text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
class PoolerStartLogits(nn.Module):
"""
Compute SQuAD start logits from sequence hidden states.
Args:
config ([`PretrainedConfig`]):
The config used by the model, will be used to grab the `hidden_size` of the model.
"""
def __init__(self, config: PretrainedConfig):
supe... | 231 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
Returns:
`torch.FloatTensor`: The start logits for SQuAD.
"""
x = self.dense(hidden_states).squeeze(-1)
if p_mask is not None:
if get_parameter_dtype(self) == torch.float16:
x = x * (1 - p_mask) - 65500 * p_mask
else:
x = x * (... | 231 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
class PoolerEndLogits(nn.Module):
"""
Compute SQuAD end logits from sequence hidden states.
Args:
config ([`PretrainedConfig`]):
The config used by the model, will be used to grab the `hidden_size` of the model and the `layer_norm_eps`
to use.
"""
def __init__(self,... | 232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
def forward(
self,
hidden_states: torch.FloatTensor,
start_states: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
p_mask: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
"""
Args:
hidden_states (... | 232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
should be masked. | 232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
<Tip>
One of `start_states` or `start_positions` should be not `None`. If both are set, `start_positions` overrides
`start_states`.
</Tip>
Returns:
`torch.FloatTensor`: The end logits for SQuAD.
"""
assert (
start_states is not None or start_pos... | 232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if p_mask is not None:
if get_parameter_dtype(self) == torch.float16:
x = x * (1 - p_mask) - 65500 * p_mask
else:
x = x * (1 - p_mask) - 1e30 * p_mask
return x | 232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
class PoolerAnswerClass(nn.Module):
"""
Compute SQuAD 2.0 answer class from classification and start tokens hidden states.
Args:
config ([`PretrainedConfig`]):
The config used by the model, will be used to grab the `hidden_size` of the model.
"""
def __init__(self, config):
... | 233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
def forward(
self,
hidden_states: torch.FloatTensor,
start_states: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
cls_index: Optional[torch.LongTensor] = None,
) -> torch.FloatTensor:
"""
Args:
hidden_states... | 233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
One of `start_states` or `start_positions` should be not `None`. If both are set, `start_positions` overrides
`start_states`.
</Tip>
Returns:
`torch.FloatTensor`: The SQuAD 2.0 answer class.
"""
# No dependency on end_feature so that we can obtain one single `cls_lo... | 233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if cls_index is not None:
cls_index = cls_index[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
cls_token_state = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, hsz)
else:
cls_token_state = hidden_states[:, -1, :] # shape (bsz, hsz)
x = sel... | 233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
class SquadHeadOutput(ModelOutput):
"""
Base class for outputs of question answering models using a [`~modeling_utils.SQuADHead`]. | 234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.
start_top_log_probs (`torch.FloatTens... | 234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
Log probabilities for the top `config.start_n_top * config.end_n_top` end token possibilities
(beam-search).
end_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
In... | 234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
"""
loss: Optional[torch.FloatTensor] = None
start_top_log_probs: Optional[torch.FloatTensor] = None
start_top_index: Optional[torch.LongTensor] = None
end_top_log_probs: Optional[torch.FloatTensor] = None
end_top_index: Optional[torch.LongTensor] = None
cls_logits: Optional[torch.FloatTensor] ... | 234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
class SQuADHead(nn.Module):
r"""
A SQuAD head inspired by XLNet.
Args:
config ([`PretrainedConfig`]):
The config used by the model, will be used to grab the `hidden_size` of the model and the `layer_norm_eps`
to use.
"""
def __init__(self, config):
super()._... | 235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
@replace_return_docstrings(output_type=SquadHeadOutput, config_class=PretrainedConfig)
def forward(
self,
hidden_states: torch.FloatTensor,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
cls_index: Optional[torch.LongTens... | 235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
Positions of the last token for the labeled span.
cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Position of the CLS token for each sentence in the batch. If `None`, takes the last token.
is_impossible (`torch.LongTensor` of shape `(batch_size,)`, *optional*... | 235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
Returns:
"""
start_logits = self.start_logits(hidden_states, p_mask=p_mask)
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, let's remove the dimension added by batch splitting
for x in (start_positions, end_positions, cls_index, is... | 235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
if cls_index is not None and is_impossible is not None:
# Predict answerability from the representation of CLS and START
cls_logits = self.answer_class(hidden_states, start_positions=start_positions, cls_index=cls_index)
loss_fct_cls = nn.BCEWithLogitsLoss()
... | 235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
start_top_log_probs, start_top_index = torch.topk(
start_log_probs, self.start_n_top, dim=-1
) # shape (bsz, start_n_top)
start_top_index_exp = start_top_index.unsqueeze(-1).expand(-1, -1, hsz) # shape (bsz, start_n_top, hsz)
start_states = torch.gather(hidden_state... | 235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
end_top_log_probs, end_top_index = torch.topk(
end_log_probs, self.end_n_top, dim=1
) # shape (bsz, end_n_top, start_n_top)
end_top_log_probs = end_top_log_probs.view(-1, self.start_n_top * self.end_n_top)
end_top_index = end_top_index.view(-1, self.start_n_top * sel... | 235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
class SequenceSummary(nn.Module):
r"""
Compute a single vector summary of a sequence hidden states.
Args:
config ([`PretrainedConfig`]):
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
config class of your model for ... | 236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
- **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
- **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
(otherwise to `config.hidden_size`).
- **summary_activation** (`Optional[str]`) -- Set to `"tan... | 236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
self.summary_type = getattr(config, "summary_type", "last")
if self.summary_type == "attn":
# We should use a standard multi-head attention module with absolute positional embedding for that.
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
# We can ... | 236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
activation_string = getattr(config, "summary_activation", None)
self.activation: Callable = get_activation(activation_string) if activation_string else Identity()
self.first_dropout = Identity()
if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
self.f... | 236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
Args:
hidden_states (`torch.FloatTensor` of shape `[batch_size, seq_len, hidden_size]`):
The hidden states of the last layer.
cls_index (`torch.LongTensor` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*):
... | 236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
Returns:
`torch.FloatTensor`: The summary of the sequence hidden states.
"""
if self.summary_type == "last":
output = hidden_states[:, -1]
elif self.summary_type == "first":
output = hidden_states[:, 0]
elif self.summary_type == "mean":
out... | 236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size)
elif self.summary_type == "attn":
raise NotImplementedError | 236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
output = self.first_dropout(output)
output = self.summary(output)
output = self.activation(output)
output = self.last_dropout(output)
return output | 236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py |
class AffineTransformed(TransformedDistribution):
def __init__(self, base_distribution: Distribution, loc=None, scale=None, event_dim=0):
self.scale = 1.0 if scale is None else scale
self.loc = 0.0 if loc is None else loc
super().__init__(base_distribution, [AffineTransform(loc=self.loc, sc... | 237 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
class ParameterProjection(nn.Module):
def __init__(
self, in_features: int, args_dim: Dict[str, int], domain_map: Callable[..., Tuple[torch.Tensor]], **kwargs
) -> None:
super().__init__(**kwargs)
self.args_dim = args_dim
self.proj = nn.ModuleList([nn.Linear(in_features, dim) for... | 238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
class LambdaLayer(nn.Module):
def __init__(self, function):
super().__init__()
self.function = function
def forward(self, x, *args):
return self.function(x, *args) | 239 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
class DistributionOutput:
distribution_class: type
in_features: int
args_dim: Dict[str, int]
def __init__(self, dim: int = 1) -> None:
self.dim = dim
self.args_dim = {k: dim * self.args_dim[k] for k in self.args_dim}
def _base_distribution(self, distr_args):
if self.dim == ... | 240 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
@property
def event_shape(self) -> Tuple:
r"""
Shape of each individual event contemplated by the distributions that this object constructs.
"""
return () if self.dim == 1 else (self.dim,)
@property
def event_dim(self) -> int:
r"""
Number of event dimensions,... | 240 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
def get_parameter_projection(self, in_features: int) -> nn.Module:
r"""
Return the parameter projection layer that maps the input to the appropriate parameters of the distribution.
"""
return ParameterProjection(
in_features=in_features,
args_dim=self.args_dim,
... | 240 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
@staticmethod
def squareplus(x: torch.Tensor) -> torch.Tensor:
r"""
Helper to map inputs to the positive orthant by applying the square-plus operation. Reference:
https://twitter.com/jon_barron/status/1387167648669048833
"""
return (x + torch.sqrt(torch.square(x) + 4.0)) / 2.... | 240 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
class StudentTOutput(DistributionOutput):
"""
Student-T distribution output class.
"""
args_dim: Dict[str, int] = {"df": 1, "loc": 1, "scale": 1}
distribution_class: type = StudentT
@classmethod
def domain_map(cls, df: torch.Tensor, loc: torch.Tensor, scale: torch.Tensor):
scale = ... | 241 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
class NormalOutput(DistributionOutput):
"""
Normal distribution output class.
"""
args_dim: Dict[str, int] = {"loc": 1, "scale": 1}
distribution_class: type = Normal
@classmethod
def domain_map(cls, loc: torch.Tensor, scale: torch.Tensor):
scale = cls.squareplus(scale).clamp_min(to... | 242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
class NegativeBinomialOutput(DistributionOutput):
"""
Negative Binomial distribution output class.
"""
args_dim: Dict[str, int] = {"total_count": 1, "logits": 1}
distribution_class: type = NegativeBinomial
@classmethod
def domain_map(cls, total_count: torch.Tensor, logits: torch.Tensor):
... | 243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
# Overwrites the parent class method. We cannot scale using the affine
# transformation since negative binomial should return integers. Instead
# we scale the parameters.
def distribution(
self, distr_args, loc: Optional[torch.Tensor] = None, scale: Optional[torch.Tensor] = None
) -> Distributio... | 243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py |
class Trainer:
"""
Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.
Args:
model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*):
The model to train, evaluate or use for predictions. If not provided, a `model_init` must be... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
args ([`TrainingArguments`], *optional*):
The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the
`output_dir` set to a directory named *tmp_trainer* in the current directory if not provided.
data_collator (`DataCollator`, *optional*):
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Note that if it's a `torch.utils.data.IterableDataset` with some randomization and you are training in a
distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a
`torch.Generator` for the randomization that must be identical on all processes (and th... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
Processing class used to process the data. If provided, will be used to automatically process the inputs
for the model, and it will be saved along the model to make i... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to
be able to choose different architectures according to hyper parameters (such as layer count, sizes of
inner layers, dropout probabilities etc).
compute_loss_func (`Callable`, *opt... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
a dictionary string to metric values. *Note* When passing TrainingArgs with `batch_eval_metrics` set to
`True`, your compute_metrics function must take a boolean `compute_result` argument. This will be triggered
after the last eval batch to signal that the function needs to calculate and return ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.
optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*, defaults to `(None, None)`):
A tuple containing the optimizer and the scheduler to use. Will default to an i... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Unlike `optimizers`, this argument avoids the need to place model parameters on the correct devices before initializing the Trainer.
preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
A function that preprocess the logits right before caching them at ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
- **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`]
subclass.
- **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the
original model. This is the model that should be used for the f... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
`TrainingArguments.place_model_on_device` is overridden to return `False` .
- **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while
in `train`) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
"""
# Those are used as methods of the Trainer in examples.
from .trainer_pt_utils import _get_learning_rate, log_metrics, metrics_format, save_metrics, save_state | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
@deprecate_kwarg("tokenizer", new_name="processing_class", version="5.0.0", raise_if_both_names=True)
def __init__(
self,
model: Union[PreTrainedModel, nn.Module] = None,
args: TrainingArguments = None,
data_collator: Optional[DataCollator] = None,
train_dataset: Optional[Uni... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
optimizer_cls_and_kwargs: Optional[Tuple[Type[torch.optim.Optimizer], Dict[str, Any]]] = None,
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
):
if args is None:
output_dir = "tmp_trainer"
logger.info(f"No `TrainingArgume... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if args.eval_strategy is not None and args.eval_strategy != "no" and eval_dataset is None:
raise ValueError(
f"You have set `args.eval_strategy` to {args.eval_strategy} but you didn't pass an `eval_dataset` to `Trainer`. Either set `args.eval_strategy` to `no` or pass an `eval_dataset`. "
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.args = args
self.compute_loss_func = compute_loss_func
# Seed must be set before instantiating the model when using model
enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)
self.hp_name = None
self.deepspeed = None
se... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if model is None:
if model_init is not None:
self.model_init = model_init
model = self.call_model_init()
else:
raise RuntimeError("`Trainer` requires either a `model` or `model_init` argument")
else:
if model_init is not None:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if model.__class__.__name__ in MODEL_MAPPING_NAMES:
raise ValueError(
f"The model you have picked ({model.__class__.__name__}) cannot be used as is for training: it only "
"computes hidden states and does not accept any labels. You should choose a model with a head "
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if getattr(model, "hf_device_map", None) is not None:
devices = [device for device in set(model.hf_device_map.values()) if device not in ["cpu", "disk"]]
if len(devices) > 1:
self.is_model_parallel = True
elif len(devices) == 1:
self.is_model_parallel ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if isinstance(model, PreTrainedModel):
# Patch the model with liger kernels. Use the default kernel configurations.
_apply_liger_kernel_to_instance(model=model)
elif hasattr(model, "get_base_model") and isinstance(model.get_base_model(), PreTrainedModel):
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
_is_quantized_and_base_model = getattr(model, "is_quantized", False) and not getattr(
model, "_hf_peft_config_loaded", False
)
_quantization_method_supports_training = (
getattr(model, "hf_quantizer", None) is not None and model.hf_quantizer.is_trainable
)
_is_mo... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# At this stage the model is already loaded
if _is_quantized_and_base_model and not _is_peft_model(model) and not _is_model_quantized_and_qat_trainable:
raise ValueError(
"You cannot perform fine-tuning on purely quantized models. Please attach trainable adapters on top of"
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
) | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.is_fsdp_xla_enabled = args.fsdp_config["xla"]
if len(args.fsdp) > 0:
if self.is_deepspeed_enabled:
raise ValueError(
"Using --fsdp xxx together with --deepspeed is not possible, deactivate one of those flags."
)
if not args.fsdp_co... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# one place to sort out whether to place the model on device or not
# postpone switching model to cuda when:
# 1. MP - since we are trying to fit a much bigger than 1 gpu model
# 2. fp16-enabled DeepSpeed loads the model in half the size and it doesn't need .to() anyway,
# and we only... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
default_collator = (
DataCollatorWithPadding(processing_class)
if processing_class is not None
and isinstance(processing_class, (PreTrainedTokenizerBase, SequenceFeatureExtractor))
else default_data_collator
)
self.data_collator = data_collator if data_col... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# later use `self.model is self.model_wrapped` to check if it's wrapped or not
self.model_wrapped = model
self.model = model
# Just in case the model was wrapped outside of the `Trainer`
unwrapped_model = self.accelerator.unwrap_model(model)
model_forward = (
unwrapp... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.compute_metrics = compute_metrics
self.preprocess_logits_for_metrics = preprocess_logits_for_metrics
self.optimizer, self.lr_scheduler = optimizers
self.optimizer_cls_and_kwargs = optimizer_cls_and_kwargs
if self.optimizer_cls_and_kwargs is not None and self.optimizer is not None:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
for param_group in self.optimizer.param_groups:
if len(param_group["params"]) > 0:
optimizer_device = param_group["params"][0].device
break
if model_device != optimizer_device:
raise ValueError(
"The model and the op... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
"You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
)
default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to)
callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks
self.call... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
self._loggers_initialized = False
# Create distant repo and output directory if needed
self.hub_model_id = None
if self.args.push_to_hub:
self.init_hf_repo()
if self.args.should_save:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if train_dataset is not None and not has_length(train_dataset) and args.max_steps <= 0:
raise ValueError(
"The train_dataset does not implement __len__, max_steps has to be specified. "
"The number of steps needs to be known in advance for the learning rate scheduler."
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Mixed precision setup for SageMaker Model Parallel
if is_sagemaker_mp_enabled():
# BF16 + model parallelism in SageMaker: currently not supported, raise an error
if args.bf16:
raise ValueError("SageMaker Model Parallelism does not support BF16 yet. Please use FP16 inste... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if IS_SAGEMAKER_MP_POST_1_10:
# When there's mismatch between SMP config and trainer argument, use SMP config as truth
if args.fp16 != smp.state.cfg.fp16:
logger.warning(
f"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16}, "
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if args.device == torch.device("cpu"):
if args.fp16:
if not is_torch_greater_or_equal_than_2_3:
raise ValueError("Tried to use `fp16` but it is not supported on cpu")
else:
args.half_precision_backend = "cpu_amp"
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if (args.fp16 or args.bf16) and not (self.is_deepspeed_enabled or is_sagemaker_mp_enabled()):
# deepspeed and SageMaker Model Parallel manage their own half precision
if args.half_precision_backend == "cpu_amp":
self.use_cpu_amp = True
self.amp_dtype = torch.bfloa... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.state = TrainerState(
is_local_process_zero=self.is_local_process_zero(),
is_world_process_zero=self.is_world_process_zero(),
stateful_callbacks=[
cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
],
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Internal variables to help with automatic batch size reduction
self._train_batch_size = args.train_batch_size
self._created_lr_scheduler = False
# very last
self._memory_tracker.stop_and_update_metrics()
# torch.compile
if args.torch_compile and not is_torch_compile_a... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
self.is_fsdp_xla_v2_enabled = args.fsdp_config.get("xla_fsdp_v2", False)
if self.is_fsdp_xla_v2_enabled:
if not IS_XLA_FSDPV2_POST_2_2:
raise ValueError("FSDPv2 requires `torch_xla` 2.2 or higher.")
# Prepare the SPMD mesh that is going to be used by the data loader and t... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
@tokenizer.setter
def tokenizer(self, processing_class) -> None:
logger.warning(
"Trainer.tokenizer is now deprecated. You should use `Trainer.processing_class = processing_class` instead."
)
self.processing_class = processing_class
def _activate_neftune(self, model):
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _deactivate_neftune(self, model):
"""
Deactivates the neftune method. Make sure to call `_activate_neftune` first.
"""
if not hasattr(self, "neftune_hook_handle"):
raise ValueError("Neftune is not activated make sure to call `trainer._activate_neftune()` first")
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Args:
callback (`type` or [`~transformers.TrainerCallback]`):
A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
first case, will instantiate a member of that class.
"""
self.callback_handler.add_callback(callb... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Returns:
[`~transformers.TrainerCallback`]: The callback removed, if found.
"""
return self.callback_handler.pop_callback(callback)
def remove_callback(self, callback):
"""
Remove a callback from the current list of [`~transformers.TrainerCallback`].
Args:
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _set_signature_columns_if_needed(self):
if self._signature_columns is None:
# Inspect model forward signature to keep only the arguments it accepts.
model_to_inspect = self.model
if _is_peft_model(self.model):
if hasattr(self.model, "get_base_model"):
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None):
if not self.args.remove_unused_columns:
return dataset
self._set_signature_columns_if_needed()
signature_columns = self._signature_columns
ignored_columns = list(set(dataset.col... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
columns = [k for k in signature_columns if k in dataset.column_names]
if len(columns) == 0:
raise ValueError(
"No columns in the dataset match the model's forward method signature. "
f"The following columns have been ignored: [{', '.join(ignored_columns)}]. "
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def _get_collator_with_removed_columns(
self, data_collator: Callable, description: Optional[str] = None
) -> Callable:
"""Wrap the data collator in a callable removing unused columns."""
if not self.args.remove_unused_columns:
return data_collator
self._set_signature_col... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Build the sampler.
if self.args.group_by_length:
if is_datasets_available() and isinstance(self.train_dataset, datasets.Dataset):
lengths = (
self.train_dataset[self.args.length_column_name]
if self.args.length_column_name in self.train_datas... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def get_train_dataloader(self) -> DataLoader:
"""
Returns the training [`~torch.utils.data.DataLoader`].
Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
training if necessary) otherwise.
Subclass and override this me... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
dataloader_params = {
"batch_size": self._train_batch_size,
"collate_fn": data_collator,
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
"persistent_workers": self.args.dataloader_persistent_workers,
}
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# Deprecated code
if self.args.use_legacy_prediction_loop:
if is_torch_xla_available():
return SequentialDistributedSampler(
eval_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()
)
elif is_sagemaker_mp_enabled():
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if self.args.group_by_length:
if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
lengths = (
eval_dataset[self.args.length_column_name]
if self.args.length_column_name in eval_dataset.column_names
else None
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
def get_eval_dataloader(self, eval_dataset: Optional[Union[str, Dataset]] = None) -> DataLoader:
"""
Returns the evaluation [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (`str` or `torch.ut... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# If we have persistent workers, don't do a fork bomb especially as eval datasets
# don't change during training
dataloader_key = eval_dataset if isinstance(eval_dataset, str) else "eval"
if (
hasattr(self, "_eval_dataloaders")
and dataloader_key in self._eval_dataloaders... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
eval_dataset = self._remove_unused_columns(eval_dataset, description="evaluation")
else:
data_collator = self._get_collator_with_removed_columns(data_collator, description="evaluation")
dataloader_params ... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
# accelerator.free_memory() will destroy the references, so
# we need to store the non-prepared version
eval_dataloader = DataLoader(eval_dataset, **dataloader_params)
if self.args.dataloader_persistent_workers:
if hasattr(self, "_eval_dataloaders"):
self._eval_datalo... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Args:
test_dataset (`torch.utils.data.Dataset`, *optional*):
The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the
`model.forward()` method are automatically removed. It must implement `__len__`.
"""
data_collator = self.data_c... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
if not isinstance(test_dataset, torch.utils.data.IterableDataset):
dataloader_params["sampler"] = self._get_eval_sampler(test_dataset)
dataloader_params["drop_last"] = self.args.dataloader_drop_last
dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or
`create_scheduler`) in a subclass.
"""
self.create_optimizer()
i... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Note that some models implement their own layernorm instead of calling nn.LayerNorm, weight decay could still
apply to those modules since this function only filter out instance of nn.LayerNorm
"""
decay_parameters = get_parameter_names(model, ALL_LAYERNORM_LAYERS)
decay_parameters = [na... | 244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.