text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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def _unpack_router_logits(self, router_outputs):
total_router_logits = []
total_expert_indexes = []
for router_output in router_outputs:
if router_output is not None:
router_logits, expert_indexes = router_output
total_router_logits.append(router_logit... | 2,831 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py |
class SEWDNoLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | 2,832 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | 2,833 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDGroupNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | 2,834 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDPositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
... | 2,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0):
self.conv = weight_norm(self.conv, name="weight", dim=2)
if hasattr(self.conv, "parametrizations"):
weight_g = self.conv.parametrizations.weight.original0
weight_v = self.conv.parametri... | 2,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = self.padding(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states | 2,835 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDSamePadLayer(nn.Module):
def __init__(self, num_conv_pos_embeddings):
super().__init__()
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def forward(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, :, : -self.num... | 2,836 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDUpsampling(nn.Module):
def __init__(self, config):
super().__init__()
self.projection = nn.Linear(config.hidden_size, config.hidden_size * config.squeeze_factor)
self.activation = ACT2FN[config.feat_extract_activation]
self.squeeze_factor = config.squeeze_factor
def fo... | 2,837 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [SEWDGroupNormConvLayer(config, layer_id=0)] + [
SEWDNoLayerNormConvLayer(c... | 2,838 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def _freeze_parameters(self):
for param in self.parameters():
param.requires_grad = False
self._requires_grad = False
def forward(self, input_values):
hidden_states = input_values[:, None]
# make sure hidden_states require grad for gradient_checkpointing
if self... | 2,838 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDFeatureExtractor(SEWDFeatureEncoder):
def __init__(self, config):
super().__init__(config)
warnings.warn(
f"The class `{self.__class__.__name__}` has been depreciated "
"and will be removed in Transformers v5. "
f"Use `{self.__class__.__bases__[0].__name... | 2,839 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class ContextPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
self.dropout = StableDropout(config.pooler_dropout)
self.config = config
def forward(self, hidden_states):
# We "po... | 2,840 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class XSoftmax(torch.autograd.Function):
"""
Masked Softmax which is optimized for saving memory
Args:
input (`torch.tensor`): The input tensor that will apply softmax.
mask (`torch.IntTensor`):
The mask matrix where 0 indicate that element will be ignored in the softmax calcula... | 2,841 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
output = input.masked_fill(rmask, torch.tensor(torch.finfo(input.dtype).min))
output = torch.softmax(output, ctx.dim)
output.masked_fill_(rmask, 0)
ctx.save_for_backward(output)
return output
@staticmethod
def backward(ctx, grad_output):
(output,) = ctx.saved_tensors
... | 2,841 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
mask_cast_value = g.op("Cast", mask, to_i=sym_help.cast_pytorch_to_onnx["Long"])
r_mask = g.op(
"Cast",
g.op("Sub", g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64)), mask_cast_value),
to_i=sym_help.cast_pytorch_to_onnx["Bool"],
)
output = masked_fi... | 2,841 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class DropoutContext:
def __init__(self):
self.dropout = 0
self.mask = None
self.scale = 1
self.reuse_mask = True | 2,842 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class XDropout(torch.autograd.Function):
"""Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
@staticmethod
def forward(ctx, input, local_ctx):
mask, dropout = get_mask(input, local_ctx)
ctx.scale = 1.0 / (1 - dropout)
if... | 2,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
dropout_p = local_ctx
if isinstance(local_ctx, DropoutContext):
dropout_p = local_ctx.dropout
# StableDropout only calls this function when training.
train = True
# TODO: We should check if the opset_version being used to export
# is > 12 here, but there's no good way... | 2,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class StableDropout(nn.Module):
"""
Optimized dropout module for stabilizing the training
Args:
drop_prob (float): the dropout probabilities
"""
def __init__(self, drop_prob):
super().__init__()
self.drop_prob = drop_prob
self.count = 0
self.context_stack = ... | 2,844 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def get_context(self):
if self.context_stack is not None:
if self.count >= len(self.context_stack):
self.context_stack.append(DropoutContext())
ctx = self.context_stack[self.count]
ctx.dropout = self.drop_prob
self.count += 1
return ctx... | 2,844 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.activation_dropout)
def forward(s... | 2,845 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class DisentangledSelfAttention(nn.Module):
"""
Disentangled self-attention module
Parameters:
config (`DebertaV2Config`):
A model config class instance with the configuration to build a new model. The schema is similar to
*BertConfig*, for more details, please refer [`Deber... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
self.share_att_key = getattr(config, "share_att_key", False)
self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []
self.relative_attention = getattr(config, "relative_attention", False)
if self.relative_attention:
self.position_buckets = getattr(config, ... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
if not self.share_att_key:
if "c2p" in self.pos_att_type:
self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
if "p2c" in self.pos_att_type:
self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size)
... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
Args:
hidden_states (`torch.FloatTensor`):
Input states to the module usually the output from previous layer, it will be the Q,K and V in
*Attention(Q,K,V)*
attention_mask (`torch.BoolTensor`):
An attention mask matrix of shape [*B*, *N*, *N*] whe... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
rel_embeddings (`torch.FloatTensor`):
The embedding of relative distances. It's a tensor of shape [\\(2 \\times
\\text{max_relative_positions}\\), *hidden_size*].
"""
if query_states is None:
query_states = hidden_states
query_layer = self.transpose_... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
rel_att = None
# Take the dot product between "query" and "key" to get the raw attention scores.
scale_factor = 1
if "c2p" in self.pos_att_type:
scale_factor += 1
if "p2c" in self.pos_att_type:
scale_factor += 1
scale = torch.sqrt(torch.tensor(query_layer.... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
if rel_att is not None:
attention_scores = attention_scores + rel_att
attention_scores = attention_scores
attention_scores = attention_scores.view(
-1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1)
) | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
# bsz x height x length x dimension
attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)
attention_probs = self.dropout(attention_probs)
context_layer = torch.bmm(
attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer
)
... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
if relative_pos is None:
q = query_layer.size(-2)
relative_pos = build_relative_position(
q,
key_layer.size(-2),
bucket_size=self.pos... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
rel_embeddings = rel_embeddings[0 : att_span * 2, :].unsqueeze(0)
if self.share_att_key:
pos_query_layer = self.transpose_for_scores(
self.query_proj(rel_embeddings), self.num_attention_heads
).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1)
pos_... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
score = 0
# content->position
if "c2p" in self.pos_att_type:
scale = torch.sqrt(torch.tensor(pos_key_layer.size(-1), dtype=torch.float) * scale_factor)
c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2))
c2p_pos = torch.clamp(relative_pos + att_span, 0, ... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
# position->content
if "p2c" in self.pos_att_type:
scale = torch.sqrt(torch.tensor(pos_query_layer.size(-1), dtype=torch.float) * scale_factor)
if key_layer.size(-2) != query_layer.size(-2):
r_pos = build_relative_position(
key_layer.size(-2),
... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2))
p2c_att = torch.gather(
p2c_att,
dim=-1,
index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_l... | 2,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = DisentangledSelfAttention(config)
self.output = SEWDSelfOutput(config)
self.config = config
def forward(
self,
hidden_states,
attention_mask,
output_attenti... | 2,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.interm... | 2,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.activation_dropout)
self.con... | 2,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = SEWDAttention(config)
self.intermediate = SEWDIntermediate(config)
self.output = SEWDOutput(config) | 2,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def forward(
self,
hidden_states,
attention_mask,
query_states=None,
relative_pos=None,
rel_embeddings=None,
output_attentions=False,
):
attention_output = self.attention(
hidden_states,
attention_mask,
output_attent... | 2,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class ConvLayer(nn.Module):
def __init__(self, config):
super().__init__()
kernel_size = getattr(config, "conv_kernel_size", 3)
groups = getattr(config, "conv_groups", 1)
self.conv_act = getattr(config, "conv_act", "tanh")
self.conv = nn.Conv1d(
config.hidden_size... | 2,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
layer_norm_input = residual_states + out
output = self.LayerNorm(layer_norm_input).to(layer_norm_input)
if input_mask is None:
output_states = output
else:
if input_mask.dim() != layer_norm_input.dim():
if input_mask.dim() == 4:
input_... | 2,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDTransformerEncoder(nn.Module):
"""Modified BertEncoder with relative position bias support"""
def __init__(self, config):
super().__init__()
self.layer = nn.ModuleList([SEWDLayer(config) for _ in range(config.num_hidden_layers)])
self.relative_attention = getattr(config, "rel... | 2,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
if "layer_norm" in self.norm_rel_ebd:
self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)
self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None
self.gradient_checkpointing = False
def get_rel_embedding(self):
... | 2,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
if self.relative_attention and relative_pos is None:
q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)
relative_pos = build_relative_position(
q,
hi... | 2,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def forward(
self,
hidden_states,
attention_mask,
output_hidden_states=True,
output_attentions=False,
query_states=None,
relative_pos=None,
return_dict=True,
):
if attention_mask.dim() <= 2:
input_mask = attention_mask
else:... | 2,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
if isinstance(hidden_states, Sequence):
next_kv = hidden_states[0]
else:
next_kv = hidden_states
rel_embeddings = self.get_rel_embedding()
output_states = next_kv
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
al... | 2,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
if self.gradient_checkpointing and self.training:
output_states = self._gradient_checkpointing_func(
layer_module.__call__,
next_kv,
attention_mask,
query_states,
relative_pos,
rel_emb... | 2,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
if query_states is not None:
query_states = output_states
if isinstance(hidden_states, Sequence):
next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
else:
next_kv = output_states
if output_attentions:
... | 2,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = SEWDPositionalConvEmbedding(config)
self.pool = nn.AvgPool1d(config.squeeze_factor, config.squeeze_factor)
self.encoder = SEWDTransformerEncoder(config)
... | 2,853 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def forward(
self,
hidden_states: torch.tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
max_encoder_length = hidden_states.shape[1] // self.config.squeeze_... | 2,853 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
input_lengths = (attention_mask.long()).sum(-1)
# apply pooling formula to get real output_lengths
output_lengths = input_lengths // self.config.squeeze_factor
attention_ids = (
torch.arange(0, max_encoder_length, device=output_lengths.device)
.view(1,... | 2,853 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
encoder_outputs = self.encoder(hidden_states, attention_mask, output_hidden_states, output_attentions)
hidden_states = self.upsample(encoder_outputs.last_hidden_state)
if hidden_states.shape[1] < n_input_timesteps:
hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, n_input_timesteps... | 2,853 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SEWDConfig
base_model_prefix = "sew-d"
main_input_name = "input_values"
supports_gradient_checkpoin... | 2,854 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, SEWDPositionalConvEmbedding):
nn.init.normal_(
module.conv.weight,
mean=0,
std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)),
... | 2,854 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
if hasattr(module, "weight_v") and hasattr(module, "weight_g"):
with deepspeed.zero.GatheredParameters([module.weight_v, module.weight_g], modifier_rank=0):
nn.init.kaiming_normal_(module.weight.data)
else:
with deepspeed.zero.GatheredParam... | 2,854 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
"""
Computes the output length of the convolutional layers
"""
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
#... | 2,854 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
attention_mask = torch.zeros(
(batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
)
# these two operations makes sure that all values before the output lengths idxs are attended to
attention_mask[(torch.arange(attention_mask.shape[0], device=... | 2,854 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDModel(SEWDPreTrainedModel):
def __init__(self, config: SEWDConfig):
super().__init__(config)
self.config = config
self.feature_extractor = SEWDFeatureEncoder(config)
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.feature_layer_norm_eps)
self.project... | 2,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states
def _mask_hidden_states(
self,
hidden_states: torch.FloatTensor,
mask_time_indices: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
):
"""... | 2,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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... | 2,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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... | 2,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
@add_start_docstrings_to_model_forward(SEWD_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,... | 2,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 2,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
extract_features = self.feature_extractor(input_values)
extract_features = extract_features.transpose(1, 2)
extract_features = self.layer_norm(extract_features)
if self.project_features:
extract_features = self.feature_projection(extract_features)
hidden_states = self.featur... | 2,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
if not return_dict:
return (hidden_states,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
) | 2,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDForCTC(SEWDPreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.sew_d = SEWDModel(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_size is None:
... | 2,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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... | 2,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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... | 2,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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.sew_d.feature_extractor._freeze_parameters()
def freeze_base_model(self):
"""... | 2,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
@add_start_docstrings_to_model_forward(SEWD_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(
... | 2,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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 | 2,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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.sew_d(
input_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
... | 2,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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... | 2,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
return CausalLMOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
) | 2,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDForSequenceClassification(SEWDPreTrainedModel):
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 SEWD adapters (config.add_adapt... | 2,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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 ... | 2,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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.sew_d.parameters():
param.re... | 2,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
@add_start_docstrings_to_model_forward(SEWD_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_SEQ_CLASS_CHECKPOINT,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss... | 2,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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).
""" | 2,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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.sew_d(
input_values,
attention_mask=attention_mask,
output_attentions=outp... | 2,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.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... | 2,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 2,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py |
class SEWDConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SEWDModel`]. It is used to instantiate a SEW-D
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the SEW-D model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`SEWD`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality ... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
position_buckets (`int`, *optional*, defaults to 256):
The maximu... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_python"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"`, `"gelu_python"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defa... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-7):
The epsilon used by the layer normalization layers in the transformer encoder.
feature_layer_norm_eps (`float`, *optional*, defaults to 1e-... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 5... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to mat... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irr... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`SEWDForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
Example:
```python
>>> from transformers import SEWDConfig, SEWDModel
>>> # Initializing a SEW-D asapp/sew-d-tiny-100k style configuration
>>> configuration = SEWDConfig()
>>> # Initializing a model (with random weights) from the asapp/sew-d-tiny-100k style configuration
>>> model = SEWDModel... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
squeeze_factor=2,
max_position_embeddings=512,
position_buckets=256,
share_att_key=True,
relative_attention=Tr... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
mask_feature_min_masks=0,
ctc_loss_reduction="me... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.squeeze_factor = ... | 2,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py |
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