text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
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|---|---|---|---|---|---|---|---|
class FlaxCausalLMOutputWithCrossAttentions(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each voca... | class_definition | 19,231 | 21,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 200 |
class FlaxMaskedLMOutput(ModelOutput):
"""
Base class for masked language models outputs.
Args:
logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
... | class_definition | 21,783 | 23,097 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 201 |
class FlaxSeq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before Sof... | class_definition | 23,165 | 27,095 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 202 |
class FlaxNextSentencePredictorOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
logits (`jnp.ndarray` of shape `(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/F... | class_definition | 27,121 | 28,490 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 203 |
class FlaxSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (... | class_definition | 28,516 | 29,815 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 204 |
class FlaxSeq2SeqSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence sentence classification models.
Args:
logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftM... | class_definition | 29,841 | 33,769 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 205 |
class FlaxMultipleChoiceModelOutput(ModelOutput):
"""
Base class for outputs of multiple choice models.
Args:
logits (`jnp.ndarray` of shape `(batch_size, num_choices)`):
*num_choices* is the second dimension of the input tensors. (see *input_ids* above).
Classification sco... | class_definition | 33,795 | 35,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 206 |
class FlaxTokenClassifierOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax).
hidden_states (`tuple(jnp.ndarray)`, *option... | class_definition | 35,165 | 36,435 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 207 |
class FlaxQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Span-start scores (before SoftMax).
end_logits (`jnp.ndarray` of shape `(batch_size, sequenc... | class_definition | 36,461 | 37,883 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 208 |
class FlaxSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence question answering models.
Args:
start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Span-start scores (before SoftMax).
end_logits (`jnp.ndarray` of... | class_definition | 37,909 | 41,960 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 209 |
class Cache(torch.nn.Module):
"""
Base, abstract class for all caches. The actual data structure is specific to each subclass.
"""
def __init__(self):
super().__init__()
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: int,
... | class_definition | 542 | 4,041 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 210 |
class CacheConfig:
"""
Base class for cache configs
"""
cache_implementation: None
@classmethod
def from_dict(cls, config_dict, **kwargs):
"""
Constructs a CacheConfig instance from a dictionary of parameters.
Args:
config_dict (Dict[str, Any]): Dictionary c... | class_definition | 4,055 | 7,863 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 211 |
class QuantizedCacheConfig(CacheConfig):
"""
Configuration class for quantized cache settings.
Attributes:
backend (`str`, *optional*, defaults to `"quanto"`):
Backend to use when performing quantization, Can be one of [`quanto`, `HQQ`]
nbits (`Optional[int]`, *optional*, defaul... | class_definition | 7,877 | 12,016 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 212 |
class StaticCacheConfig(CacheConfig):
"""
Configuration class for static cache settings.
"""
cache_implementation = "static"
def __init__(self, batch_size: int, max_cache_len: int, device="cpu"):
self.batch_size = batch_size
self.max_cache_len = max_cache_len
self.device = ... | class_definition | 12,030 | 13,173 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 213 |
class DynamicCache(Cache):
"""
A cache that grows dynamically as more tokens are generated. This is the default for generative models.
It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
`[batch_size, num_heads, seq_len, head_dim]`.
Ex... | class_definition | 13,176 | 22,658 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 214 |
class OffloadedCache(DynamicCache):
"""
A drop-in replacement for DynamicCache that conserves GPU memory at the expense of more CPU memory.
Useful for generating from models with very long context.
In addition to the default CUDA stream, where all forward() computations happen,
this class uses anot... | class_definition | 22,661 | 28,434 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 215 |
class QuantizedCache(DynamicCache):
"""
A quantizer cache similar to what is described in the [KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache paper](https://arxiv.org/abs/2402.02750).
It allows the model to generate longer sequence length without allocating too much memory for Key and Value c... | class_definition | 28,437 | 33,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 216 |
class QuantoQuantizedCache(QuantizedCache):
"""
Quantized Cache class that uses `quanto` as a backend to perform quantization. Current implementation supports `int2` and `int4` dtypes only.
Parameters:
cache_config (`QuantizedCacheConfig`):
A configuration containing all the arguments t... | class_definition | 33,700 | 36,767 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 217 |
class HQQQuantizedCache(QuantizedCache):
"""
Quantized Cache class that uses `HQQ` as a backend to perform quantization. Current implementation supports `int2`, `int4`, `int8` dtypes.
Parameters:
cache_config (`QuantizedCacheConfig`):
A configuration containing all the arguments to be u... | class_definition | 36,770 | 39,346 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 218 |
class SinkCache(Cache):
"""
A cache that as described in the [Attention Sinks paper](https://arxiv.org/abs/2309.17453). It allows the model to
generate beyond the length of its context window, without losing fluency in the conversation. As it discards past
tokens, the model will lose the ability to gene... | class_definition | 39,349 | 48,396 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 219 |
class StaticCache(Cache):
"""
Static Cache class to be used with `torch.compile(model)` and `torch.export()`.
Parameters:
config (`PretrainedConfig`):
The configuration file defining the shape-related attributes required to initialize the static cache.
batch_size (`int`):
... | class_definition | 48,399 | 57,273 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 220 |
class SlidingWindowCache(StaticCache):
"""
Sliding Window Cache class to be used with `torch.compile` for models like Mistral that support sliding window attention.
Every time when we try to update the cache, we compute the `indices` based on `cache_position >= self.config.sliding_window - 1`,
if true(w... | class_definition | 57,276 | 64,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 221 |
class EncoderDecoderCache(Cache):
"""
Base, abstract class for all encoder-decoder caches. Can be used to hold combinations of self-attention and
cross-attention caches.
Example:
```python
>>> from transformers import AutoProcessor, AutoModelForCausalLM, DynamicCache, EncoderDecoderCac... | class_definition | 64,333 | 73,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 222 |
class HybridCache(Cache):
"""
Hybrid Cache class to be used with `torch.compile` for Gemma2 models that alternate between a local sliding window attention
and global attention in every other layer. Under the hood, Hybrid Cache leverages ["SlidingWindowCache"] for sliding window attention
and ["StaticCac... | class_definition | 73,472 | 83,284 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 223 |
class MambaCache:
"""
Cache for mamba model which does not have attention mechanism and key value states.
Arguments:
config (`PretrainedConfig):
The configuration file defining the shape-related attributes required to initialize the static cache.
batch_size (`int`):
... | class_definition | 83,287 | 88,150 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 224 |
class OffloadedStaticCache(StaticCache):
"""
Static cache class to be used with `torch.compile(model)` that offloads to the CPU or
another device.
Args:
config (`PretrainedConfig):
The configuration file defining the shape-related attributes required to initialize
the st... | class_definition | 88,153 | 100,342 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py | null | 225 |
class AttentionMaskConverter:
"""
A utility attention mask class that allows one to:
- Create a causal 4d mask
- Create a causal 4d mask with slided window
- Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
key_value_le... | class_definition | 773 | 13,019 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_attn_mask_utils.py | null | 226 |
class TransposeType(ExplicitEnum):
"""
Possible ...
"""
NO = "no"
SIMPLE = "simple"
CONV1D = "conv1d"
CONV2D = "conv2d" | class_definition | 1,132 | 1,280 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_pytorch_utils.py | null | 227 |
class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
"""
Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
Inherits from [`~tokenization_utils_base.PreTrainedTokenizerBase`].
Handles all the shared methods for tokenization and special tokens, as well as methods for
d... | class_definition | 2,915 | 40,723 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils_fast.py | null | 228 |
class ModuleUtilsMixin:
"""
A few utilities for `torch.nn.Modules`, to be used as a mixin.
"""
@staticmethod
def _hook_rss_memory_pre_forward(module, *args, **kwargs):
try:
import psutil
except ImportError:
raise ImportError("You need to install psutil (pip i... | class_definition | 35,280 | 49,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py | null | 229 |
class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMixin, PeftAdapterMixin):
r"""
Base class for all models.
[`PreTrainedModel`] takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models as well as a few methods common... | class_definition | 49,662 | 259,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py | null | 230 |
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... | class_definition | 259,400 | 260,716 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py | null | 231 |
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,... | class_definition | 260,719 | 263,574 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py | null | 232 |
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):
... | class_definition | 263,577 | 266,361 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py | null | 233 |
class SquadHeadOutput(ModelOutput):
"""
Base class for outputs of question answering models using a [`~modeling_utils.SQuADHead`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
Classification loss as th... | class_definition | 266,375 | 268,500 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py | null | 234 |
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()._... | class_definition | 268,503 | 274,587 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py | null | 235 |
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 ... | class_definition | 274,590 | 279,614 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_utils.py | null | 236 |
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... | class_definition | 1,002 | 1,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py | null | 237 |
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... | class_definition | 1,852 | 2,410 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py | null | 238 |
class LambdaLayer(nn.Module):
def __init__(self, function):
super().__init__()
self.function = function
def forward(self, x, *args):
return self.function(x, *args) | class_definition | 2,413 | 2,609 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py | null | 239 |
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 == ... | class_definition | 2,612 | 5,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py | null | 240 |
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 = ... | class_definition | 5,337 | 5,822 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py | null | 241 |
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... | class_definition | 5,825 | 6,222 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py | null | 242 |
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):
... | class_definition | 6,225 | 7,520 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/time_series_utils.py | null | 243 |
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... | class_definition | 9,609 | 253,063 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer.py | null | 244 |
class HyperParamSearchBackendBase:
name: str
pip_package: str = None
@staticmethod
def is_available():
raise NotImplementedError
def run(self, trainer, n_trials: int, direction: str, **kwargs):
raise NotImplementedError
def default_hp_space(self, trial):
raise NotImple... | class_definition | 1,078 | 1,736 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py | null | 245 |
class OptunaBackend(HyperParamSearchBackendBase):
name = "optuna"
@staticmethod
def is_available():
return is_optuna_available()
def run(self, trainer, n_trials: int, direction: str, **kwargs):
return run_hp_search_optuna(trainer, n_trials, direction, **kwargs)
def default_hp_spac... | class_definition | 1,739 | 2,120 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py | null | 246 |
class RayTuneBackend(HyperParamSearchBackendBase):
name = "ray"
pip_package = "'ray[tune]'"
@staticmethod
def is_available():
return is_ray_tune_available()
def run(self, trainer, n_trials: int, direction: str, **kwargs):
return run_hp_search_ray(trainer, n_trials, direction, **kwa... | class_definition | 2,123 | 2,530 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py | null | 247 |
class SigOptBackend(HyperParamSearchBackendBase):
name = "sigopt"
@staticmethod
def is_available():
return is_sigopt_available()
def run(self, trainer, n_trials: int, direction: str, **kwargs):
return run_hp_search_sigopt(trainer, n_trials, direction, **kwargs)
def default_hp_spac... | class_definition | 2,533 | 2,914 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py | null | 248 |
class WandbBackend(HyperParamSearchBackendBase):
name = "wandb"
@staticmethod
def is_available():
return is_wandb_available()
def run(self, trainer, n_trials: int, direction: str, **kwargs):
return run_hp_search_wandb(trainer, n_trials, direction, **kwargs)
def default_hp_space(se... | class_definition | 2,917 | 3,293 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hyperparameter_search.py | null | 249 |
class KerasMetricCallback(keras.callbacks.Callback):
"""
Callback to compute metrics at the end of every epoch. Unlike normal Keras metrics, these do not need to be
compilable by TF. It is particularly useful for common NLP metrics like BLEU and ROUGE that require string
operations or generation loops t... | class_definition | 430 | 13,457 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py | null | 250 |
class PushToHubCallback(keras.callbacks.Callback):
"""
Callback that will save and push the model to the Hub regularly. By default, it pushes once per epoch, but this can
be changed with the `save_strategy` argument. Pushed models can be accessed like any other model on the hub, such
as with the `from_p... | class_definition | 13,460 | 20,674 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/keras_callbacks.py | null | 251 |
class TrainerState:
"""
A class containing the [`Trainer`] inner state that will be saved along the model and optimizer when checkpointing
and passed to the [`TrainerCallback`].
<Tip>
In all this class, one step is to be understood as one update step. When using gradient accumulation, one update
... | class_definition | 1,050 | 6,994 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py | null | 252 |
class ExportableState:
"""
A class for objects that include the ability to have its state
be saved during `Trainer._save_checkpoint` and loaded back in during
`Trainer._load_from_checkpoint`.
These must implement a `state` function that gets called during the respective
Trainer function call. I... | class_definition | 6,997 | 8,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py | null | 253 |
class TrainerControl(ExportableState):
"""
A class that handles the [`Trainer`] control flow. This class is used by the [`TrainerCallback`] to activate some
switches in the training loop.
Args:
should_training_stop (`bool`, *optional*, defaults to `False`):
Whether or not the traini... | class_definition | 8,757 | 11,209 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py | null | 254 |
class TrainerCallback:
# no-format
"""
A class for objects that will inspect the state of the training loop at some events and take some decisions. At
each of those events the following arguments are available:
Args:
args ([`TrainingArguments`]):
The training arguments used to i... | class_definition | 11,212 | 17,078 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py | null | 255 |
class CallbackHandler(TrainerCallback):
"""Internal class that just calls the list of callbacks in order."""
def __init__(self, callbacks, model, processing_class, optimizer, lr_scheduler):
self.callbacks = []
for cb in callbacks:
self.add_callback(cb)
self.model = model
... | class_definition | 17,081 | 23,033 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py | null | 256 |
class DefaultFlowCallback(TrainerCallback):
"""
A [`TrainerCallback`] that handles the default flow of the training loop for logs, evaluation and checkpoints.
"""
def on_step_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
# Log
if state.globa... | class_definition | 23,036 | 24,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py | null | 257 |
class ProgressCallback(TrainerCallback):
"""
A [`TrainerCallback`] that displays the progress of training or evaluation.
You can modify `max_str_len` to control how long strings are truncated when logging.
"""
def __init__(self, max_str_len: int = 100):
"""
Initialize the callback w... | class_definition | 24,909 | 28,058 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py | null | 258 |
class PrinterCallback(TrainerCallback):
"""
A bare [`TrainerCallback`] that just prints the logs.
"""
def on_log(self, args, state, control, logs=None, **kwargs):
_ = logs.pop("total_flos", None)
if state.is_local_process_zero:
print(logs) | class_definition | 28,061 | 28,345 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py | null | 259 |
class EarlyStoppingCallback(TrainerCallback, ExportableState):
"""
A [`TrainerCallback`] that handles early stopping.
Args:
early_stopping_patience (`int`):
Use with `metric_for_best_model` to stop training when the specified metric worsens for
`early_stopping_patience` eval... | class_definition | 28,348 | 31,866 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_callback.py | null | 260 |
class DistributedSamplerWithLoop(DistributedSampler):
"""
Like a torch.utils.data.distributed.DistributedSampler` but loops at the end back to the beginning of the shuffled
samples to make each process have a round multiple of batch_size samples.
Args:
dataset (`torch.utils.data.Dataset`):
... | class_definition | 9,937 | 11,171 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 261 |
class EvalLoopContainer:
"""
Container to store intermediate results of evaluation loop
Args:
do_nested_concat (`bool`, *optional*, defaults to `True`):
If set to `True`, each iteration will recursively concatenate a new object containing tensors to
the existing stored tenso... | class_definition | 11,174 | 13,295 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 262 |
class SequentialDistributedSampler(Sampler):
"""
Distributed Sampler that subsamples indices sequentially, making it easier to collate all results at the end.
Even though we only use this sampler for eval and predict (no training), which means that the model params won't
have to be synced (i.e. will no... | class_definition | 13,298 | 15,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 263 |
class DistributedTensorGatherer:
"""
A class responsible for properly gathering tensors (or nested list/tuple of tensors) on the CPU by chunks.
If our dataset has 16 samples with a batch size of 2 on 3 processes and we gather then transfer on CPU at every
step, our sampler will generate the following i... | class_definition | 17,124 | 22,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 264 |
class LabelSmoother:
"""
Adds label-smoothing on a pre-computed output from a Transformers model.
Args:
epsilon (`float`, *optional*, defaults to 0.1):
The label smoothing factor.
ignore_index (`int`, *optional*, defaults to -100):
The index in the labels to ignore w... | class_definition | 22,064 | 23,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 265 |
class LengthGroupedSampler(Sampler):
r"""
Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while
keeping a bit of randomness.
"""
def __init__(
self,
batch_size: int,
dataset: Optional[Dataset] = None,
leng... | class_definition | 25,750 | 27,460 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 266 |
class DistributedLengthGroupedSampler(DistributedSampler):
r"""
Distributed Sampler that samples indices in a way that groups together features of the dataset of roughly the same
length while keeping a bit of randomness.
"""
# Copied and adapted from PyTorch DistributedSampler.
def __init__(
... | class_definition | 27,463 | 31,148 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 267 |
class ShardSampler(Sampler):
"""
Sampler that shards batches between several processes. Dispatches indices batch by batch: on 2 processes with batch
size 4, the first two batches are `[0, 1, 2, 3, 4, 5, 6, 7]` and `[8, 9, 10, 11, 12, 13, 14, 15]`, which shard into
`[0, 1, 2, 3]` and `[8, 9, 10, 11]` for... | class_definition | 31,151 | 33,051 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 268 |
class IterableDatasetShard(IterableDataset):
"""
Wraps a PyTorch `IterableDataset` to generate samples for one of the processes only. Instances of this class will
always yield a number of samples that is a round multiple of the actual batch size (which is `batch_size x
num_processes`). Depending on the ... | class_definition | 33,054 | 37,781 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 269 |
class AcceleratorConfig:
"""
A subset of arguments relating to the underlying [`accelerate.Accelerator`]
implementation utilized in the `Trainer` that can be customized.
Mostly relating to data.
Parameters:
split_batches (`bool`, *optional*, defaults to `False`):
Whether or not ... | class_definition | 50,573 | 58,541 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 270 |
class LayerWiseDummyOptimizer(torch.optim.Optimizer):
"""
For Layer-wise optimizers such as GaLoRE optimizer, the optimization
step is already done through the post gradient hooks. Therefore
the trick is to create a dummy optimizer that can take arbitrary
args and kwargs and return a no-op during tr... | class_definition | 58,544 | 59,455 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 271 |
class LayerWiseDummyScheduler(LRScheduler):
"""
For Layer-wise optimizers such as GaLoRE optimizer, the optimization and scheduling step
are already done through the post gradient hooks. Therefore
the trick is to create a dummy scheduler that can take arbitrary
args and kwargs and return a no-op dur... | class_definition | 59,458 | 60,566 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_pt_utils.py | null | 272 |
class TFModelUtilsMixin:
"""
A few utilities for `keras.Model`, to be used as a mixin.
"""
def num_parameters(self, only_trainable: bool = False) -> int:
"""
Get the number of (optionally, trainable) parameters in the model.
Args:
only_trainable (`bool`, *optional*,... | class_definition | 3,632 | 4,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 273 |
class TFCausalLanguageModelingLoss:
"""
Loss function suitable for causal language modeling (CLM), that is, the task of guessing the next token.
<Tip>
Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
</Tip>
"""
def hf_compute_loss(self, lab... | class_definition | 6,775 | 8,159 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 274 |
class TFQuestionAnsweringLoss:
"""
Loss function suitable for question answering.
"""
def hf_compute_loss(self, labels, logits):
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.NONE)
start_loss = loss_fn(labels["start_position"], l... | class_definition | 8,162 | 8,599 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 275 |
class TFTokenClassificationLoss:
"""
Loss function suitable for token classification.
<Tip>
Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
</Tip>
"""
def hf_compute_loss(self, labels, logits):
loss_fn = keras.losses.SparseCategori... | class_definition | 8,602 | 10,607 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 276 |
class TFSequenceClassificationLoss:
"""
Loss function suitable for sequence classification.
"""
def hf_compute_loss(self, labels, logits):
if logits.shape.rank == 1 or logits.shape[1] == 1:
loss_fn = keras.losses.MeanSquaredError(reduction=keras.losses.Reduction.NONE)
if... | class_definition | 10,610 | 11,310 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 277 |
class TFMultipleChoiceLoss:
"""Loss function suitable for multiple choice tasks."""
def hf_compute_loss(self, labels, logits):
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.NONE)
return loss_fn(labels, logits) | class_definition | 11,313 | 11,605 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 278 |
class TFMaskedLanguageModelingLoss(TFCausalLanguageModelingLoss):
"""
Loss function suitable for masked language modeling (MLM), that is, the task of guessing the masked tokens.
<Tip>
Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
</Tip>
""" | class_definition | 11,608 | 11,926 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 279 |
class TFNextSentencePredictionLoss:
"""
Loss function suitable for next sentence prediction (NSP), that is, the task of guessing the next sentence.
<Tip>
Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
</Tip>
"""
def hf_compute_loss(self, ... | class_definition | 11,929 | 13,440 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 280 |
class TFPreTrainedModel(keras.Model, TFModelUtilsMixin, TFGenerationMixin, PushToHubMixin):
r"""
Base class for all TF models.
[`TFPreTrainedModel`] takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models as well as a few methods common to al... | class_definition | 47,939 | 154,641 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 281 |
class TFConv1D(keras.layers.Layer):
"""
1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).
Basically works like a linear layer but the weights are transposed.
Args:
nf (`int`):
The number of output features.
nx (`int`):
... | class_definition | 154,644 | 156,043 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 282 |
class TFSharedEmbeddings(keras.layers.Layer):
r"""
Construct shared token embeddings.
The weights of the embedding layer is usually shared with the weights of the linear decoder when doing language
modeling.
Args:
vocab_size (`int`):
The size of the vocabulary, e.g., the number... | class_definition | 156,046 | 160,275 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 283 |
class TFSequenceSummary(keras.layers.Layer):
"""
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 ... | class_definition | 160,278 | 166,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py | null | 284 |
class DebugUnderflowOverflow:
"""
This debug class helps detect and understand where the model starts getting very large or very small, and more
importantly `nan` or `inf` weight and activation elements.
There are 2 working modes:
1. Underflow/overflow detection (default)
2. Specific batch abs... | class_definition | 774 | 11,162 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py | null | 285 |
class DebugOption(ExplicitEnum):
UNDERFLOW_OVERFLOW = "underflow_overflow"
TPU_METRICS_DEBUG = "tpu_metrics_debug" | class_definition | 12,784 | 12,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/debug_utils.py | null | 286 |
class TFBaseModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
... | class_definition | 804 | 2,119 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 287 |
class TFBaseModelOutputWithNoAttention(ModelOutput):
"""
Base class for model's outputs, with potential hidden states.
Args:
last_hidden_state (`tf.Tensor` shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output of the last layer of the model.
... | class_definition | 2,133 | 3,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 288 |
class TFBaseModelOutputWithPooling(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last la... | class_definition | 3,042 | 5,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 289 |
class TFBaseModelOutputWithPoolingAndNoAttention(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output o... | class_definition | 5,014 | 6,146 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 290 |
class TFBaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the out... | class_definition | 6,160 | 9,186 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 291 |
class TFBaseModelOutputWithPast(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the outpu... | class_definition | 9,200 | 11,228 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 292 |
class TFBaseModelOutputWithCrossAttentions(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last laye... | class_definition | 11,242 | 13,094 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 293 |
class TFBaseModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-s... | class_definition | 13,108 | 15,677 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 294 |
class TFSeq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-... | class_definition | 15,691 | 19,600 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 295 |
class TFCausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Language modeling loss (for next-token predict... | class_definition | 19,614 | 21,157 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 296 |
class TFCausalLMOutputWithPast(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Language modeling loss (for next-token... | class_definition | 21,171 | 23,254 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 297 |
class TFCausalLMOutputWithCrossAttentions(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Language modeling loss (for... | class_definition | 23,268 | 25,880 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 298 |
class TFMaskedLMOutput(ModelOutput):
"""
Base class for masked language models outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Masked language modeling (MLM) loss.
logits (`tf.Tensor` ... | class_definition | 25,894 | 27,403 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 299 |
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