text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
bbox_loss_coefficient=5,
giou_loss_coefficient=2,
eos_coefficient=0.1,
**kwargs,
):
# We default to values which were previously hard-coded in the model. This enables configurability of the config
# while keeping the default behavior the same.
if use_timm_backbone and... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
backbone_model_type = backbone_config.get("model_type")
config_class = CONFIG_MAPPING[backbone_model_type]
backbone_config = config_class.from_dict(backbone_config)
backbone = None
# set timm attributes to None
dilation = None | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
verify_backbone_config_arguments(
use_timm_backbone=use_timm_backbone,
use_pretrained_backbone=use_pretrained_backbone,
backbone=backbone,
backbone_config=backbone_config,
backbone_kwargs=backbone_kwargs,
) | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
self.use_timm_backbone = use_timm_backbone
self.backbone_config = backbone_config
self.num_channels = num_channels
self.num_queries = num_queries
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attent... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
self.position_embedding_type = position_embedding_type
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.backbone_kwargs = backbone_kwargs
self.dilation = dilation
# Hungarian matcher
self.class_cost = class_cost
self.bbox_cost =... | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
@property
def num_attention_heads(self) -> int:
return self.encoder_attention_heads
@property
def hidden_size(self) -> int:
return self.d_model | 2,876 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
class TableTransformerOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
... | 2,877 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py |
class BarkSemanticGenerationConfig(GenerationConfig):
model_type = "semantic"
def __init__(
self,
eos_token_id=10_000,
renormalize_logits=True,
max_new_tokens=768,
output_scores=False,
return_dict_in_generate=False,
output_hidden_states=False,
out... | 2,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
Args:
eos_token_id (`int`, *optional*, defaults to 10_000):
The id of the *end-of-sequence* token.
renormalize_logits (`bool`, *optional*, defaults to `True`):
Whether to renormalize the logits after applying all the logits processors (including the
... | 2,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
outpu... | 2,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
Text pad token.
semantic_infer_token (`int`, *optional*, defaults to 129_599):
Semantic infer token.
semantic_vocab_size (`int`, *optional*, defaults to 10_000):
Semantic vocab size.
max_input_semantic_length (`int`, *optional*, defaults to 256):
... | 2,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
max_new_tokens=max_new_tokens,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
**kwargs,
) | 2,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
self.text_encoding_offset = text_encoding_offset
self.text_pad_token = text_pad_token
self.semantic_pad_token = eos_token_id
self.semantic_infer_token = semantic_infer_token
self.semantic_vocab_size = semantic_vocab_size
self.max_input_semantic_length = max_input_semantic_length
... | 2,878 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
class BarkCoarseGenerationConfig(GenerationConfig):
model_type = "coarse_acoustics"
def __init__(
self,
renormalize_logits=True,
output_scores=False,
return_dict_in_generate=False,
output_hidden_states=False,
output_attentions=False,
temperature=1.0,
... | 2,879 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
Args:
renormalize_logits (`bool`, *optional*, defaults to `True`):
Whether to renormalize the logits after applying all the logits processors (including the
custom ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the
s... | 2,879 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
temperature (`float`, *optional*, defaults to 1.0):
The value used t... | 2,879 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
max_coarse_input_length (`int`, *optional*, defaults to 256):
Max length of input coarse vector.
max_coarse_history (`int`, *optional*, defaults to 630):
Max length of the output of the coarse acoustics model used in the fine generation step.
sliding_window_len (`... | 2,879 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
self.coarse_semantic_pad_token = coarse_semantic_pad_token
self.coarse_rate_hz = coarse_rate_hz
self.n_coarse_codebooks = n_coarse_codebooks
self.coarse_infer_token = coarse_infer_token
self.max_coarse_input_length = max_coarse_input_length
self.max_coarse_history = max_coarse_hi... | 2,879 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
class BarkFineGenerationConfig(GenerationConfig):
model_type = "fine_acoustics"
def __init__(
self,
temperature=1.0,
max_fine_history_length=512,
max_fine_input_length=1024,
n_fine_codebooks=8,
**kwargs,
):
"""Class that holds a generation configurati... | 2,880 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
Args:
temperature (`float`, *optional*):
The value used to modulate the next token probabilities.
max_fine_history_length (`int`, *optional*, defaults to 512):
Max length of the fine history vector.
max_fine_input_length (`int`, *optional*, defaults to... | 2,880 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
class BarkGenerationConfig(GenerationConfig):
model_type = "bark"
is_composition = True
# TODO (joao): nested from_dict
def __init__(
self,
semantic_config: Dict = None,
coarse_acoustics_config: Dict = None,
fine_acoustics_config: Dict = None,
sample_rate=24_000... | 2,881 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
Args:
semantic_config (`Dict`, *optional*):
Semantic generation configuration.
coarse_acoustics_config (`Dict`, *optional*):
Coarse generation configuration.
fine_acoustics_config (`Dict`, *optional*):
Fine generation configuration.
... | 2,881 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
if fine_acoustics_config is None:
fine_acoustics_config = {}
logger.info("fine_acoustics_config is None. initializing the fine model with default values.")
self.semantic_config = BarkSemanticGenerationConfig(**semantic_config)
self.coarse_acoustics_config = BarkCoarseGenerationC... | 2,881 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
Returns:
[`BarkGenerationConfig`]: An instance of a configuration object
"""
return cls(
semantic_config=semantic_config.to_dict(),
coarse_acoustics_config=coarse_acoustics_config.to_dict(),
fine_acoustics_config=fine_acoustics_config.to_dict(),
... | 2,881 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py |
class BarkProcessor(ProcessorMixin):
r"""
Constructs a Bark processor which wraps a text tokenizer and optional Bark voice presets into a single processor.
Args:
tokenizer ([`PreTrainedTokenizer`]):
An instance of [`PreTrainedTokenizer`].
speaker_embeddings (`Dict[Dict[str]]`, *... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
def __init__(self, tokenizer, speaker_embeddings=None):
super().__init__(tokenizer)
self.speaker_embeddings = speaker_embeddings
@classmethod
def from_pretrained(
cls, pretrained_processor_name_or_path, speaker_embeddings_dict_path="speaker_embeddings_path.json", **kwargs
):
... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
- a string, the *model id* of a pretrained [`BarkProcessor`] hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a processor saved using the [`~BarkProcessor.save_pretrained`]
method, e.g., `./my_model_directory/`.
speaker_em... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
if speaker_embeddings_dict_path is not None:
speaker_embeddings_path = get_file_from_repo(
pretrained_processor_name_or_path,
speaker_embeddings_dict_path,
subfolder=kwargs.pop("subfolder", None),
cache_dir=kwargs.pop("cache_dir", None),
... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""
)
speaker_embeddings = None
else:
with open(speaker_embeddings_pat... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
tokenizer = AutoTokenizer.from_pretrained(pretrained_processor_name_or_path, **kwargs)
return cls(tokenizer=tokenizer, speaker_embeddings=speaker_embeddings)
def save_pretrained(
self,
save_directory,
speaker_embeddings_dict_path="speaker_embeddings_path.json",
speaker_embe... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
Args:
save_directory (`str` or `os.PathLike`):
Directory where the tokenizer files and the speaker embeddings will be saved (directory will be created
if it does not exist).
speaker_embeddings_dict_path (`str`, *optional*, defaults to `"speaker_embeddings_path.jso... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs:
Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
if self.speaker_embeddings is not None:
... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
embeddings_dict = {}
embeddings_dict["repo_or_path"] = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
voice_preset = self._load_voice_preset(prompt_key)
tmp_dict = {}
for ke... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
with open(os.path.join(save_directory, speaker_embeddings_dict_path), "w") as fp:
json.dump(embeddings_dict, fp)
super().save_pretrained(save_directory, push_to_hub, **kwargs)
def _load_voice_preset(self, voice_preset: str = None, **kwargs):
voice_preset_paths = self.speaker_embedd... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
path = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path", "/"),
voice_preset_paths[key],
subfolder=kwargs.pop("subfolder", None),
cache_dir=kwargs.pop("cache_dir", None),
force_download=kwargs.pop("force_download", False),
... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
voice_preset_dict[key] = np.load(path)
return voice_preset_dict
def _validate_voice_preset_dict(self, voice_preset: Optional[dict] = None):
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f"Voice preset unreco... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
def __call__(
self,
text=None,
voice_preset=None,
return_tensors="pt",
max_length=256,
add_special_tokens=False,
return_attention_mask=True,
return_token_type_ids=False,
**kwargs,
):
"""
Main method to prepare for the model one ... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_sp... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
Returns:
Tuple([`BatchEncoding`], [`BatchFeature`]): A tuple composed of a [`BatchEncoding`], i.e the output of the
`tokenizer` and a [`BatchFeature`], i.e the voice preset with... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
if voice_preset is not None:
self._validate_voice_preset_dict(voice_preset, **kwargs)
voice_preset = BatchFeature(data=voice_preset, tensor_type=return_tensors)
encoded_text = self.tokenizer(
text,
return_tensors=return_tensors,
padding="max_length",
... | 2,882 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py |
class BarkSubModelConfig(PretrainedConfig):
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
"vocab_size": "input_vocab_size",
"window_size": "block_size",
} | 2,883 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
def __init__(
self,
block_size=1024,
input_vocab_size=10_048,
output_vocab_size=10_048,
num_layers=12,
num_heads=12,
hidden_size=768,
dropout=0.0,
bias=True, # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
... | 2,883 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
class BarkSemanticConfig(BarkSubModelConfig):
model_type = "semantic"
base_config_key = "semantic_config" | 2,884 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
class BarkCoarseConfig(BarkSubModelConfig):
model_type = "coarse_acoustics"
base_config_key = "coarse_acoustics_config" | 2,885 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
class BarkFineConfig(BarkSubModelConfig):
model_type = "fine_acoustics"
base_config_key = "fine_acoustics_config"
def __init__(self, tie_word_embeddings=True, n_codes_total=8, n_codes_given=1, **kwargs):
self.n_codes_total = n_codes_total
self.n_codes_given = n_codes_given
super().... | 2,886 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
class BarkConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BarkModel`]. It is used to instantiate a Bark
model according to the specified sub-models configurations, defining the model architecture.
Instantiating a configuration with the defaults will yield... | 2,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
Args:
semantic_config ([`BarkSemanticConfig`], *optional*):
Configuration of the underlying semantic sub-model.
coarse_acoustics_config ([`BarkCoarseConfig`], *optional*):
Configuration of the underlying coarse acoustics sub-model.
fine_acoustics_config ([`BarkFineConfig`], *optional*):
... | 2,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
>>> # Initializing a Bark module style configuration
>>> configuration = BarkConfig.from_sub_model_configs(
... semantic_config, coarse_acoustics_config, fine_acoustics_config, codec_config
... )
>>> # Initializing a model (with random weights)
>>> model = BarkModel(configuration)
>>> # Ac... | 2,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
def __init__(
self,
semantic_config: Dict = None,
coarse_acoustics_config: Dict = None,
fine_acoustics_config: Dict = None,
codec_config: Dict = None,
initializer_range=0.02,
**kwargs,
):
if semantic_config is None:
semantic_config = {}
... | 2,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
self.semantic_config = BarkSemanticConfig(**semantic_config)
self.coarse_acoustics_config = BarkCoarseConfig(**coarse_acoustics_config)
self.fine_acoustics_config = BarkFineConfig(**fine_acoustics_config)
codec_model_type = codec_config["model_type"] if "model_type" in codec_config else "encodec... | 2,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
Returns:
[`BarkConfig`]: An instance of a configuration object
"""
return cls(
semantic_config=semantic_config.to_dict(),
coarse_acoustics_config=coarse_acoustics_config.to_dict(),
fine_acoustics_config=fine_acoustics_config.to_dict(),
codec_co... | 2,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py |
class BarkSelfAttention(nn.Module):
# adapted from GPTNeoSelfAttention and Bark code
# BarkSelfAttention can have two attention type, i.e full attention or causal attention
def __init__(self, config, is_causal=False):
super().__init__()
# regularization
self.dropout = config.dropou... | 2,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# key, query, value projections for all heads, but in a batch
self.att_proj = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=config.bias)
# output projection
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=config.bias)
self.is_causal = is_causal
... | 2,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def _merge_heads(self, tensor, num_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden_size
"""
# re-assemble all head outputs side by side
# (batch, num_heads, seq_len, attn_head_size) -> (batch, seq_len, num_heads*attn_head_size)
te... | 2,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# fill the upper left part of the attention weights with inf
attn_weights = attn_weights.masked_fill(
self.bias[:, :, key_length - query_length : key_length, :key_length] == 0,
torch.finfo(attn_weights.dtype).min,
)
if attention_mask is not None:
... | 2,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def forward(
self,
hidden_states,
attention_mask=None,
past_key_values=None,
head_mask=None,
use_cache=False,
output_attentions=False,
):
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
query, key... | 2,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
... | 2,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkSelfFlashAttention2(BarkSelfAttention):
"""
Bark flash attention module. This module inherits from `BarkSelfAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal... | 2,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-... | 2,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def _split_heads(self, tensor, num_heads, attn_head_size):
"""
Splits hidden_size dim into attn_head_size and num_heads
"""
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
tensor = tensor.view(new_shape)
# Flash attention requires the input to have the shape
... | 2,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def forward(
self,
hidden_states,
attention_mask=None,
past_key_values=None,
head_mask=None,
use_cache=False,
output_attentions=False,
):
batch_size, query_len, _ = hidden_states.size()
# calculate query, key, values for all heads in batch and... | 2,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if past_key_values is not None:
# (batch, head, seq_length, head_features) -> (batch, seq_length, head, head_features)
past_key = past_key_values[0].transpose(1, 2)
past_value = past_key_values[1].transpose(1, 2)
# and merge on seq_length
key = torch.cat((past... | 2,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
attn_weights = None
outputs += (attn_weights,... | 2,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkLayerNorm(nn.Module):
"""LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False."""
def __init__(self, hidden_size, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size)) if ... | 2,890 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.in_proj = nn.Linear(config.hidden_size, 4 * config.hidden_size, bias=config.bias)
self.out_proj = nn.Linear(4 * config.hidden_size, config.hidden_size, bias=config.bias)
self.dropout = nn.Dropout(config.dro... | 2,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkBlock(nn.Module):
def __init__(self, config, is_causal=False):
super().__init__()
if is_causal:
# if causal, uses handmade LayerNorm, so that the layerNorm bias is optional
# this handmade layerNorm is used to stick with Bark choice of leaving optional bias in
... | 2,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def forward(
self,
hidden_states,
past_key_values=None,
attention_mask=None,
head_mask=None,
use_cache=False,
output_attentions=False,
):
intermediary_hidden_states = self.layernorm_1(hidden_states)
attn_outputs = self.attn(
interm... | 2,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if use_cache:
outputs = (intermediary_hidden_states,) + outputs
else:
outputs = (intermediary_hidden_states,) + outputs[1:]
return outputs # hidden_states, ((present), attentions) | 2,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BarkConfig
supports_gradient_checkpointing = False
_supports_flash_attn_2 = True | 2,893 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear,)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mea... | 2,893 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
@property
def device(self) -> torch.device:
"""
`torch.device`: The device on which the module is (assuming that all the module parameters are on the same
device).
"""
# if has _hf_hook, has been offloaded so the device has to be found in the hook
if not hasattr(self... | 2,893 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkCausalModel(BarkPreTrainedModel, GenerationMixin):
config_class = BarkSubModelConfig
def __init__(self, config):
super().__init__(config)
self.config = config
# initialize as an autoregressive GPT-like model
self.input_embeds_layer = nn.Embedding(config.input_vocab_si... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
def get_input_embeddings(self):
return self.input_embeds_layer
def set_input_embeddings(self, new_embeddings):
self.input_embeds_layer = new_embeddings
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
# Overwritten -- bark has a model-specific hack
... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
input_ids = input_ids[:, remove_prefix_length:]
# input_embeds have already been used and is not required anymore
input_embeds = None
else:
if input_embeds is not None and kwargs.get("use_cache"):
seq_len = input_embeds.shape[1]
else:
... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if input_embeds is not None and kwargs.get("use_cache"):
return {
"input_ids": None,
"input_embeds": input_embeds,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
@add_start_docstrings_to_model_forward(BARK_CAUSAL_MODEL_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tens... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
loss = None
if labels is not None:
raise NotImplementedError(
"Training is not implemented yet for Bark - ensure you do not pass `labels` to the model."
) | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# Verify if input_embeds already exists
# then compute embeddings.
if input_ids is not None and input_embeds is not None:
raise ValueError("You cannot specify both input_ids and input_embeds at the same time")
elif input_embeds is not None and past_key_values is None:
# w... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
device = input_ids.device if input_ids is not None else input_embeds.device
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.layers))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# Attention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
if self._use_flash_attention_2:
attention_mask = attention_mask if 0 in attention_mask else None
else:
... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
hidden_states = self.drop(input_embeds + position_embeds)
output_shape = input_shape + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpoin... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
attention_mask,
head_mask[i],
use_cache,
... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
hidden_states = self.layernorm_final(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
logits = self.lm_head(hidden_states)
if not return_dict:
... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is call... | 2,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkSemanticModel(BarkCausalModel):
base_model_prefix = "semantic"
config_class = BarkSemanticConfig
def generate(
self,
input_ids: torch.Tensor,
semantic_generation_config: BarkSemanticGenerationConfig = None,
history_prompt: Optional[Dict[str, torch.Tensor]] = None,
... | 2,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
Args:
input_ids (`Optional[torch.Tensor]` of shape (batch_size, seq_len), *optional*):
Input ids, i.e tokenized input sentences. Will be truncated up to
semantic_generation_config.max_input_semantic_length tokens. Note that the output audios will be as
long as... | 2,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
[What are attention masks?](../glossary#attention-mask)
Returns:
torch.LongTensor: Output semantic tokens.
"""
if semantic_generation_config is None:
raise ValueError("`semantic_generation_config` has to be provided")
batch_size = input_ids.shape[0]
max_... | 2,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
if history_prompt is not None:
semantic_history = history_prompt["semantic_prompt"][-max_input_semantic_length:]
semantic_history = nn.functional.pad(
semantic_history,
(0, max_input_semantic_length - len(semantic_history)),
value=semantic_generati... | 2,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
input_embeds = torch.cat(
[
self.input_embeds_layer(input_ids[:, :max_input_semantic_length])
+ self.input_embeds_layer(semantic_history[:, : max_input_semantic_length + 1]),
self.input_embeds_layer(infer_array),
],
dim=1,
)
... | 2,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
min_eos_p = kwargs.get("min_eos_p", semantic_generation_config.min_eos_p)
early_stopping_logits_processor = BarkEosPrioritizerLogitsProcessor(
eos_token_id=semantic_generation_config.eos_token_id, min_eos_p=min_eos_p, device=input_ids.device
)
# pass input_ids in order to stay consi... | 2,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
return semantic_output | 2,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
class BarkCoarseModel(BarkCausalModel):
base_model_prefix = "coarse_acoustics"
config_class = BarkCoarseConfig
def preprocess_histories(
self,
max_coarse_history: int,
semantic_to_coarse_ratio: int,
batch_size: int,
semantic_generation_config: int,
codebook_s... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
Args:
max_coarse_history (`int`):
Maximum size of coarse tokens used.
semantic_to_coarse_ratio (`int`):
Ratio of semantic to coarse frequency
batch_size (`int`):
Batch size, i.e the number of samples.
semantic_generation_con... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
x_semantic_history = torch.repeat_interleave(history_prompt["semantic_prompt"][None], batch_size, dim=0)
# clone to avoid modifying history_prompt.coarse_prompt
x_coarse_history = history_prompt["coarse_prompt"].clone() | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
# offset x_coarse_history
if codebook_size is not None:
for n in range(1, x_coarse_history.shape[0]):
# offset
x_coarse_history[n, :] += codebook_size * n
# flatten x_coarse_history
x_coarse_history = torch.transpose(x_coarse_h... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
max_semantic_history = int(np.floor(max_coarse_history / semantic_to_coarse_ratio))
# trim histories correctly
n_semantic_hist_provided = min(
[
max_semantic_history,
x_semantic_history.shape[1] - x_semantic_history.shape[1] % 2,
... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
else:
# shape: (batch_size, 0)
x_semantic_history = torch.tensor([[]] * batch_size, dtype=torch.int).to(self.device)
x_coarse_history = torch.tensor([[]] * batch_size, dtype=torch.int).to(self.device)
return x_semantic_history, x_coarse_history
def generate(
sel... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
Args:
semantic_output (`torch.Tensor` of shape (batch_size, seq_len), *optional*):
Input text semantic ids, i.e the output of `BarkSemanticModel.generate`.
semantic_generation_config (`BarkSemanticGenerationConfig`):
Generation config indicating how to generate th... | 2,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py |
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