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1242 lines
47 KiB
1242 lines
47 KiB
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from dataclasses import dataclass
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from functools import cached_property
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ... import initialization as init
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from ...cache_utils import Cache
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from ...generation import GenerationMixin
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from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutputWithPast
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from ...modeling_utils import PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import auto_docstring, can_return_tuple, logging, torch_compilable_check
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from ...utils.generic import check_model_inputs
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from ..chameleon.modeling_chameleon import (
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ChameleonPreTrainedModel,
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ChameleonVQVAEEncoderConvDownsample,
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)
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from ..llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM, LlamaModel, TransformersKwargs
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from ..siglip.modeling_siglip import SiglipAttention
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from .configuration_emu3 import Emu3Config, Emu3TextConfig, Emu3VQVAEConfig
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logger = logging.get_logger(__name__)
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@dataclass
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@auto_docstring
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class Emu3VQVAEModelOutput(BaseModelOutputWithPooling):
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r"""
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image_tokens (`torch.LongTensor` of shape `(batch_size, config.vocab_size`):
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Indices of the image tokens predicted by the VQ-VAE model.
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"""
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image_tokens: torch.LongTensor | None = None
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class Emu3Attention(LlamaAttention):
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pass
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# Has extra dropout which no other model in the library has
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class Emu3DecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: Emu3Config, layer_idx: int):
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super().__init__(config, layer_idx)
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self.dropout = nn.Dropout(config.attention_dropout)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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position_ids: torch.LongTensor | None = None,
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past_key_values: Cache | None = None,
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use_cache: bool | None = False,
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cache_position: torch.LongTensor | None = None,
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position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states, _ = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = residual + self.dropout(hidden_states)
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + self.dropout(hidden_states)
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return hidden_states
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class Emu3VQVAEVectorQuantizer(nn.Module):
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"""
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A module for vector quantization using learned embedding vectors.
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This module implements the quantization process similar to te one described in
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the VQ-VAE (Vector Quantized Variational AutoEncoder) paper. It quantizes continuous
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input vectors into discrete codebook vectors, which are learned during training.
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Current implementation improves over previous ones by avoiding costly matrix multiplications
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and allowing for post-hoc remapping of indices.
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"""
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def __init__(self, config: Emu3VQVAEConfig):
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super().__init__()
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self.embedding = nn.Embedding(config.codebook_size, config.embed_dim)
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self.embedding.weight.data.uniform_(-1.0 / config.codebook_size, 1.0 / config.codebook_size)
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def forward(self, hidden_state: torch.Tensor):
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batch_size, temporal, channels, height, width = hidden_state.shape
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hidden_state = hidden_state.permute(0, 1, 3, 4, 2).contiguous()
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hidden_state_flattened = hidden_state.view(-1, channels)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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hidden_state_sum = torch.sum(hidden_state_flattened**2, dim=1, keepdim=True)
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embedding_sum = torch.sum(self.embedding.weight**2, dim=1)
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# "bd,dn->bn",
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distances = 2 * torch.matmul(hidden_state_flattened, self.embedding.weight.transpose(0, 1))
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distances = hidden_state_sum + embedding_sum - distances
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min_encoding_indices = torch.argmin(distances, dim=1)
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min_encoding_indices = min_encoding_indices.view(batch_size, temporal, height, width)
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return min_encoding_indices
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class Emu3VQVAEEncoderConvDownsample(ChameleonVQVAEEncoderConvDownsample):
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pass
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class Emu3VQVAEEncoderConvUpsample(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
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def forward(self, hidden_states):
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hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
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hidden_states = self.conv(hidden_states)
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return hidden_states
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class Emu3VQVAEConv3d(nn.Module):
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def __init__(
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self,
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in_channel: int,
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out_channel: int,
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kernel_size: tuple[int],
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stride: tuple[int],
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):
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super().__init__()
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padding_sizes = [one_kernel - one_stride for one_kernel, one_stride in zip(kernel_size[1:], stride[1:])]
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self.padding = ()
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for pad_size in padding_sizes[::-1]:
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self.padding += (pad_size // 2 + pad_size % 2, pad_size // 2)
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self.padding += (2, 0)
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self.conv = nn.Conv3d(
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in_channel,
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out_channel,
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kernel_size,
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stride=stride,
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)
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def forward(self, hidden_states: torch.Tensor):
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hidden_states = F.pad(hidden_states, self.padding)
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hidden_states = self.conv(hidden_states)
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return hidden_states
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class Emu3VQVAESpatialNorm(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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):
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super().__init__()
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self.norm_layer = nn.GroupNorm(
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num_channels=out_channels,
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num_groups=32,
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eps=1e-6,
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affine=True,
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)
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self.conv_y = nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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)
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self.conv_b = nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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)
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def forward(self, hidden_states: torch.Tensor, quant_states: torch.Tensor):
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quant_states = F.interpolate(quant_states, size=hidden_states.shape[-2:], mode="nearest")
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hidden_states = self.norm_layer(hidden_states)
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hidden_states = hidden_states * self.conv_y(quant_states) + self.conv_b(quant_states)
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return hidden_states
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class Emu3VQVAETemporalUpsample(nn.Module):
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def __init__(
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self,
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in_channel: int,
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out_channel: int,
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):
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super().__init__()
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self.conv = Emu3VQVAEConv3d(
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in_channel,
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out_channel,
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kernel_size=(3, 3, 3),
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stride=(1, 1, 1),
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)
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def forward(self, hidden_states: torch.Tensor):
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batch_size, channels, temporal, height, width = hidden_states.shape
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hidden_states = hidden_states.permute(0, 1, 3, 4, 2).contiguous().view(batch_size, -1, temporal)
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hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
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hidden_states = hidden_states.view(batch_size, channels, height, width, -1).permute(0, 1, 4, 2, 3).contiguous()
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hidden_states = self.conv(hidden_states)
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return hidden_states
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class Emu3VQVAETemporalDownsample(nn.Module):
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def __init__(
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self,
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in_channel: int,
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out_channel: int,
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):
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super().__init__()
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self.conv = Emu3VQVAEConv3d(
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in_channel,
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out_channel,
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kernel_size=(4, 3, 3),
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stride=(2, 1, 1),
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)
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def forward(self, hidden_states: torch.Tensor):
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hidden_states = self.conv(hidden_states)
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return hidden_states
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class Emu3VQVAETemporalResnetBlock(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels=None,
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):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = in_channels if out_channels is None else out_channels
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self.norm1 = nn.BatchNorm3d(in_channels)
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self.conv1 = Emu3VQVAEConv3d(
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in_channels,
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out_channels,
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kernel_size=(3, 3, 3),
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stride=(1, 1, 1),
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)
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self.norm2 = nn.BatchNorm3d(out_channels)
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self.conv2 = Emu3VQVAEConv3d(
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out_channels,
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out_channels,
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kernel_size=(3, 3, 3),
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stride=(1, 1, 1),
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)
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if self.in_channels != self.out_channels:
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self.nin_shortcut = nn.Conv3d(
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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)
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def forward(self, hidden_states):
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residual = hidden_states
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hidden_states = self.norm1(hidden_states)
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hidden_states *= torch.sigmoid(hidden_states)
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hidden_states = self.conv1(hidden_states)
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hidden_states = self.norm2(hidden_states)
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hidden_states *= torch.sigmoid(hidden_states)
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hidden_states = self.conv2(hidden_states)
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if self.in_channels != self.out_channels:
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residual = self.nin_shortcut(residual)
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return residual + hidden_states
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class Emu3VQVAEResnetBlock(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int | None = None,
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quant_channels: int | None = None,
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):
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super().__init__()
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.quant_channels = quant_channels
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if quant_channels is None:
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self.norm1 = nn.GroupNorm(num_channels=in_channels, num_groups=32, eps=1e-6, affine=True)
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self.norm2 = nn.GroupNorm(num_channels=out_channels, num_groups=32, eps=1e-6, affine=True)
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else:
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self.norm1 = Emu3VQVAESpatialNorm(quant_channels, in_channels)
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self.norm2 = Emu3VQVAESpatialNorm(quant_channels, out_channels)
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self.conv1 = nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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)
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self.conv2 = nn.Conv2d(
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out_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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)
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if self.in_channels != self.out_channels:
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self.nin_shortcut = nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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)
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def forward(self, hidden_states: torch.Tensor, quant_channels: torch.Tensor | None = None):
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norm_args = () if self.quant_channels is None else (quant_channels,)
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residual = hidden_states
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hidden_states = self.norm1(hidden_states, *norm_args)
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hidden_states *= torch.sigmoid(hidden_states)
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hidden_states = self.conv1(hidden_states)
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hidden_states = self.norm2(hidden_states, *norm_args)
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hidden_states *= torch.sigmoid(hidden_states)
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hidden_states = self.conv2(hidden_states)
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if self.in_channels != self.out_channels:
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residual = self.nin_shortcut(residual)
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return residual + hidden_states
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class Emu3VQVAEAttentionBlock(SiglipAttention):
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def __init__(self, config: Emu3VQVAEConfig):
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super().__init__(config)
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# for compatibility with the attention interface
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self.num_key_value_groups = 1
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class Emu3VQVAEGroupNorm(nn.GroupNorm):
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"""
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Same as the torch GroupNorm with the only difference that this ones accepts
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an optional kwarg `quant_states` which is not used. This class makes it easier to
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use SpatialNorm or GroupNorm without conditionals
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"""
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def forward(self, input, quant_states=None):
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return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)
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class Emu3VQVAEMiddleBlock(nn.Module):
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def __init__(self, config, in_channels, quant_channels=None):
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super().__init__()
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self.block_1 = Emu3VQVAEResnetBlock(
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in_channels=in_channels,
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out_channels=in_channels,
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quant_channels=quant_channels,
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)
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self.attn_1 = Emu3VQVAEAttentionBlock(config)
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if quant_channels is None:
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self.attn_norm = Emu3VQVAEGroupNorm(num_channels=in_channels, num_groups=32, eps=1e-6, affine=True)
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else:
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self.attn_norm = Emu3VQVAESpatialNorm(quant_channels, in_channels)
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self.block_2 = Emu3VQVAEResnetBlock(
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in_channels=in_channels,
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out_channels=in_channels,
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quant_channels=quant_channels,
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)
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def forward(self, hidden_states: torch.FloatTensor, quant_states: torch.FloatTensor | None = None):
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hidden_states = self.block_1(hidden_states, quant_states)
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residual = hidden_states
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hidden_states = self.attn_norm(hidden_states, quant_states)
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batch_size, channels, height, width = hidden_states.shape
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hidden_states = hidden_states.view(batch_size, channels, height * width).transpose(1, 2)
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hidden_states = self.attn_1(hidden_states)[0]
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hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
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hidden_states = residual + hidden_states
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hidden_states = self.block_2(hidden_states, quant_states)
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return hidden_states
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class Emu3VQVAEDownBlock(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.num_resolutions = len(config.channel_multiplier)
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self.num_res_blocks = config.num_res_blocks
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base_channels = config.base_channels
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channel_multiplier = config.channel_multiplier
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in_channel_multiplier = (1,) + tuple(channel_multiplier)
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self.in_channel_multiplier = in_channel_multiplier
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self.down = nn.ModuleList()
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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attn_norms = nn.ModuleList()
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block_in = base_channels * in_channel_multiplier[i_level]
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block_out = base_channels * channel_multiplier[i_level]
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for i_block in range(self.num_res_blocks):
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block.append(
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Emu3VQVAEResnetBlock(
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in_channels=block_in,
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out_channels=block_out,
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)
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)
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block_in = block_out
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if config.attn_resolutions is not None and i_level in config.attn_resolutions:
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attn.append(Emu3VQVAEAttentionBlock(config))
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attn_norms.append(nn.GroupNorm(num_channels=block_in, num_groups=32, eps=1e-6, affine=True))
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down = nn.Module()
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down.block = block
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down.attn = attn
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down.attn_norms = attn_norms
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if i_level != self.num_resolutions - 1:
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down.downsample = Emu3VQVAEEncoderConvDownsample(block_in)
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self.down.append(down)
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def forward(self, hidden_states: torch.FloatTensor):
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for i_level, blocks in enumerate(self.down):
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for i_block in range(self.num_res_blocks):
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hidden_states = blocks.block[i_block](hidden_states)
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if len(blocks.attn) > 0:
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residual = hidden_states
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hidden_states = blocks.attn_norms[i_block](hidden_states)
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batch_size, channels, height, width = hidden_states.shape
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hidden_states = hidden_states.view(batch_size, channels, height * width).transpose(1, 2)
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hidden_states = blocks.attn[i_block](hidden_states)[0]
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hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
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hidden_states = residual + hidden_states
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if i_level != self.num_resolutions - 1:
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hidden_states = blocks.downsample(hidden_states)
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return hidden_states
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class Emu3VQVAEUpBlock(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.num_resolutions = len(config.channel_multiplier)
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self.num_res_blocks = config.num_res_blocks
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|
|
quant_channels = config.embed_dim
|
|
block_in = config.base_channels * config.channel_multiplier[-1]
|
|
|
|
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.num_res_blocks + 1):
|
|
block.append(
|
|
Emu3VQVAEResnetBlock(
|
|
in_channels=block_in,
|
|
out_channels=block_out,
|
|
quant_channels=quant_channels,
|
|
)
|
|
)
|
|
block_in = block_out
|
|
if i_level in config.attn_resolutions:
|
|
attn.append(Emu3VQVAEAttentionBlock(config))
|
|
attn_norms.append(Emu3VQVAESpatialNorm(quant_channels, block_in))
|
|
|
|
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: torch.FloatTensor):
|
|
for i_level, blocks in enumerate(self.up[::-1]):
|
|
for i_block in range(self.num_res_blocks + 1):
|
|
hidden_states = blocks.block[i_block](hidden_states, quant_states)
|
|
if len(blocks.attn) > 0:
|
|
residual = hidden_states
|
|
hidden_states = blocks.attn_norms[i_block](hidden_states, quant_states)
|
|
|
|
batch_size, channels, height, width = hidden_states.shape
|
|
hidden_states = hidden_states.view(batch_size, channels, height * width).transpose(1, 2)
|
|
hidden_states = blocks.attn[i_block](hidden_states)[0]
|
|
|
|
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
|
|
|
|
|
|
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_multiplier
|
|
out_channels = 2 * latent_channels if double_latent else latent_channels
|
|
block_in = base_channels * channel_multiplier[-1]
|
|
|
|
self.conv_in = torch.nn.Conv2d(in_channels, base_channels, kernel_size=3, stride=1, padding=1)
|
|
self.down_block = Emu3VQVAEDownBlock(config)
|
|
self.middle_block = Emu3VQVAEMiddleBlock(config, block_in)
|
|
|
|
self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
|
|
self.conv_out = torch.nn.Conv2d(
|
|
block_in,
|
|
out_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
)
|
|
|
|
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)
|
|
|
|
for _ in range(config.num_res_blocks):
|
|
time_res_conv = Emu3VQVAETemporalResnetBlock(
|
|
in_channels=out_channels,
|
|
out_channels=out_channels,
|
|
)
|
|
self.time_res_stack.append(time_res_conv)
|
|
|
|
def forward(self, pixel_values: torch.LongTensor):
|
|
temporal_dim = pixel_values.shape[1]
|
|
pixel_values = pixel_values.reshape(-1, *pixel_values.shape[2:])
|
|
|
|
# downsampling & middle
|
|
hidden_states = self.conv_in(pixel_values)
|
|
hidden_states = self.down_block(hidden_states)
|
|
hidden_states = self.middle_block(hidden_states)
|
|
|
|
# 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)
|
|
|
|
# temporal convs
|
|
for conv in self.time_conv:
|
|
hidden_states = conv(hidden_states)
|
|
hidden_states *= torch.sigmoid(hidden_states)
|
|
|
|
for layer in self.time_res_stack:
|
|
hidden_states = layer(hidden_states)
|
|
|
|
hidden_states = hidden_states.permute(0, 2, 1, 3, 4)
|
|
|
|
return hidden_states
|
|
|
|
|
|
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):
|
|
time_res_conv = Emu3VQVAETemporalResnetBlock(
|
|
in_channels=config.latent_channels, out_channels=config.latent_channels
|
|
)
|
|
self.time_res_stack.append(time_res_conv)
|
|
|
|
temp_upsample_block_num = int(math.log2(config.temporal_downsample_factor))
|
|
self.time_conv = nn.ModuleList()
|
|
for i in range(temp_upsample_block_num):
|
|
conv = Emu3VQVAETemporalUpsample(config.latent_channels, config.latent_channels)
|
|
self.time_conv.append(conv)
|
|
|
|
self.conv_in = nn.Conv2d(
|
|
config.latent_channels,
|
|
block_in,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
)
|
|
|
|
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(
|
|
block_in,
|
|
config.out_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, quant_states: torch.Tensor):
|
|
hidden_quant_states = torch.cat((hidden_states, quant_states), dim=0)
|
|
hidden_quant_states = hidden_quant_states.permute(0, 2, 1, 3, 4)
|
|
|
|
# temporal convs
|
|
for layer in self.time_res_stack:
|
|
hidden_quant_states = layer(hidden_quant_states)
|
|
|
|
for layer in self.time_conv:
|
|
hidden_quant_states = layer(hidden_quant_states)
|
|
hidden_quant_states *= torch.sigmoid(hidden_quant_states)
|
|
|
|
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 = self.conv_in(hidden_states)
|
|
|
|
# middle & upsampling
|
|
hidden_states = self.middle_block(hidden_states, quant_states)
|
|
hidden_states = self.up_block(hidden_states, quant_states)
|
|
|
|
hidden_states = self.norm_out(hidden_states, quant_states)
|
|
hidden_states *= torch.sigmoid(hidden_states)
|
|
hidden_states = self.conv_out(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The VQ-VAE model used in Emu3 for encoding/decoding images into discrete tokens.
|
|
This model follows the "Make-a-scene: Scene-based text-to-image generation with human priors" paper from
|
|
[ Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv
|
|
Taigman](https://huggingface.co/papers/2203.13131).
|
|
"""
|
|
)
|
|
class Emu3VQVAE(PreTrainedModel):
|
|
config: Emu3VQVAEConfig
|
|
base_model_prefix = "emuvideovq"
|
|
main_input_name = "pixel_values"
|
|
input_modalities = ("image",)
|
|
_supports_sdpa = True
|
|
_supports_flash_attn = True
|
|
_supports_flex_attn = True
|
|
_supports_attention_backend = True
|
|
_no_split_modules = [
|
|
"Emu3VQVAETemporalResnetBlock",
|
|
"Emu3VQVAEAttentionBlock",
|
|
"Emu3VQVAEResnetBlock",
|
|
"Emu3VQVAEVectorQuantizer",
|
|
]
|
|
_can_record_outputs = {
|
|
"hidden_states": [Emu3VQVAEResnetBlock, Emu3VQVAETemporalResnetBlock],
|
|
"attentions": Emu3VQVAEAttentionBlock,
|
|
}
|
|
|
|
@torch.no_grad()
|
|
def _init_weights(self, module):
|
|
if isinstance(module, (nn.Conv2d, nn.Conv3d)):
|
|
init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
|
|
if module.bias is not None:
|
|
fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(module.weight)
|
|
bound = 1 / math.sqrt(fan_in)
|
|
init.uniform_(module.bias, -bound, bound)
|
|
elif isinstance(module, nn.Linear):
|
|
init.kaiming_uniform_(module.weight, a=math.sqrt(5))
|
|
if module.bias is not None:
|
|
fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(module.weight)
|
|
bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
|
|
init.uniform_(module.bias, -bound, bound)
|
|
elif isinstance(module, (nn.BatchNorm2d, nn.BatchNorm3d, nn.GroupNorm)):
|
|
init.constant_(module.weight, 1.0)
|
|
init.constant_(module.bias, 0.0)
|
|
if getattr(module, "running_mean", None) is not None:
|
|
init.zeros_(module.running_mean)
|
|
init.ones_(module.running_var)
|
|
init.zeros_(module.num_batches_tracked)
|
|
elif isinstance(module, nn.Embedding):
|
|
init.normal_(module.weight)
|
|
# Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag
|
|
if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False):
|
|
init.zeros_(module.weight[module.padding_idx])
|
|
|
|
def __init__(self, config: Emu3VQVAEConfig):
|
|
super().__init__(config)
|
|
|
|
self.config = config
|
|
|
|
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, kernel_size=(3, 1, 1), stride=(1, 1, 1)
|
|
)
|
|
self.post_quant_conv = Emu3VQVAEConv3d(
|
|
config.embed_dim, config.latent_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1)
|
|
)
|
|
self.spatial_scale_factor = 2 ** (len(config.channel_multiplier) - 1)
|
|
self.eval() # Emu3's VQ model is frozen
|
|
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
def encode(
|
|
self, pixel_values: torch.Tensor, image_sizes: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
|
|
) -> Emu3VQVAEModelOutput:
|
|
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).repeat(1, temporal, 1, 1, 1)
|
|
else:
|
|
batch_size, temporal, channels, height, width = pixel_values.shape
|
|
|
|
hidden_states = self.encoder(pixel_values)
|
|
|
|
# b t c h w -> b c t h w
|
|
conv_hidden_states = hidden_states.permute(0, 2, 1, 3, 4)
|
|
conv_hidden_states = self.quant_conv(conv_hidden_states)
|
|
|
|
# b c t h w -> b t c h w
|
|
conv_hidden_states = conv_hidden_states.permute(0, 2, 1, 3, 4)
|
|
codes = self.quantize(conv_hidden_states)
|
|
|
|
image_tokens = codes.squeeze(1) if is_image else codes
|
|
|
|
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 Emu3VQVAEModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
image_tokens=image_tokens,
|
|
)
|
|
|
|
def decode(self, hidden_states: torch.Tensor):
|
|
is_image = hidden_states.ndim == 3
|
|
if is_image:
|
|
hidden_states = hidden_states.unsqueeze(1)
|
|
|
|
batch_size, temporal, height, width = hidden_states.shape
|
|
quant = self.quantize.embedding(hidden_states.flatten())
|
|
|
|
channels = quant.shape[-1]
|
|
quant = quant.view(batch_size, temporal, height, width, channels).permute(0, 4, 1, 2, 3).contiguous()
|
|
post_quant = self.post_quant_conv(quant)
|
|
|
|
quant = quant.permute(0, 2, 1, 3, 4)
|
|
post_quant = post_quant.permute(0, 2, 1, 3, 4)
|
|
|
|
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 video[:, 0] if is_image else video
|
|
|
|
|
|
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_property
|
|
def image_tokens(self):
|
|
return sorted([val for name, val in self.vocab_map.items() if name.startswith("<|visual token")])
|
|
|
|
@cached_property
|
|
def image_tokens_str(self):
|
|
return sorted([name for name, val in self.vocab_map.items() if name.startswith("<|visual token")])
|
|
|
|
@cached_property
|
|
def img2bpe(self):
|
|
return {int(token[-8:-2]): self.vocab_map[token] for token in self.image_tokens_str}
|
|
|
|
@cached_property
|
|
def bpe2img(self):
|
|
return {v: k for k, v in self.img2bpe.items()}
|
|
|
|
@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(self.img2bpe.keys()) + 1, dtype=torch.int)
|
|
for k, v in self.img2bpe.items():
|
|
mapping[k] = v
|
|
return mapping
|
|
|
|
def convert_img2bpe(self, img_batch: list[torch.Tensor]) -> torch.Tensor:
|
|
device = img_batch.device
|
|
eol_row = torch.ones((img_batch.shape[0], 1), dtype=torch.int) * self.eol_token_id
|
|
img_tokens = self.img2bpe_mapping_tensor[img_batch.to("cpu")]
|
|
img_tokens = torch.cat([img_tokens, eol_row], dim=-1)
|
|
return img_tokens.to(device)
|
|
|
|
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)
|
|
|
|
|
|
class Emu3PreTrainedModel(ChameleonPreTrainedModel):
|
|
_no_split_modules = [
|
|
"Emu3DecoderLayer",
|
|
]
|
|
_supports_flex_attn = True
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": Emu3DecoderLayer,
|
|
"attentions": Emu3Attention,
|
|
}
|
|
|
|
|
|
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)]
|
|
)
|
|
|
|
|
|
class Emu3ForCausalLM(LlamaForCausalLM, Emu3PreTrainedModel, GenerationMixin):
|
|
config: Emu3TextConfig
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = Emu3TextModel(config)
|
|
|
|
def forward(**super_kwargs):
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import Emu3Processor, Emu3ForConditionalGeneration
|
|
>>> import torch
|
|
>>> import httpx
|
|
>>> from io import BytesIO
|
|
>>> from PIL import Image
|
|
|
|
>>> model = Emu3ForCausalLM.from_pretrained("BAAI/Emu3-Chat-hf", dtype=torch.bfloat16)
|
|
>>> processor = Emu3Processor.from_pretrained("BAAI/Emu3-Chat-hf")
|
|
|
|
>>> inputs = processor(text=["Can you write me a poem about winter."], return_tensors="pt").to(model.device)
|
|
|
|
>>> generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False)
|
|
>>> processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
|
```"""
|
|
super().forward()
|
|
|
|
|
|
class Emu3Model(Emu3PreTrainedModel):
|
|
_checkpoint_conversion_mapping = {"text_model.model": "text_model"}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.text_model = Emu3TextModel._from_config(config.text_config)
|
|
self.vqmodel = Emu3VQVAE(config.vq_config)
|
|
self.vocabulary_mapping = Emu3ImageVocabularyMapping(config.vocabulary_map)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.text_model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.text_model.set_input_embeddings(value)
|
|
|
|
def get_image_tokens(self, pixel_values: torch.FloatTensor, image_sizes: torch.LongTensor) -> torch.LongTensor:
|
|
"""
|
|
Tokenizes images into discrete tokens with VQGAN module. Converts
|
|
obtained image tokens into BPE tokens and wraps with "boi" and "eoi"
|
|
special tokens.
|
|
|
|
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, width) for each image.
|
|
"""
|
|
vqmodel_outputs: Emu3VQVAEModelOutput = self.vqmodel.encode(pixel_values, image_sizes, return_dict=True)
|
|
bpe_tokens_list = [
|
|
self.vocabulary_mapping.convert_img2bpe(tokens).flatten() for tokens in vqmodel_outputs.image_tokens
|
|
]
|
|
bpe_tokens = torch.cat(bpe_tokens_list)
|
|
return bpe_tokens
|
|
|
|
@can_return_tuple
|
|
@auto_docstring(
|
|
custom_intro="Tokenizes images into discrete tokens with VQGAN module and embeds them with text embeddings layer"
|
|
)
|
|
def get_image_features(
|
|
self, pixel_values: torch.FloatTensor, image_sizes: torch.LongTensor, **kwargs: Unpack[TransformersKwargs]
|
|
) -> tuple | Emu3VQVAEModelOutput:
|
|
r"""
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
|
|
The tensors corresponding to the input images.
|
|
"""
|
|
vqmodel_outputs: Emu3VQVAEModelOutput = self.vqmodel.encode(
|
|
pixel_values, image_sizes, return_dict=True, **kwargs
|
|
)
|
|
split_sizes = [
|
|
(height // self.vqmodel.vision_spatial_factor) * (width // self.vqmodel.vision_spatial_factor + 1)
|
|
for height, width in image_sizes
|
|
]
|
|
bpe_tokens_list = [
|
|
self.vocabulary_mapping.convert_img2bpe(tokens).flatten() for tokens in vqmodel_outputs.image_tokens
|
|
]
|
|
bpe_tokens = torch.cat(bpe_tokens_list)
|
|
image_embeddings = self.get_input_embeddings()(bpe_tokens)
|
|
image_features = torch.split(image_embeddings, split_sizes)
|
|
vqmodel_outputs.pooler_output = image_features
|
|
|
|
return vqmodel_outputs
|
|
|
|
@torch.no_grad()
|
|
def decode_image_tokens(self, image_tokens: torch.LongTensor, height: int, width: int):
|
|
"""
|
|
Decodes generated image tokens from language model to continuous pixel values
|
|
with VQGAN module via upsampling.
|
|
|
|
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 before upsampling.
|
|
"""
|
|
sequences = image_tokens[:, :-3].view(-1, height, width + 1)
|
|
image_tokens = self.vocabulary_mapping.convert_bpe2img(sequences)
|
|
image = self.vqmodel.decode(image_tokens)
|
|
return image
|
|
|
|
def get_placeholder_mask(
|
|
self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
|
|
):
|
|
"""
|
|
Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
|
|
equal to the length of multimodal features. If the lengths are different, an error is raised.
|
|
"""
|
|
if input_ids is None:
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(self.vocabulary_mapping.image_token_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
else:
|
|
special_image_mask = input_ids == self.vocabulary_mapping.image_token_id
|
|
|
|
n_image_tokens = special_image_mask.sum()
|
|
n_image_features = image_features.shape[0] * image_features.shape[1]
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
torch_compilable_check(
|
|
inputs_embeds[special_image_mask].numel() == image_features.numel(),
|
|
f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
|
|
)
|
|
return special_image_mask
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
image_sizes: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple | CausalLMOutputWithPast:
|
|
r"""
|
|
image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`):
|
|
The sizes of the images in the batch, being (height, width) for each image. Image sizes can be obtained using
|
|
[`AutoImageProcessor`]. See [`Emu3ImageProcessor.__call__`] for details ([]`Emu3Processor`] uses
|
|
[`Emu3ImageProcessor`] for processing images).
|
|
"""
|
|
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 inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
|
|
|
if pixel_values is not None:
|
|
image_features = self.get_image_features(pixel_values, image_sizes).pooler_output
|
|
image_features = torch.cat(image_features, dim=0)
|
|
special_image_mask = self.get_placeholder_mask(
|
|
input_ids, inputs_embeds=inputs_embeds, image_features=image_features
|
|
)
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs = self.text_model(
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
return outputs
|
|
|
|
|
|
class Emu3ForConditionalGeneration(Emu3PreTrainedModel, GenerationMixin):
|
|
output_modalities = ("image", "text")
|
|
_tied_weights_keys = {"lm_head.weight": "model.text_model.embed_tokens.weight"}
|
|
_checkpoint_conversion_mapping = {
|
|
"^text_model.model": "model.text_model",
|
|
"^vqmodel": "model.vqmodel",
|
|
"^text_model.lm_head": "lm_head",
|
|
}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = Emu3Model(config)
|
|
self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
|
|
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.set_input_embeddings(value)
|
|
|
|
def get_output_embeddings(self) -> nn.Module:
|
|
return self.lm_head
|
|
|
|
def decode_image_tokens(self, **kwargs):
|
|
return self.model.decode_image_tokens(**kwargs)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
image_sizes: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple | CausalLMOutputWithPast:
|
|
r"""
|
|
image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`):
|
|
The sizes of the images in the batch, being (height, width) for each image. Image sizes can be obtained using
|
|
[`AutoImageProcessor`]. See [`Emu3ImageProcessor.__call__`] for details ([]`Emu3Processor`] uses
|
|
[`Emu3ImageProcessor`] for processing images).
|
|
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
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import Emu3Processor, Emu3ForConditionalGeneration
|
|
>>> import torch
|
|
>>> import httpx
|
|
>>> from io import BytesIO
|
|
>>> from PIL import Image
|
|
|
|
>>> model = Emu3ForConditionalGeneration.from_pretrained("BAAI/Emu3-Chat-hf", dtype=torch.bfloat16)
|
|
>>> processor = Emu3Processor.from_pretrained("BAAI/Emu3-Chat-hf")
|
|
|
|
>>> conversation = [
|
|
... {
|
|
... "role": "system",
|
|
... "content": [
|
|
... {"type": "text", "text": "You are a helpful assistant."},
|
|
... ],
|
|
... },
|
|
... {
|
|
... "role": "user",
|
|
... "content": [
|
|
... {"type": "image"},
|
|
... {"type": "text", "text": "Please describe the image."},
|
|
... ],
|
|
... },
|
|
... ]
|
|
|
|
>>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
|
|
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
|
>>> with httpx.stream("GET", url) as response:
|
|
... image = Image.open(BytesIO(response.read()))
|
|
|
|
>>> inputs = processor(images=[image], text=[prompt], return_tensors="pt").to(model.device, torch.bfloat16)
|
|
|
|
>>> generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False)
|
|
>>> processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
|
```"""
|
|
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,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(
|
|
logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
|
|
)
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
cache_position=None,
|
|
position_ids=None,
|
|
use_cache=True,
|
|
pixel_values=None,
|
|
is_first_iteration=False,
|
|
**kwargs,
|
|
):
|
|
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
|
|
|
|
model_inputs = super().prepare_inputs_for_generation(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
cache_position=cache_position,
|
|
position_ids=position_ids,
|
|
pixel_values=pixel_values,
|
|
use_cache=use_cache,
|
|
is_first_iteration=is_first_iteration,
|
|
**kwargs,
|
|
)
|
|
|
|
if not is_first_iteration and use_cache:
|
|
model_inputs["pixel_values"] = None
|
|
|
|
return model_inputs
|
|
|
|
|
|
__all__ = [
|
|
"Emu3ForConditionalGeneration",
|
|
"Emu3ForCausalLM",
|
|
"Emu3TextModel",
|
|
"Emu3PreTrainedModel",
|
|
"Emu3VQVAE",
|
|
"Emu3Model",
|
|
]
|