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# if eval is called w/o train, handle model prep here
if self.is_deepspeed_enabled and self.deepspeed is None:
_, _ = deepspeed_init(self, num_training_steps=0, inference=True)
model = self._wrap_model(self.model, training=False, dataloader=dataloader)
if len(self.accelerator._mode... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# backward compatibility
if self.is_deepspeed_enabled:
self.deepspeed = self.model_wrapped
# if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
# while ``train`` is running, cast it to the right dtype first and then put on device
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
model.eval()
if hasattr(self.optimizer, "eval") and callable(self.optimizer.eval):
self.optimizer.eval()
self.callback_handler.eval_dataloader = dataloader
# Do this before wrapping.
eval_dataset = getattr(dataloader, "dataset", None)
if args.past_index >= 0:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Main evaluation loop
for step, inputs in enumerate(dataloader):
# Update the observed num examples
observed_batch_size = find_batch_size(inputs)
if observed_batch_size is not None:
observed_num_examples += observed_batch_size
# For batch samp... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Update containers
if losses is not None:
losses = self.gather_function((losses.repeat(batch_size)))
all_losses.add(losses)
if inputs_decode is not None:
inputs_decode = self.accelerator.pad_across_processes(inputs_decode, dim=1, pad_index=-100)
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
logits = self.preprocess_logits_for_metrics(logits, labels)
logits = self.gather_function((logits))
if not self.args.batch_eval_metrics or description == "Prediction":
all_preds.add(logits)
if labels is not None:
labels = self.gather_functi... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)
if self.args.batch_eval_metrics:
if self.compute_metrics is not None and logits is not None and labels is not None:
is_last_step = self.accelerator.gradient_state.end_of_dataloader
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
elif args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0:
all_losses.to_cpu_and_numpy()
all_preds.to_cpu_and_numpy()
all_labels... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Gather all remaining tensors and put them back on the CPU
all_losses = all_losses.get_arrays()
all_preds = all_preds.get_arrays()
all_labels = all_labels.get_arrays()
all_inputs = all_inputs.get_arrays() | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Number of samples
if has_length(eval_dataset):
num_samples = len(eval_dataset)
# The instance check is weird and does not actually check for the type, but whether the dataset has the right
# methods. Therefore we need to make sure it also has the attribute.
elif isinstance(... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Metrics!
if (
self.compute_metrics is not None
and all_preds is not None
and all_labels is not None
and not self.args.batch_eval_metrics
):
eval_set_kwargs["losses"] = all_losses if "loss" in args.include_for_metrics else None
eva... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if isinstance(all_losses, list) and all_losses:
metrics[f"{metric_key_prefix}_loss"] = np.concatenate(all_losses).mean().item()
elif isinstance(all_losses, np.ndarray):
metrics[f"{metric_key_prefix}_loss"] = all_losses.mean().item()
if hasattr(self, "jit_compilation_time"):
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _nested_gather(self, tensors, name=None):
"""
Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
concatenating them to `gathered`
"""
if tensors is None:
return
if is_torch_xla_available():
if na... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def prediction_step(
self,
model: nn.Module,
inputs: Dict[str, Union[torch.Tensor, Any]],
prediction_loss_only: bool,
ignore_keys: Optional[List[str]] = None,
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
"""
Perform an ev... | 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.
prediction_loss_only (`bool`):
Whether or not to return the loss only.
ignore_ke... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Return:
Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss,
logits and labels (each being optional).
"""
has_labels = False if len(self.label_names) == 0 else all(inputs.get(k) is not None for k in self.label_names)
# For ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
inputs = self._prepare_inputs(inputs)
if ignore_keys is None:
if hasattr(self.model, "config"):
ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", [])
else:
ignore_keys = []
# labels may be popped when computing the loss (labe... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
with torch.no_grad():
if is_sagemaker_mp_enabled():
raw_outputs = smp_forward_only(model, inputs)
if has_labels or loss_without_labels:
if isinstance(raw_outputs, dict):
loss_mb = raw_outputs["loss"]
logits_m... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
loss = loss_mb.reduce_mean().detach().cpu()
logits = smp_nested_concat(logits_mb)
else:
loss = None
if isinstance(raw_outputs, dict):
logits_mb = tuple(v for k, v in raw_outputs.items() if k not in ignore_keys)
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if isinstance(outputs, dict):
logits = tuple(v for k, v in outputs.items() if k not in ignore_keys + ["loss"])
else:
logits = outputs[1:]
else:
loss = None
with self.compute_loss_context_manager()... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]):
"""
For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point
operations for every backward + forward pass. If using another model, either implement such a method in the
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.hub_model_id is None:
repo_name = Path(self.args.output_dir).absolute().name
else:
repo_name = self.args.hub_model_id
token = token if token is not None else self.args.hub_token
repo_url = create_repo(repo_name, token=token, private=self.args.hub_private_rep... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Args:
language (`str`, *optional*):
The language of the model (if applicable)
license (`str`, *optional*):
The license of the model. Will default to the license of the pretrained model used, if the original
model given to the `Trainer` comes from a... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
dataset_tags (`str` or `List[str]`, *optional*):
One or several dataset tags, to be included in the metadata of the model card.
dataset (`str` or `List[str]`, *optional*):
One or several dataset identifiers, to be included in the metadata of the model card.
datase... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
model_card_filepath = os.path.join(self.args.output_dir, "README.md")
is_peft_library = False
if os.path.exists(model_card_filepath):
library_name = ModelCard.load(model_card_filepath).data.get("library_name")
is_peft_library = library_name == "peft"
# Append existin... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
training_summary = TrainingSummary.from_trainer(
self,
language=language,
license=license,
tags=tags,
model_name=model_name,
finetuned_from=finetuned_from,
tasks=tasks,
dataset_tags=dataset_tags,
dataset=dataset,... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _push_from_checkpoint(self, checkpoint_folder):
# Only push from one node.
if not self.is_world_process_zero() or self.args.hub_strategy == HubStrategy.END:
return
# If we haven't finished the last push, we don't do this one unless args.hub_always_push=True.
if not self.a... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
output_dir = self.args.output_dir
# To avoid a new synchronization of all model weights, we just copy the file from the checkpoint folder
modeling_files = [CONFIG_NAME, WEIGHTS_NAME, SAFE_WEIGHTS_NAME]
# Add sharded checkpoints if we have an index
for index_file in [WEIGHTS_INDEX_NAME, ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
shutil.copy(os.path.join(checkpoint_folder, modeling_file), os.path.join(output_dir, modeling_file))
# Saving the processing class is fast and we don't know how many files it may have spawned, so we resave it to be sure.
if self.processing_class is not None:
self.processing_class.save_pretra... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.save_strategy == SaveStrategy.STEPS:
commit_message = f"Training in progress, step {self.state.global_step}"
else:
commit_message = f"Training in progress, epoch {int(self.state.epoch)}"
model_push_job = upload_folder(
repo_id=self.hub_model_id,
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.hub_strategy in [HubStrategy.CHECKPOINT, HubStrategy.ALL_CHECKPOINTS]:
path_in_repo = (
"last-checkpoint" if self.args.hub_strategy == HubStrategy.CHECKPOINT else Path(checkpoint_folder).name
)
checkpoint_push = upload_folder(
repo_id=self... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _finish_current_push(self):
if not hasattr(self, "push_in_progress"):
return
if self.push_in_progress is not None and not self.push_in_progress.is_done():
logger.info("Waiting for the current checkpoint push to be finished, this might take a couple of minutes.")
s... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Parameters:
commit_message (`str`, *optional*, defaults to `"End of training"`):
Message to commit while pushing.
blocking (`bool`, *optional*, defaults to `True`):
Whether the function should return only when the `git push` has finished.
token (`str`,... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Returns:
The URL of the repository where the model was pushed if `blocking=False`, or a `Future` object tracking the
progress of the commit if `blocking=True`.
"""
model_name = kwargs.pop("model_name", None)
if model_name is None and self.args.should_save:
if ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Only push from one node.
if not self.is_world_process_zero():
return
# Add additional tags in the case the model has already some tags and users pass
# "tags" argument to `push_to_hub` so that trainer automatically handles internal tags
# from all models since Trainer does... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Wait for the current upload to be finished.
self._finish_current_push()
return upload_folder(
repo_id=self.hub_model_id,
folder_path=self.args.output_dir,
commit_message=commit_message,
token=token,
run_as_future=not blocking,
ign... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else args.prediction_loss_only
# if eval is called w/o train, handle model prep here
if self.is_deepspeed_enabled and self.deepspeed is None:
_, _ = deepspeed_init(self, num_training_steps=0, inference=True)
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# backward compatibility
if self.is_deepspeed_enabled:
self.deepspeed = self.model_wrapped
# if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
# while ``train`` is running, cast it to the right dtype first and then put on device
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
num_examples = self.num_examples(dataloader)
logger.info(f"\n***** Running {description} *****")
logger.info(f" Num examples = {num_examples}")
logger.info(f" Batch size = {batch_size}")
losses_host: torch.Tensor = None
preds_host: Union[torch.Tensor, List[torch.Tensor]] = Non... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
eval_losses_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=batch_size)
if not prediction_loss_only:
# The actual number of eval_sample can be greater than num_examples in distributed settings (when we pass
# a batch size to the sampler)
make_m... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if args.past_index >= 0:
self._past = None
self.callback_handler.eval_dataloader = dataloader
for step, inputs in enumerate(dataloader):
loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
main_input_name = getat... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if loss is not None:
losses = loss.repeat(batch_size)
losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)
if logits is not None:
preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.batch_eval_metrics:
if self.compute_metrics is not None and preds_host is not None and labels_host is not None:
is_last_step = self.accelerator.gradient_state.end_of_dataloader
batch_kwargs = {}
batch_kwargs["losses"] = losses_host... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.batch_eval_metrics or (
args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0
):
# Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
eval_losses_gatherer.add_arrays(sel... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if args.past_index and hasattr(self, "_past"):
# Clean the state at the end of the evaluation loop
delattr(self, "_past")
# Gather all remaining tensors and put them back on the CPU
eval_losses_gatherer.add_arrays(self._gather_and_numpify(losses_host, "eval_losses"))
if ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if (
self.compute_metrics is not None
and preds is not None
and label_ids is not None
and not self.args.batch_eval_metrics
):
eval_set_kwargs["losses"] = eval_loss if "loss" in args.include_for_metrics else None
eval_set_kwargs["inputs"] = ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys()):
if not key.startswith(f"{metric_key_prefix}_"):
metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
return EvalLoopOutput(predictions=preds, label_ids=label_ids, metrics=metrics, num_samples... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _add_sm_patterns_to_gitignore(self) -> None:
"""Add SageMaker Checkpointing patterns to .gitignore file."""
# Make sure we only do this on the main process
if not self.is_world_process_zero():
return
patterns = ["*.sagemaker-uploading", "*.sagemaker-uploaded"]
#... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Write the .gitignore file if it has changed
if content != current_content:
with open(os.path.join(self.repo.local_dir, ".gitignore"), "w") as f:
logger.debug(f"Writing .gitignore file. Content: {content}")
f.write(content)
self.repo.git_add(".gitignore")
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# check if num_steps is attempted to be passed in gradient_accumulation_kwargs
if "num_steps" in grad_acc_kwargs:
if self.args.gradient_accumulation_steps > 1:
# raise because we do not know which setting is intended.
raise ValueError(
"The `Accele... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if is_accelerate_available("0.28.0"):
dataloader_config = DataLoaderConfiguration(
split_batches=accelerator_config.pop("split_batches"),
dispatch_batches=accelerator_config.pop("dispatch_batches"),
even_batches=accelerator_config.pop("even_batches"),
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
non_blocking = accelerator_config.pop("non_blocking")
if not is_accelerate_available("0.30.0"):
if non_blocking:
raise ImportError(
"`non_blocking` is only supported in accelerate v0.30.0 and above. Please upgrade accelerate to use this feature."
)... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
args = {
"deepspeed_plugin": self.args.deepspeed_plugin,
}
if is_accelerate_available("0.28.0"):
args["dataloader_config"] = dataloader_config
else:
args.update(accelerator_config)
# create accelerator object
self.accelerator = Accelerator(**a... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# deepspeed and accelerate flags covering both trainer args and accelerate launcher
self.is_deepspeed_enabled = getattr(self.accelerator.state, "deepspeed_plugin", None) is not None
self.is_fsdp_enabled = getattr(self.accelerator.state, "fsdp_plugin", None) is not None | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# post accelerator creation setup
if self.is_fsdp_enabled:
fsdp_plugin = self.accelerator.state.fsdp_plugin
fsdp_plugin.limit_all_gathers = self.args.fsdp_config.get(
"limit_all_gathers", fsdp_plugin.limit_all_gathers
)
fsdp_plugin.activation_check... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# `save_only_model` can't be used with DeepSpeed/FSDP along with `load_best_model_at_end`
if (
self.args.save_only_model
and (self.is_deepspeed_enabled or self.is_fsdp_enabled)
and self.args.load_best_model_at_end
):
wrapper = "DeepSpeed" if self.is_deepsp... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def propagate_args_to_deepspeed(self, auto_find_batch_size=False):
"""
Sets values in the deepspeed plugin based on the Trainer args
"""
from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
ds_plugin = self.accelerator.state.deepspeed_plugin
ds_plugi... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if isinstance(self.model.active_peft_config, LoraConfig):
fsdp_plugin = self.accelerator.state.fsdp_plugin
fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(self.model)
if (
getattr(self.model, "quantization_method", None) == QuantizationMethod.BITS_AND_BYT... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if len(batch_samples) > 0 and "labels" in batch_samples[0]:
# For now we don't support object detection
try:
num_items_in_batch = sum([(batch["labels"].ne(-100)).sum() for batch in batch_samples])
except (TypeError, AttributeError):
pass
if se... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
class HyperParamSearchBackendBase:
name: str
pip_package: str = None
@staticmethod
def is_available():
raise NotImplementedError
def run(self, trainer, n_trials: int, direction: str, **kwargs):
raise NotImplementedError
def default_hp_space(self, trial):
raise NotImple... | 245 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py |
class OptunaBackend(HyperParamSearchBackendBase):
name = "optuna"
@staticmethod
def is_available():
return is_optuna_available()
def run(self, trainer, n_trials: int, direction: str, **kwargs):
return run_hp_search_optuna(trainer, n_trials, direction, **kwargs)
def default_hp_spac... | 246 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py |
class RayTuneBackend(HyperParamSearchBackendBase):
name = "ray"
pip_package = "'ray[tune]'"
@staticmethod
def is_available():
return is_ray_tune_available()
def run(self, trainer, n_trials: int, direction: str, **kwargs):
return run_hp_search_ray(trainer, n_trials, direction, **kwa... | 247 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py |
class SigOptBackend(HyperParamSearchBackendBase):
name = "sigopt"
@staticmethod
def is_available():
return is_sigopt_available()
def run(self, trainer, n_trials: int, direction: str, **kwargs):
return run_hp_search_sigopt(trainer, n_trials, direction, **kwargs)
def default_hp_spac... | 248 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py |
class WandbBackend(HyperParamSearchBackendBase):
name = "wandb"
@staticmethod
def is_available():
return is_wandb_available()
def run(self, trainer, n_trials: int, direction: str, **kwargs):
return run_hp_search_wandb(trainer, n_trials, direction, **kwargs)
def default_hp_space(se... | 249 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py |
class KerasMetricCallback(keras.callbacks.Callback):
"""
Callback to compute metrics at the end of every epoch. Unlike normal Keras metrics, these do not need to be
compilable by TF. It is particularly useful for common NLP metrics like BLEU and ROUGE that require string
operations or generation loops t... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
def rouge_fn(predictions, labels):
decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
result = rouge_metric.compute(predictions=decoded_predictions, references=decoded_labels)
retur... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
Args:
metric_fn (`Callable`):
Metric function provided by the user. It will be called with two arguments - `predictions` and `labels`.
These contain the model's outputs and matching labels from the dataset. It should return a dict mapping
metric names to numerical values.
... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
predict_with_generate (`bool`, *optional*, defaults to `False`):
Whether we should use `model.generate()` to get outputs for the model.
use_xla_generation (`bool`, *optional*, defaults to `False`):
If we're generating, whether to compile model generation with XLA. This can massively incr... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
""" | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
def __init__(
self,
metric_fn: Callable,
eval_dataset: Union[tf.data.Dataset, np.ndarray, tf.Tensor, tuple, dict],
output_cols: Optional[List[str]] = None,
label_cols: Optional[List[str]] = None,
batch_size: Optional[int] = None,
predict_with_generate: bool = Fals... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
self.eval_dataset = eval_dataset
self.predict_with_generate = predict_with_generate
self.output_cols = output_cols | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
# This next block attempts to parse out which elements of the dataset should be appended to the labels list
# that is passed to the metric_fn
if isinstance(eval_dataset.element_spec, tuple) and len(eval_dataset.element_spec) == 2:
input_spec, label_spec = eval_dataset.element_spec
el... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
self.label_cols = ["labels"]
self.use_keras_label = False
logging.warning("No label_cols specified for KerasMetricCallback, assuming you want the 'labels' key.")
elif "start_positions" in input_spec and "end_positions" in input_spec:
self.label_cols = ["start_positions", "end... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
self.use_xla_generation = use_xla_generation
self.generate_kwargs = {} if generate_kwargs is None else generate_kwargs
self.generation_function = None
@staticmethod
def _concatenate_batches(batches, padding_index=-100):
# If all batches are unidimensional or same length, do a simple co... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
# Welp, they're not the same length. Let's do some padding
max_len = max([batch.shape[1] for batch in batches])
num_samples = sum([batch.shape[0] for batch in batches])
output = np.full_like(
batches[0], fill_value=padding_index, shape=[num_samples, max_len] + list(batches[0].shape[2... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
def _postprocess_predictions_or_labels(self, inputs):
if isinstance(inputs[0], dict):
outputs = {}
for key in inputs[0].keys():
outputs[key] = self._concatenate_batches([batch[key] for batch in inputs])
# If it's a dict with only one key, just return the array... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
raise TypeError(f"Couldn't handle batch of type {type(inputs[0])}!")
return outputs | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
def on_epoch_end(self, epoch, logs=None):
if hasattr(self.model, "config"):
ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", [])
else:
ignore_keys = []
main_input_name = None
if self.predict_with_generate:
# This dense condition... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
self.generation_function = tf.function(generation_function, jit_compile=True)
prediction_list = []
label_list = [] | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
# The whole predict/generate loop is handled inside this method
for batch in self.eval_dataset:
if isinstance(batch, tuple):
batch, labels = batch
else:
labels = None
if self.predict_with_generate:
if isinstance(batch, dict):
... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
if isinstance(predictions, dict):
# This converts any dict-subclass to a regular dict
# Keras REALLY doesn't like it when we pass around a BatchEncoding or other derived class
predictions = dict(predictions)
if self.output_cols is not None:... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
labels = [array.numpy() for array in labels]
elif isinstance(labels, tf.Tensor):
labels = labels.numpy()
else:
raise TypeError(f"Confused by labels of type {type(labels)}")
label_list.append(labels) | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
all_preds = self._postprocess_predictions_or_labels(prediction_list)
all_labels = self._postprocess_predictions_or_labels(label_list)
metric_output = self.metric_fn((all_preds, all_labels))
if not isinstance(metric_output, dict):
raise TypeError(
f"metric_fn should r... | 250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
class PushToHubCallback(keras.callbacks.Callback):
"""
Callback that will save and push the model to the Hub regularly. By default, it pushes once per epoch, but this can
be changed with the `save_strategy` argument. Pushed models can be accessed like any other model on the hub, such
as with the `from_p... | 251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
- `"no"`: Save is done at the end of training.
- `"epoch"`: Save is done at the end of each epoch.
- `"steps"`: Save is done every `save_steps`
save_steps (`int`, *optional*):
The number of steps between saves when using the "steps" `save_strategy`.
tokenizer ... | 251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
Will default to the name of `output_dir`.
hub_token (`str`, *optional*):
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
`huggingface-cli login`.
checkpoint (`bool`, *optional*, defaults to `False`):
Whether t... | 251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
def __init__(
self,
output_dir: Union[str, Path],
save_strategy: Union[str, IntervalStrategy] = "epoch",
save_steps: Optional[int] = None,
tokenizer: Optional[PreTrainedTokenizerBase] = None,
hub_model_id: Optional[str] = None,
hub_token: Optional[str] = None,
... | 251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
# Create repo and retrieve repo_id
if hub_model_id is None:
hub_model_id = output_dir.absolute().name
self.hub_model_id = create_repo(repo_id=hub_model_id, exist_ok=True, token=hub_token).repo_id
self.output_dir = output_dir
self.repo = Repository(str(self.output_dir), clone... | 251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
def on_train_batch_end(self, batch, logs=None):
if self.save_strategy == IntervalStrategy.STEPS and (batch + 1) % self.save_steps == 0:
if self.last_job is not None and not self.last_job.is_done:
return # The last upload is still running, don't start another
self.model.s... | 251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
def on_epoch_end(self, epoch, logs=None):
logs = logs.copy() # Don't accidentally write things that Keras will read later
if "epoch" not in logs:
logs["epoch"] = epoch
self.training_history.append(logs)
if self.save_strategy == IntervalStrategy.EPOCH:
if self.las... | 251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
)
model_card = train_summary.to_model_card()
with (self.output_dir / "README.md").open("w") as f:
f.write(model_card)
_, self.last_job = self.repo.push_to_hub(
commit_message=f"Training in progress epoch {epoch}", blocking=False
) | 251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
def on_train_end(self, logs=None):
# Makes sure the latest version of the model is uploaded
if self.last_job is not None and not self.last_job.is_done:
logging.info("Pushing the last epoch to the Hub, this may take a while...")
while not self.last_job.is_done:
sle... | 251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py |
class TrainerState:
"""
A class containing the [`Trainer`] inner state that will be saved along the model and optimizer when checkpointing
and passed to the [`TrainerCallback`].
<Tip>
In all this class, one step is to be understood as one update step. When using gradient accumulation, one update
... | 252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py |
Args:
epoch (`float`, *optional*):
Only set during training, will represent the epoch the training is at (the decimal part being the
percentage of the current epoch completed).
global_step (`int`, *optional*, defaults to 0):
During training, represents the number of u... | 252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py |
When tracking the inputs tokens, the number of tokens seen during training (number of input tokens, not the
number of prediction tokens).
total_flos (`float`, *optional*, defaults to 0):
The total number of floating operations done by the model since the beginning of training (stored as ... | 252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py |
several machines) main process.
is_world_process_zero (`bool`, *optional*, defaults to `True`):
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be `True` for one process).
is_hyper_param_sea... | 252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py |
epoch: Optional[float] = None
global_step: int = 0
max_steps: int = 0
logging_steps: int = 500
eval_steps: int = 500
save_steps: int = 500
train_batch_size: int = None
num_train_epochs: int = 0
num_input_tokens_seen: int = 0
total_flos: float = 0
log_history: List[Dict[str, float... | 252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py |
def __post_init__(self):
if self.log_history is None:
self.log_history = []
if self.stateful_callbacks is None:
self.stateful_callbacks = {}
elif isinstance(self.stateful_callbacks, dict):
# We are loading the callbacks in from the state file, no need to proce... | 252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py |
if not isinstance(stateful_callbacks[name], list):
stateful_callbacks[name] = [stateful_callbacks[name]]
stateful_callbacks[name].append(callback.state())
else:
stateful_callbacks[name] = callback.state()
self.stateful_callbacks... | 252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py |
def save_to_json(self, json_path: str):
"""Save the content of this instance in JSON format inside `json_path`."""
json_string = json.dumps(dataclasses.asdict(self), indent=2, sort_keys=True) + "\n"
with open(json_path, "w", encoding="utf-8") as f:
f.write(json_string)
@classmet... | 252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py |
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