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class GPTNeoXForQuestionAnswering(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.gpt_neox = GPTNeoXModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply final pr... | 3,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
)... | 3,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequ... | 3,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
outputs = self.gpt_neox(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 3,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1).to(start_logits.device)
if len(end_positions.size(... | 3,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 3,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
class PoolFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of [`PoolFormerModel`]. It is used to instantiate a
PoolFormer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield... | 3,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/configuration_poolformer.py |
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of channels in the input image.
patch_size (`int`, *optional*, defaults to 16):
The size of the input patch.
stride (`int`, *optional*, defaults to 16):
The stride of the input patch.
po... | 3,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/configuration_poolformer.py |
strides (`list`, *optional*, defaults to `[4, 2, 2, 2]`):
The stride of the input patch for each encoder block.
padding (`list`, *optional*, defaults to `[2, 1, 1, 1]`):
The padding of the input patch for each encoder block.
num_encoder_blocks (`int`, *optional*, defaults to 4):
... | 3,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/configuration_poolformer.py |
Example:
```python
>>> from transformers import PoolFormerConfig, PoolFormerModel
>>> # Initializing a PoolFormer sail/poolformer_s12 style configuration
>>> configuration = PoolFormerConfig()
>>> # Initializing a model (with random weights) from the sail/poolformer_s12 style configuration
>>... | 3,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/configuration_poolformer.py |
def __init__(
self,
num_channels=3,
patch_size=16,
stride=16,
pool_size=3,
mlp_ratio=4.0,
depths=[2, 2, 6, 2],
hidden_sizes=[64, 128, 320, 512],
patch_sizes=[7, 3, 3, 3],
strides=[4, 2, 2, 2],
padding=[2, 1, 1, 1],
num_encod... | 3,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/configuration_poolformer.py |
self.use_layer_scale = use_layer_scale
self.layer_scale_init_value = layer_scale_init_value
self.initializer_range = initializer_range
super().__init__(**kwargs) | 3,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/configuration_poolformer.py |
class PoolFormerOnnxConfig(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"}),
]
... | 3,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/configuration_poolformer.py |
class PoolFormerDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) ->... | 3,663 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerEmbeddings(nn.Module):
"""
Construct Patch Embeddings.
"""
def __init__(self, hidden_size, num_channels, patch_size, stride, padding, norm_layer=None):
super().__init__()
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_... | 3,664 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerGroupNorm(nn.GroupNorm):
"""
Group Normalization with 1 group. Input: tensor in shape [B, C, H, W]
"""
def __init__(self, num_channels, **kwargs):
super().__init__(1, num_channels, **kwargs) | 3,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerPooling(nn.Module):
def __init__(self, pool_size):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False)
def forward(self, hidden_states):
return self.pool(hidden_states) - hidden_states | 3,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerOutput(nn.Module):
def __init__(self, config, dropout_prob, hidden_size, intermediate_size):
super().__init__()
self.conv1 = nn.Conv2d(hidden_size, intermediate_size, 1)
self.conv2 = nn.Conv2d(intermediate_size, hidden_size, 1)
self.drop = PoolFormerDropPath(dropout_p... | 3,667 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerLayer(nn.Module):
"""This corresponds to the 'PoolFormerBlock' class in the original implementation."""
def __init__(self, config, num_channels, pool_size, hidden_size, intermediate_size, drop_path):
super().__init__()
self.pooling = PoolFormerPooling(pool_size)
self.out... | 3,668 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
# Useful for training neural nets
self.drop_path = PoolFormerDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.use_layer_scale = config.use_layer_scale
if config.use_layer_scale:
self.layer_scale_1 = nn.Parameter(
config.layer_scale_init_value * torch.one... | 3,668 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
layer_output = self.output(self.after_norm(hidden_states))
scaled_op = self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * layer_output
# Second residual connection
output = hidden_states + self.drop_path(scaled_op)
outputs = (output,) + outputs
return outputs
... | 3,668 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
# patch embeddings
embeddings = []
for... | 3,669 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
# Transformer blocks
blocks = []
cur = 0
for i in range(config.num_encoder_blocks):
# each block consists of layers
layers = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i]):
layers.append(
... | 3,669 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
hidden_states = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings, self.block)):
embedding_layer, block_layer = layers
# Get patch embeddings from hidden_states
hidden_states = embedding_layer(hidden_states)
# Send the embeddings through the bloc... | 3,669 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = PoolFormerConfig
base_model_prefix = "poolformer"
main_input_name = "pixel_values"
_no_split_... | 3,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerModel(PoolFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = PoolFormerEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
... | 3,671 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
@add_start_docstrings_to_model_forward(POOLFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def ... | 3,671 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
encoder_outputs = self.encoder(
pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseM... | 3,671 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerFinalPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
def forward(self, hidden_states):
output = self.dense(hidden_states)
return output | 3,672 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerForImageClassification(PoolFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.poolformer = PoolFormerModel(config)
# Final norm
self.norm = PoolFormerGroupNorm(config.hidden_sizes[-1])
... | 3,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
@add_start_docstrings_to_model_forward(POOLFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
... | 3,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
outputs = self.poolformer(
pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(self.norm(sequence_output).mean([-2, -1]))
loss = None
if labels is not None... | 3,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 3,673 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py |
class PoolFormerImageProcessor(BaseImageProcessor):
r"""
Constructs a PoolFormer image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in t... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
If crop_pct is set:
- size is `{"height": h, "width": w}`: the image is resized to `(int(floor(h/crop_pct)),
int(floor(w/crop_pct)))`
- size is `{"height": c, "width": c}`: the shortest edge of the image is resized to `int(floor(c/crop_pct)`
whilst maintaining the asp... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
is padded with 0's and then center cropped. Can be overridden by `do_center_crop` in the `preprocess`
method.
crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "w... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
do_normalize (`bool`, *optional*, defaults to `True`):
Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
`preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use i... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
crop_pct: int = 0.9,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
self.do_resize = do_resize
self.size = size
self.crop_pct = crop_pct
self.resample = resample
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
If crop_pct is unset:
- size is `{"height": h, "width": w}`: the image is resized to `(h, w)`.
- size is `{"shortest_edge": s}`: the shortest edge of the image is resized to s whilst maintaining the
aspect ratio.
if crop_pct is set:
- size is `{"height": h, "wi... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
crop_pct (`float`, *optional*):
Percentage of the image that will be cropped from the center. If set, the image is resized
resam... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
raise ValueError(f"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
if crop_pct is not None:
if "shortest_edge" in size:
scale_size = int(size["shortest_edge"] / crop_pct)
elif "height" in size and "width" in size:
if ... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
output_size = get_resize_output_image_size(
image, size=scale_size, default_to_square=False, input_data_format=input_data_format
)
else:
if "shortest_edge" in size:
output_size = get_resize_output_image_size(
image, size=size["shortest_... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
crop_pct: int = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resiz... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
Whether to center crop the image.
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
Size of the image after applying center crop.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 -... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `Tenso... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimens... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
crop_size = crop_size if crop_size is not None else self.crop_size
crop_size = get_size_dict(crop_size, param_name="crop_size")
images = make_list_of_images(images)
if not valid_i... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if do_rescale and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pi... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
if do_center_crop:
images = [
self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images
]
if do_rescale:
images = [
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_forma... | 3,674 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py |
class PoolFormerFeatureExtractor(PoolFormerImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use PoolFormerImageProcessor instead.",
... | 3,675 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/feature_extraction_poolformer.py |
class MimiConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`MimiModel`]. It is used to instantiate a
Mimi model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar conf... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
Args:
sampling_rate (`int`, *optional*, defaults to 24000):
The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
frame_rate (`float`, *optional*, defaults to 12.5):
Framerate of the model.
audio_channels (`int`, *optional*, defaults... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
will use the ratios in the reverse order to the ones specified here that must match the decoder order.
If not specified, will defaults to `[8, 6, 5, 4]`
kernel_size (`int`, *optional*, defaults to 7):
Kernel size for the initial convolution.
last_kernel_size (`int`, *optional*, d... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
trim_right_ratio (`float`, *optional*, defaults to 1.0):
Ratio for trimming at the right of the transposed convolution under the `use_causal_conv = True` setup. If
equal to 1.0, it means that all the trimming is done at the right.
codebook_size (`int`, *optional*, defaults to 2048):
... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
Intermediate representation dimension in the residual vector quantization space.
num_semantic_quantizers (`int`, *optional*, defaults to 1):
Number of semantic quantizer channels, or codebooks, in the semantic quantizer. Must be lower than `num_quantizers`.
upsample_groups (`int`, *optional*... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
The maximum sequence length that this model might ever be used with. Mimi's sliding window attention
allows sequence of up to 8000 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight mat... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
The dropout ratio for the attention probabilities.
layer_scale_initial_scale (`float`, *optional*, defaults to 0.01):
Initiale scale of the residual rescaling operation done in the Transformer models.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
```python
>>> from transformers import MimiModel, MimiConfig
>>> # Initializing a "kyutai/mimi" style configuration
>>> configuration = MimiConfig()
>>> # Initializing a model (with random weights) from the "kyutai/mimi" style configuration
>>> model = MimiModel(configuration)
>>> # Accessing... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
def __init__(
self,
sampling_rate=24_000,
frame_rate=12.5,
audio_channels=1,
hidden_size=512,
num_filters=64,
num_residual_layers=1,
upsampling_ratios=None,
kernel_size=7,
last_kernel_size=3,
residual_kernel_size=3,
dilation... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
sliding_window=250,
attention_dropout=0.0,
layer_scale_initial_scale=0.01,
attention_bias=False,
**kwargs,
):
self.sampling_rate = sampling_rate
self.frame_rate = frame_rate
self.audio_channels = audio_channels
self.hidden_size = hidden_size
se... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
self.num_quantizers = num_quantizers
self.use_conv_shortcut = use_conv_shortcut
self.vector_quantization_hidden_dimension = vector_quantization_hidden_dimension
self.upsample_groups = upsample_groups
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
if num_semantic_quantizers >= self.num_quantizers:
raise ValueError(
f"The number of semantic quantizers should be lower than the total number of quantizers {self.num_quantizers}, but is currently {num_semantic_quantizers}."
)
self.num_semantic_quantizers = num_semantic_q... | 3,676 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py |
class MimiOutput(ModelOutput):
"""
Args:
audio_codes (`torch.LongTensor` of shape `(batch_size, num_quantizers, codes_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
audio_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*)... | 3,677 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
have their past key value states given to this model).
decoder_past_key_values (`Cache`, *optional*):
Pre-computed hidden-states (key and values in the self-attenti... | 3,677 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
audio_codes: torch.LongTensor = None
audio_values: torch.FloatTensor = None
encoder_past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None
decoder_past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None | 3,677 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiEncoderOutput(ModelOutput):
"""
Args:
audio_codes (`torch.LongTensor` of shape `(batch_size, num_quantizers, codes_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
encoder_past_key_values (`Cache`, *optional*):
Pre-computed hidden-s... | 3,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
audio_codes: torch.LongTensor = None
encoder_past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None | 3,678 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiDecoderOutput(ModelOutput):
"""
Args:
audio_values (`torch.FloatTensor` of shape `(batch_size, segment_length)`, *optional*):
Decoded audio values, obtained using the decoder part of Mimi.
decoder_past_key_values (`Cache`, *optional*):
Pre-computed hidden-state... | 3,679 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
audio_values: torch.FloatTensor = None
decoder_past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None | 3,679 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiConv1d(nn.Module):
"""Conv1d with asymmetric or causal padding and normalization."""
def __init__(
self,
config,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
dilation: int = 1,
groups: int = 1,
pad_mode... | 3,680 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
kernel_size = self.conv.kernel_size[0]
stride = torch.tensor(self.conv.stride[0], dtype=torch.int64)
dilation = self.conv.dilation[0]
# Effective kernel size with dilations.
kernel_size = torch.tensor((kernel_size - 1) * dilation + 1, dtype=torch.int64)
self.register_buffer("st... | 3,680 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def remove_weight_norm(self):
nn.utils.remove_weight_norm(self.conv)
# Copied from transformers.models.encodec.modeling_encodec.EncodecConv1d._get_extra_padding_for_conv1d
def _get_extra_padding_for_conv1d(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
"""See `pad_fo... | 3,680 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
@staticmethod
# Copied from transformers.models.encodec.modeling_encodec.EncodecConv1d._pad1d
def _pad1d(hidden_states: torch.Tensor, paddings: Tuple[int, int], mode: str = "zero", value: float = 0.0):
"""Tiny wrapper around torch.nn.functional.pad, just to allow for reflect padding on small input.
... | 3,680 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def forward(self, hidden_states):
extra_padding = self._get_extra_padding_for_conv1d(hidden_states)
if self.causal:
# Left padding for causal
hidden_states = self._pad1d(hidden_states, (self.padding_total, extra_padding), mode=self.pad_mode)
else:
hidden_stat... | 3,680 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiConvTranspose1d(nn.Module):
"""ConvTranspose1d with asymmetric or causal padding and normalization."""
def __init__(
self,
config,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
groups: int = 1,
bias=True,
):... | 3,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
# removed at the very end, when keeping only the right length for the output,
# as removing it here would require also passing the length at the matching layer
# in the encoder.
if self.causal:
# ... | 3,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def remove_weight_norm(self):
nn.utils.remove_weight_norm(self.conv)
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
# unpad
end = hidden_states.shape[-1] - self.padding_right
hidden_states = hidden_states[..., self.padding_left : end]
ret... | 3,681 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiResnetBlock(nn.Module):
"""
Residual block from SEANet model as used by Mimi.
"""
def __init__(self, config: MimiConfig, dim: int, dilations: List[int]):
super().__init__()
kernel_sizes = (config.residual_kernel_size, 1)
if len(kernel_sizes) != len(dilations):
... | 3,682 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def forward(self, hidden_states):
residual = hidden_states
for layer in self.block:
hidden_states = layer(hidden_states)
return self.shortcut(residual) + hidden_states | 3,682 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiEncoder(nn.Module):
"""SEANet encoder as used by Mimi."""
def __init__(self, config: MimiConfig):
super().__init__()
model = [MimiConv1d(config, config.audio_channels, config.num_filters, config.kernel_size)]
scaling = 1
# Downsample to raw audio scale
for rat... | 3,683 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# Copied from transformers.models.encodec.modeling_encodec.EncodecEncoder.forward
def forward(self, hidden_states):
for layer in self.layers:
hidden_states = layer(hidden_states)
return hidden_states | 3,683 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiLayerScale(nn.Module):
"""Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
This rescales diagonally the residual outputs close to 0, with a learnt scale.
"""
def __init__(self, config):
super().__init__()
channels = config.hidden_size
initi... | 3,684 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiRotaryEmbedding(nn.Module):
def __init__(self, config: MimiConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", conf... | 3,685 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... | 3,685 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_f... | 3,685 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device... | 3,685 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.fc2 = nn.Linear(config.intermediate_size, config.hi... | 3,686 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: MimiConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logge... | 3,687 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = config.head_dim
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_val... | 3,687 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=c... | 3,687 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position... | 3,687 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": ... | 3,687 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
i... | 3,687 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
class MimiFlashAttention2(MimiAttention):
"""
Mimi flash attention module. This module inherits from `MimiAttention` 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 with paddin... | 3,688 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.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-... | 3,688 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py |
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