code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def nested_concat(tensors, new_tensors, padding_index=-100):
"""
Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or
nested list/tuples/dict of tensors.
"""
assert type(tensors) == type(
new_tensors
), f"Expected `tensors` and... |
Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or
nested list/tuples/dict of tensors.
| nested_concat | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def find_batch_size(tensors):
"""
Find the first dimension of a tensor in a nested list/tuple/dict of tensors.
"""
if isinstance(tensors, (list, tuple)):
for t in tensors:
result = find_batch_size(t)
if result is not None:
return result
elif isinstance... |
Find the first dimension of a tensor in a nested list/tuple/dict of tensors.
| find_batch_size | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def nested_numpify(tensors):
"Numpify `tensors` (even if it's a nested list/tuple/dict of tensors)."
if isinstance(tensors, (list, tuple)):
return type(tensors)(nested_numpify(t) for t in tensors)
if isinstance(tensors, Mapping):
return type(tensors)({k: nested_numpify(t) for k, t in tensors... | Numpify `tensors` (even if it's a nested list/tuple/dict of tensors). | nested_numpify | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def torch_distributed_zero_first(local_rank: int):
"""
Decorator to make all processes in distributed training wait for each local_master to do something.
Args:
local_rank (`int`): The rank of the local process.
"""
if local_rank not in [-1, 0]:
dist.barrier()
yield
if local... |
Decorator to make all processes in distributed training wait for each local_master to do something.
Args:
local_rank (`int`): The rank of the local process.
| torch_distributed_zero_first | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def nested_new_like(arrays, num_samples, padding_index=-100):
"""Create the same nested structure as `arrays` with a first dimension always at `num_samples`."""
if isinstance(arrays, (list, tuple)):
return type(arrays)(nested_new_like(x, num_samples) for x in arrays)
return np.full_like(arrays, padd... | Create the same nested structure as `arrays` with a first dimension always at `num_samples`. | nested_new_like | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def expand_like(arrays, new_seq_length, padding_index=-100):
"""Expand the `arrays` so that the second dimension grows to `new_seq_length`. Uses `padding_index` for padding."""
result = np.full_like(arrays, padding_index, shape=(arrays.shape[0], new_seq_length) + arrays.shape[2:])
result[:, : arrays.shape[1... | Expand the `arrays` so that the second dimension grows to `new_seq_length`. Uses `padding_index` for padding. | expand_like | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def add_arrays(self, arrays):
"""
Add `arrays` to the internal storage, Will initialize the storage to the full size at the first arrays passed
so that if we're bound to get an OOM, it happens at the beginning.
"""
if arrays is None:
return
if self._storage is... |
Add `arrays` to the internal storage, Will initialize the storage to the full size at the first arrays passed
so that if we're bound to get an OOM, it happens at the beginning.
| add_arrays | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def finalize(self):
"""
Return the properly gathered arrays and truncate to the number of samples (since the sampler added some extras
to get each process a dataset of the same length).
"""
if self._storage is None:
return
if self._offsets[0] != self.process_l... |
Return the properly gathered arrays and truncate to the number of samples (since the sampler added some extras
to get each process a dataset of the same length).
| finalize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def get_length_grouped_indices(lengths, batch_size, mega_batch_mult=None, generator=None):
"""
Return a list of indices so that each slice of `batch_size` consecutive indices correspond to elements of similar
lengths. To do this, the indices are:
- randomly permuted
- grouped in mega-batches of siz... |
Return a list of indices so that each slice of `batch_size` consecutive indices correspond to elements of similar
lengths. To do this, the indices are:
- randomly permuted
- grouped in mega-batches of size `mega_batch_mult * batch_size`
- sorted by length in each mega-batch
The result is the ... | get_length_grouped_indices | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def metrics_format(self, metrics: Dict[str, float]) -> Dict[str, float]:
"""
Reformat Trainer metrics values to a human-readable format
Args:
metrics (`Dict[str, float]`):
The metrics returned from train/evaluate/predict
Returns:
metrics (`Dict[str, float]`): The reformatte... |
Reformat Trainer metrics values to a human-readable format
Args:
metrics (`Dict[str, float]`):
The metrics returned from train/evaluate/predict
Returns:
metrics (`Dict[str, float]`): The reformatted metrics
| metrics_format | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def log_metrics(self, split, metrics):
"""
Log metrics in a specially formatted way
Under distributed environment this is done only for a process with rank 0.
Args:
split (`str`):
Mode/split name: one of `train`, `eval`, `test`
metrics (`Dict[str, float]`):
The ... |
Log metrics in a specially formatted way
Under distributed environment this is done only for a process with rank 0.
Args:
split (`str`):
Mode/split name: one of `train`, `eval`, `test`
metrics (`Dict[str, float]`):
The metrics returned from train/evaluate/predictme... | log_metrics | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def save_metrics(self, split, metrics, combined=True):
"""
Save metrics into a json file for that split, e.g. `train_results.json`.
Under distributed environment this is done only for a process with rank 0.
Args:
split (`str`):
Mode/split name: one of `train`, `eval`, `test`, `all`... |
Save metrics into a json file for that split, e.g. `train_results.json`.
Under distributed environment this is done only for a process with rank 0.
Args:
split (`str`):
Mode/split name: one of `train`, `eval`, `test`, `all`
metrics (`Dict[str, float]`):
The metrics... | save_metrics | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def save_state(self):
"""
Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model
Under distributed environment this is done only for a process with rank 0.
"""
if not self.is_world_process_zero():
return
path = os.path.join(self.args.output_dir, "trai... |
Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model
Under distributed environment this is done only for a process with rank 0.
| save_state | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def get_parameter_names(model, forbidden_layer_types):
"""
Returns the names of the model parameters that are not inside a forbidden layer.
"""
result = []
for name, child in model.named_children():
result += [
f"{name}.{n}"
for n in get_parameter_names(child, forbidd... |
Returns the names of the model parameters that are not inside a forbidden layer.
| get_parameter_names | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def get_module_class_from_name(module, name):
"""
Gets a class from a module by its name.
Args:
module (`torch.nn.Module`): The module to get the class from.
name (`str`): The name of the class.
"""
modules_children = list(module.children())
if module.__class__.__name__ == name:... |
Gets a class from a module by its name.
Args:
module (`torch.nn.Module`): The module to get the class from.
name (`str`): The name of the class.
| get_module_class_from_name | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def evaluate(
self,
eval_dataset: Optional[Dataset] = None,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
**gen_kwargs
) -> Dict[str, float]:
"""
Run evaluation and returns metrics.
The calling script will be responsible fo... |
Run evaluation and returns metrics.
The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init `compute_metrics` argument).
You can also subclass and override this method to inject custom behavior.
Args:
... | evaluate | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_seq2seq.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_seq2seq.py | Apache-2.0 |
def predict(
self,
test_dataset: Dataset,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "test",
**gen_kwargs
) -> PredictionOutput:
"""
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your... |
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in `evaluate()`.
Args:
test_dataset (`Dataset`):
Dataset... | predict | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_seq2seq.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_seq2seq.py | Apache-2.0 |
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[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
"""
Perform an evaluatio... |
Perform an evaluation step on `model` using `inputs`.
Subclass and override to inject custom behavior.
Args:
model (`nn.Module`):
The model to evaluate.
inputs (`Dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.... | prediction_step | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_seq2seq.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_seq2seq.py | Apache-2.0 |
def get_train_tfdataset(self) -> tf.data.Dataset:
"""
Returns the training [`~tf.data.Dataset`].
Subclass and override this method if you want to inject some custom behavior.
"""
if self.train_dataset is None:
raise ValueError("Trainer: training requires a train_data... |
Returns the training [`~tf.data.Dataset`].
Subclass and override this method if you want to inject some custom behavior.
| get_train_tfdataset | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def get_eval_tfdataset(self, eval_dataset: Optional[tf.data.Dataset] = None) -> tf.data.Dataset:
"""
Returns the evaluation [`~tf.data.Dataset`].
Args:
eval_dataset ([`~tf.data.Dataset`], *optional*):
If provided, will override *self.eval_dataset*. The dataset should... |
Returns the evaluation [`~tf.data.Dataset`].
Args:
eval_dataset ([`~tf.data.Dataset`], *optional*):
If provided, will override *self.eval_dataset*. The dataset should yield tuples of `(features, labels)`
where `features` is a dict of input features and `labe... | get_eval_tfdataset | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def get_test_tfdataset(self, test_dataset: tf.data.Dataset) -> tf.data.Dataset:
"""
Returns a test [`~tf.data.Dataset`].
Args:
test_dataset ([`~tf.data.Dataset`]):
The dataset to use. The dataset should yield tuples of `(features, labels)` where `features` is a dict
... |
Returns a test [`~tf.data.Dataset`].
Args:
test_dataset ([`~tf.data.Dataset`]):
The dataset to use. The dataset should yield tuples of `(features, labels)` where `features` is a dict
of input features and `labels` is the labels. If `labels` is a tensor, the ... | get_test_tfdataset | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def create_optimizer_and_scheduler(self, num_training_steps: int):
"""
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
TFTrainer's init through `optimizers`, or subclass ... |
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
TFTrainer's init through `optimizers`, or subclass and override this method.
| create_optimizer_and_scheduler | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def setup_wandb(self):
"""
Setup the optional Weights & Biases (`wandb`) integration.
One can subclass and override this method to customize the setup if needed. Find more information `here
<https://docs.wandb.com/huggingface>`__. You can also override the following environment variable... |
Setup the optional Weights & Biases (`wandb`) integration.
One can subclass and override this method to customize the setup if needed. Find more information `here
<https://docs.wandb.com/huggingface>`__. You can also override the following environment variables:
Environment:
... | setup_wandb | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def setup_comet(self):
"""
Setup the optional Comet.ml integration.
Environment:
COMET_MODE:
(Optional): str - "OFFLINE", "ONLINE", or "DISABLED"
COMET_PROJECT_NAME:
(Optional): str - Comet.ml project name for experiments
COMET... |
Setup the optional Comet.ml integration.
Environment:
COMET_MODE:
(Optional): str - "OFFLINE", "ONLINE", or "DISABLED"
COMET_PROJECT_NAME:
(Optional): str - Comet.ml project name for experiments
COMET_OFFLINE_DIRECTORY:
... | setup_comet | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def prediction_loop(
self,
dataset: tf.data.Dataset,
steps: int,
num_examples: int,
description: str,
prediction_loss_only: Optional[bool] = None,
) -> PredictionOutput:
"""
Prediction/evaluation loop, shared by [`~TFTrainer.evaluate`] and [`~TFTrainer... |
Prediction/evaluation loop, shared by [`~TFTrainer.evaluate`] and [`~TFTrainer.predict`].
Works both with or without labels.
| prediction_loop | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def evaluate(self, eval_dataset: Optional[tf.data.Dataset] = None) -> Dict[str, float]:
"""
Run evaluation and returns metrics.
The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init `compute_metrics` argument).
... |
Run evaluation and returns metrics.
The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init `compute_metrics` argument).
Args:
eval_dataset ([`~tf.data.Dataset`], *optional*):
Pass a ... | evaluate | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def prediction_step(
self, features: tf.Tensor, labels: tf.Tensor, nb_instances_in_global_batch: tf.Tensor
) -> tf.Tensor:
"""
Compute the prediction on features and update the loss with labels.
Subclass and override to inject some custom behavior.
"""
per_example_lo... |
Compute the prediction on features and update the loss with labels.
Subclass and override to inject some custom behavior.
| prediction_step | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def train(self) -> None:
"""
Train method to train the model.
"""
train_ds = self.get_train_tfdataset()
if self.args.debug:
tf.summary.trace_on(graph=True, profiler=True)
self.gradient_accumulator.reset()
num_update_steps_per_epoch = self.num_train_... |
Train method to train the model.
| train | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def training_step(self, features, labels, nb_instances_in_global_batch):
"""
Perform a training step on features and labels.
Subclass and override to inject some custom behavior.
"""
per_example_loss, _ = self.run_model(features, labels, True)
scaled_loss = per_example_l... |
Perform a training step on features and labels.
Subclass and override to inject some custom behavior.
| training_step | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def run_model(self, features, labels, training):
"""
Computes the loss of the given features and labels pair.
Subclass and override this method if you want to inject some custom behavior.
Args:
features (`tf.Tensor`): A batch of input features.
labels (`tf.Tenso... |
Computes the loss of the given features and labels pair.
Subclass and override this method if you want to inject some custom behavior.
Args:
features (`tf.Tensor`): A batch of input features.
labels (`tf.Tensor`): A batch of labels.
training (`bool`): Wheth... | run_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def predict(self, test_dataset: tf.data.Dataset) -> PredictionOutput:
"""
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in `evaluate... |
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in `evaluate()`.
Args:
test_dataset ([`~tf.data.Dataset`]):
... | predict | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def save_model(self, output_dir: Optional[str] = None):
"""
Will save the model, so you can reload it using `from_pretrained()`.
"""
output_dir = output_dir if output_dir is not None else self.args.output_dir
logger.info(f"Saving model in {output_dir}")
if not isinstanc... |
Will save the model, so you can reload it using `from_pretrained()`.
| save_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_tf.py | Apache-2.0 |
def set_seed(seed: int):
"""
Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch` and/or `tf` (if installed).
Args:
seed (`int`): The seed to set.
"""
random.seed(seed)
np.random.seed(seed)
if is_torch_available():
torch.manual_seed(seed)
... |
Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch` and/or `tf` (if installed).
Args:
seed (`int`): The seed to set.
| set_seed | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def default_compute_objective(metrics: Dict[str, float]) -> float:
"""
The default objective to maximize/minimize when doing an hyperparameter search. It is the evaluation loss if no
metrics are provided to the [`Trainer`], the sum of all metrics otherwise.
Args:
metrics (`Dict[str, float]`): T... |
The default objective to maximize/minimize when doing an hyperparameter search. It is the evaluation loss if no
metrics are provided to the [`Trainer`], the sum of all metrics otherwise.
Args:
metrics (`Dict[str, float]`): The metrics returned by the evaluate method.
Return:
`float`: ... | default_compute_objective | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def is_main_process(local_rank):
"""
Whether or not the current process is the local process, based on `xm.get_ordinal()` (for TPUs) first, then on
`local_rank`.
"""
if is_torch_tpu_available(check_device=True):
import torch_xla.core.xla_model as xm
return xm.get_ordinal() == 0
... |
Whether or not the current process is the local process, based on `xm.get_ordinal()` (for TPUs) first, then on
`local_rank`.
| is_main_process | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def total_processes_number(local_rank):
"""
Return the number of processes launched in parallel. Works with `torch.distributed` and TPUs.
"""
if is_torch_tpu_available(check_device=True):
import torch_xla.core.xla_model as xm
return xm.xrt_world_size()
elif local_rank != -1 and is_t... |
Return the number of processes launched in parallel. Works with `torch.distributed` and TPUs.
| total_processes_number | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def speed_metrics(split, start_time, num_samples=None, num_steps=None):
"""
Measure and return speed performance metrics.
This function requires a time snapshot `start_time` before the operation to be measured starts and this function
should be run immediately after the operation to be measured has com... |
Measure and return speed performance metrics.
This function requires a time snapshot `start_time` before the operation to be measured starts and this function
should be run immediately after the operation to be measured has completed.
Args:
- split: name to prefix metric (like train, eval, test..... | speed_metrics | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def derive_stage(self):
"""derives the stage/caller name automatically"""
caller = inspect.currentframe().f_back.f_back.f_code.co_name
if caller in self.stages:
return self.stages[caller]
else:
raise ValueError(
f"was called from {caller}, but only... | derives the stage/caller name automatically | derive_stage | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def start(self):
"""start tracking for the caller's stage"""
if self.skip_memory_metrics:
return
stage = self.derive_stage()
# deal with nested calls of eval during train - simply ignore those
if self.cur_stage is not None and self.cur_stage != stage:
ret... | start tracking for the caller's stage | start | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def stop(self, stage):
"""stop tracking for the passed stage"""
# deal with nested calls of eval during train - simply ignore those
if self.cur_stage is not None and self.cur_stage != stage:
return
# this sends a signal to peak_monitor_func to complete its loop
self... | stop tracking for the passed stage | stop | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def stop_and_update_metrics(self, metrics=None):
"""combine stop and metrics update in one call for simpler code"""
if self.skip_memory_metrics:
return
stage = self.derive_stage()
self.stop(stage)
# init doesn't have metrics to update so we just save that data for l... | combine stop and metrics update in one call for simpler code | stop_and_update_metrics | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def has_length(dataset):
"""
Checks if the dataset implements __len__() and it doesn't raise an error
"""
try:
return len(dataset) is not None
except TypeError:
# TypeError: len() of unsized object
return False |
Checks if the dataset implements __len__() and it doesn't raise an error
| has_length | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def denumpify_detensorize(metrics):
"""
Recursively calls `.item()` on the element of the dictionary passed
"""
if isinstance(metrics, (list, tuple)):
return type(metrics)(denumpify_detensorize(m) for m in metrics)
elif isinstance(metrics, dict):
return type(metrics)({k: denumpify_de... |
Recursively calls `.item()` on the element of the dictionary passed
| denumpify_detensorize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def number_of_arguments(func):
"""
Return the number of arguments of the passed function, even if it's a partial function.
"""
if isinstance(func, functools.partial):
total_args = len(inspect.signature(func.func).parameters)
return total_args - len(func.args) - len(func.keywords)
ret... |
Return the number of arguments of the passed function, even if it's a partial function.
| number_of_arguments | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def find_executable_batch_size(
function: callable = None, starting_batch_size: int = 128, auto_find_batch_size: bool = False
):
"""
Args:
A basic decorator that will try to execute `function`. If it fails from exceptions related to out-of-memory or
CUDNN, the batch size is cut in half and passed to... |
Args:
A basic decorator that will try to execute `function`. If it fails from exceptions related to out-of-memory or
CUDNN, the batch size is cut in half and passed to `function` `function` must take in a `batch_size` parameter as
its first argument.
function (`callable`, *optional*)
... | find_executable_batch_size | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_utils.py | Apache-2.0 |
def get_int_from_env(env_keys, default):
"""Returns the first positive env value found in the `env_keys` list or the default."""
for e in env_keys:
val = int(os.environ.get(e, -1))
if val >= 0:
return val
return default | Returns the first positive env value found in the `env_keys` list or the default. | get_int_from_env | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def get_xla_device_type(device: "torch.device") -> Optional[str]:
"""
Returns the xla device type (CPU|GPU|TPU) or None if the device is a non-xla device.
"""
if is_torch_tpu_available():
return xm.xla_real_devices([device])[0].split(":")[0]
return None |
Returns the xla device type (CPU|GPU|TPU) or None if the device is a non-xla device.
| get_xla_device_type | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def train_batch_size(self) -> int:
"""
The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training).
"""
if self.per_gpu_train_batch_size:
logger.warning(
"Using deprecated `--per_gpu_train_batch_size` argument which ... |
The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training).
| train_batch_size | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def eval_batch_size(self) -> int:
"""
The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training).
"""
if self.per_gpu_eval_batch_size:
logger.warning(
"Using deprecated `--per_gpu_eval_batch_size` argument which wi... |
The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training).
| eval_batch_size | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def n_gpu(self):
"""
The number of GPUs used by this process.
Note:
This will only be greater than one when you have multiple GPUs available but are not using distributed
training. For distributed training, it will always be 1.
"""
# Make sure `self._n_gp... |
The number of GPUs used by this process.
Note:
This will only be greater than one when you have multiple GPUs available but are not using distributed
training. For distributed training, it will always be 1.
| n_gpu | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def parallel_mode(self):
"""
The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:
- `ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU).
- `ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses `torch.nn.DataParallel`).
... |
The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:
- `ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU).
- `ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses `torch.nn.DataParallel`).
- `ParallelMode.DISTRIBUTED`: sev... | parallel_mode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def world_size(self):
"""
The number of processes used in parallel.
"""
if is_torch_tpu_available():
return xm.xrt_world_size()
elif is_sagemaker_mp_enabled():
return smp.dp_size() if not smp.state.cfg.prescaled_batch else smp.rdp_size()
elif is_sa... |
The number of processes used in parallel.
| world_size | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def process_index(self):
"""
The index of the current process used.
"""
if is_torch_tpu_available():
return xm.get_ordinal()
elif is_sagemaker_mp_enabled():
return smp.dp_rank() if not smp.state.cfg.prescaled_batch else smp.rdp_rank()
elif is_sagem... |
The index of the current process used.
| process_index | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def local_process_index(self):
"""
The index of the local process used.
"""
if is_torch_tpu_available():
return xm.get_local_ordinal()
elif is_sagemaker_mp_enabled():
return smp.local_rank()
elif is_sagemaker_dp_enabled():
return dist.g... |
The index of the local process used.
| local_process_index | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def should_log(self):
"""
Whether or not the current process should produce log.
"""
if self.log_on_each_node:
return self.local_process_index == 0
else:
if is_sagemaker_mp_enabled():
return smp.rank() == 0
else:
... |
Whether or not the current process should produce log.
| should_log | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def should_save(self):
"""
Whether or not the current process should write to disk, e.g., to save models and checkpoints.
"""
if self.save_on_each_node:
return self.local_process_index == 0
else:
if is_sagemaker_mp_enabled():
return smp.ran... |
Whether or not the current process should write to disk, e.g., to save models and checkpoints.
| should_save | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def get_process_log_level(self):
"""
Returns the log level to be used depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process.
For the main process the log level defaults to `logging.INFO` unless overridden by `log_level` argume... |
Returns the log level to be used depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process.
For the main process the log level defaults to `logging.INFO` unless overridden by `log_level` argument.
For the replica processes the l... | get_process_log_level | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def main_process_first(self, local=True, desc="work"):
"""
A context manager for torch distributed environment where on needs to do something on the main process, while
blocking replicas, and when it's finished releasing the replicas.
One such use is for `datasets`'s `map` feature which... |
A context manager for torch distributed environment where on needs to do something on the main process, while
blocking replicas, and when it's finished releasing the replicas.
One such use is for `datasets`'s `map` feature which to be efficient should be run once on the main process,
w... | main_process_first | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def get_warmup_steps(self, num_training_steps: int):
"""
Get number of steps used for a linear warmup.
"""
warmup_steps = (
self.warmup_steps if self.warmup_steps > 0 else math.ceil(num_training_steps * self.warmup_ratio)
)
return warmup_steps |
Get number of steps used for a linear warmup.
| get_warmup_steps | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args.py | Apache-2.0 |
def n_gpu(self) -> int:
"""
The number of replicas (CPUs, GPUs or TPU cores) used in this training.
"""
warnings.warn(
"The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.",
FutureWarning,
)
return self... |
The number of replicas (CPUs, GPUs or TPU cores) used in this training.
| n_gpu | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/training_args_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/training_args_tf.py | Apache-2.0 |
def __init__(self, **kwargs):
"""
This __init__ is there for legacy code. When removing deprecated args completely, the class can simply be
deleted
"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
positive_arg = deprecated_a... |
This __init__ is there for legacy code. When removing deprecated args completely, the class can simply be
deleted
| __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/benchmark/benchmark_args.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/benchmark/benchmark_args.py | Apache-2.0 |
def separate_process_wrapper_fn(func: Callable[[], None], do_multi_processing: bool) -> Callable[[], None]:
"""
This function wraps another function into its own separated process. In order to ensure accurate memory
measurements it is important that the function is executed in a separate process
Args:
... |
This function wraps another function into its own separated process. In order to ensure accurate memory
measurements it is important that the function is executed in a separate process
Args:
- `func`: (`callable`): function() -> ... generic function which will be executed in its own separate proce... | separate_process_wrapper_fn | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | Apache-2.0 |
def measure_peak_memory_cpu(function: Callable[[], None], interval=0.5, device_idx=None) -> int:
"""
measures peak cpu memory consumption of a given `function` running the function for at least interval seconds and
at most 20 * interval seconds. This function is heavily inspired by: `memory_usage` of the pa... |
measures peak cpu memory consumption of a given `function` running the function for at least interval seconds and
at most 20 * interval seconds. This function is heavily inspired by: `memory_usage` of the package
`memory_profiler`:
https://github.com/pythonprofilers/memory_profiler/blob/895c4ac7a08020d... | measure_peak_memory_cpu | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | Apache-2.0 |
def get_cpu_memory(process_id: int) -> int:
"""
measures current cpu memory usage of a given `process_id`
Args:
- `process_id`: (`int`) process_id for which to measure memory
Returns
- `memory`: (`int`) consumed memory in Bytes
"""
process = psu... |
measures current cpu memory usage of a given `process_id`
Args:
- `process_id`: (`int`) process_id for which to measure memory
Returns
- `memory`: (`int`) consumed memory in Bytes
| get_cpu_memory | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | Apache-2.0 |
def traceit(frame, event, args):
"""
Tracing method executed before running each line in a module or sub-module Record memory allocated in a list
with debugging information
"""
global _is_memory_tracing_enabled
if not _is_memory_tracing_enabled:
return tracei... |
Tracing method executed before running each line in a module or sub-module Record memory allocated in a list
with debugging information
| traceit | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | Apache-2.0 |
def stop_memory_tracing(
memory_trace: Optional[MemoryTrace] = None, ignore_released_memory: bool = True
) -> Optional[MemorySummary]:
"""
Stop memory tracing cleanly and return a summary of the memory trace if a trace is given.
Args:
`memory_trace` (optional output of start_memory_tracing, def... |
Stop memory tracing cleanly and return a summary of the memory trace if a trace is given.
Args:
`memory_trace` (optional output of start_memory_tracing, default: None):
memory trace to convert in summary
`ignore_released_memory` (boolean, default: None):
if True we only... | stop_memory_tracing | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/benchmark/benchmark_utils.py | Apache-2.0 |
def find_indent(line: str) -> int:
"""
Returns the number of spaces that start a line indent.
"""
search = re.search("^(\s*)(?:\S|$)", line)
if search is None:
return 0
return len(search.groups()[0]) |
Returns the number of spaces that start a line indent.
| find_indent | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def parse_module_content(content: str) -> List[str]:
"""
Parse the content of a module in the list of objects it defines.
Args:
content (`str`): The content to parse
Returns:
`List[str]`: The list of objects defined in the module.
"""
objects = []
current_object = []
li... |
Parse the content of a module in the list of objects it defines.
Args:
content (`str`): The content to parse
Returns:
`List[str]`: The list of objects defined in the module.
| parse_module_content | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def add_content_to_text(
text: str,
content: str,
add_after: Optional[Union[str, Pattern]] = None,
add_before: Optional[Union[str, Pattern]] = None,
exact_match: bool = False,
) -> str:
"""
A utility to add some content inside a given text.
Args:
text (`str`): The text in which w... |
A utility to add some content inside a given text.
Args:
text (`str`): The text in which we want to insert some content.
content (`str`): The content to add.
add_after (`str` or `Pattern`):
The pattern to test on a line of `text`, the new content is added after the first instan... | add_content_to_text | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def add_content_to_file(
file_name: Union[str, os.PathLike],
content: str,
add_after: Optional[Union[str, Pattern]] = None,
add_before: Optional[Union[str, Pattern]] = None,
exact_match: bool = False,
):
"""
A utility to add some content inside a given file.
Args:
file_name (`str... |
A utility to add some content inside a given file.
Args:
file_name (`str` or `os.PathLike`): The name of the file in which we want to insert some content.
content (`str`): The content to add.
add_after (`str` or `Pattern`):
The pattern to test on a line of `text`, the new conte... | add_content_to_file | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def replace_model_patterns(
text: str, old_model_patterns: ModelPatterns, new_model_patterns: ModelPatterns
) -> Tuple[str, str]:
"""
Replace all patterns present in a given text.
Args:
text (`str`): The text to treat.
old_model_patterns (`ModelPatterns`): The patterns for the old model... |
Replace all patterns present in a given text.
Args:
text (`str`): The text to treat.
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The patterns for the new model.
Returns:
`Tuple(str, str)`: A tuple of with the ... | replace_model_patterns | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def simplify_replacements(replacements):
"""
Simplify a list of replacement patterns to make sure there are no needless ones.
For instance in the sequence "Bert->BertNew, BertConfig->BertNewConfig, bert->bert_new", the replacement
"BertConfig->BertNewConfig" is implied by "Bert->BertNew" so not needed.... |
Simplify a list of replacement patterns to make sure there are no needless ones.
For instance in the sequence "Bert->BertNew, BertConfig->BertNewConfig, bert->bert_new", the replacement
"BertConfig->BertNewConfig" is implied by "Bert->BertNew" so not needed.
Args:
replacements (`List[Tuple[st... | simplify_replacements | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def get_module_from_file(module_file: Union[str, os.PathLike]) -> str:
"""
Returns the module name corresponding to a module file.
"""
full_module_path = Path(module_file).absolute()
module_parts = full_module_path.with_suffix("").parts
# Find the first part named transformers, starting from th... |
Returns the module name corresponding to a module file.
| get_module_from_file | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def duplicate_module(
module_file: Union[str, os.PathLike],
old_model_patterns: ModelPatterns,
new_model_patterns: ModelPatterns,
dest_file: Optional[str] = None,
add_copied_from: bool = True,
):
"""
Create a new module from an existing one and adapting all function and classes names from ol... |
Create a new module from an existing one and adapting all function and classes names from old patterns to new ones.
Args:
module_file (`str` or `os.PathLike`): Path to the module to duplicate.
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`Mo... | duplicate_module | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def filter_framework_files(
files: List[Union[str, os.PathLike]], frameworks: Optional[List[str]] = None
) -> List[Union[str, os.PathLike]]:
"""
Filter a list of files to only keep the ones corresponding to a list of frameworks.
Args:
files (`List[Union[str, os.PathLike]]`): The list of files t... |
Filter a list of files to only keep the ones corresponding to a list of frameworks.
Args:
files (`List[Union[str, os.PathLike]]`): The list of files to filter.
frameworks (`List[str]`, *optional*): The list of allowed frameworks.
Returns:
`List[Union[str, os.PathLike]]`: The list ... | filter_framework_files | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def get_model_files(model_type: str, frameworks: Optional[List[str]] = None) -> Dict[str, Union[Path, List[Path]]]:
"""
Retrieves all the files associated to a model.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
frameworks (`List[str]`, *optional*):
If pa... |
Retrieves all the files associated to a model.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
frameworks (`List[str]`, *optional*):
If passed, will only keep the model files corresponding to the passed frameworks.
Returns:
`Dict[str, Union[Path, L... | get_model_files | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def find_base_model_checkpoint(
model_type: str, model_files: Optional[Dict[str, Union[Path, List[Path]]]] = None
) -> str:
"""
Finds the model checkpoint used in the docstrings for a given model.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
model_files (`Dict[st... |
Finds the model checkpoint used in the docstrings for a given model.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
model_files (`Dict[str, Union[Path, List[Path]]`, *optional*):
The files associated to `model_type`. Can be passed to speed up the function, oth... | find_base_model_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def get_default_frameworks():
"""
Returns the list of frameworks (PyTorch, TensorFlow, Flax) that are installed in the environment.
"""
frameworks = []
if is_torch_available():
frameworks.append("pt")
if is_tf_available():
frameworks.append("tf")
if is_flax_available():
... |
Returns the list of frameworks (PyTorch, TensorFlow, Flax) that are installed in the environment.
| get_default_frameworks | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def retrieve_model_classes(model_type: str, frameworks: Optional[List[str]] = None) -> Dict[str, List[str]]:
"""
Retrieve the model classes associated to a given model.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
frameworks (`List[str]`, *optional*):
The... |
Retrieve the model classes associated to a given model.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
frameworks (`List[str]`, *optional*):
The frameworks to look for. Will default to `["pt", "tf", "flax"]`, passing a smaller list will restrict
th... | retrieve_model_classes | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def retrieve_info_for_model(model_type, frameworks: Optional[List[str]] = None):
"""
Retrieves all the information from a given model_type.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
frameworks (`List[str]`, *optional*):
If passed, will only keep the in... |
Retrieves all the information from a given model_type.
Args:
model_type (`str`): A valid model type (like "bert" or "gpt2")
frameworks (`List[str]`, *optional*):
If passed, will only keep the info corresponding to the passed frameworks.
Returns:
`Dict`: A dictionary wi... | retrieve_info_for_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def clean_frameworks_in_init(
init_file: Union[str, os.PathLike], frameworks: Optional[List[str]] = None, keep_processing: bool = True
):
"""
Removes all the import lines that don't belong to a given list of frameworks or concern tokenizers/feature
extractors/processors in an init.
Args:
in... |
Removes all the import lines that don't belong to a given list of frameworks or concern tokenizers/feature
extractors/processors in an init.
Args:
init_file (`str` or `os.PathLike`): The path to the init to treat.
frameworks (`List[str]`, *optional*):
If passed, this will remove... | clean_frameworks_in_init | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def add_model_to_main_init(
old_model_patterns: ModelPatterns,
new_model_patterns: ModelPatterns,
frameworks: Optional[List[str]] = None,
with_processing: bool = True,
):
"""
Add a model to the main init of Transformers.
Args:
old_model_patterns (`ModelPatterns`): The patterns for t... |
Add a model to the main init of Transformers.
Args:
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The patterns for the new model.
frameworks (`List[str]`, *optional*):
If specified, only the models implemented in... | add_model_to_main_init | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def insert_tokenizer_in_auto_module(old_model_patterns: ModelPatterns, new_model_patterns: ModelPatterns):
"""
Add a tokenizer to the relevant mappings in the auto module.
Args:
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The p... |
Add a tokenizer to the relevant mappings in the auto module.
Args:
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The patterns for the new model.
| insert_tokenizer_in_auto_module | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def add_model_to_auto_classes(
old_model_patterns: ModelPatterns, new_model_patterns: ModelPatterns, model_classes: Dict[str, List[str]]
):
"""
Add a model to the relevant mappings in the auto module.
Args:
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_... |
Add a model to the relevant mappings in the auto module.
Args:
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The patterns for the new model.
model_classes (`Dict[str, List[str]]`): A dictionary framework to list of model cla... | add_model_to_auto_classes | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def duplicate_doc_file(
doc_file: Union[str, os.PathLike],
old_model_patterns: ModelPatterns,
new_model_patterns: ModelPatterns,
dest_file: Optional[Union[str, os.PathLike]] = None,
frameworks: Optional[List[str]] = None,
):
"""
Duplicate a documentation file and adapts it for a new model.
... |
Duplicate a documentation file and adapts it for a new model.
Args:
module_file (`str` or `os.PathLike`): Path to the doc file to duplicate.
old_model_patterns (`ModelPatterns`): The patterns for the old model.
new_model_patterns (`ModelPatterns`): The patterns for the new model.
... | duplicate_doc_file | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def get_user_field(
question: str,
default_value: Optional[str] = None,
is_valid_answer: Optional[Callable] = None,
convert_to: Optional[Callable] = None,
fallback_message: Optional[str] = None,
) -> Any:
"""
A utility function that asks a question to the user to get an answer, potentially l... |
A utility function that asks a question to the user to get an answer, potentially looping until it gets a valid
answer.
Args:
question (`str`): The question to ask the user.
default_value (`str`, *optional*): A potential default value that will be used when the answer is empty.
is_... | get_user_field | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def get_user_input():
"""
Ask the user for the necessary inputs to add the new model.
"""
model_types = list(auto_module.configuration_auto.MODEL_NAMES_MAPPING.keys())
# Get old model type
valid_model_type = False
while not valid_model_type:
old_model_type = input(
"What... |
Ask the user for the necessary inputs to add the new model.
| get_user_input | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/add_new_model_like.py | Apache-2.0 |
def convert_command_factory(args: Namespace):
"""
Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint.
Returns: ServeCommand
"""
return ConvertCommand(
args.model_type, args.tf_checkpoint, args.pytorch_dump_output, args.config, args.finetuning_task_name
) |
Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint.
Returns: ServeCommand
| convert_command_factory | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/convert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/convert.py | Apache-2.0 |
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
train_parser = parser.add_parser(
"convert",
... |
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
| register_subcommand | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/convert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/convert.py | Apache-2.0 |
def convert_command_factory(args: Namespace):
"""
Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint.
Returns: ServeCommand
"""
return PTtoTFCommand(
args.model_name,
args.local_dir,
args.max_error,
args.new_weights,
args... |
Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint.
Returns: ServeCommand
| convert_command_factory | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/pt_to_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/pt_to_tf.py | Apache-2.0 |
def find_pt_tf_differences(pt_outputs, tf_outputs):
"""
Compares the TensorFlow and PyTorch outputs, returning a dictionary with all tensor differences.
"""
# 1. All output attributes must be the same
pt_out_attrs = set(pt_outputs.keys())
tf_out_attrs = set(tf_outputs.key... |
Compares the TensorFlow and PyTorch outputs, returning a dictionary with all tensor differences.
| find_pt_tf_differences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/pt_to_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/pt_to_tf.py | Apache-2.0 |
def get_inputs(self, pt_model, config):
"""
Returns the right inputs for the model, based on its signature.
"""
def _get_audio_input():
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
speech_samples = ds.sort("id").sele... |
Returns the right inputs for the model, based on its signature.
| get_inputs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/pt_to_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/pt_to_tf.py | Apache-2.0 |
def serve_command_factory(args: Namespace):
"""
Factory function used to instantiate serving server from provided command line arguments.
Returns: ServeCommand
"""
nlp = pipeline(
task=args.task,
model=args.model if args.model else None,
config=args.config,
tokenizer... |
Factory function used to instantiate serving server from provided command line arguments.
Returns: ServeCommand
| serve_command_factory | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/serving.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/serving.py | Apache-2.0 |
def tokenize(self, text_input: str = Body(None, embed=True), return_ids: bool = Body(False, embed=True)):
"""
Tokenize the provided input and eventually returns corresponding tokens id: - **text_input**: String to
tokenize - **return_ids**: Boolean flags indicating if the tokens have to be conve... |
Tokenize the provided input and eventually returns corresponding tokens id: - **text_input**: String to
tokenize - **return_ids**: Boolean flags indicating if the tokens have to be converted to their integer
mapping.
| tokenize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/serving.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/serving.py | Apache-2.0 |
def detokenize(
self,
tokens_ids: List[int] = Body(None, embed=True),
skip_special_tokens: bool = Body(False, embed=True),
cleanup_tokenization_spaces: bool = Body(True, embed=True),
):
"""
Detokenize the provided tokens ids to readable text: - **tokens_ids**: List of... |
Detokenize the provided tokens ids to readable text: - **tokens_ids**: List of tokens ids -
**skip_special_tokens**: Flag indicating to not try to decode special tokens - **cleanup_tokenization_spaces**:
Flag indicating to remove all leading/trailing spaces and intermediate ones.
| detokenize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/serving.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/serving.py | Apache-2.0 |
def tabulate(rows: List[List[Union[str, int]]], headers: List[str]) -> str:
"""
Inspired by:
- stackoverflow.com/a/8356620/593036
- stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
"""
col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)]
row_format =... |
Inspired by:
- stackoverflow.com/a/8356620/593036
- stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
| tabulate | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/commands/user.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/commands/user.py | Apache-2.0 |
def default_data_collator(features: List[InputDataClass], return_tensors="pt") -> Dict[str, Any]:
"""
Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:
- `label`: handles a single value (int or float) per object
... |
Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:
- `label`: handles a single value (int or float) per object
- `label_ids`: handles a list of values per object
Does not do any additional preprocessing: prop... | default_data_collator | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/data_collator.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/data_collator.py | Apache-2.0 |
def _torch_collate_batch(examples, tokenizer, pad_to_multiple_of: Optional[int] = None):
"""Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary."""
import torch
# Tensorize if necessary.
if isinstance(examples[0], (list, tuple, np.ndarray)):
examples =... | Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary. | _torch_collate_batch | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/data_collator.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/data_collator.py | Apache-2.0 |
def _whole_word_mask(self, input_tokens: List[str], max_predictions=512):
"""
Get 0/1 labels for masked tokens with whole word mask proxy
"""
if not isinstance(self.tokenizer, (BertTokenizer, BertTokenizerFast)):
warnings.warn(
"DataCollatorForWholeWordMask is... |
Get 0/1 labels for masked tokens with whole word mask proxy
| _whole_word_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/data_collator.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/data_collator.py | Apache-2.0 |
def torch_mask_tokens(self, inputs: Any, mask_labels: Any) -> Tuple[Any, Any]:
"""
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
'mask_labels' means we use whole word mask (wwm), we directly mask idxs according to it's ref.
"""
... |
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
'mask_labels' means we use whole word mask (wwm), we directly mask idxs according to it's ref.
| torch_mask_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/data_collator.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/data_collator.py | Apache-2.0 |
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