text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True):
return self.tokenizer.get_decoder_prompt_ids(task=task, language=language, no_timestamps=no_timestamps)
def __call__(self, *args, **kwargs):
"""
Forwards the `audio` argument to WhisperFeatureExtractor's [`~Whis... | 9,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/processing_whisper.py |
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process.")
if audio is not None:
inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
if text is not None:
encodings = self.t... | 9,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/processing_whisper.py |
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to WhisperTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def get_prompt_ids(self, ... | 9,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/processing_whisper.py |
class WhisperPositionalEmbedding(nn.Embedding):
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__(num_positions, embedding_dim)
def forward(self, input_ids, past_key_values_length=0, position_ids=None):
if position_ids is None:
... | 9,893 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | 9,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if layer_idx is None and is_decoder:
logger.warning_once(
f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
"will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
... | 9,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[EncoderDecoderCache] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions:... | 9,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if past_key_value is not None:
is_updated = past_key_value.is_updated.get(self.layer_idx)
if is_cross_attention:
# after the first generated id, we can subsequently re-use all key/value_states from cache
past_key_value.is_updated[self.layer_idx] = True
... | 9,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# use key_value_states if cross attention
current_states = key_value_states if key_value_states is not None else hidden_states
if is_cross_attention and past_key_value and is_updated:
# reuse k,v, cross_attentions
key_states = past_key_value.key_cache[self.layer_idx]
... | 9,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.so... | 9,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
... | 9,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperFlashAttention2(WhisperAttention):
"""
Whisper flash attention module. This module inherits from `WhisperAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal... | 9,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-... | 9,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[EncoderDecoderCache] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions:... | 9,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
raise ValueError("WhisperFlashAttention2 attention does not support output_attentions") | 9,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = torch.reshape(self.q_proj(hidden_states), (bsz, tgt_len, se... | 9,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# use key_value_states if cross attention
current_states = key_value_states if key_value_states is not None else hidden_states
if is_cross_attention and past_key_value and is_updated:
# reuse k,v, cross_attentions
key_states = past_key_value.key_cache[self.layer_idx]
... | 9,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]
# We would need to refactor the KV cache to be able to avoid many of these transpose/reshape/view.
key_states = key_states.transpose(1, 2)
value_states = valu... | 9,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
... | 9,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
causal_mask,
tgt_len,
dropout=self.dropout if self.training else 0.0,
is_causal=self.is_causal,
use_top_left_mask=self._flash_attn_uses_top_left... | 9,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperSdpaAttention(WhisperAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[EncoderDecoderCache] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optiona... | 9,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states,
key... | 9,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self._shape(self.q_proj(hidden_states), tgt_len, bsz)
... | 9,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# use key_value_states if cross attention
current_states = key_value_states if key_value_states is not None else hidden_states
if is_cross_attention and past_key_value and is_updated:
# reuse k,v, cross_attentions
key_states = past_key_value.key_cache[self.layer_idx]
... | 9,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
causal_mask = attention_mask
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional... | 9,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask,
# but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
attn_output = torch.nn.functional.scaled_dot_prod... | 9,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return a... | 9,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperEncoderLayer(nn.Module):
def __init__(self, config: WhisperConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_h... | 9,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq... | 9,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, traini... | 9,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
... | 9,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperDecoderLayer(nn.Module):
def __init__(self, config: WhisperConfig, layer_idx: int = None):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=con... | 9,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
layer_idx=layer_idx,... | 9,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_l... | 9,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatT... | 9,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
hidden_states = self.self_attn_layer_norm(hidden_states) | 9,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
... | 9,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# Cross-Attention Block
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
... | 9,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self... | 9,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperPreTrainedModel(PreTrainedModel):
config_class = WhisperConfig
base_model_prefix = "model"
main_input_name = "input_features"
supports_gradient_checkpointing = True
_no_split_modules = ["WhisperEncoderLayer", "WhisperDecoderLayer"]
_supports_flash_attn_2 = True
_supports_sdpa = ... | 9,899 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
... | 9,899 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperEncoder(WhisperPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`WhisperEncoderLayer`].
Args:
config: WhisperConfig
"""
def __init__(self, config: WhisperConfig):
super().__init__(config)
... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
self.layers = nn.ModuleList([WhisperEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def _freeze_parameters(self):
... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def forward(
self,
input_features,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_features (`torch.LongTensor` of shape `(batch_size, feature_size, seque... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
Whisper does not support masking of the `input_features`, this argument is preserved for compatibility,
but it is not used. By default the silence in the input log mel spectrogram are ignored.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0]
if input_features.shape[-1] != expected_seq_length:
raise ValueError(
f"Whisper expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of l... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_p... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
la... | 9,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperDecoder(WhisperPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`WhisperDecoderLayer`]
Args:
config: WhisperConfig
"""
main_input_name = "input_ids"
def __init__(self, config: WhisperConfig):
super().__init__... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
self.layers = nn.ModuleList(
[WhisperDecoderLayer(config, layer_idx) for layer_idx in range(config.decoder_layers)]
)
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
self._use_sdpa = config._attn_implementation == "sdpa"
self.layer_norm = nn.... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
position_ids=None,
use_cache=None,
output_attentions=None,
... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in encoder to avoid performing cros... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
past_key_values (`EncoderDecoderCache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
Pre-computed hidden-states that can be used to speed up auto-regressive (sequential) decoding. There are
four sets of pre-computed hidden-states: key and values states in the self-attention blocks (... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
input... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a pl... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
return_legacy_cache = False
return_self_attention_cache = False
if use_cache or past_key_values is not None:
if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache):
return_self_attention_cache = True
past_key_values = En... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
past_key_values_length = 0
if cache_position is not None:
past_key_values_length = cache_position[0]
elif past_key_values is not None:
past_key_values_length = past_key_values.get_seq_length()
if cache_position is None:
cache_position = torch.arange(
... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
hidden_states = inputs_embeds + positions.to(inputs_embeds.device)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
causal_mask = self._update_causal_mask(
attention_mask,
inputs_embeds,
cache_position,
past_key... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
assert attn_mask.size()[0] == (len(self.layers)), (
... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
encoder_hidden_states,
None, # encoder attention m... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_values if use_cache else None,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
hidden_states = self.layer_norm(hidden_states)
# add hidden states from the last decoder layer
if ou... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
next_cache = past_key_values if use_cache else None
if return_self_attention_cache:
next_cache = past_key_values.self_attention_cache
if return_legacy_cache:
next_cache = past_key_values.to_legacy_cache()
if not return_dict:
return tuple(
v
... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.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,
... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.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... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.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... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dt... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.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.
... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.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(... | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.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
) | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
return causal_mask | 9,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperModel(WhisperPreTrainedModel):
def __init__(self, config: WhisperConfig):
super().__init__(config)
self.encoder = WhisperEncoder(config)
self.decoder = WhisperDecoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_emb... | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def _mask_input_features(
self,
input_features: torch.FloatTensor,
attention_mask: Optional[torch.LongTensor] = None,
):
"""
Masks extracted features along time axis and/or along feature axis according to
[SpecAugment](https://arxiv.org/abs/1904.08779).
"""
... | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if self.config.mask_time_prob > 0 and self.training:
# generate indices & apply SpecAugment along time axis
mask_time_indices = _compute_mask_indices(
(batch_size, sequence_length),
mask_prob=self.config.mask_time_prob,
mask_length=self.config.mask... | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if self.config.mask_feature_prob > 0 and self.training:
# generate indices & apply SpecAugment along feature axis
mask_feature_indices = _compute_mask_indices(
(batch_size, hidden_size),
mask_prob=self.config.mask_feature_prob,
mask_length=self.con... | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_features: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
deco... | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
r"""
Returns: | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
Example:
```python
>>> import torch
>>> from transformers import AutoFeatureExtractor, WhisperModel
>>> from datasets import load_dataset | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
>>> model = WhisperModel.from_pretrained("openai/whisper-base")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = feature_extractor(ds[0]["audio"]["ar... | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.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 | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if encoder_outputs is None:
input_features = self._mask_input_features(input_features, attention_mask=attention_mask)
encoder_outputs = self.encoder(
input_features,
head_mask=head_mask,
output_attentions=output_attentions,
output_... | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
head_mask=decoder_head_mask,
... | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_output... | 9,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperForConditionalGeneration(WhisperGenerationMixin, WhisperPreTrainedModel):
base_model_prefix = "model"
_tied_weights_keys = ["proj_out.weight"]
def __init__(self, config: WhisperConfig):
super().__init__(config)
self.model = WhisperModel(config)
self.proj_out = nn.Linear... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def freeze_encoder(self):
"""
Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will
not be updated during training.
"""
self.model.encoder._freeze_parameters() | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_features: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
decoder... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_siz... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
Returns:
Example:
```python
>>> import torch
>>> from transformers import AutoProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = WhisperForConditio... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if labels is not None:
if labels.shape[1] > self.max_target_positions:
raise ValueError(
f"Labels' sequence length {labels.shape[1]} cannot exceed the maximum allowed length of {self.max_target_positions} tokens."
)
if decoder_input_ids is None... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
outputs = self.model(
input_features,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_hea... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# move labels to correct device to enable PP
labels = labels.to(lm_logits.device)
loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.reshape(-1))
if not return_dict:
... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
use_cache=None,
encoder_outputs=None,
attention_mask=None,
decoder_attention_mask=None,
cache_position=None,
**kwargs,
):
# Overwritten -- encoder-decoder... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
past_length = 0
if past_key_values is not None:
if isinstance(past_key_values, EncoderDecoderCache):
past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
else:
past_length = past_key_values[0][0].shape[2]
... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
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