text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_posi... | 3,688 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len... | 3,688 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.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.
query_states = query_states.transpose(1, 2)
key_states = ke... | 3,688 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.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"):
... | 3,688 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
position_ids=position_ids,
dropout=dropout_rate,
sliding_window=getattr(self, "sliding_window", None),
is_cau... | 3,688 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiSdpaAttention(MimiAttention):
"""
Mimi attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`MimiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
""" | 3,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# Adapted from MimiAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_ca... | 3,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
'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=hidden_states,
attent... | 3,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.v... | 3,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficien... | 3,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
is_causal = True if ca... | 3,689 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiTransformerLayer(nn.Module):
def __init__(self, config: MimiConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = MIMI_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = MimiMLP(config... | 3,690 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 3,690 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
... | 3,690 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value... | 3,690 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiTransformerModel(nn.Module):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MimiTransformerLayer`]
Args:
config: MimiConfig
"""
def __init__(self, config: MimiConfig):
super().__init__()
self.layers = nn.ModuleList(
... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def forward(
self,
hidden_states: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
ou... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] f... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
Two formats are allowed:
- a [`~cache_utils.Cache`] instance;
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
use_cache (`bool`, *o... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
)
if position_ids is N... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if output_attentions:
all_self_attns += (layer_outputs[1],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask with Phi3->Mimi
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_leng... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.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 or using_sliding_window_cache)
and not output_attentions
):
if Atten... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
# SlidingWindowCache or StaticCache
if using_sliding_window_cache or using_static_cache:
target_length = past_key_values.get_max_cache_shape()
... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.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... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.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... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
@staticmethod
# Copied from transformers.models.mistral.modeling_mistral.MistralModel._prepare_4d_causal_attention_mask_with_cache_position with Mistral->Mimi
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.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.
target... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
past_key_values (`Cache`):
The cache class that is being used currently to generate
"""
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_... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
sliding_attend_mask = torch.arange(target_length, device=device) <= (
cache_position.reshape(-1, 1) - config.sliding_window
)
diagonal_attend_mas... | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask | 3,691 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiDecoder(nn.Module):
"""SEANet decoder as used by Mimi."""
def __init__(self, config: MimiConfig):
super().__init__()
scaling = int(2 ** len(config.upsampling_ratios))
model = [MimiConv1d(config, config.hidden_size, scaling * config.num_filters, config.kernel_size)]
# ... | 3,692 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# Add final layers
model += [nn.ELU()]
model += [MimiConv1d(config, config.num_filters, config.audio_channels, config.last_kernel_size)]
self.layers = nn.ModuleList(model)
# Copied from transformers.models.encodec.modeling_encodec.EncodecDecoder.forward
def forward(self, hidden_states):... | 3,692 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiEuclideanCodebook(nn.Module):
"""Codebook with Euclidean distance."""
def __init__(self, config: MimiConfig, epsilon: float = 1e-5):
super().__init__()
embed = torch.zeros(config.codebook_size, config.codebook_dim)
self.codebook_size = config.codebook_size
self.regis... | 3,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def quantize(self, hidden_states):
# Projects each vector in `hidden_states` over the nearest centroid and return its index.
# `hidden_states` should be `[N, D]` with `N` the number of input vectors and `D` the dimension.
dists = torch.cdist(hidden_states[None], self.embed[None], p=2)[0]
... | 3,693 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiVectorQuantization(nn.Module):
"""
Vector quantization implementation. Currently supports only euclidean distance.
"""
def __init__(self, config: MimiConfig):
super().__init__()
self.codebook = MimiEuclideanCodebook(config)
def encode(self, hidden_states):
hidden_... | 3,694 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiResidualVectorQuantizer(nn.Module):
"""Residual Vector Quantizer."""
def __init__(self, config: MimiConfig, num_quantizers: int = None):
super().__init__()
self.codebook_size = config.codebook_size
self.frame_rate = config.frame_rate
self.num_quantizers = num_quantizer... | 3,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def encode(self, embeddings: torch.Tensor, num_quantizers: Optional[int] = None) -> torch.Tensor:
"""
Encode a given input tensor with the specified frame rate at the given number of quantizers / codebooks. The RVQ encode method sets
the appropriate number of quantizers to use and returns indice... | 3,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def decode(self, codes: torch.Tensor) -> torch.Tensor:
"""Decode the given codes of shape [B, K, T] to the quantized representation."""
quantized_out = torch.tensor(0.0, device=codes.device)
codes = codes.transpose(0, 1)
for i, indices in enumerate(codes):
layer = self.layers... | 3,695 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiSplitResidualVectorQuantizer(nn.Module):
"""Split Residual Vector Quantizer."""
def __init__(self, config: MimiConfig):
super().__init__()
self.codebook_size = config.codebook_size
self.frame_rate = config.frame_rate
self.max_num_quantizers = config.num_quantizers
... | 3,696 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def encode(self, embeddings: torch.Tensor, num_quantizers: Optional[float] = None) -> torch.Tensor:
"""
Encode a given input tensor with the specified frame rate at the given number of quantizers / codebooks. The RVQ encode method sets
the appropriate number of quantizers to use and returns indi... | 3,696 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if num_quantizers < self.num_semantic_quantizers:
raise ValueError(
f"The number of quantizers (i.e codebooks) asked should be higher than the number of semantic quantizers {self.num_semantic_quantizers}, but is currently {num_quantizers}."
)
# codes is [K, B, T], with T... | 3,696 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# The first num_semantic_quantizers codebooks are decoded using the semantic RVQ
quantized_out = self.semantic_residual_vector_quantizer.decode(codes[:, : self.num_semantic_quantizers])
# The rest of the codebooks are decoded using the acoustic RVQ
if codes.shape[1] > self.num_semantic_quantize... | 3,696 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MimiConfig
base_model_prefix = "mimi"
main_input_name = "input_values"
supports_gradient_checkpoint... | 3,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# Copied from transformers.models.encodec.modeling_encodec.EncodecPreTrainedModel._init_weights
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bi... | 3,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LSTM):
for name, param in module.named_parameters():
if "weight" in name:
nn.init.xavier_uniform_(param)
elif "bias" in name:
nn.init.constant_(param, 0.0... | 3,697 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiModel(MimiPreTrainedModel):
def __init__(self, config: MimiConfig):
super().__init__(config)
self.config = config
self.encoder = MimiEncoder(config)
self.encoder_transformer = MimiTransformerModel(config)
self.downsample = None
self.upsample = None
... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
self.upsample = MimiConvTranspose1d(
config,
config.hidden_size,
config.hidden_size,
kernel_size=2 * int(config.encodec_frame_rate / config.frame_rate),
stride=2,
bias=False,
groups=config.upsample_groups,
... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def _encode_frame(
self,
input_values: torch.Tensor,
num_quantizers: int,
padding_mask: int,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
return_dict: Optional[bool] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""
... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
codes = self.quantizer.encode(embeddings, num_quantizers)
codes = codes.transpose(0, 1)
return codes, past_key_values
def encode(
self,
input_values: torch.Tensor,
padding_mask: torch.Tensor = None,
num_quantizers: Optional[float] = None,
encoder_past_key_val... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
Args:
input_values (`torch.Tensor` of shape `(batch_size, channels, sequence_length)`):
Float values of the input audio waveform.
padding_mask (`torch.Tensor` of shape `(batch_size, channels, sequence_length)`):
Indicates which inputs are to be ignored due to padd... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
The model will output the same cache format that is fed as input.
If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
have their past key value states given to this model).
return_dict (`bool`, *optional... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if num_quantizers > self.config.num_quantizers:
raise ValueError(
f"The number of quantizers (i.e codebooks) asked should be lower than the total number of quantizers {self.config.num_quantizers}, but is currently {num_quantizers}."
)
_, channels, input_length = input_va... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
return MimiEncoderOutput(encoded_frames, encoder_past_key_values)
def _decode_frame(
self,
codes: torch.Tensor,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
return_dict: Optional[bool] = None,
) -> torch.Tensor:
embeddings = self.quantizer.dec... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def decode(
self,
audio_codes: torch.Tensor,
padding_mask: Optional[torch.Tensor] = None,
decoder_past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor, torch.Tensor], MimiDecoderOutput]:
... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
Args:
audio_codes (`torch.LongTensor` of shape `(batch_size, num_quantizers, codes_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
padding_mask (`torch.Tensor` of shape `(batch_size, channels, sequence_length)`):
Indicates which inpu... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
have their past key value states given to this model).
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a ... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if not return_dict:
return (
audio_values,
decoder_past_key_values,
)
return MimiDecoderOutput(audio_values, decoder_past_key_values)
@add_start_docstrings_to_model_forward(MIMI_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=MimiOutput, ... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
```python
>>> from datasets import load_dataset
>>> from transformers import AutoFeatureExtractor, MimiModel
>>> dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
>>> audio_sample = dataset["train"]["audio"][0]["array"]
>>> model_id = "kyutai/mimi"
... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if audio_codes is None:
encoder_outputs = self.encode(
input_values, padding_mask, num_quantizers, encoder_past_key_values, return_dict=return_dict
)
audio_codes = encoder_outputs[0]
if return_dict:
encoder_past_key_values = encoder_outputs... | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
return MimiOutput(
audio_codes=audio_codes,
audio_values=audio_values,
encoder_past_key_values=encoder_past_key_values,
decoder_past_key_values=decoder_past_key_values,
) | 3,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class WavLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`WavLMModel`]. It is used to instantiate an WavLM
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the WavLM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`WavLMModel`]. Vocabulary size of the model. Defines the different tokens
that can... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout (`float`, *optional*, defaults to 0.1):
... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
details.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
apply_spec_augment (`bool`, *optional*, defaults to `True`):
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).
... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Propability of each feature vector along the feature axis to be chosen as the start of the vector spa... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
contrastive_logits_temperature (`float`, *optional*, defaults to 0.1):
The temperature *kappa* in the contrastive loss.
num_negatives (`int`, *optional*, defaults to 100):
Number of negative samples for the contrastive loss.
codevector_dim (`int`, *optional*, defaults to 256):
... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`WavLMForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
tdnn_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the
*XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*.
tdnn_dilation ... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
adapter_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adapter_stride (`int`, *optional*, defaults to 2):
Stride of the convolutional layers in the adapter network. Only relevant if `... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
Example:
```python
```
Example:
```python
>>> from transformers import WavLMConfig, WavLMModel
>>> # Initializing a WavLM facebook/wavlm-base-960h style configuration
>>> configuration = WavLMConfig()
>>> # Initializing a model (with random weights) from the facebook/wavlm-base-960... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_dropout=0.0,
... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
mask_feature_length=10,
num_codevectors_per_group=320,
num_codevector_groups=2,
contrastive_logits_temperature=0.1,
num_negatives=100,
codevector_dim=256,
proj_codevector_dim=256,
diversity_loss_weight=0.1,
ctc_loss_reduction="mean",
ctc_zero_infin... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
self.feat_extract_norm = feat_extract_norm
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_buckets = num_buckets
sel... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.num_ctc_classes = num_ctc_classes
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.use_weighted_layer_sum = use_weighted_layer... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrec... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mas... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
# adapter
self.add_adapter = add_adapter
self.adapter_kernel_size = adapter_kernel_size
self.adapter_stride = adapter_stride
self.num_adapter_layers = num_adapter_layers
self.output_hidden_size = output_hidden_size or hidden_size
# SequenceClassification-specific paramet... | 3,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py |
class WavLMNoLayerNormConvLayer(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,
... | 3,700 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMLayerNormConvLayer(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,
... | 3,701 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMGroupNormConvLayer(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,
... | 3,702 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMPositionalConvEmbedding(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,
... | 3,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.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... | 3,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
hidden_states = self.conv(hidden_states)
hidden_states = self.padding(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
return hidden_states | 3,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMSamePadLayer(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.nu... | 3,704 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [WavLMGroupNormConvLayer(config, layer_id=0)] + [
WavLMNoLayerNormConvLaye... | 3,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.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... | 3,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMFeatureExtractor(WavLMFeatureEncoder):
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].__na... | 3,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_dropo... | 3,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
class WavLMAttention(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,
num_buckets: int = 320,
max_distance: int = 800,
has_relative_position_bias: boo... | 3,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py |
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