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<Tip>
`nan_inf_filter` only influences the logging of loss values, it does not change the behavior the
gradient is computed or applied to the model.
</Tip>
on_each_node (`bool`, *optional*, defaults to `True`):
In multinode distributed train... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
>>> args = TrainingArguments("working_dir")
>>> args = args.set_logging(strategy="steps", steps=100)
>>> args.logging_steps
100
```
"""
self.logging_strategy = IntervalStrategy(strategy)
if self.logging_strategy == IntervalStrategy.STEPS and steps == 0:
... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
def set_push_to_hub(
self,
model_id: str,
strategy: Union[str, HubStrategy] = "every_save",
token: Optional[str] = None,
private_repo: Optional[bool] = None,
always_push: bool = False,
):
"""
A method that regroups all arguments linked to synchronizing... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
Args:
model_id (`str`):
The name of the repository to keep in sync with the local *output_dir*. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository
name, for instance `"user_name/mod... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
- `"end"`: push the model, its configuration, the processing_class e.g. tokenizer (if passed along to the [`Trainer`]) and a
draft of a model card when the [`~Trainer.save_model`] method is called.
- `"every_save"`: push the model, its configuration, the processing_class e.g. tokenizer (... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
- `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the
output
folder (so you will get one checkpoint folder per folder in your final repository) | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
token (`str`, *optional*):
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained
with `huggingface-cli login`.
private_repo (`bool`, *optional*, defaults to `False`):
Whether to make the repo private. If `None` (... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
>>> args = TrainingArguments("working_dir")
>>> args = args.set_push_to_hub("me/awesome-model")
>>> args.hub_model_id
'me/awesome-model'
```
"""
self.push_to_hub = True
self.hub_model_id = model_id
self.hub_strategy = HubStrategy(strategy)
self.hub... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
Args:
name (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch"`):
The optimizer to use: `"adamw_hf"`, `"adamw_torch"`, `"adamw_torch_fused"`, `"adamw_apex_fused"`,
`"adamw_anyprecision"` or `"adafactor"`.
learning_rate (`float`, *opt... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
args (`str`, *optional*):
Optional arguments that are supplied to AnyPrecisionAdamW (only useful when
`optim="adamw_anyprecision"`). | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_optimizer(name="adamw_torch", beta1=0.8)
>>> args.optim
'adamw_torch'
```
"""
self.optim = OptimizerNames(name)
... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
Args:
name (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`):
The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values.
num_epochs(`float`, *optional*, defaults to 3.0):
Total number of training epochs to perform... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of
`warmup_ratio`. | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_lr_scheduler(name="cosine", warmup_ratio=0.05)
>>> args.warmup_ratio
0.05
```
"""
self.lr_scheduler_type = SchedulerType... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
def set_dataloader(
self,
train_batch_size: int = 8,
eval_batch_size: int = 8,
drop_last: bool = False,
num_workers: int = 0,
pin_memory: bool = True,
persistent_workers: bool = False,
prefetch_factor: Optional[int] = None,
auto_find_batch_size: bo... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
Args:
drop_last (`bool`, *optional*, defaults to `False`):
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch
size) or not.
num_workers (`int`, *optional*, defaults to 0):
Number of subprocesses to... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
prefetch_factor (`int`, *optional*):
Number of batches loaded in advance by each worker.
2 means there will be a total of 2 * num_workers batches prefetched across all workers.
auto_find_batch_size (`bool`, *optional*, defaults to `False`)
Whether to find a ba... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
Random seed to be used with data samplers. If not set, random generators for data sampling will use the
same seed as `self.seed`. This can be used to ensure reproducibility of data sampling, independent of
the model seed. | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_dataloader(train_batch_size=16, eval_batch_size=64)
>>> args.per_device_train_batch_size
16
```
"""
self.per_device_trai... | 179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
class ParallelMode(Enum):
NOT_PARALLEL = "not_parallel"
NOT_DISTRIBUTED = "not_distributed"
DISTRIBUTED = "distributed"
SAGEMAKER_MODEL_PARALLEL = "sagemaker_model_parallel"
SAGEMAKER_DATA_PARALLEL = "sagemaker_data_parallel"
TPU = "tpu" | 180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py |
class GGUFTensor(NamedTuple):
weights: np.ndarray
name: str
metadata: dict | 181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class TensorProcessor:
def __init__(self, config=None):
self.config = config or {}
def process(self, weights, name, **kwargs):
return GGUFTensor(weights, name, {}) | 182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class LlamaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if ".attn_k." in name or ".attn_q." in name:
num_heads = self.config.get("num_attention_heads")
num_kv_heads = self.c... | 183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
def _reverse_permute_weights(
self, weights: np.ndarray, n_head: int, num_kv_heads: Optional[int] = None
) -> np.ndarray:
# Original permutation implementation
# https://github.com/ggerganov/llama.cpp/blob/a38b884c6c4b0c256583acfaaabdf556c62fabea/convert_hf_to_gguf.py#L1402-L1408
if ... | 183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class Qwen2MoeTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if "_exp" in name:
tensor_key_mapping = kwargs.get("tensor_key_mapping")
parsed_parameters = kwargs.get("parsed_pa... | 184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
def _split_moe_expert_tensor(
self, weights: np.ndarray, parsed_parameters: Dict[str, Dict], name: str, tensor_key_mapping: dict
):
# Original merge implementation
# https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L1994-L2022
name = tensor_key_mapping[name]
... | 184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class BloomTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if "attn_qkv" in name:
num_heads = self.config["n_head"]
n_embed = self.config["hidden_size"]
if "weight"... | 185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
q = q.reshape(n_head, n_embed // n_head, n_embed)
k = k.reshape(n_head, n_embed // n_head, n_embed)
v = v.reshape(n_head, n_embed // n_head, n_embed)
qkv_weights = np.stack([q, k, v], axis=1)
return qkv_weights.reshape(n_head * 3 * (n_embed // n_head), n_embed)
def _reverse_reshape... | 185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class T5TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
bid = None
for chunk in name.split("."):
if chunk.isdigit():
bid = int(chunk)
break
... | 186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class GPT2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
# Original transpose implementation
# https://github.com/ggerganov/llama.cpp/blob/a38b884c6c4b0c256583acfaaabdf556c62fabea/convert_hf_... | 187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
# Handle special case for output.weight
if name == "output.weight":
# output.weight has conflicts with attn_output.weight in name checking
# Store the tensor directly and signal to skip further processing
name = "lm_head.weight"
parsed_parameters = kwargs.get("par... | 187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class MambaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if "ssm_conv1d.weight" in name:
# for compatibility tensor ssm_conv1d must be (5120, 1, 4]) dim,
# quantized one is (... | 188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class NemotronTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
# ref : https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L4666
def process(self, weights, name, **kwargs):
if "norm.weight" in name:
weights =... | 189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class Gemma2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
# ref: https://github.com/ggerganov/llama.cpp/blob/d79d8f39b4da6deca4aea8bf130c6034c482b320/convert_hf_to_gguf.py#L3191
# ref: https://github.com/huggingface/transformers/blob/fc37f3891537... | 190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py |
class FlaxBaseModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.... | 191 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 191 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
class FlaxBaseModelOutputWithNoAttention(ModelOutput):
"""
Base class for model's outputs, with potential hidden states.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output of the last layer of the model.
... | 192 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
class FlaxBaseModelOutputWithPoolingAndNoAttention(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the outp... | 193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
last_hidden_state: jnp.ndarray = None
pooler_output: jnp.ndarray = None
hidden_states: Optional[Tuple[jnp.ndarray]] = None | 193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
class FlaxImageClassifierOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of image 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... | 194 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
class FlaxBaseModelOutputWithPast(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of th... | 195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
class FlaxBaseModelOutputWithPooling(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states. | 196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`):
Last layer hidden-state of the first token of... | 196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
class FlaxBaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states. | 197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`):
Last layer hidden-state of the first token of... | 197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. | 197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_si... | 197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
... | 197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
last_hidden_state: jnp.ndarray = None
pooler_output: jnp.ndarray = None
hidden_states: Optional[Tuple[jnp.ndarray]] = None
past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
attentions: Optional[Tuple[jnp.ndarray]] = None
cross_attentions: Optional[Tuple[jnp.ndarray]] = None | 197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
class FlaxBaseModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidd... | 198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(jnp.ndarray)` o... | 198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(jnp.ndarray)`, *optiona... | 198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
... | 198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
class FlaxSeq2SeqModelOutput(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 (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hid... | 199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is pa... | 199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.n... | 199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-state... | 199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_s... | 199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
last_hidden_state: jnp.ndarray = None
past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
decoder_attentions: Optional[Tuple[jnp.ndarray]] = None
cross_attentions: Optional[Tuple[jnp.ndarray]] = None
encoder_last_hidden_state: Option... | 199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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... | 200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `jnp.ndarray` tuples of l... | 200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
logits: jnp.ndarray = None
past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
hidden_states: Optional[Tuple[jnp.ndarray]] = None
attentions: Optional[Tuple[jnp.ndarray]] = None
cross_attentions: Optional[Tuple[jnp.ndarray]] = None | 200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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).
... | 201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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... | 202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is pa... | 202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.n... | 202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-state... | 202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_s... | 202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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... | 203 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 203 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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 (... | 204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
logits: jnp.ndarray = None
hidden_states: Optional[Tuple[jnp.ndarray]] = None
attentions: Optional[Tuple[jnp.ndarray]] = None | 204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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... | 205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is pa... | 205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.n... | 205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-state... | 205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_s... | 205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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... | 206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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... | 207 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
logits: jnp.ndarray = None
hidden_states: Optional[Tuple[jnp.ndarray]] = None
attentions: Optional[Tuple[jnp.ndarray]] = None | 207 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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... | 208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 208 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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... | 209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is pa... | 209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.n... | 209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-state... | 209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_s... | 209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
start_logits: jnp.ndarray = None
end_logits: jnp.ndarray = None
past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None
decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None
decoder_attentions: Optional[Tuple[jnp.ndarray]] = None
cross_attentions: Optional[Tuple[jnp.ndarray]] = None
enc... | 209 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py |
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,
... | 210 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py |
Parameters:
key_states (`torch.Tensor`):
The new key states to cache.
value_states (`torch.Tensor`):
The new value states to cache.
layer_idx (`int`):
The index of the layer to cache the states for.
cache_kwargs (`Dict[str, ... | 210 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py |
def get_max_cache_shape(self) -> Optional[int]:
"""Returns the maximum sequence length (i.e. max capacity) of the cache object"""
raise NotImplementedError("Make sure to implement `get_max_cache_shape` in a subclass.")
def get_usable_length(self, new_seq_length: int, layer_idx: Optional[int] = 0) -... | 210 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py |
def reorder_cache(self, beam_idx: torch.LongTensor):
"""Reorders the cache for beam search, given the selected beam indices."""
for layer_idx in range(len(self.key_cache)):
if self.key_cache[layer_idx] != []:
device = self.key_cache[layer_idx].device
self.key_... | 210 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py |
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... | 211 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py |
# Copied from transformers.utils.quantization_config.QuantizationConfigMixin.to_json_file
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
"""
Save this instance to a JSON file.
Args:
json_file_path (`str` or `os.PathLike`):
Path to the JSON file ... | 211 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py |
# Copied from transformers.utils.quantization_config.QuantizationConfigMixin.to_dict
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary. Returns:
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
"""... | 211 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/cache_utils.py |
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