text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if input_name == "input_ids" and "inputs_embeds" in model_kwargs:
model_kwargs["input_ids"] = self._maybe_initialize_input_ids_for_generation(
inputs, bos_token_id, model_kwargs=model_kwargs
)
inputs, input_name = model_kwargs["inputs_embeds"], "inputs_embeds"
... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
if conditioning_embeds is not None:
mel_start_token_embedding = self.model.decoder.input_embeds_layer(
torch.full(
(conditioning_embeds.shape[0], 1),
fill_value=self.config.bos_token_id,
device=conditioning_embeds.device,
... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
# subtract the positional_ids here
if hasattr(model_kwargs, "attention_mask"):
position_ids = model_kwargs["attention_mask"].long().cumsum(-1) - 1
else:
position_ids = torch.range(
0, conditioning_embeds.shape[1] - 1, dtype=torch.long, device=c... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
inputs = self._maybe_initialize_input_ids_for_generation(inputs, bos_token_id, model_kwargs)
return inputs, input_name, model_kwargs
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, inputs_embeds=None, conditioning_embeds=None, **kwargs
):
# Overwritten: has `co... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
input_ids = input_ids[:, remove_prefix_length:]
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -input_ids.shape[1] :]
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
@add_start_docstrings_to_model_forward(CLVP_DECODER_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[to... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_siz... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
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.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
lm_logits = self.final_norm(hidden_states)
lm_logits = self.lm_head(lm_logits)
loss = None
if labels is not None:
labels = labels.to(lm_logits.device)
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_la... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
@staticmethod
# Copied from transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel._reorder_cache
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... | 3,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
class ClvpModelForConditionalGeneration(ClvpPreTrainedModel, GenerationMixin):
config_class = ClvpConfig
def __init__(self, config: ClvpConfig):
super().__init__(config)
if not isinstance(config.text_config, ClvpEncoderConfig):
raise TypeError(
"config.text_config i... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
self.conditioning_encoder = ClvpConditioningEncoder(config)
self.speech_decoder_model = ClvpForCausalLM(config.decoder_config)
self.text_encoder_model = ClvpEncoder(config.text_config)
self.speech_encoder_model = ClvpEncoder(config.speech_config)
self.logit_scale = nn.Parameter(torch.... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
Args:
speech_ids (`torch.LongTensor`):
This refers to the output of the decoder model.
"""
decoder_fixing_codes = self.config.decoder_config.decoder_fixing_codes
speech_ids = speech_ids[:, 1:]
stop_token_indices = torch.where(speech_ids == self.speech_decoder... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
stm = each_seq_stop_token_index.argmax()
speech_ids[i, stm:] = decoder_fixing_codes[0]
if stm - 3 < speech_ids.shape[1]:
speech_ids[i, -3:] = torch.tensor(
[decoder_fixing_codes[1:]], device=speech_ids.device, dtype=torch.long
)
return... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
[What are input IDs?](../glossary#input-ids)
text_encoder_... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
Returns:
`torch.FloatTensor` of shape `(batch_size, output_dim)`:
The text embeddings obtained by applying the projection layer to the pooled output of the CLVP Text
Model.
Examples:
```python
>>> from transformers import ClvpProcessor, ClvpModelForC... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
outputs = self.text_encoder_model(
input_ids=input_ids,
inputs_embeds=text_encoder_inputs_embeds,
attention_mask=attention_mask,
)
return outputs[0]
def get_speech_features(
self,
speech_ids: Optional[torch.LongTensor] = None,
input_ids: ... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
Args:
speech_ids (`torch.LongTensor` of shape `(batch_size, num_speech_ids)`, *optional*):
Speech Tokens. Padding will be ignored by default should you provide it. If speech_ids are provided
then input_ids and input_features will be automatically ignored.
input_id... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
inputs_embeds for `ClvpConditioningEncoder`. Can be used in place of `input_ids`.
attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding speech token indices. Mask values selected in `[0, 1]`: | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
generation_config (`GenerationConfig`, *optional*):
generation config to control the generation of speech_ids if they are not p... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
>>> # Define the Text and Load the Audio (We are taking an audio example from HuggingFace Hub using `datasets` library)
>>> text = "This is an example text."
>>> ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.cast_column("audio", ... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
if speech_ids is None:
if (input_ids is None and conditioning_encoder_inputs_embeds is None) or input_features is None:
raise ValueError(
"Either speech_ids or input_ids/conditioning_encoder_inputs_embeds and input_features must be provided."
)
... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
outputs = self.speech_encoder_model(
input_ids=speech_ids,
attention_mask=attention_mask,
)
return outputs[0]
@add_start_docstrings_to_model_forward(CLVP_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ClvpOutput, config_class=ClvpConfig)
def forward(
... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
```python
>>> import datasets
>>> from transformers import ClvpProcessor, ClvpModelForConditionalGeneration
>>> # Define the Text and Load the Audio (We are taking an audio example from HuggingFace Hub using `datasets` library)
>>> text = "This is an example text."
>>> ds = dat... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
>>> # processor outputs and model outputs
>>> processor_output = processor(raw_speech=audio, sampling_rate=sr, text=text, return_tensors="pt")
>>> outputs = model(
... input_ids=processor_output["input_ids"],
... input_features=processor_output["input_features"],
... ... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
decoder_outputs = self.speech_decoder_model(
inputs_embeds=conditioning_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
speech_ids = decoder_outputs[0]
# since we will get the embeds of shape `(batch_size, seq_len, embedding_dim... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
text_outputs = self.text_encoder_model(
input_ids=input_ids,
inputs_embeds=text_encoder_inputs_embeds,
attention_mask=attention_mask,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
speech_embeds = speech_outputs[0]
... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
if not return_dict:
output = (
logits_per_speech,
logits_per_text,
text_embeds,
speech_embeds,
text_outputs[2],
speech_outputs[2],
)
if output_hidden_states:
output += (
... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
return ClvpOutput(
loss=loss,
logits_per_speech=logits_per_speech,
logits_per_text=logits_per_text,
text_embeds=text_embeds,
speech_embeds=speech_embeds,
text_model_output=text_outputs[2],
speech_model_output=speech_outputs[2],
... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
@torch.no_grad()
def generate(
self,
input_ids: torch.LongTensor = None,
input_features: torch.FloatTensor = None,
attention_mask: Optional[torch.LongTensor] = None,
generation_config: Optional[GenerationConfig] = None,
pad_to_max_mel_tokens: Optional[int] = None,
... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
Args:
input_ids (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Input text Tokens. Processed from the [`ClvpTokenizer`].
input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, time_dim)`, *optional*):
Indicates log-mel... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
[What are attention masks?](../glossary#attention-mask)
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call. `**kwargs`
passed to generate matching the attributes of `generatio... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
repo, link: https://github.com/neonbjb/tortoise-tts/blob/80f89987a5abda5e2b082618cd74f9c7411141dc/tortoise/api.py#L430
and to make sure the logits are same.
This does not affect generation quality so please don't consider using it since it is less efficient.
output_hidden_sta... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
Returns:
`ClvpOutput` or tuple: A `ClvpOutput` (if `return_dict_in_generate=True` or when
`config.return_dict_in_generate=True`) or a tuple.
"""
# If the input sequences are larger than (self.config.decoder_config.max_text_tokens - 3) then raise error,
# because we need ... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
generation_config.validate()
self._validate_model_kwargs(model_kwargs.copy())
# pad input_ids as specified in the original repo
# link... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
decoder_outputs = self.speech_decoder_model.generate(
conditioning_embeds=conditioning_embeds,
generation_config=generation_config,
output_hidden_states=output_hidden_states,
return_dict=generation_config.return_dict_in_generate,
)
if isinstance(decoder_ou... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
speech_outputs = self.speech_encoder_model(
input_ids=speech_ids,
output_hidden_states=output_hidden_states,
return_dict=generation_config.return_dict_in_generate,
)
text_outputs = self.text_encoder_model(
input_ids=input_ids,
attention_mask=at... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
if not generation_config.return_dict_in_generate:
output = (
speech_ids,
logits_per_speech,
logits_per_text,
text_embeds,
speech_embeds,
text_outputs[2],
speech_outputs[2],
)
... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
return ClvpOutput(
speech_ids=speech_ids,
logits_per_speech=logits_per_speech,
logits_per_text=logits_per_text,
text_embeds=text_embeds,
speech_embeds=speech_embeds,
text_model_output=text_outputs[2],
speech_model_output=speech_outputs[... | 3,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py |
class ClvpProcessor(ProcessorMixin):
r"""
Constructs a CLVP processor which wraps a CLVP Feature Extractor and a CLVP Tokenizer into a single processor.
[`ClvpProcessor`] offers all the functionalities of [`ClvpFeatureExtractor`] and [`ClvpTokenizer`]. See the
[`~ClvpProcessor.__call__`], [`~ClvpProces... | 3,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/processing_clvp.py |
def __call__(self, *args, **kwargs):
"""
Forwards the `audio` and `sampling_rate` arguments to [`~ClvpFeatureExtractor.__call__`] and the `text`
argument to [`~ClvpTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more
information.
"""
raw_... | 3,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/processing_clvp.py |
if text is None:
return inputs
elif raw_speech is None:
return encodings
else:
inputs["input_ids"] = encodings["input_ids"]
inputs["attention_mask"] = encodings["attention_mask"]
return inputs
# Copied from transformers.models.whisper.proc... | 3,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/processing_clvp.py |
# Copied from transformers.models.whisper.processing_whisper.WhisperProcessor.decode with Whisper->Clvp
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to ClvpTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more info... | 3,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/processing_clvp.py |
class ClvpEncoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ClvpEncoder`]. It is used to instantiate a CLVP
text or CLVP speech encoder according to the specified arguments. Instantiating a configuration with the defaults
will yield a similar configu... | 3,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
Args:
vocab_size (`int`, *optional*, defaults to 256):
Vocabulary size of the CLVP Encoder model.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 1536):
... | 3,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabiliti... | 3,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
testing).
bos_token_id (`int`, *optional*, defaults to 255):
Beginning of sequence token id.
eos_token_i... | 3,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
Example:
```python
>>> from transformers import ClvpEncoderConfig, ClvpEncoder
>>> # Initializing a ClvpEncoderConfig with susnato/clvp_dev style configuration
>>> encoder_configuration = ClvpEncoderConfig()
>>> # Initializing a ClvpEncoder (with random weights) from the susnato/clvp_dev style co... | 3,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
def __init__(
self,
vocab_size=256,
hidden_size=768,
intermediate_size=1536,
projection_dim=768,
num_hidden_layers=20,
num_attention_heads=12,
hidden_act="gelu",
layer_norm_eps=1e-5,
attention_dropout=0.1,
dropout=0.1,
use_r... | 3,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
self.use_rotary_embedding = use_rotary_embedding
self.use_attention_bias = use_attention_bias
self.summary_type = summary_type
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id | 3,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@classmethod
def from_pretrained(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], config_type: str = "text_config", **kwargs
) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
confi... | 3,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all config... | 3,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
class ClvpDecoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ClvpDecoder`]. It is used to instantiate a CLVP
Decoder Model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yiel... | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
Args:
vocab_size (`int`, *optional*, defaults to 8194):
Vocabulary size of the model.
max_position_embeddings (`int`, *optional*, defaults to 608):
The maximum sequence length of mel tokens that this model might ever be used with. Similar to `n_positions`
in `GPT2Conf... | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times `hidden_size`.
num_mel_attn_blocks (`int`, *optional*, defaults to 6):
Denotes the number of self attention layers in [`ClvpConditioningEncoder`].
activation_function (`str`, *optional*, defaults to `"gelu_new"`):... | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
summary_type (`string`, *optional*, defaults to `"cls_index"`):
Argument used when doing sequence summary. | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
Has to be one of the following options: | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
- `"last"`: Take the last token hidden state (like XLNet).
- `"first"`: Take the first token hidden state (like BERT).
- `"mean"`: Take the mean of all tokens hidden states.
- `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
... | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
The dropout ratio to be used after the projection and activation.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
bos_token_id (`int`, *optional*, defaults to 8192):
Beginning of s... | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
testing).
decoder_fixing_codes (`list`, *optional*, defaults to `[83, 45, 45, 248]`):
These values are used in the method `fix_speech_decoder_output` to fix decoder generated outputs. | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
Example:
```python
>>> from transformers import ClvpDecoderConfig, ClvpDecoder
>>> # Initializing a ClvpDecoderConfig with susnato/clvp_dev style configuration
>>> decoder_configuration = ClvpDecoderConfig()
>>> # Initializing a ClvpDecoder (with random weights) from the susnato/clvp_dev style co... | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
def __init__(
self,
vocab_size=8194,
max_position_embeddings=608,
max_text_tokens=404,
hidden_size=1024,
num_hidden_layers=30,
num_attention_heads=16,
n_inner=None,
num_mel_attn_blocks=6,
activation_function="gelu_new",
resid_pdrop=... | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_inner = n_inner
self.num_mel_attn_blocks = num_mel_attn_blocks
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
... | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | 3,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
class ClvpConfig(PretrainedConfig):
r"""
[`ClvpConfig`] is the configuration class to store the configuration of a [`ClvpModelForConditionalGeneration`]. It
is used to instantiate a CLVP model according to the specified arguments, defining the text model, speech model and
decoder model configs. Instanti... | 3,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize the CLVP text encoder.
speech_config (`dict`, *optional*):
Dictionary of configuration options used to initialize CLVP speech encoder.
decoder_config (`dict`, *optional*):
... | 3,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
```python
>>> from transformers import ClvpConfig, ClvpModelForConditionalGeneration
>>> # Initializing a ClvpConfig with susnato/clvp_dev style configuration
>>> configuration = ClvpConfig()
>>> # Initializing a ClvpModelForConditionalGeneration (with random weights) from the susnato/clvp_dev style c... | 3,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
model_type = "clvp"
sub_configs = {
"text_config": ClvpEncoderConfig,
"speech_config": ClvpEncoderConfig,
"decoder_config": ClvpDecoderConfig,
}
def __init__(
self,
text_config=None,
speech_config=None,
decoder_config=None,
projection_dim=768,... | 3,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
self.text_config = ClvpEncoderConfig(**text_config)
self.speech_config = ClvpEncoderConfig(**speech_config)
self.decoder_config = ClvpDecoderConfig(**decoder_config)
self.projection_dim = projection_dim
self.logit_scale_init_value = logit_scale_init_value
self.initializer_factor... | 3,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
Args:
text_config (`ClvpEncoderConfig`):
Text model configuration of type [`ClvpEncoderConfig`].
speech_config (`ClvpEncoderConfig`):
Speech model configuration of type [`ClvpEncoderConfig`].
decoder_config (`ClvpDecoderConfig`):
Decode... | 3,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py |
class M2M100ScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding... | 3,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = embeddi... | 3,431 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
@staticmethod
def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
"""
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
"Attention Is All You Need".
... | 3,431 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
@torch.no_grad()
def forward(
self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0
):
if input_ids is not None:
bsz, seq_len = input_ids.size()
# Create the position ids from the input token ids. Any padded tokens remai... | 3,431 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequenti... | 3,431 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
c... | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int... | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size() | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support... | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_sta... | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (d... | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
... | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = la... | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_we... | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
... | 3,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100FlashAttention2(M2M100Attention):
"""
M2M100 flash attention module. This module inherits from `M2M100Attention` 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 wit... | 3,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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-... | 3,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions:... | 3,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
# get query proj
query_states = self._reshape(self.q_proj(hidden_states), -1, bsz)
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` t... | 3,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
key_states = self._reshape(self.k_proj(hidden_states), -1, bsz)
value_states = self._reshape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0].transpose(1, 2), key_states], dim=1)
value_states = torch.cat([past_key_value[1].transpose(1, 2), value_states],... | 3,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (d... | 3,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference ... | 3,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_s... | 3,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
return attn_output, attn_weights, past_key_value | 3,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100SdpaAttention(M2M100Attention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[... | 3,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states,
key... | 3,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size() | 3,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
# get query proj
query_states = self.q_proj(hidden_states)
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
... | 3,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_sta... | 3,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
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