text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
self.num_buckets = num_buckets
self.max_distance = max_distance
self.gru_rel_pos_const = nn.Parameter(torch.ones(1, self.num_heads, 1, 1))
self.gru_rel_pos_linear = nn.Linear(self.head_dim, 8)
if has_relative_position_bias:
self.rel_attn_embed = nn.Embedding(self.num_bucket... | 3,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# first pass of attention layer creates position bias
if position_bias is None:
position_bias = self.compute_bias(tgt_len, tgt_len)
position_bias = (
position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1).view(bsz * self.num_heads, tgt_len, tgt_len)
)
# Comp... | 3,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# 4) apply gate to position bias to compute gated position_bias
gated_position_bias = gate_output.view(bsz * self.num_heads, -1, 1) * position_bias
gated_position_bias = gated_position_bias.view((-1, tgt_len, tgt_len))
attn_output, attn_weights = self.torch_multi_head_self_attention(
... | 3,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def torch_multi_head_self_attention(
self,
hidden_states: torch.FloatTensor,
attention_mask: Union[torch.LongTensor, torch.BoolTensor],
gated_position_bias: torch.FloatTensor,
output_attentions: bool,
) -> (torch.FloatTensor, torch.FloatTensor):
"""simple wrapper arou... | 3,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# PyTorch 1.3.0 has F.multi_head_attention_forward defined
# so no problem with backwards compatibility
attn_output, attn_weights = F.multi_head_attention_forward(
query,
key,
value,
self.embed_dim,
self.num_heads,
torch.empty([0]),... | 3,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if attn_weights is not None:
# IMPORTANT: Attention weights are averaged weights
# here which should not be the case. This is an open issue
# on PyTorch: https://github.com/pytorch/pytorch/issues/32590
attn_weights = attn_weights[:, None].broadcast_to(
att... | 3,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def compute_bias(self, query_length: int, key_length: int) -> torch.FloatTensor:
context_position = torch.arange(query_length, dtype=torch.long)[:, None]
memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
relative_position = memory_position - context_position
relative_... | 3,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
relative_positions_if_large = torch.log(relative_positions.float() / max_exact)
relative_positions_if_large = relative_positions_if_large / math.log(self.max_distance / max_exact)
relative_positions_if_large = relative_positions_if_large * (num_buckets - max_exact)
relative_position_if_large = (... | 3,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.intermediate_dropout = nn.Dropout(config.activation_dropout)
self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
se... | 3,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMEncoderLayer(nn.Module):
def __init__(self, config: WavLMConfig, has_relative_position_bias: bool = True):
super().__init__()
self.attention = WavLMAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_d... | 3,710 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def forward(self, hidden_states, attention_mask=None, position_bias=None, output_attentions=False, index=0):
attn_residual = hidden_states
hidden_states, attn_weights, position_bias = self.attention(
hidden_states,
attention_mask=attention_mask,
position_bias=position... | 3,710 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMEncoderLayerStableLayerNorm(nn.Module):
def __init__(self, config: WavLMConfig, has_relative_position_bias: bool = True):
super().__init__()
self.attention = WavLMAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=con... | 3,711 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def forward(self, hidden_states, attention_mask=None, position_bias=None, output_attentions=False):
attn_residual = hidden_states
hidden_states = self.layer_norm(hidden_states)
hidden_states, attn_weights, position_bias = self.attention(
hidden_states,
attention_mask=atte... | 3,711 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = WavLMPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_... | 3,712 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if attention_mask is not None:
# make sure padded tokens output 0
expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
hidden_states[~expand_attention_mask] = 0
position_embeddings = self.pos_conv_embed(hidden_states)
hidden_state... | 3,712 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
skip_the_layer = self.training and i > 0 and (dropout_probability < self.config.layerdrop)
if not skip_the_layer or synced_gpus:
# under fsdp or deepspeed zero3 all gpus must run in sync
if self.gradient_checkpointing and self.training:
layer_outputs = sel... | 3,712 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if skip_the_layer:
layer_outputs = (None, None, None)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
... | 3,712 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMEncoderStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = WavLMPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout... | 3,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if attention_mask is not None:
# make sure padded tokens are not attended to
expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
hidden_states[~expand_attention_mask] = 0
position_embeddings = self.pos_conv_embed(hidden_states)
h... | 3,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
skip_the_layer = self.training and i > 0 and (dropout_probability < self.config.layerdrop)
if not skip_the_layer or synced_gpus:
# under fsdp or deepspeed zero3 all gpus must run in sync
# XXX: could optimize this like synced_gpus in generate_utils but not sure if it's worth ... | 3,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
hidden_states, position_bias = layer_outputs[:2] | 3,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if skip_the_layer:
layer_outputs = (None, None, None)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[2],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_st... | 3,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMGumbelVectorQuantizer(nn.Module):
"""
Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH
GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
"""
def __init__(self, config):
super().__init__()
self.num_groups = config... | 3,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# can be decayed for training
self.temperature = 2
@staticmethod
def _compute_perplexity(probs):
marginal_probs = probs.mean(dim=0)
perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum()
return perplexity
def forward(self, hidden... | 3,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# compute perplexity
codevector_soft_dist = torch.softmax(
hidden_states.view(batch_size * sequence_length, self.num_groups, -1).float(), dim=-1
)
perplexity = self._compute_perplexity(codevector_soft_dist)
else:
# take argmax in non-differentiable... | 3,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
codevector_probs = codevector_probs.view(batch_size * sequence_length, -1)
# use probs to retrieve codevectors
codevectors_per_group = codevector_probs.unsqueeze(-1) * self.codevectors
codevectors = codevectors_per_group.view(batch_size * sequence_length, self.num_groups, self.num_vars, -1)
... | 3,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMAdapter(nn.Module):
def __init__(self, config):
super().__init__()
# feature dim might need to be down-projected
if config.output_hidden_size != config.hidden_size:
self.proj = nn.Linear(config.hidden_size, config.output_hidden_size)
self.proj_layer_norm =... | 3,715 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
for layer in self.layers:
layerdrop_prob = np.random.random()
if not self.training or (layerdrop_prob > self.layerdrop):
hidden_states = layer(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
return hidden_states | 3,715 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMAdapterLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.output_hidden_size,
2 * config.output_hidden_size,
config.adapter_kernel_size,
stride=config.adapter_stride,
padding=1,
... | 3,716 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = WavLMConfig
base_model_prefix = "wavlm"
main_input_name = "input_values"
supports_gradient_checkpo... | 3,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def _init_weights(self, module):
"""Initialize the weights"""
# gumbel softmax requires special init
if isinstance(module, WavLMGumbelVectorQuantizer):
module.weight_proj.weight.data.normal_(mean=0.0, std=1)
module.weight_proj.bias.data.zero_()
nn.init.uniform... | 3,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) | 3,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Conv1d):
nn.init.kaiming_normal_(module.weight)
i... | 3,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1
for kernel_size... | 3,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter)
output_lengths = output_lengths.to(torch.long)
batch_size = attention_mask.shape[0]
attention_mask = torch.zeros(
(batch_size, feature_vector_length), dtype=attention_mask.dtype, dev... | 3,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMModel(WavLMPreTrainedModel):
def __init__(self, config: WavLMConfig):
super().__init__(config)
self.config = config
self.feature_extractor = WavLMFeatureEncoder(config)
self.feature_projection = WavLMFeatureProjection(config)
# model only needs masking vector if m... | 3,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
not be updated during training.
"""
warnings.warn(
"The method `freeze_feature_extractor` is deprecated and will be ... | 3,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def _mask_hidden_states(
self,
hidden_states: torch.FloatTensor,
mask_time_indices: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
):
"""
Masks extracted features along time axis and/or along feature axis according to
[S... | 3,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if mask_time_indices is not None:
# apply SpecAugment along time axis with given mask_time_indices
hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
elif self.config.mask_time_prob > 0 and self.training:
mask_time_indices = _compute_mask_indice... | 3,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.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... | 3,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Wav2Vec2BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
... | 3,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
extract_features = self.feature_extractor(input_values)
extract_features = extract_features.transpose(1, 2)
if attention_mask is not None:
# compute reduced attention_mask corresponding to feature vectors
attention_mask = self._get_feature_vector_attention_mask(
... | 3,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if self.adapter is not None:
hidden_states = self.adapter(hidden_states)
if not return_dict:
return (hidden_states, extract_features) + encoder_outputs[1:]
return Wav2Vec2BaseModelOutput(
last_hidden_state=hidden_states,
extract_features=extract_features... | 3,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMForCTC(WavLMPreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.wavlm = WavLMModel(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_size is None:
... | 3,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# Initialize weights and apply final processing
self.post_init()
def tie_weights(self):
"""
This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
passing `target_lang=...` to `from_pretrained(...)`.
This method is **not... | 3,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
logge... | 3,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def freeze_feature_encoder(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
self.wavlm.feature_extractor._freeze_parameters()
def freeze_base_model(self):
"""... | 3,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_CTC_EXPECTED_OUTPUT,
expected_loss=_CTC_EXPECTED_LOSS,
)
def forward(... | 3,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
if labels is not None and labels.max() >= self.config.vocab_size:
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
outputs = self.wavlm(
input_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
... | 3,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# assuming that padded tokens are filled with -100
# when not being attended to
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
# ctc_loss doesn't support fp16
log_probs = nn.fun... | 3,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
return CausalLMOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
) | 3,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMForSequenceClassification(WavLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Sequence classification does not support the use of WavLM adapters (config.add_ad... | 3,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_extractor
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
not be updated durin... | 3,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_encoder with wav2vec2->wavlm
def freeze_feature_encoder(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not ... | 3,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav... | 3,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
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).
""" | 3,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
outputs = self.wavlm(
input_values,
attention_mask=attention_mask,
output_attentions=outp... | 3,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
hidden_states = self.projector(hidden_states)
if attention_mask is None:
pooled_output = hidden_states.mean(dim=1)
else:
padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
expand_padding_mask = padding_mask.unsqueeze(-1).rep... | 3,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMForAudioFrameClassification(WavLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Audio frame classification does not support the use of WavLM adapters (config.a... | 3,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
warnings.warn(
"The method `freeze_feature_extractor` is deprecated and will be r... | 3,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
be updated during training. Only the classification head will be updated.
"""
for param in self.wavlm.parameters():
param.re... | 3,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_FRAME_CLASS_CHECKPOINT,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_FRAME_EXPECTED_OUTPUT,
)
def forward(
... | 3,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
""" | 3,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
outputs = self.wavlm(
input_values,
attention_mask=attention_mask,
output_attentions=outp... | 3,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), torch.argmax(labels.view(-1, self.num_labels), axis=1))
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
ret... | 3,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class AMSoftmaxLoss(nn.Module):
def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
super(AMSoftmaxLoss, self).__init__()
self.scale = scale
self.margin = margin
self.num_labels = num_labels
self.weight = nn.Parameter(torch.randn(input_dim, num_labels), require... | 3,722 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class TDNNLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
self.out_conv_dim = config.tdnn_dim[layer_id]
self.kernel_size = config.tdnn_kernel[layer_id]
... | 3,723 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# for backward compatibility, we keep nn.Linear but call F.conv1d for speed up
hidden_states = hidden_states.transpose(1, 2)
weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2)
hidden_states = nn.functional.conv1d(hidden_states, weight, self.ke... | 3,723 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMForXVector(WavLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wavlm = WavLMModel(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
self.layer_weights = nn.... | 3,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
warnings.warn(
"The method `freeze_feature_extractor` is deprecated and will be r... | 3,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
be updated during training. Only the classification head will be updated.
"""
for param in self.wavlm.parameters():
param.re... | 3,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_XVECTOR_CHECKPOINT,
output_type=XVectorOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_XVECTOR_EXPECTED_OUTPUT,
)
def forward(
self... | 3,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
""" | 3,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
outputs = self.wavlm(
input_values,
attention_mask=attention_mask,
output_attentions=outp... | 3,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
# Statistic Pooling
if attention_mask is None:
mean_features = hidden_states.mean(dim=1)
std_features = hidden_states.std(dim=1)
else:
feat_extract_output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(dim=1))
tdnn_output_lengths = self... | 3,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
loss = None
if labels is not None:
loss = self.objective(logits, labels)
if not return_dict:
output = (logits, output_embeddings) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return XVectorOutput(
... | 3,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class TatoebaConverter:
"""
Convert Tatoeba-Challenge models to huggingface format.
Steps:
1. Convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
2. Rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique
... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
def __init__(self, save_dir="marian_converted"):
assert Path(DEFAULT_REPO).exists(), "need git clone git@github.com:Helsinki-NLP/Tatoeba-Challenge.git"
self.download_lang_info()
self.model_results = json.load(open("Tatoeba-Challenge/models/released-model-results.json"))
self.alpha3_to_al... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
def convert_models(self, tatoeba_ids, dry_run=False):
models_to_convert = [self.parse_metadata(x) for x in tatoeba_ids]
save_dir = Path("marian_ckpt")
dest_dir = Path(self.model_card_dir)
dest_dir.mkdir(exist_ok=True)
for model in tqdm(models_to_convert): # k, prepro, download, ... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
self.write_model_card(model, dry_run=dry_run) | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
def expand_group_to_two_letter_codes(self, grp_name):
return [self.alpha3_to_alpha2.get(x, x) for x in GROUP_MEMBERS[grp_name][1]]
def is_group(self, code, name):
return "languages" in name or len(GROUP_MEMBERS.get(code, [])) > 1
def get_tags(self, code, name):
if len(code) == 2:
... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
@staticmethod
def model_type_info_from_model_name(name):
info = {"_has_backtranslated_data": False}
if "1m" in name:
info["_data_per_pair"] = str(1e6)
if "2m" in name:
info["_data_per_pair"] = str(2e6)
if "4m" in name:
info["_data_per_pair"] = str(... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
def write_model_card(self, model_dict, dry_run=False) -> str:
"""
Construct card from data parsed from YAML and the model's name. upload command: aws s3 sync model_card_dir
s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
"""
model_dir_url = f"{TATOEBA_MODELS_URL}/{model_di... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
# This messy part tries to deal with language tags in multilingual models, possibly
# not all having three-letter codes
resolved_src_tags, resolved_tgt_tags = self.resolve_lang_code(a3_src, a3_tgt)
a2_src_tags, a2_tgt_tags = [], []
for tag in resolved_src_tags:
if tag not in ... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
metadata = {
"hf_name": model_dict["_name"],
"source_languages": s,
"target_languages": t,
"opus_readme_url": f"{model_dir_url}/README.md",
"original_repo": "Tatoeba-Challenge",
"tags": ["translation"],
"languages": lang_tags,
}... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
tuned = ""
if "_tuned" in model_dict:
tuned = f"* multilingual model tuned for: {model_dict['_tuned']}\n"
model_base_filename = model_dict["release"].split("/")[-1]
download = f"* download original weights: [{model_base_filename}]({model_dir_url}/{model_dict['release']})\n"
... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
scorestable = ""
for k, v in model_dict.items():
if "scores" in k:
this_score_table = f"* {k}\n|Test set|score|\n|---|---|\n"
pairs = sorted(v.items(), key=lambda x: x[1], reverse=True)
for pair in pairs:
this_score_table += f"|{pai... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
datainfo = ""
if "training-data" in model_dict:
datainfo += "* Training data: \n"
for k, v in model_dict["training-data"].items():
datainfo += f" * {str(k)}: {str(v)}\n"
if "validation-data" in model_dict:
datainfo += "* Validation data: \n"
... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
# combine with Tatoeba markdown
readme_url = f"{TATOEBA_MODELS_URL}/{model_dict['_name']}/README.md"
extra_markdown = f"""
### {model_dict['_name']}
* source language name: {self.tag2name[a3_src]}
* target language name: {self.tag2name[a3_tgt]}
* OPUS readme: [README.md]({readme_url})
"""
cont... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
items = "\n".join([f"* {k}: {v}" for k, v in metadata.items()])
sec3 = "\n### System Info: \n" + items
content += sec3
if dry_run:
print("CONTENT:")
print(content)
print("METADATA:")
print(metadata)
return
sub_dir = self.model_c... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
if not os.path.exists(ISO_PATH):
wget.download(ISO_URL, ISO_PATH)
if not os.path.exists(LANG_CODE_PATH):
LANG_CODE_PATH = hf_hub_download(
repo_id="huggingface/language_codes_marianMT", filename="language-codes-3b2.csv", repo_type="dataset"
)
def parse_me... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
if method == "best":
# Sort by how early they appear in released-models-results
results = [url_to_name(model["download"]) for model in self.model_results[model_name]]
ymls = [f for f in os.listdir(p) if f.endswith(".yml") and f[:-4] in results]
ymls.sort(key=lambda x: res... | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
metadata["_name"] = model_name
return metadata | 3,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py |
class MarianSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
self.weight =... | 3,726 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
@staticmethod
def _init_weight(out: nn.Parameter) -> nn.Parameter:
"""
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
"""
n_pos, dim = out.shape
position_enc = np.a... | 3,726 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
@torch.no_grad()
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0) -> torch.Tensor:
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length ... | 3,726 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianAttention(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,
c... | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
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