text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
... | 3,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3DecoderLayer(nn.Module):
def __init__(self, config: Emu3Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Emu3Attention(config=config, layer_idx=layer_idx)
self.mlp = Emu3MLP(config)
self.input_layernorm = Emu3RMSNo... | 3,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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,
... | 3,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3VQVAEEncoderConvDownsample(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
def forward(self, hidden_states):
# no asymmetric padding in torch conv, must do it ourselves
... | 3,497 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,498 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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[... | 3,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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,
)... | 3,500 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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),
... | 3,501 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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),
... | 3,502 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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,
... | 3,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
if self.in_channels != self.out_channels:
residual = self.nin_shortcut(residual)
return residual + hidden_states | 3,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3VQVAEAttentionBlock(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim... | 3,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time x Channel"""
batch_size, q_len, _ = hidden_states.... | 3,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
raise ValueError(
f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
... | 3,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1... | 3,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,506 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,507 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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.... | 3,507 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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 | 3,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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()
... | 3,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
attn_norms.append(nn.GroupNorm(num_channels=block_in, num_groups=32, eps=1e-6, affine=True)) | 3,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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: ... | 3,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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 | 3,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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[... | 3,509 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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.... | 3,509 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,509 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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 | 3,509 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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)
... | 3,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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):
... | 3,511 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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(
... | 3,511 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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 = ... | 3,511 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3VQVAE(PreTrainedModel):
config_class = Emu3VQVAEConfig
base_model_prefix = "emuvideovq"
main_input_name = "pixel_values"
_no_split_modules = [
"Emu3VQVAETemporalResnetBlock",
"Emu3VQVAEAttentionBlock",
"Emu3VQVAEResnetBlock",
"Emu3VQVAEVectorQuantizer",
] | 3,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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).... | 3,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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_... | 3,513 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,513 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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) | 3,513 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3PreTrainedModel(PreTrainedModel):
config_class = Emu3Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = [
"Emu3DecoderLayer",
]
_skip_keys_device_placement = ["past_key_values", "causal_mask"]
_supports_flash_attn_2 = True
_sup... | 3,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
def _init_weights(self, module):
std = self.config.get_text_config().initializer_range
if isinstance(module, Emu3VQVAE):
module.apply(module._init_weights)
elif isinstance(module, (nn.Linear, nn.Conv2d)):
module.weight.data.normal_(mean=0.0, std=std)
if module... | 3,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3RotaryEmbedding(nn.Module):
def __init__(self, config: Emu3Config, 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... | 3,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.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 ... | 3,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.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... | 3,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.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... | 3,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3TextModel(Emu3PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Emu3TextDecoderLayer`]
Args:
config: Emu3TextConfig
"""
def __init__(self, config: Emu3Config):
super().__init__(config)
self.padding_idx = c... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
@add_start_docstrings_to_model_forward(EMU3_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds:... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states,... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
return causal_mask | 3,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 3,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class Emu3ForCausalLM(Emu3PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
config_class = Emu3TextConfig
def __init__(self, config):
super().__init__(config)
self.model = Emu3TextModel(config)
self.vocab_size = conf... | 3,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
@add_start_docstrings_to_model_forward(EMU3_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class="Emu3TextConfig")
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Opt... | 3,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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
... | 3,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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)
... | 3,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
>>> generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False)
>>> processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states =... | 3,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 3,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_sta... | 3,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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)... | 3,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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,... | 3,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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 ... | 3,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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
... | 3,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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... | 3,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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)
... | 3,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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(
... | 3,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_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,
... | 3,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py |
class DiffLlamaMLP(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=False)
... | 3,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: DiffLlamaConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | 3,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
self.num_key_value_heads = config.num_key_value_heads
self.num_key_v... | 3,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
self.lambda_init = lambda_init_fn(layer_idx)
self.lambda_q1 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
self.lambda_k1 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
self.lambda_q2 = nn.Parameter(torch.normal(0, config.lambda_std_d... | 3,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool ... | 3,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos... | 3,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
value_states = torch.cat(torch.chunk(value_states, 2, dim=1), dim=-1)
value_states = value_states.repeat(1, 2, 1, 1)
attn_weights = torch.matmul(query_states, ... | 3,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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)
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, d... | 3,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights | 3,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaFlashAttention2(DiffLlamaAttention):
"""
DiffLlama flash attention module. This module inherits from `DiffLlamaAttention` 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 ... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
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