text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
" you can ignore this message."
)
legacy = True
self.legacy = legacy | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
if add_prefix_space is not None:
kwargs["from_slow"] = True
super().__init__(
vocab_file=vocab_file,
tokenizer_file=tokenizer_file,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
unk_token=unk_token,
bos_token=bos_token,
... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
def update_post_processor(self):
"""
Updates the underlying post processor with the current `bos_token` and `eos_token`.
"""
bos = self.bos_token
bos_token_id = self.bos_token_id
if bos is None and self.add_bos_token:
raise ValueError("add_bos_token = True but... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
special_tokens = []
if self.add_bos_token:
special_tokens.append((bos, bos_token_id))
if self.add_eos_token:
special_tokens.append((eos, eos_token_id))
self._tokenizer.post_processor = processors.TemplateProcessing(
single=single, pair=pair, special_tokens=spe... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer."
... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
# TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.build_inputs_with_special_tokens
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
bos_token_id = [self.bos_token_i... | 9,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py |
class LlamaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
LlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | 9,879 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaRotaryEmbedding(nn.Module):
def __init__(self, config: LlamaConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", co... | 9,880 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... | 9,880 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_f... | 9,880 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device... | 9,880 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
... | 9,881 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: LlamaConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hid... | 9,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
... | 9,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttenti... | 9,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 9,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
... | 9,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaDecoderLayer(nn.Module):
def __init__(self, config: LlamaConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = LlamaAttention(config=config, layer_idx=layer_idx)
self.mlp = LlamaMLP(config)
self.input_layernorm = Llama... | 9,883 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 9,883 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cac... | 9,883 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaPreTrainedModel(PreTrainedModel):
config_class = LlamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlamaDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supp... | 9,884 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaModel(LlamaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
Args:
config: LlamaConfig
"""
def __init__(self, config: LlamaConfig):
super().__init__(config)
self.padding_idx = config.p... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states,... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
return causal_mask | 9,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 9,886 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = LlamaModel(config)
self.vocab_size = config.vocab_size
self.lm_head ... | 9,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional... | 9,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
... | 9,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 9,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
... | 9,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 9,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_sta... | 9,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaForSequenceClassification(LlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = LlamaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights a... | 9,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.Fl... | 9,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
o... | 9,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token foun... | 9,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_va... | 9,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaForQuestionAnswering(LlamaPreTrainedModel):
base_model_prefix = "transformer"
# Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Llama
def __init__(self, config):
super().__init__(config)
self.transformer = LlamaModel(config)
... | 9,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[tor... | 9,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled spa... | 9,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_state... | 9,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
return QuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class LlamaForTokenClassification(LlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = LlamaModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classi... | 9,890 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
atte... | 9,890 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 9,890 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden... | 9,890 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py |
class WhisperTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" Whisper tokenizer (backed by HuggingFace's *tokenizers* library).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information rega... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
Args:
vocab_file (`str`, *optional*):
Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
normalizer_file (`str`, *optional*):
Path to the normalizer_file file.
tokenizer_file (`str`, *optional*):
Path to ... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
The end of sequence token.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (Whisper tokenizer detect beginning of words by the preceding space).
language... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
predict_timestamps (`bool`, *optional*, defaults to `False`):
Whether to omit the `<|notimestamps|>` token at the start of the sequence.
""" | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = WhisperTokenizer | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
def __init__(
self,
vocab_file=None,
merges_file=None,
normalizer_file=None,
tokenizer_file=None,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
language=None,
task=None,
... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
add_prefix_space=add_prefix_space,
**kwargs,
)
self.add_bos_token = kw... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast._batch_encode_plus
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"Y... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
return super()._encode_plus(*args, **kwargs)
# Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._decode_with_timestamps
def _decode_with_timestamps(
self, token_ids, skip_special_tokens=False, time_precision=0.02, segment_size=1500
) -> str:
"""
Timestam... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
if timestamp < cur_max_timestamp:
# next segment has started
last_was_single_ending = i >= 2 and not (
token_ids[i - 1] >= timestamp_begin and token_ids[i - 2] >= timestamp_begin
)
if last_was_single_ending:
... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
outputs.append(f"<|{(timestamp + prev_segments_len):.2f}|>")
outputs.append([])
else:
outputs[-1].append(token)
outputs = [
s if isinstance(s, str) else self.decode(s, skip_special_tokens=skip_special_tokens) for s in outputs
]
return "".jo... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
time_precision (`float`, *optional*, defaults to 0.02):
The time ratio to convert from token to time.
... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
consecutive = np.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + 1
if consecutive.shape[0] == 0 and timestamp_tokens.sum() <= 1:
# either there are no timestamps or there are no consecutive ones
return []
elif np.where(timestamp_tokens)[0][-1] + 1 not in consecutive:
... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
if start_timestamp_position < cur_max_timestamp:
# next segment has started
is_single_ending = last_slice >= 2 and not (
token_ids[last_slice - 2] >= timestamp_begin and token_ids[last_slice - 1] >= timestamp_begin
)
... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
# strip timestamp tokens from the text output
sliced_tokens = self._preprocess_token_ids(sliced_tokens)
text = self._decode(sliced_tokens)
text = self._filter_timestamp_ids(text)
offsets.append(
{
"text": text,
... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
Args:
time_precision (`float`, *optional*, defaults to 0.02):
The time ratio to convert from token to time.
"""
return self.convert_tokens_to_ids([("<|%.2f|>" % (i * time_precision)) for i in range(1500 + 1)])
# Copied from transformers.models.whisper.tokenization_whispe... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Typically, obtained using the `__call__` method of the tokenizer.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove s... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
# Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.decode
def decode(
self,
token_ids,
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
output_offsets: bool = False,
time_precision: float = 0.02,
decode... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the ... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
time_precision (`float`, *optional*, defaults to 0.02):
The time ratio to convert from token to time.
decode_with_timestamps (`bool`, *optional*, defaults to `False`):
Whether or not to decode with timestamps included in the raw text.
normalize (`bool`, *optional*... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
destroy information in the decoded text, hence it should be used with caution.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`str`: The decoded sentence.
"""
filtered_ids = self._... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
text = super().decode(
filtered_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
normalize=normalize,
basic_normalize=basic_normalize,
remove_diacritics=remove_diacritics,
**kwargs... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
def _decode(
self, *args, normalize: bool = False, basic_normalize: bool = False, remove_diacritics: bool = False, **kwargs
) -> str:
text = super()._decode(*args, **kwargs)
if normalize:
clean_text = self._normalize(text)
return clean_text
elif basic_normali... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
# Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._basic_normalize
def _basic_normalize(self, text, remove_diacritics=False):
warnings.warn(
"The private method `_basic_normalize` is deprecated and will be removed in v5 of Transformers."
"You can normali... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
@staticmethod
# Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.basic_normalize
def basic_normalize(text, remove_diacritics=False):
"""
Normalize a given string using the `BasicTextNormalizer` class, which preforms commons transformation on
multilingual text... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
if self.english_spelling_normalizer is not None:
with open(normalizer_file, "w", encoding="utf-8") as f:
f.write(
json.dumps(self.english_spelling_normalizer, indent=2, sort_keys=True, ensure_ascii=False) + "\n"
)
return tuple(files) + (normalizer... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
Args:
language (`str`, *optional*, defaults to `None`):
The language of the transcription text.
task (`str`, *optional*, defaults to `None`):
Task identifier to append at the start of sequence (if any).
predict_timestamps (`bool`, *optional*, defaults ... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
prefix_token_ids = self.prefix_tokens
prefixes = self.convert_ids_to_tokens(prefix_token_ids)
eos = self.eos_token
eos_token_id = self.eos_token_id
prefix_template = " ".join([f"{token}:0" for token in prefixes])
self.backend_tokenizer.post_processor = processors.TemplateProcessi... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
@property
# Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.prefix_tokens
def prefix_tokens(self) -> List[int]:
bos_token_id = self.convert_tokens_to_ids("<|startoftranscript|>")
translate_token_id = self.convert_tokens_to_ids("<|translate|>")
transcribe_tok... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
if self.language is not None:
self.language = self.language.lower()
if self.language in TO_LANGUAGE_CODE:
language_id = TO_LANGUAGE_CODE[self.language]
elif self.language in TO_LANGUAGE_CODE.values():
language_id = self.language
else:
... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
bos_sequence = [bos_token_id]
if self.language is not None:
bos_sequence.append(bos_token_id + 1 + langs.index(language_id))
if self.task is not None:
bos_sequence.append(transcribe_token_id if self.task == "transcribe" else translate_token_id)
if not self.predict_timesta... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
# Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.get_special_tokens_mask
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids f... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
prefix_ones = [1] * len(self.prefix_tokens)
suffix_ones = [1]
if token_ids_1 is None:
re... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
# Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.get_decoder_prompt_ids
def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True):
self.set_prefix_tokens(task=task, language=language, predict_timestamps=not no_timestamps)
# prefix tokens are of the... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
def _decode_asr(self, model_outputs, *, return_timestamps, return_language, time_precision):
return _decode_asr(
self,
model_outputs,
return_timestamps=return_timestamps,
return_language=return_language,
time_precision=time_precision,
)
# ... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
# Check for special tokens
prompt_text_ids = batch_encoding["input_ids"][1:]
special_token_id = next((x for x in prompt_text_ids if x >= self.all_special_ids[0]), None)
if special_token_id is not None:
token = self.convert_ids_to_tokens(special_token_id)
raise ValueError(... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
# handle case of empty token_ids for decoding with timestamps.
# at this point token_ids is a list, so it is safe to use if not check.
if not token_ids:
return token_ids
has_prompt = token_ids[0] == prompt_token_id
if has_prompt:
if decoder_start_token_id in toke... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
@staticmethod
# Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._convert_to_list
def _convert_to_list(token_ids):
# convert type to ndarray if necessary
if hasattr(token_ids, "numpy"):
if "torch" in str(type(token_ids)):
token_ids = token... | 9,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py |
class WhisperProcessor(ProcessorMixin):
r"""
Constructs a Whisper processor which wraps a Whisper feature extractor and a Whisper tokenizer into a single
processor.
[`WhisperProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`WhisperTokenizer`]. See
the [`~WhisperProcess... | 9,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/processing_whisper.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.