def shard_output(output, mesh):
from .modeling_outputs import CausalLMOutputWithPast
real_output = None
if isinstance(output, torch.Tensor):
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.model = model = FSDPv2(
model,
shard_output=shard_output,
auto_wrap_policy=auto_wrap_policy,
auto_wrapper_callable=auto_wrapper_callable,
)
else:
self.model = model = FSDP(
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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_to... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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 = DistributedDataParallel... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Args:
resume_from_checkpoint (`str` or `bool`, *optional*):
If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a
`bool` and equals `True`, load the last checkpoint in *args.output_dir* as saved by a previous instance
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
resume_from_checkpoint = None | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# memory metrics - must set up as early as possible
self._memory_tracker.start()
args = self.args
self.is_in_train = True
# Attach NEFTune hooks if necessary
if self.neftune_noise_alpha is not None:
self.model = self._activate_neftune(self.model)
# do_trai... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if "model_path" in kwargs:
resume_from_checkpoint = kwargs.pop("model_path")
warnings.warn(
"`model_path` is deprecated and will be removed in a future version. Use `resume_from_checkpoint` "
"instead.",
FutureWarning,
)
if len(... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Model re-init
model_reloaded = False
if self.model_init is not None:
# Seed must be set before instantiating the model when using model_init.
enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)
self.model = self.call_m... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if resume_from_checkpoint is not None:
if not is_sagemaker_mp_enabled() and not self.is_deepspeed_enabled and not self.is_fsdp_enabled:
self._load_from_checkpoint(resume_from_checkpoint)
# In case of repeating the find_executable_batch_size, set `self._train_batch_size` properly
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
inner_training_loop = find_executable_batch_size(
self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
)
if args.push_to_hub:
try:
# Disable progress bars when uploading models during checkpoints to avoid polluting stdout
hf... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _inner_training_loop(
self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None
):
self.accelerator.free_memory()
self._train_batch_size = batch_size
if self.args.auto_find_batch_size:
if self.state.train_batch_size != self._... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Check for DeepSpeed *after* the intial pass and modify the config
if self.is_deepspeed_enabled:
# Temporarily unset `self.args.train_batch_size`
original_bs = self.args.per_device_train_batch_size
self.args.per_device_train_batch_size = self.... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Setting up training control variables:
# number of training epochs: num_train_epochs
# number of training steps per epoch: num_update_steps_per_epoch
# total number of training steps to execute: max_steps
total_train_batch_size = self._train_batch_size * args.gradient_accumulation_step... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
len_dataloader = None
num_train_tokens = None
if has_length(train_dataloader):
len_dataloader = len(train_dataloader)
num_update_steps_per_epoch = len_dataloader // args.gradient_accumulation_steps
num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.num_tokens(train_dataloader, args.max_steps) * args.gradient_accumulation_steps
)
else:
max_steps = math.ceil(args.num_train_epochs * num_update_steps_per_epoch)
num_train_epochs = math.ceil(args.num_train_epochs)
num_train_samples = s... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
num_train_samples = args.max_steps * total_train_batch_size
if args.include_tokens_per_second:
num_train_tokens = self.num_tokens(train_dataloader, args.max_steps) * args.gradient_accumulation_steps
else:
raise ValueError(
"args.max_steps must be set to a ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if DebugOption.UNDERFLOW_OVERFLOW in self.args.debug:
if self.args.n_gpu > 1:
# nn.DataParallel(model) replicates the model, creating new variables and module
# references registered here no longer work on other gpus, breaking the module
raise ValueError(
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.is_deepspeed_enabled:
self.optimizer, self.lr_scheduler = deepspeed_init(self, num_training_steps=max_steps)
if not delay_optimizer_creation:
self.create_optimizer_and_scheduler(num_training_steps=max_steps)
self.state = TrainerState(
stateful_callbacks=[
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Compute absolute values for logging, eval, and save if given as ratio
if args.logging_steps is not None:
if args.logging_steps < 1:
self.state.logging_steps = math.ceil(max_steps * args.logging_steps)
else:
self.state.logging_steps = args.logging_steps
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
model = self._wrap_model(self.model_wrapped)
# as the model is wrapped, don't use `accelerator.prepare`
# this is for unhandled cases such as
# FSDP-XLA, SageMaker MP/DP, DataParallel, IPEX
use_accelerator_prepare = True if model is self.model else False
if use_accelerator_prep... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# prepare using `accelerator` prepare
if use_accelerator_prepare:
self.model.train()
if hasattr(self.lr_scheduler, "step"):
if self.use_apex:
model = self.accelerator.prepare(self.model)
else:
model, self.optimizer =... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# for the rest of this function `model` is the outside model, whether it was wrapped or not
if model is not self.model:
self.model_wrapped = model
# backward compatibility
if self.is_deepspeed_enabled:
self.deepspeed = self.model_wrapped
# ckpt loading
i... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# important: at this point:
# self.model is the Transformers Model
# self.model_wrapped is DDP(Transformers Model), Deepspeed(Transformers Model),
# FSDP(Transformers Model), Dynamo Optimized Module(Transformers Model) etc. | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Train!
logger.info("***** Running training *****")
logger.info(f" Num examples = {num_examples:,}")
logger.info(f" Num Epochs = {num_train_epochs:,}")
logger.info(f" Instantaneous batch size per device = {self.args.per_device_train_batch_size:,}")
if self.args.per_device_tra... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.state.epoch = 0
start_time = time.time()
epochs_trained = 0
steps_trained_in_current_epoch = 0
steps_trained_progress_bar = None
# Check if continuing training from a checkpoint
if resume_from_checkpoint is not None and os.path.isfile(
os.path.join(resum... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
logger.info(" Continuing training from checkpoint, will skip to saved global_step")
logger.info(f" Continuing training from epoch {epochs_trained}")
logger.info(f" Continuing training from global step {self.state.global_step}")
if not args.ignore_data_skip:
logger.... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Update the references
self.callback_handler.model = self.model
self.callback_handler.optimizer = self.optimizer
self.callback_handler.lr_scheduler = self.lr_scheduler
self.callback_handler.train_dataloader = train_dataloader
if self.hp_name is not None and self._trial is not No... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.state.num_train_epochs = num_train_epochs
self.state.is_local_process_zero = self.is_local_process_zero()
self.state.is_world_process_zero = self.is_world_process_zero() | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# tr_loss is a tensor to avoid synchronization of TPUs through .item()
tr_loss = torch.tensor(0.0).to(args.device)
# _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses
self._total_loss_scalar = 0.0
self._globalstep_last_logged = s... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
steps_in_epoch = (
len(epoch_dataloader)
if len_dataloader is not None
else args.max_steps * args.gradient_accumulation_steps
)
self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)
if epoch == epochs_trai... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
step = -1
epoch_iterator = iter(epoch_dataloader)
# We chunkify the epoch iterator into gradient accumulation steps `n` batches
remainder = num_examples % args.gradient_accumulation_steps
if remainder == 0:
remainder = args.gradient_accumulation_steps
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Since we perform prefetching, we need to manually set sync_gradients
if not do_sync_step:
self.accelerator.gradient_state._set_sync_gradients(False)
else:
self.accelerator.gradient_state._set_sync_gradients(True) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.include_num_input_tokens_seen:
main_input_name = getattr(self.model, "main_input_name", "input_ids")
if main_input_name not in inputs:
logger.warning(
"Tried to track the number of tokens seen, howev... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if rng_to_sync:
self._load_rng_state(resume_from_checkpoint)
rng_to_sync = False | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
if steps_trained_progress_bar is not None:
steps_trained_progress_bar.update(1)
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# We explicitly want to avoid relying on `accelerator.accumulate` for generation training
context = (
functools.partial(self.accelerator.no_sync, model=model)
if i != len(batch_samples) - 1
and self.accelerator.distributed_type ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if (
args.logging_nan_inf_filter
and not is_torch_xla_available()
and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
):
# if loss is nan or inf simply add the average of previous logged losses
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if do_sync_step:
# Since we perform prefetching, we need to manually set sync_gradients to True
self.accelerator.gradient_state._set_sync_gradients(True)
# Gradient clipping
if args.max_grad_norm is not None and args.max_gr... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if is_sagemaker_mp_enabled() and args.fp16:
_grad_norm = self.optimizer.clip_master_grads(args.max_grad_norm)
elif self.use_apex:
# Revert to normal clipping otherwise, handling Apex or full precision
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if (
is_accelerate_available()
and self.accelerator.distributed_type == DistributedType.DEEPSPEED
):
grad_norm = model.get_global_grad_norm()
# In some cases the gr... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_was_run = not self.accelerator.optimizer_step_was_skipped
if optimizer_was_run:
# Delay optimizer scheduling until metrics are generated
if not isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# PyTorch/XLA relies on the data loader to insert the mark_step for
# each step. Since we are breaking the loop early, we need to manually
# insert the mark_step here.
if self.control.should_epoch_stop or self.control.should_training_stop:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
f" num_steps ({max_steps}) higher than the number of available samples."
)
self.control.should_training_stop = True | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time)
if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
if is_torch_xla_available():
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n")
if args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
# Wait for everyone to get here so we are sure the model has been saved by process 0.
if is_torch_xla_... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
metrics = speed_metrics(
"train",
start_time,
num_samples=num_train_samples,
num_steps=self.state.max_steps,
num_tokens=num_train_tokens,
)
self.store_flos()
metrics["total_flos"] = self.state.total_flos
metrics["train_loss"] = ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Delete the last checkpoint when save_total_limit=1 if it's different from the best checkpoint and process allowed to save.
if self.args.should_save and self.state.best_model_checkpoint is not None and self.args.save_total_limit == 1:
for checkpoint in checkpoints_sorted:
if not os.... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
return TrainOutput(self.state.global_step, train_loss, metrics)
def _get_output_dir(self, trial):
if self.hp_search_backend is not None and trial is not None:
if self.hp_search_backend == HPSearchBackend.OPTUNA:
run_id = trial.number
elif self.hp_search_backend == HP... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
if model is None:
model = self.model | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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