text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
class Emu3TextKwargs(TextKwargs, total=False):
return_for_image_generation: bool | 3,462 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
class Emu3ImagesKwargs(ImagesKwargs, total=False):
ratio: str
image_area: int | 3,463 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
class Emu3ProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: Emu3TextKwargs
images_kwargs: Emu3ImagesKwargs
_defaults = {
"text_kwargs": {
"return_for_image_generation": False,
},
"images_kwargs": {
"ratio": "1:1",
"image_area": 518400,
... | 3,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
class Emu3Processor(ProcessorMixin):
r"""
Constructs a Emu3 processor which wraps a Emu3 image processor and a GPT2 tokenizer into a single
processor.
[`Emu3Processor`] offers all the functionalities of [`Emu3ImageProcessor`] and [`GPT2TokenizerFast`].
See the [`~Emu3Processor.__call__`] and [`~Emu... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
def __init__(
self,
image_processor,
tokenizer,
chat_template=None,
**kwargs,
):
self.image_token = tokenizer.image_token # image_token as placeholder to be replaced by vq-vae tokens
self.image_start_token = tokenizer.boi_token # "<|image start|>" fixed toke... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
def __call__(
self,
images: Optional[ImageInput] = None,
text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
audio=None,
videos=None,
**kwargs: Unpack[Emu3ProcessorKwargs],
) -> BatchFeature:
"""
Main m... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last ... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the follow... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise TypeError("Invalid input text. Please provide a string, or a list of strings")
output_kwargs = self._merge_kwargs(
Emu3ProcessorKwargs,
tokeniz... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
image_features = {}
image_start_tokens = f"{self.image_start_token}"
image_end_tokens = f"{self.eof_token}{self.image_end_token}"
# generate text from image + text input, so we add placeholders for image tokens
if not return_for_image_generation and images is not None:
image... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
image_placeholder = f"{image_start_tokens}{height}*{width}{self.fake_token_around_image}{'<placeholder>' * image_seq_length}{image_end_tokens}"
sample = sample.replace(self.image_token, image_placeholder, 1)
sample = f"{self.bos_token}{sample}" # add BOS because PT tokenizer doe... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
# else just generate from text-only input, and we do no special treatment for text
data = self.tokenizer(text, **output_kwargs["text_kwargs"])
data.update(**image_features)
return BatchFeature(data=data, tensor_type=output_kwargs["common_kwargs"]["return_tensors"])
def calculate_generate_s... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Emu3TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def de... | 3,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py |
class Emu3VQVAEConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Emu3VQVAE`]. It is used to instantiate an VQ-VAE
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a confi... | 3,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
codebook_size (`int`, *optional*, defaults to 32768):
Codebook size of the VQ model.
embed_dim (`int`, *o... | 3,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
base_channels (`int`, *optional*, defaults to 256):
Basic channel number of the intermediate blocks.
channel_multiplier (`List[int]`, *optional*, defaults to `[1, 2, 2, 4]`):
Channel scaling factor of the intermediate blocks.
num_res_blocks (`int`, *optional*, defaults to 2):
... | 3,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
```python
>>> from transformers import Emu3VQVAE, Emu3VQVAEConfig
>>> # Initializing a video VQ model of Emu3 configuration
>>> configuration = Emu3VQVAEConfig()
>>> # Initializing a model from the Emu3 VQ model style configuration
>>> model = Emu3VQVAE(configuration)
>>> # Accessing the mode... | 3,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
def __init__(
self,
codebook_size: int = 32768,
embed_dim: int = 4,
latent_channels: int = 4,
double_latent: bool = False,
in_channels: int = 3,
out_channels: int = 3,
temporal_downsample_factor: int = 4,
base_channels: int = 256,
channel_m... | 3,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
self.codebook_size = codebook_size
self.embed_dim = embed_dim
self.latent_channels = latent_channels
self.double_latent = double_latent
self.in_channels = in_channels
self.out_channels = out_channels
self.temporal_downsample_factor = temporal_downsample_factor
sel... | 3,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
class Emu3TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Emu3TextModel`]. It is used to instantiate a
emu3 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a simil... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
Args:
vocab_size (`int`, *optional*, defaults to 184622):
Vocabulary size of the Emu3 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Emu3Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimensi... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
`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
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be construct... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaul... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested va... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to l... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
```python
>>> from transformers import Emu3Model, Emu3Config
>>> # Initializing a Emu3-community/Emu3-Chat-hf style configuration
>>> configuration = Emu3Config()
>>> # Initializing a model from the Emu3-community/Emu3-Chat-hf style configuration
>>> model = Emu3Model(configuration)
>>> # Acc... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
def __init__(
self,
vocab_size: int = 184622,
hidden_size: int = 4096,
intermediate_size: int = 14336,
num_hidden_layers: int = 32,
num_attention_heads: int = 32,
num_key_value_heads: Optional[int] = 8,
hidden_act: str = "silu",
max_position_embedd... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope... | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
) | 3,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
class Emu3Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`Emu3Model`]. It is used to instantiate a
emu3 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar config... | 3,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
Args:
vq_config (`Union[Dict, Emu3VQVAEConfig]`, *optional*):
Emu3VQVAEConfig instance containing the configuration for the VQ-VAE model.
text_config (`Union[Dict, Emu3TextConfig]``, *optional*):
Emu3TextConfig instance containing the configuration for the language model.
... | 3,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
def __init__(
self,
vq_config: Union[Dict, Emu3VQVAEConfig] = None,
text_config: Union[Dict, Emu3TextConfig] = None,
vocabulary_map: Dict[int, int] = None,
**kwargs,
):
if vq_config is None:
vq_config = Emu3VQVAEConfig()
elif isinstance(vq_config, ... | 3,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py |
class Emu3DecoderLayer(LlamaDecoderLayer):
def __init__(self, config: Emu3Config, layer_idx: int):
super().__init__(config, layer_idx)
self.dropout = nn.Dropout(config.attention_dropout) | 3,469 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 3,469 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are r... | 3,469 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
outpu... | 3,469 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEVectorQuantizer(nn.Module):
"""
A module for vector quantization using learned embedding vectors.
This module implements the quantization process similar to te one described in
the VQ-VAE (Vector Quantized Variational AutoEncoder) paper. It quantizes continuous
input vectors into dis... | 3,470 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
def forward(self, hidden_state: torch.Tensor):
batch_size, temporal, channels, height, width = hidden_state.shape
hidden_state = hidden_state.permute(0, 1, 3, 4, 2).contiguous()
hidden_state_flattened = hidden_state.view(-1, channels)
# distances from z to embeddings e_j (z - e)^2 = z^2... | 3,470 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEEncoderConvDownsample(ChameleonVQVAEEncoderConvDownsample):
pass | 3,471 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEEncoderConvUpsample(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
def forward(self, hidden_states):
hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="n... | 3,472 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEConv3d(nn.Module):
def __init__(
self,
in_channel: int,
out_channel: int,
kernel_size: Tuple[int],
stride: Tuple[int],
):
super().__init__()
padding_sizes = [one_kernel - one_stride for one_kernel, one_stride in zip(kernel_size[1:], stride[... | 3,473 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAESpatialNorm(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
):
super().__init__()
self.norm_layer = nn.GroupNorm(
num_channels=out_channels,
num_groups=32,
eps=1e-6,
affine=True,
)... | 3,474 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAETemporalUpsample(nn.Module):
def __init__(
self,
in_channel: int,
out_channel: int,
):
super().__init__()
self.conv = Emu3VQVAEConv3d(
in_channel,
out_channel,
kernel_size=(3, 3, 3),
stride=(1, 1, 1),
... | 3,475 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAETemporalDownsample(nn.Module):
def __init__(
self,
in_channel: int,
out_channel: int,
):
super().__init__()
self.conv = Emu3VQVAEConv3d(
in_channel,
out_channel,
kernel_size=(4, 3, 3),
stride=(2, 1, 1),
... | 3,476 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAETemporalResnetBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels=None,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = in_channels if out_channels is None else out_channels
self.norm1 = nn.BatchNorm3d(i... | 3,477 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
def forward(self, hidden_states):
residual = hidden_states
hidden_states = self.norm1(hidden_states)
hidden_states *= torch.sigmoid(hidden_states)
hidden_states = self.conv1(hidden_states)
hidden_states = self.norm2(hidden_states)
hidden_states *= torch.sigmoid(hidden_st... | 3,477 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEResnetBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
quant_channels: Optional[int] = None,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None e... | 3,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
self.conv2 = nn.Conv2d(
out_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
)
if self.in_channels != self.out_channels:
self.nin_shortcut = nn.Conv2d(
in_channels,
out_channels,
... | 3,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
if self.in_channels != self.out_channels:
residual = self.nin_shortcut(residual)
return residual + hidden_states | 3,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEAttentionBlock(SiglipAttention):
pass | 3,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEGroupNorm(nn.GroupNorm):
"""
Same as the torch GroupNorm with the only difference that this ones accepts
an optional kwarg `quant_states` which is not used. This class makes it easier to
use SpatialNorm or GroupNorm without conditionals
"""
def __init__(self, **kwargs):
s... | 3,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEMiddleBlock(nn.Module):
def __init__(self, config, in_channels, quant_channels=None):
super().__init__()
self.block_1 = Emu3VQVAEResnetBlock(
in_channels=in_channels,
out_channels=in_channels,
quant_channels=quant_channels,
)
self.a... | 3,481 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
def forward(self, hidden_states: torch.FloatTensor, quant_states: torch.FloatTensor = None):
hidden_states = self.block_1(hidden_states, quant_states)
residual = hidden_states
hidden_states = self.attn_norm(hidden_states, quant_states)
batch_size, channels, height, width = hidden_states.... | 3,481 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEDownBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_resolutions = len(config.channel_multiplier)
self.num_res_blocks = config.num_res_blocks
base_channels = config.base_channels
channel_multiplier = config.channel_multiplier | 3,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
in_channel_multiplier = (1,) + tuple(channel_multiplier)
self.in_channel_multiplier = in_channel_multiplier
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
attn_norms = nn.ModuleList()
... | 3,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
attn_norms.append(nn.GroupNorm(num_channels=block_in, num_groups=32, eps=1e-6, affine=True)) | 3,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
down = nn.Module()
down.block = block
down.attn = attn
down.attn_norms = attn_norms
if i_level != self.num_resolutions - 1:
down.downsample = Emu3VQVAEEncoderConvDownsample(block_in)
self.down.append(down)
def forward(self, hidden_states: ... | 3,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
hidden_states = residual + hidden_states
if i_level != self.num_resolutions - 1:
hidden_states = blocks.downsample(hidden_states)
return hidden_states | 3,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEUpBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_resolutions = len(config.channel_multiplier)
self.num_res_blocks = config.num_res_blocks
quant_channels = config.embed_dim
block_in = config.base_channels * config.channel_multiplier[... | 3,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
attn_norms = nn.ModuleList()
block_out = config.base_channels * config.channel_multiplier[i_level]
for i_block in range(self.... | 3,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
up = nn.Module()
up.block = block
up.attn = attn
up.attn_norms = attn_norms
if i_level != 0:
up.upsample = Emu3VQVAEEncoderConvUpsample(block_in)
self.up.insert(0, up)
def forward(self, hidden_states: torch.FloatTensor, quant_states: torc... | 3,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
hidden_states = residual + hidden_states
if i_level != len(self.up) - 1:
hidden_states = blocks.upsample(hidden_states)
return hidden_states | 3,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEEncoder(nn.Module):
def __init__(self, config):
super().__init__()
base_channels = config.base_channels
in_channels = config.in_channels
double_latent = config.double_latent
latent_channels = config.latent_channels
channel_multiplier = config.channel_m... | 3,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
temporal_down_blocks = int(math.log2(config.temporal_downsample_factor))
self.time_conv = nn.ModuleList()
self.time_res_stack = nn.ModuleList()
for i in range(temporal_down_blocks):
conv = Emu3VQVAETemporalDownsample(out_channels, out_channels)
self.time_conv.append(conv... | 3,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
# end
hidden_states = self.norm_out(hidden_states)
hidden_states *= torch.sigmoid(hidden_states)
hidden_states = self.conv_out(hidden_states)
hidden_states = hidden_states.reshape(-1, temporal_dim, *hidden_states.shape[1:])
hidden_states = hidden_states.permute(0, 2, 1, 3, 4)
... | 3,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAEDecoder(nn.Module):
def __init__(self, config: Emu3VQVAEConfig):
super().__init__()
quant_channels = config.embed_dim
block_in = config.base_channels * config.channel_multiplier[-1]
self.time_res_stack = nn.ModuleList()
for _ in range(config.num_res_blocks):
... | 3,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
self.middle_block = Emu3VQVAEMiddleBlock(config, block_in, quant_channels=quant_channels)
self.up_block = Emu3VQVAEUpBlock(config)
block_in = config.base_channels * config.channel_multiplier[0]
self.norm_out = Emu3VQVAESpatialNorm(quant_channels, block_in)
self.conv_out = nn.Conv2d(
... | 3,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
hidden_quant_states = hidden_quant_states.permute(0, 2, 1, 3, 4)
hidden_states, quant_states = torch.chunk(hidden_quant_states, 2, dim=0)
hidden_states = hidden_states.reshape(-1, *hidden_states.shape[2:])
quant_states = quant_states.reshape(-1, *quant_states.shape[2:])
hidden_states = ... | 3,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3VQVAE(PreTrainedModel):
config_class = Emu3VQVAEConfig
base_model_prefix = "emuvideovq"
main_input_name = "pixel_values"
_no_split_modules = [
"Emu3VQVAETemporalResnetBlock",
"Emu3VQVAEAttentionBlock",
"Emu3VQVAEResnetBlock",
"Emu3VQVAEVectorQuantizer",
] | 3,486 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
def _init_weights(self, module):
if isinstance(module, (nn.Conv2d, nn.Conv3d)):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(module, nn.Linear):
nn.init.kaiming_uniform_(module.weight, a=math.sqrt(5))
if module.bias is no... | 3,486 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
self.encoder = Emu3VQVAEEncoder(config)
self.decoder = Emu3VQVAEDecoder(config)
self.quantize = Emu3VQVAEVectorQuantizer(config)
self.vision_spatial_factor = 2 ** (len(config.channel_multiplier) - 1)
self.quant_conv = Emu3VQVAEConv3d(
config.latent_channels, config.embed_dim... | 3,486 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
def encode(self, pixel_values: torch.Tensor, image_sizes: torch.Tensor):
is_image = pixel_values.ndim == 4
if is_image:
temporal = self.config.temporal_downsample_factor
batch_size, channels, height, width = pixel_values.shape
pixel_values = pixel_values.unsqueeze(1).... | 3,486 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
image_tokens = [
single_image[: int(size[0] / self.vision_spatial_factor), : int(size[1] / self.vision_spatial_factor)]
for single_image, size in zip(image_tokens, image_sizes)
]
return image_tokens
def decode(self, hidden_states: torch.Tensor):
is_image = hidden_st... | 3,486 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
video = self.decoder(post_quant, quant)
video = video.reshape(
batch_size,
temporal * self.config.temporal_downsample_factor,
self.config.out_channels,
height * self.spatial_scale_factor,
width * self.spatial_scale_factor,
)
return vide... | 3,486 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3ImageVocabularyMapping:
"""
A class for mapping discrete image tokens from VQGAN to BPE tokens.
"""
def __init__(self, vocab_map):
self.vocab_map = vocab_map
self.eol_token_id = vocab_map.get("<|extra_200|>")
self.image_token_id = vocab_map.get("<image>")
@cached_... | 3,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
@cached_property
def bpe2img_mapping_tensor(self):
mapping = torch.zeros(max(self.bpe2img.keys()) + 1, dtype=torch.int)
for k, v in self.bpe2img.items():
mapping[k] = v
return mapping
@cached_property
def img2bpe_mapping_tensor(self):
mapping = torch.zeros(max(se... | 3,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
def convert_bpe2img(self, img_batch: torch.Tensor) -> torch.Tensor:
device = img_batch.device
img_batch = img_batch[..., :-1] # remove last row of EOL tokens
img_tokens = self.bpe2img_mapping_tensor[img_batch.to("cpu")]
return img_tokens.to(device) | 3,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3PreTrainedModel(ChameleonPreTrainedModel, Emu3VQVAE):
_no_split_modules = [
"Emu3DecoderLayer",
]
_supports_flex_attn = True
def _init_weights(self, module):
std = self.config.get_text_config().initializer_range
if isinstance(module, Emu3VQVAE):
module.appl... | 3,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3TextModel(LlamaModel, Emu3PreTrainedModel):
def __init__(self, config: Emu3Config):
super().__init__(config)
self.layers = nn.ModuleList(
[Emu3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
) | 3,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3ForCausalLM(LlamaForCausalLM, Emu3PreTrainedModel, GenerationMixin):
config_class = Emu3TextConfig
def __init__(self, config):
super().__init__(config)
self.model = Emu3TextModel(config) | 3,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
@add_start_docstrings_to_model_forward(EMU3_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class="Emu3TextConfig")
def forward(**super_kwargs):
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
... | 3,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
token can save memory, which becomes pretty significant for long sequences or large vocabulary size. | 3,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
Returns:
Example:
```python
>>> from transformers import Emu3Processor, Emu3ForConditionalGeneration
>>> import torch
>>> import requests
>>> from PIL import Image
>>> model = Emu3ForCausalLM.from_pretrained("BAAI/Emu3-Chat-hf", torch_dtype=torch.bfloat16)
... | 3,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3ForConditionalGeneration(Emu3PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["text_model.lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.text_model = Emu3ForCausalLM._from_config(config.text_config)
self.vqmodel = Emu3VQVAE(config.vq_config)... | 3,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
The tensors corresponding to the input images.
image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`):
The sizes of the images in the batch, being (height,... | 3,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
Args:
image_tokens (`torch.LongTensor` of shape `(batch_size, num_of_tokens)`):
The tensors corresponding to the input images.
height (`int`):
Height of the generated image before upsampling.
width (`int`):
Width of the generated image ... | 3,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
@add_start_docstrings_to_model_forward(EMU3_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
image_sizes: torch.Tensor = No... | 3,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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
... | 3,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
Returns:
Example:
```python
>>> from transformers import Emu3Processor, Emu3ForConditionalGeneration
>>> import torch
>>> import requests
>>> from PIL import Image
>>> model = Emu3ForConditionalGeneration.from_pretrained("BAAI/Emu3-Chat-hf", torch_dtype=torch.b... | 3,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
>>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
>>> image = Image.open(requests.get("https://www.ilankelman.org/stopsigns/australia.jpg", stream=True).raw)
>>> inputs = processor(images=[image], text=[prompt], return_tensors="pt").to(model.device, torch.bfloat16)
... | 3,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
)
if pixel_values is not None and inputs_embeds is not None:
raise ValueError(
... | 3,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 3,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py |
class Emu3RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Emu3RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | 3,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
... | 3,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Emu3Config, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidde... | 3,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
... | 3,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttenti... | 3,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
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": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 3,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
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