text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
... | 9,953 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
class Qwen2AudioPreTrainedModel(PreTrainedModel):
config_class = Qwen2AudioConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Qwen2AudioAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = True... | 9,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding... | 9,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
class Qwen2AudioEncoder(Qwen2AudioPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`Qwen2AudioEncoderLayer`].
Args:
config: Qwen2AudioEncoderConfig
"""
# Ignore copy
config_class = Qwen2AudioEncoderConfig
ma... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)
self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim)
self.embed_positions.requires_grad_(False)
self.laye... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
def forward(
self,
input_features,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_features (`torch.LongTensor` of shape `(batch_size, feature_size, seque... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
Qwen2Audio does not support masking of the `input_features`, this argument is preserved for compatibility,
but it is not used. By default the silence in the input log mel spectrogram are ignored.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0]
if input_features.shape[-1] != expected_seq_length:
raise ValueError(
f"Qwen2Audio expects the mel input features to be of length {expected_seq_length}, but found {input_features.shap... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
inputs_embeds = nn.functional.gelu(self.conv1(input_features))
inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
inputs_embeds = inputs_embeds.permute(0, 2, 1)
embed_pos = self.embed_positions.weight
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.funct... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_p... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# Ignore copy
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_sta... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# Ignore copy
hidden_states = hidden_states.permute(0, 2, 1)
hidden_states = self.avg_pooler(hidden_states)
hidden_states = hidden_states.permute(0, 2, 1)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidde... | 9,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
class Qwen2AudioMultiModalProjector(nn.Module):
def __init__(self, config: Qwen2AudioConfig):
super().__init__()
self.linear = nn.Linear(config.audio_config.d_model, config.text_config.hidden_size, bias=True)
def forward(self, audio_features):
hidden_states = self.linear(audio_features)... | 9,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
class Qwen2AudioForConditionalGeneration(Qwen2AudioPreTrainedModel, GenerationMixin):
def __init__(self, config: Qwen2AudioConfig):
super().__init__(config)
self.audio_tower = AutoModel.from_config(config.audio_config)
self.multi_modal_projector = Qwen2AudioMultiModalProjector(config)
... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
@padding_side.setter
def padding_side(self, padding_side: str):
if padding_side not in ["left", "right"]:
raise ValueError(f"{padding_side} is not `left` or `right`.")
self._padding_side = padding_side
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGenerat... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.language_model.set_output_embeddings(new_embeddings)
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
Args:
audio_features (`torch.Tensor` of shape `(num_audios, max_audio_tokens, embed_dim)`):
All audio vectors of all audios in the batch
num_audio_tokens (`torch.LongTensor` of shape `(num_audios)`):
The length of audio embeddings of each audio as stacked in `audi... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
final_embedding, final_attention_mask, final_labels, position_ids, final_input_ids | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
Explanation:
each audio has variable length embeddings, with length specified by num_audio_tokens
audio_features is concatenation of all audio embed vectors
task: fill each <|AUDIO|> with the correct number of audio embeddings
Example:
X (5 tokens), Y (3 t... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
a b c d e f X g h i j k Y l m
_ _ _ _ _ _ o p q r Z s t u v
]
input_ids should be: [
a b c d e f X X X X X g h i j k Y Y Y l m
_ _ _ _ _ o p q r Z Z Z Z Z Z Z Z s t u v
]
labels should be: [
... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
audio2, _ = librosa.load(BytesIO(urlopen(url2).read()), sr=processor.feature_extractor.sampling_rate)
prompts = [
"[INST] <|AUDIO|>\nWhat is that in this audio? [/INST]",
"[INST] <|AUDIO|>\nWhat is that in this audio? [/INST]",
]
in... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
input_ids: [
a b c d X g h
i j Y k l m n
]
where X is 3 tokens while Y is 5, this mean after merge
if left-padding (batched generation)
input_ids should be: [
_ _ a b c d X X X g h
... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
_left_padding = torch.any(attention_mask[:, 0] == 0)
_right_padding = torch.any(attention_mask[:, -1] == 0) | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
left_padding = True
if batch_size > 1:
if _left_padding and not _right_padding:
left_padding = True
elif not _left_padding and _right_padding:
left_padding = False
elif not _left_padding and not _right_padding:
# both side is 1,... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# In case the Audio model or the Language model has been offloaded to CPU, we need to manually
# set the corresponding tensors into their correct target device.
target_device = inputs_embeds.device
attention_mask = attention_mask.to(target_device)
input_ids = input_ids.to(target_device)
... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# 2. Compute the positions where text should be written
# Calculate new positions for text tokens in merged audio-text sequence.
# `special_audio_token_mask` identifies audio tokens. Each audio token will be replaced by `audio_feat_lengths - 1` text tokens.
# `torch.cumsum` computes how each aud... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
batch_indices, non_audio_indices, text_to_overwrite = (
batch_indices.to(target_device),
non_audio_indices.to(target_device),
text_to_overwrite.to(target_device),
) | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# 3. Create the full embedding, already padded to the maximum position
final_embedding = torch.zeros(
batch_size, max_token_num, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
)
final_attention_mask = torch.zeros(
batch_size, max_token_num, dtype=attent... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# 4. Fill the embeddings based on the mask. If we have ["hey" "<audio>", "how", "are"]
# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the audio features
final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_audio_indices]
final_attention... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# 5. Fill the embeddings corresponding to the audios. Anything that is still zeros needs filling
audio_to_overwrite = torch.full(
(batch_size, max_token_num), True, dtype=torch.bool, device=inputs_embeds.device
)
audio_to_overwrite[batch_indices, text_to_overwrite] = False
se... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
if audio_to_overwrite.sum() != num_audio_tokens.sum():
raise ValueError(
f"The input provided to the model are wrong. The number of audio tokens is {num_special_audio_tokens} while"
f" the number of audio given to the model is {num_audios}. This prevents correct indexing and ... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
@add_start_docstrings_to_model_forward(QWEN2AUDIO_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Qwen2AudioCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
input_features: torch.FloatTensor = None,
attention_ma... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_si... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
Returns:
Example:
```python
>>> from io import BytesIO
>>> from urllib.request import urlopen
>>> import librosa
>>> from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration
>>> model = Qwen2AudioForConditionalGeneration.from_pretrained("Qwen/... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
>>> # Generate
>>> generate_ids = model.generate(**inputs, max_length=30)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Generate the caption in English: Glass is breaking."
```"""
output_attentions = output_attentions ... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# 2. Merge text and audios
if input_features is not None and input_ids.shape[1] != 1:
audio_feat_lengths, audio_output_lengths = self.audio_tower._get_feat_extract_output_lengths(
feature_attention_mask.sum(-1)
)
batch_size, _, max_mel_seq_... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
audio_attention_mask_ = padding_mask.view(batch_size, 1, 1, max_seq_len).expand(
batch_size, 1, max_seq_len, max_seq_len
)
audio_attention_mask = audio_attention_mask_.to(
dtype=self.audio_tower.conv1.weight.dtype, device=self.audio_tower.conv1.wei... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
if legacy_processing:
logger.warning_once(
"Expanding inputs for audio tokens in Qwen2Audio should be done in processing."
)
inputs_embeds, attention_mask, labels, position_ids, _ = self._merge_input_ids_with_audio_features(
... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
if n_audio_tokens != n_audio_features:
raise ValueError(
f"Audio features and audio tokens do not match: tokens: {n_audio_tokens}, features {n_audio_features}"
)
special_audio_mask = (input_ids == self.config.audio_token_ind... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
outputs = self.language_model(
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
loss = None
if labels is not None:
# Shift so that tokens < n predict n
if attention_mask is not None:
shift_attention_mask = attention_mask[..., 1:]
shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
return Qwen2AudioCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
attention_mask=attention_mask,
)
def prepare_inputs_for_gen... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# Here, we get the attention_mask, which was previously stored in the state after _merge_input_ids_with_audio_features.
if input_features is not None and kwargs.get("attention_mask") is not None:
attention_mask = kwargs["attention_mask"]
attention_mask = torch.cat(
... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# Keep only the unprocessed tokens:
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
# input)
if attention_mask is ... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
# older attention values, as their corresponding values are not part of the input.
if cache_length < past_length and attention_mask is not None:
attention_mask = attention_mas... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
position_ids = kwargs.get("position_ids", None)
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 p... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
feature_attention_mask = kwargs.get("feature_attention_mask", None)
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# update attention_mask
if getattr(outputs, "attention_mask", None) is not None:
model_kwargs["attention_mask"] = outputs.attention_mask
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
if not is_encoder_decoder:
# update attention mask
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
if model_kwargs.get("use_cache", True):
model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + num_new_tokens
else:
past_positions = model_kwargs.pop("cache_position")
new_positions = torch.arange(
past_positions[-1] + 1, past_positions[-1] + n... | 9,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
class Qwen2AudioProcessor(ProcessorMixin):
r"""
Constructs a Qwen2Audio processor which wraps a Qwen2Audio feature extractor and a Qwen2Audio tokenizer into a single processor.
[`Qwen2AudioProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`Qwen2TokenizerFast`]. See the
[`~Q... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
Args:
feature_extractor ([`WhisperFeatureExtractor`], *optional*):
The feature extractor is a required input.
tokenizer ([`Qwen2TokenizerFast`], *optional*):
The tokenizer is a required input.
chat_template (`Optional[str]`, *optional*):
The Jinja template... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
def __init__(
self,
feature_extractor=None,
tokenizer=None,
chat_template=None,
audio_token="<|AUDIO|>",
audio_bos_token="<|audio_bos|>",
audio_eos_token="<|audio_eos|>",
):
if chat_template is None:
chat_template = self.default_chat_templa... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
audios: Union[np.ndarray, List[np.ndarray]] = None,
padding: Union[bool, str, PaddingStrategy] = False,
sampling_rate: Optional[int] = None,
**kwargs,
) -> ... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.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... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
if text is None:
raise ValueError("You need to specify either a `text` input to process.")
elif isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError("Invalid input text. Please provide a string, or a l... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
if audios is not None:
audio_inputs = self.feature_extractor(
audios, sampling_rate=sampling_rate, return_attention_mask=True, padding="max_length", **kwargs
)
audio_inputs["feature_attention_mask"] = audio_inputs.pop(
"attention_mask"
) #... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
audio_token_start_idx = sample.find(self.audio_token)
audio_token_end_idx = audio_token_start_idx + len(self.audio_token)
has_bos = (
sample[audio_token_start_idx - len(self.audio_bos_token) : audio_token_start_idx]
== self.audio_b... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
while "<placeholder>" in sample:
sample = sample.replace("<placeholder>", replace_str.pop(0), 1)
expanded_text.append(sample)
text = expanded_text
inputs = self.tokenizer(text, padding=padding, **kwargs)
if audios is not None:
inputs.update(a... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
feature_extractor_input_names = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names + ["feature_attention_mask"]))
@proper... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
```python
messages = [
{'role': 'system', 'content': 'You are a helpful assistant.'},
{"role": "user", "content": [
{"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"},
{"type": "tex... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
result = template.render(messages=messages, add_generation_prompt=True)
```
"""
# fmt: off
return (
"{% set audio_count = namespace(value=0) %}"
"{% for message in messages %}"
"{% if loop.first and message['role'] != 'system' %}"
... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
"{{ content['text'] }}"
"{% endif %}"
"{% endfor %}"
"<|im_end|>\n"
"{% endif %}"
"{% endfor %}"
"{% if add_generation_prompt %}"
"<|im_start|>assistant\n"
"{% endif %}"
)
... | 9,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py |
class Qwen2AudioEncoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2AudioEncoder`]. It is used to instantiate a
Qwen2-Audio audio encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the... | 9,959 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
Args:
num_mel_bins (`int`, *optional*, defaults to 128):
Number of mel features used per input features. Should correspond to the value used in the
`Qwen2AudioProcessor` class.
encoder_layers (`int`, *optional*, defaults to 32):
Number of encoder layers.
encod... | 9,959 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_function (`str`, *optional*, defaults to `"gelu"`):
The non-linea... | 9,959 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
The maximum sequence length of log-mel filter-bank features that this model might ever be used with. | 9,959 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
Example:
```python
>>> from transformers import Qwen2AudioEncoderConfig, Qwen2AudioEncoder
>>> # Initializing a Qwen2AudioEncoderConfig
>>> configuration = Qwen2AudioEncoderConfig()
>>> # Initializing a Qwen2AudioEncoder (with random weights)
>>> model = Qwen2AudioEncoder(configuration)
... | 9,959 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
self.num_mel_bins = num_mel_bins
self.d_model = d_model
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.encoder_ffn_dim = encoder_ffn_dim
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activa... | 9,959 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
class Qwen2AudioConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2AudioForConditionalGeneration`]. It is used to instantiate an
Qwen2-Audio model according to the specified arguments, defining the model architecture. Instantiating a configuration
with ... | 9,960 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
Args:
audio_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
The config object or dictionary of the audio backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
The config object or dictionary of the text backbone... | 9,960 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
>>> # Initializing a model from the qwen2-audio style configuration
>>> model = Qwen2AudioForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2_audio"
sub_configs = {"text_config": AutoConfig, "audio_config":... | 9,960 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
if isinstance(audio_config, dict):
audio_config["model_type"] = (
audio_config["model_type"] if "model_type" in audio_config else "qwen2_audio_encoder"
)
audio_config = CONFIG_MAPPING[audio_config["model_type"]](**audio_config)
elif audio_config is None:
... | 9,960 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
if isinstance(text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "qwen2"
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
text_config = CONFIG_MAPPING["qwen2"]()
... | 9,960 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py |
class RemBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(
config.vocab_size, config.input_embedding_size, padding_idx=config.pad_token_id
... | 9,961 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = Non... | 9,961 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embedding... | 9,961 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hi... | 9,962 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the num... | 9,963 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = N... | 9,963 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(sel... | 9,963 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
query_layer = self.transpose_for_scores(mixed_query_layer)
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 ... | 9,963 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in RemBertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention s... | 9,963 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
... | 9,963 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
de... | 9,964 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = RemBertSelfAttention(config)
self.output = RemBertSelfOutput(config)
self.pruned_heads = set()
# Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
def pru... | 9,965 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads) | 9,965 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
# Copied from transformers.models.bert.modeling_bert.BertAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = N... | 9,965 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.int... | 9,966 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | 9,967 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = RemBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = co... | 9,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
# Copied from transformers.models.bert.modeling_bert.BertLayer.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,... | 9,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
attention_output = self_attention_outputs[0] | 9,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
... | 9,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
... | 9,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = out... | 9,968 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.embedding_hidden_mapping_in = nn.Linear(config.input_embedding_size, config.hidden_size)
self.layer = nn.ModuleList([RemBertLayer(config) for _ in range(config.num_hidden_layers... | 9,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 9,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
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