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config_file = os.path.join(resume_from_checkpoint, CONFIG_NAME)
adapter_weights_file = os.path.join(resume_from_checkpoint, ADAPTER_WEIGHTS_NAME)
adapter_safe_weights_file = os.path.join(resume_from_checkpoint, ADAPTER_SAFE_WEIGHTS_NAME)
weights_file = os.path.join(resume_from_checkpoint, WEIGHT... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# this checks the FSDP state dict when `FULL_STATE_DICT` is used
or os.path.isfile(os.path.join(resume_from_checkpoint, f"{FSDP_MODEL_NAME}.bin"))
)
# if multiple adapters exist, they get saved in sub directories
adapter_subdirs = (
[
folder_name
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if is_fsdp_ckpt and not self.is_fsdp_enabled:
raise ValueError(f"Checkpoint found at {resume_from_checkpoint} is only supported when using PyTorch FSDP")
if not (
any(
os.path.isfile(f)
for f in [
weights_file,
safe... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if os.path.isfile(config_file):
config = PretrainedConfig.from_json_file(config_file)
checkpoint_version = config.transformers_version
if checkpoint_version is not None and checkpoint_version != __version__:
logger.warning(
f"You are resuming train... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if os.path.isfile(weights_file) or os.path.isfile(safe_weights_file) or is_fsdp_ckpt:
weights_only_kwarg = {"weights_only": True}
# If the model is on the GPU, it still works!
if is_sagemaker_mp_enabled():
if os.path.isfile(os.path.join(resume_from_checkpoint, "user_c... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
"Enabling FP16 and loading from smp < 1.10 checkpoint together is not suppported."
)
state_dict = torch.load(
weights_file,
map_location="cpu",
**weights_only_kwarg,
)
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# We load the model state dict on the CPU to avoid an OOM error.
if self.args.save_safetensors and os.path.isfile(safe_weights_file):
state_dict = safetensors.torch.load_file(safe_weights_file, device="cpu")
else:
state_dict = torch.load(
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
# which takes *args instead of **kwargs
load_result = model.load_state_dict(state_dict, False)
# release memory
del state_dict
self._issue_warnings_after_load(load_re... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Load adapters following PR # 24096
elif _is_peft_model(model):
# If train a model using PEFT & LoRA, assume that adapter have been saved properly.
# TODO: in the future support only specific min PEFT versions
if (hasattr(model, "active_adapter") or hasattr(model, "active_ad... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if adapter_subdirs:
for subdir_name in adapter_subdirs:
peft_id = os.path.join(resume_from_checkpoint, subdir_name)
model.load_adapter(peft_id, subdir_name, is_trainable=(subdir_name == active_adapter))
model.set_ada... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# We load the sharded checkpoint
load_result = load_sharded_checkpoint(
model, resume_from_checkpoint, strict=is_sagemaker_mp_enabled(), prefer_safe=self.args.save_safetensors
)
if not is_sagemaker_mp_enabled():
self._issue_warnings_after_load(load_res... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _load_best_model(self):
logger.info(f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric}).")
best_model_path = os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME)
best_safe_model_path = os.path.join(self.state.best_model_checkpoint, SAFE_WEIG... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
if self.is_deepspeed_enabled:
deepspeed_load_checkpoint(
self.model_wrapped,
self.state.best_model_checkpoint,
load_module_strict=not _is_peft_model(self.model),
)
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if os.path.isfile(os.path.join(self.state.best_model_checkpoint, "user_content.pt")):
# If the 'user_content.pt' file exists, load with the new smp api.
# Checkpoint must have been saved with the new smp api.
smp.resume_from_checkpoint(
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
best_model_path,
map_location="cpu",
**weights_only_kwarg,
) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
state_dict["_smp_is_partial"] = False
load_result = model.load_state_dict(state_dict, strict=True)
else:
if _is_peft_model(model):
# If train a model using PEFT & LoRA, assume that adapter have been saved properly.
# TODO: in the fu... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
active_adapter = model.active_adapter | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if os.path.exists(best_adapter_model_path) or os.path.exists(best_safe_adapter_model_path):
try:
model.load_adapter(self.state.best_model_checkpoint, active_adapter)
except RuntimeError as exc:
if mod... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
"directory using PeftModel.from_pretrained(base_model, <path>) after training "
"has finished."
)
raise RuntimeError(msg) from exc
else:
rai... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
load_result = _IncompatibleKeys([], [])
else:
logger.warning(
"The intermediate checkpoints of PEFT may not be saved correctly, "
f"consider using a custom callback to save {ADAPTER_WEIGHTS_NAME} in corre... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
state_dict = safetensors.torch.load_file(best_safe_model_path, device="cpu")
else:
state_dict = torch.load(
best_model_path,
map_location="cpu",
**weights_only_kwarg,
) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# If the model is on the GPU, it still works!
# workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
# which takes *args instead of **kwargs
load_result = model.load_state_dict(state_dict, False)
if not is_sagemaker_mp_enable... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
f"Could not locate the best model at {best_model_path}, if you are running a distributed training "
"on multiple nodes, you should activate `--save_on_each_node`."
) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _issue_warnings_after_load(self, load_result):
if len(load_result.missing_keys) != 0:
if self.model._keys_to_ignore_on_save is not None and set(load_result.missing_keys) == set(
self.model._keys_to_ignore_on_save
):
self.model.tie_weights()
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Run delayed LR scheduler now that metrics are populated
if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau) and not skip_scheduler:
metric_to_check = self.args.metric_for_best_model
if not metric_to_check.startswith("eval_"):
metric_to_check = f... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
return metrics | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time):
if self.control.should_log and self.state.global_step > self._globalstep_last_logged:
if is_torch_xla_available():
xm.mark_step()
logs: Dict[str, float] = {}
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.log(logs, start_time)
metrics = None
if self.control.should_evaluate:
metrics = self._evaluate(trial, ignore_keys_for_eval)
is_new_best_metric = self._determine_best_metric(metrics=metrics, trial=trial)
if self.args.save_strategy == SaveStrategy.BEST:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.world_size > 1:
process_index = self.args.process_index
rng_file = os.path.join(checkpoint, f"rng_state_{process_index}.pth")
if not os.path.isfile(rng_file):
logger.info(
f"Didn't find an RNG file for process {process_index}, if you a... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
with safe_globals():
checkpoint_rng_state = torch.load(rng_file)
random.setstate(checkpoint_rng_state["python"])
np.random.set_state(checkpoint_rng_state["numpy"])
torch.random.set_rng_state(checkpoint_rng_state["cpu"])
if torch.cuda.is_available():
if self.args.p... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
torch.npu.random.set_rng_state_all(checkpoint_rng_state["npu"])
else:
try:
torch.npu.random.set_rng_state(checkpoint_rng_state["npu"])
except Exception as e:
logger... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
f"Didn't manage to set back the RNG states of the MLU because of the following error:\n {e}"
"\nThis won't yield the same results as if the training had not been interrupted."
)
if is_torch_musa_available():
if self.args.parallel_mode == ParallelMode.DISTR... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _determine_best_metric(self, metrics, trial):
"""
Determine if the model should be saved based on the evaluation metrics.
Returns:
bool: True if a new best metric was found, else False
"""
is_new_best_metric = False
if self.args.metric_for_best_model is ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
operator = np.greater if self.args.greater_is_better else np.less
if self.state.best_metric is None:
self.state.best_metric = float("-inf") if self.args.greater_is_better else float("inf")
if operator(metric_value, self.state.best_metric):
run_dir = self._get_ou... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# 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)
s... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Save the Trainer state
if self.args.should_save:
# Update `ExportableState` callbacks and `TrainerControl` state to where we are currently
for cb in [
cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
]:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Maybe delete some older checkpoints.
if self.args.should_save:
# Solely rely on numerical checkpoint id for rotation.
# mtime is not reliable especially on some fuse fs in cloud environments.
self._rotate_checkpoints(use_mtime=False, output_dir=run_dir)
def _save_rng_s... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if is_torch_xla_available():
rng_states["xla"] = xm.get_rng_state()
if is_torch_npu_available():
if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
rng_states["npu"] = torch.npu.random.get_rng_state_all()
else:
rng_states["npu"] = torch.n... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# 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:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _save_optimizer_and_scheduler(self, output_dir):
if is_torch_xla_available():
xm.rendezvous("saving_optimizer_states")
if self.is_fsdp_xla_v1_enabled:
optm = {
"optimizer": self.optimizer.state_dict(),
"shard_metadata": self.mod... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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),
par... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.model_wrapped.save_checkpoint(output_dir)
elif self.is_fsdp_enabled:
# save fsdp specific ckpt for resuming from ckpt
save_fsdp_model(
self.accelerator.state.fsdp_plugin, self.accelerator, self.model, output_dir, **_get_fsdp_ckpt_kwargs()
)
sa... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Save SCHEDULER & SCALER
is_deepspeed_custom_scheduler = self.is_deepspeed_enabled and not isinstance(
self.lr_scheduler, DeepSpeedSchedulerWrapper
)
if (
self.args.should_save
and (not self.is_deepspeed_enabled or is_deepspeed_custom_scheduler)
a... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.is_deepspeed_enabled:
# deepspeed loads optimizer/lr_scheduler together with the model in deepspeed_init
if not isinstance(self.lr_scheduler, DeepSpeedSchedulerWrapper):
with warnings.catch_warnings(record=True) as caught_warnings:
self.lr_scheduler.lo... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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))
or os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME_BIN))
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Load in optimizer and scheduler states
if is_torch_xla_available():
# On TPU we have to take some extra precautions to properly load the states on the right device.
if self.is_fsdp_xla_v1_enabled:
optimizer_state = torch.load(
os.... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
reissue_pt_warnings(caught_warnings) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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_sa... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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, OPTIMIZ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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_w... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _load_callback_state(self):
"""If callback states exist and were passed in, restore their states if enabled"""
if not self.args.restore_callback_states_from_checkpoint:
return
# Callback states are stored in stateful_callbacks
not_found = []
new_callbacks = []
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
attributes = callback_data.get("attributes", {})
new_callback = type(callback)(**args)
for attribute, value in attributes.items():
setattr(new_callback, attribute, value)
if isinstance(callback, TrainerControl):
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
f"Checkpoint included callbacks not included in current configuration. Ignoring. ({', '.join(not_found)})"
)
for callback in new_callbacks:
self.callback_handler.add_callback(callback) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def hyperparameter_search(
self,
hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
n_trials: int = 20,
direction: Union[str, List[str]] = "minimize",
backend: Optional[Union["s... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to
reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to
subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`]... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Args:
hp_space (`Callable[["optuna.Trial"], Dict[str, float]]`, *optional*):
A function that defines the hyperparameter search space. Will default to
[`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or
[`~trainer_utils.defa... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
should pick `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or
several metrics. If it's multi objectives optimization, direction is `List[str]`, can be List of
`"minimize"` and `"maximize"`, you should pick `"minimize"` when optimizing the validation lo... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
- `optuna`: parameters from
[optuna.study.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html)
and also the parameters `timeout`, `n_jobs` and `gc_after_trial` from
[optuna.study.Study.optimize](https://optuna.rea... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
[sigopt.Connection.set_proxies](https://docs.sigopt.com/support/faq#how-do-i-use-sigopt-with-a-proxy). | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Returns:
[`trainer_utils.BestRun` or `List[trainer_utils.BestRun]`]: All the information about the best run or best
runs for multi-objective optimization. Experiment summary can be found in `run_summary` attribute for Ray
backend.
"""
if backend is None:
b... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
best_run = backend_obj.run(self, n_trials, direction, **kwargs)
self.hp_search_backend = None
return best_run
def log(self, logs: Dict[str, float], start_time: Optional[float] = None) -> None:
"""
Log `logs` on the various objects watching training.
Subclass and override t... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
output = {**logs, **{"step": self.state.global_step}}
self.state.log_history.append(output)
self.control = self.callback_handler.on_log(self.args, self.state, self.control, logs) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any]:
"""
Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
"""
if isinstance(data, Mapping):
return type(data)({k: self._prepare_input(v) ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
kwargs.update({"dtype": self.accelerator.state.deepspeed_plugin.hf_ds_config.dtype()})
return data.to(**kwargs)
return data | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
"""
Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and
handling potential state.
"""
inputs = self._prepare_input(... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def autocast_smart_context_manager(self, cache_enabled: Optional[bool] = True):
"""
A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired
arguments, depending on the situation.
"""
if self.use_cpu_amp:
ctx_manager = t... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument `labels`. Check your model's documentation for all accepted arguments.
Return:
`torch.Tensor`: The tensor with training loss on this batch.
"""
model.trai... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
del inputs
if (
self.args.torch_empty_cache_steps is not None
and self.state.global_step % self.args.torch_empty_cache_steps == 0
):
if is_torch_xpu_available():
torch.xpu.empty_cache()
elif is_torch_mlu_available():
torch.m... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if self.use_apex:
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
# Finally we need to normalize the loss for reporti... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Subclass and override for custom behavior.
"""
if (self.label_smoother is not None or self.compute_loss_func is not None) and "labels" in inputs:
labels = inputs.pop("labels")
else:
labels = None
if self.model_accepts_loss_kwargs:
loss_kwargs = {}
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if labels is not None:
unwrapped_model = self.accelerator.unwrap_model(model)
if _is_peft_model(unwrapped_model):
model_name = unwrapped_model.base_model.model._get_name()
else:
model_name = unwrapped_model._get_name()
# User-defined comput... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}."
)
# We don't use .loss here since the model may return tuples instead of ModelOutput.
loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0] | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.average_tokens_across_devices and self.model_accepts_loss_kwargs:
loss *= self.accelerator.num_processes
return (loss, outputs) if return_outputs else loss
def is_local_process_zero(self) -> bool:
"""
Whether or not this process is the local (e.g., on one machine i... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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 | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if is_torch_xla_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()
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self._save(output_dir, state_dict=state_dict)
elif self.is_deepspeed_enabled:
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:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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_tpu(self, output_dir: Optional[str] = None):
output... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Save a trained model and configuration using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
supported_classes = (PushToHubMixin,)
xm.rendezvous("saving_checkpoint")
if self.is_fsdp_xla_v1_enabled:
ckpt = {
"model": model.state_dic... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
full_state_dict, _ = consolidate_sharded_model_checkpoints(
ckpt_prefix=os.path.join(output_dir, ""),
ckpt_suffix=f"rank*-of-*-{WEIGHTS_NAME}",
save_model=False,
)
model = model.module.module
unwrapped_model = se... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if isinstance(self.accelerator.unwrap_model(model), supported_classes):
self.accelerator.unwrap_model(model).save_pretrained(
output_dir,
is_main_process=self.args.should_save,
state_dict=xm._maybe_convert_to_cpu(model.state_dict()),
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
state_dict=xm._maybe_convert_to_cpu(model.state_dict()),
)
if self.processing_class is not None and self.args.should_save:
self.processing_class.save_pretrained(output_dir) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _save(self, output_dir: Optional[str] = None, state_dict=None):
# If we are executing this function, we are the process zero, so we don't check for that.
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"S... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if isinstance(self.accelerator.unwrap_model(self.model), supported_classes):
self.accelerator.unwrap_model(self.model).save_pretrained(
output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
)
else:
logger.info("Tr... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Good practice: save your training arguments together with the trained model
torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
def store_flos(self):
# Storing the number of floating-point operations that went into the model
if self.args.parallel_mode == ParallelMode.DISTR... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
for path in glob_checkpoints:
if use_mtime:
ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
else:
regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
if regex_match is not None and regex_match.groups() is not None:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
checkpoints_sorted = sorted(ordering_and_checkpoint_path)
checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
# Make sure we don't delete the best model.
if (
self.state.best_model_checkpoint is not None
and str(Path(self.state.best_model_checkpoint)... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Check if we should delete older checkpoint(s)
checkpoints_sorted = self._sorted_checkpoints(use_mtime=use_mtime, output_dir=output_dir)
if len(checkpoints_sorted) <= self.args.save_total_limit:
return
# If save_total_limit=1 with load_best_model_at_end=True, we could end up deleti... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)
checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
for checkpoint in checkpoints_to_be_deleted:
logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_lim... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Args:
eval_dataset (Union[`Dataset`, Dict[str, `Dataset`]), *optional*):
Pass a dataset if you wish to override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns
not accepted by the `model.forward()` method are automatically removed. If it is a dictionary, it wil... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
If you pass a dictionary with names of datasets as keys and datasets as values, evaluate will run
separate evaluations on each dataset. This can be useful to monitor how training affects other
datasets or simply to get a more fine-grained evaluation.
When used with `load_... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
ignore_keys (`List[str]`, *optional*):
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.
metric_key_prefix (`str`, *optional*, defaults to `"eval"`):
An optional prefix to be used as the metri... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Returns:
A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
dictionary also contains the epoch number which comes from the training state.
"""
# handle multipe eval datasets
override = eval_dataset is not None
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
eval_dataloader = self.get_eval_dataloader(eval_dataset)
if self.is_fsdp_xla_v2_enabled:
eval_dataloader = tpu_spmd_dataloader(eval_dataloader)
start_time = time.time()
eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
output... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
total_batch_size = self.args.eval_batch_size * self.args.world_size
if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
if f"{metric_key_prefix}_model_preparation_time" in output.metrics:
star... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics)
self._memory_tracker.stop_and_update_metrics(output.metrics)
return output.metrics
def predict(
self, test_dataset: Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: st... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Args:
test_dataset (`Dataset`):
Dataset to run the predictions on. If it is an `datasets.Dataset`, columns not accepted by the
`model.forward()` method are automatically removed. Has to implement the method `__len__`
ignore_keys (`List[str]`, *optional*):
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
</Tip>
Returns: *NamedTuple* A namedtuple with the following keys:
- predictions (`np.ndarray`): The predictions on `test_dataset`.
- label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some).
- metrics (`Dict[str, float]`, *optional*): The potential ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
output = eval_loop(
test_dataloader, description="Prediction", ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix
)
total_batch_size = self.args.eval_batch_size * self.args.wo... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.control = self.callback_handler.on_predict(self.args, self.state, self.control, output.metrics)
self._memory_tracker.stop_and_update_metrics(output.metrics)
return PredictionOutput(predictions=output.predictions, label_ids=output.label_ids, metrics=output.metrics)
def evaluation_loop(
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
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