# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/emu3/modular_emu3.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_emu3.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2024 HuggingFace Inc. team. All rights reserved. # # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from functools import cached_property from typing import Callable, Optional, Union import torch import torch.nn as nn import torch.nn.functional as F from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache from ...generation import GenerationMixin from ...integrations import use_kernel_forward_from_hub from ...masking_utils import create_causal_mask from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, auto_docstring, can_return_tuple from ...utils.generic import check_model_inputs from .configuration_emu3 import Emu3Config, Emu3TextConfig, Emu3VQVAEConfig def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], scaling: float, dropout: float = 0.0, **kwargs: Unpack[TransformersKwargs], ): key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights class Emu3Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Emu3Config, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias ) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, torch.Tensor]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] 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, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights @use_kernel_forward_from_hub("RMSNorm") class Emu3RMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Emu3RMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class Emu3MLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj class Emu3DecoderLayer(GradientCheckpointingLayer): 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 = Emu3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = Emu3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.dropout = nn.Dropout(config.attention_dropout) 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, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = residual + self.dropout(hidden_states) residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + self.dropout(hidden_states) return hidden_states 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 discrete codebook vectors, which are learned during training. Current implementation improves over previous ones by avoiding costly matrix multiplications and allowing for post-hoc remapping of indices. """ def __init__(self, config: Emu3VQVAEConfig): super().__init__() self.embedding = nn.Embedding(config.codebook_size, config.embed_dim) self.embedding.weight.data.uniform_(-1.0 / config.codebook_size, 1.0 / config.codebook_size) 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 + e^2 - 2 e * z hidden_state_sum = torch.sum(hidden_state_flattened**2, dim=1, keepdim=True) embedding_sum = torch.sum(self.embedding.weight**2, dim=1) # "bd,dn->bn", distances = 2 * torch.matmul(hidden_state_flattened, self.embedding.weight.transpose(0, 1)) distances = hidden_state_sum + embedding_sum - distances min_encoding_indices = torch.argmin(distances, dim=1) min_encoding_indices = min_encoding_indices.view(batch_size, temporal, height, width) return min_encoding_indices 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 hidden_states = F.pad(hidden_states, pad=(0, 1, 0, 1), mode="constant", value=0) hidden_states = self.conv(hidden_states) return hidden_states 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="nearest") hidden_states = self.conv(hidden_states) return hidden_states 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[1:])] self.padding = () for pad_size in padding_sizes[::-1]: self.padding += (pad_size // 2 + pad_size % 2, pad_size // 2) self.padding += (2, 0) self.conv = nn.Conv3d( in_channel, out_channel, kernel_size, stride=stride, ) def forward(self, hidden_states: torch.Tensor): hidden_states = F.pad(hidden_states, self.padding) hidden_states = self.conv(hidden_states) return hidden_states 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, ) self.conv_y = nn.Conv2d( in_channels, out_channels, kernel_size=1, stride=1, padding=0, ) self.conv_b = nn.Conv2d( in_channels, out_channels, kernel_size=1, stride=1, padding=0, ) def forward(self, hidden_states: torch.Tensor, quant_states: torch.Tensor): quant_states = F.interpolate(quant_states, size=hidden_states.shape[-2:], mode="nearest") hidden_states = self.norm_layer(hidden_states) hidden_states = hidden_states * self.conv_y(quant_states) + self.conv_b(quant_states) return hidden_states 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), ) def forward(self, hidden_states: torch.Tensor): batch_size, channels, temporal, height, width = hidden_states.shape hidden_states = hidden_states.permute(0, 1, 3, 4, 2).contiguous().view(batch_size, -1, temporal) hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest") hidden_states = hidden_states.view(batch_size, channels, height, width, -1).permute(0, 1, 4, 2, 3).contiguous() hidden_states = self.conv(hidden_states) return hidden_states 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), ) def forward(self, hidden_states: torch.Tensor): hidden_states = self.conv(hidden_states) return hidden_states 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(in_channels) self.conv1 = Emu3VQVAEConv3d( in_channels, out_channels, kernel_size=(3, 3, 3), stride=(1, 1, 1), ) self.norm2 = nn.BatchNorm3d(out_channels) self.conv2 = Emu3VQVAEConv3d( out_channels, out_channels, kernel_size=(3, 3, 3), stride=(1, 1, 1), ) if self.in_channels != self.out_channels: self.nin_shortcut = nn.Conv3d( in_channels, out_channels, kernel_size=1, stride=1, padding=0, ) 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_states) hidden_states = self.conv2(hidden_states) if self.in_channels != self.out_channels: residual = self.nin_shortcut(residual) return residual + hidden_states 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 else out_channels self.out_channels = out_channels self.quant_channels = quant_channels if quant_channels is None: self.norm1 = nn.GroupNorm(num_channels=in_channels, num_groups=32, eps=1e-6, affine=True) self.norm2 = nn.GroupNorm(num_channels=out_channels, num_groups=32, eps=1e-6, affine=True) else: self.norm1 = Emu3VQVAESpatialNorm(quant_channels, in_channels) self.norm2 = Emu3VQVAESpatialNorm(quant_channels, out_channels) self.conv1 = nn.Conv2d( in_channels, out_channels, kernel_size=3, stride=1, padding=1, ) 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, kernel_size=1, stride=1, padding=0, ) def forward(self, hidden_states: torch.Tensor, quant_channels: Optional[torch.Tensor] = None): norm_args = () if self.quant_channels is None else (quant_channels,) residual = hidden_states hidden_states = self.norm1(hidden_states, *norm_args) hidden_states *= torch.sigmoid(hidden_states) hidden_states = self.conv1(hidden_states) hidden_states = self.norm2(hidden_states, *norm_args) hidden_states *= torch.sigmoid(hidden_states) hidden_states = self.conv2(hidden_states) if self.in_channels != self.out_channels: residual = self.nin_shortcut(residual) return residual + hidden_states class Emu3VQVAEAttentionBlock(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Emu3VQVAEConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = config.attention_dropout self.is_causal = False self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) # for compatibility with the attention interface self.num_key_value_groups = 1 def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: """Input shape: Batch x Time x Channel""" batch_size, seq_length, embed_dim = hidden_states.shape queries = self.q_proj(hidden_states) keys = self.k_proj(hidden_states) values = self.v_proj(hidden_states) queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2) keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2) values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, queries, keys, values, attention_mask, is_causal=self.is_causal, scaling=self.scale, dropout=0.0 if not self.training else self.dropout, ) attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous() attn_output = self.out_proj(attn_output) return attn_output, attn_weights 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): super().__init__(**kwargs) def forward(self, input, quant_states=None): return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps) 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.attn_1 = Emu3VQVAEAttentionBlock(config) if quant_channels is None: self.attn_norm = Emu3VQVAEGroupNorm(num_channels=in_channels, num_groups=32, eps=1e-6, affine=True) else: self.attn_norm = Emu3VQVAESpatialNorm(quant_channels, in_channels) self.block_2 = Emu3VQVAEResnetBlock( in_channels=in_channels, out_channels=in_channels, quant_channels=quant_channels, ) def forward(self, hidden_states: torch.FloatTensor, quant_states: Optional[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.shape hidden_states = hidden_states.view(batch_size, channels, height * width).transpose(1, 2) hidden_states = self.attn_1(hidden_states)[0] hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2) hidden_states = residual + hidden_states hidden_states = self.block_2(hidden_states, quant_states) return hidden_states 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 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() block_in = base_channels * in_channel_multiplier[i_level] block_out = base_channels * channel_multiplier[i_level] for i_block in range(self.num_res_blocks): block.append( Emu3VQVAEResnetBlock( in_channels=block_in, out_channels=block_out, ) ) block_in = block_out if config.attn_resolutions is not None and i_level in config.attn_resolutions: attn.append(Emu3VQVAEAttentionBlock(config)) attn_norms.append(nn.GroupNorm(num_channels=block_in, num_groups=32, eps=1e-6, affine=True)) 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: torch.FloatTensor): for i_level, blocks in enumerate(self.down): for i_block in range(self.num_res_blocks): hidden_states = blocks.block[i_block](hidden_states) if len(blocks.attn) > 0: residual = hidden_states hidden_states = blocks.attn_norms[i_block](hidden_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 != self.num_resolutions - 1: hidden_states = blocks.downsample(hidden_states) return hidden_states 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[-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" _supports_sdpa = True _supports_flash_attn = True _supports_flex_attn = True _supports_attention_backend = True _no_split_modules = [ "Emu3VQVAETemporalResnetBlock", "Emu3VQVAEAttentionBlock", "Emu3VQVAEResnetBlock", "Emu3VQVAEVectorQuantizer", ] def _init_weights(self, module): if isinstance(module, (nn.Conv2d, nn.Conv3d)): nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") if module.bias is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(module.weight) bound = 1 / math.sqrt(fan_in) nn.init.uniform_(module.bias, -bound, bound) elif isinstance(module, nn.Linear): nn.init.kaiming_uniform_(module.weight, a=math.sqrt(5)) if module.bias is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(module.weight) bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 nn.init.uniform_(module.bias, -bound, bound) elif isinstance(module, (nn.BatchNorm2d, nn.BatchNorm3d, nn.GroupNorm)): nn.init.constant_(module.weight, 1.0) nn.init.constant_(module.bias, 0.0) elif isinstance(module, nn.Embedding): module.weight.data.normal_() if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() 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() 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).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 hidden_states = hidden_states.permute(0, 2, 1, 3, 4) hidden_states = self.quant_conv(hidden_states) # b c t h w -> b t c h w hidden_states = hidden_states.permute(0, 2, 1, 3, 4) codes = self.quantize(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 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("") @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) @auto_docstring class Emu3PreTrainedModel(PreTrainedModel): config: 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 = True _supports_sdpa = True _can_compile_fullgraph = True _supports_param_buffer_assignment = False _supports_flex_attn = True _supports_attention_backend = True 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 isinstance(config.rope_scaling, dict): self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) @auto_docstring class Emu3TextModel(Emu3PreTrainedModel): _can_record_outputs = { "hidden_states": Emu3DecoderLayer, "attentions": Emu3Attention, } def __init__(self, config: Emu3Config): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList( [Emu3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = Emu3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Emu3RotaryEmbedding(config=config) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() @check_model_inputs @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, cache_position: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, **kwargs: Unpack[TransformersKwargs], ) -> BaseModelOutputWithPast: if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = DynamicCache() if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position: torch.Tensor = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = create_causal_mask( config=self.config, input_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, past_key_values=past_key_values, position_ids=position_ids, ) hidden_states = inputs_embeds position_embeddings = self.rotary_emb(hidden_states, position_ids) for decoder_layer in self.layers[: self.config.num_hidden_layers]: hidden_states = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = self.norm(hidden_states) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, ) @auto_docstring class Emu3ForCausalLM(Emu3PreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} config: Emu3TextConfig def __init__(self, config): super().__init__(config) self.model = Emu3TextModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[TransformersKwargs], ) -> CausalLMOutputWithPast: r""" 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) >>> 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] ```""" outputs: BaseModelOutputWithPast = 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.last_hidden_state # 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.vocab_size, **kwargs) return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) 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 set_decoder(self, decoder): self.text_model = decoder def get_decoder(self): return self.text_model def get_image_tokens(self, pixel_values: torch.FloatTensor, image_sizes: 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. """ image_tokens_list = self.vqmodel.encode(pixel_values, image_sizes) bpe_tokens_list = [self.vocabulary_mapping.convert_img2bpe(tokens).flatten() for tokens in image_tokens_list] bpe_tokens = torch.cat(bpe_tokens_list) return bpe_tokens def get_image_features(self, pixel_values: torch.FloatTensor, image_sizes: torch.LongTensor): """ Tokenizes images into discrete tokens with VQGAN module and embeds them with text embeddings layer Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): The tensors corresponding to the input images. """ image_tokens = self.get_image_tokens(pixel_values, image_sizes) split_sizes = [ (height // self.vqmodel.vision_spatial_factor) * (width // self.vqmodel.vision_spatial_factor + 1) for height, width in image_sizes ] image_features = self.get_input_embeddings()(image_tokens) image_features = torch.split(image_features, split_sizes) return image_features @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 placeholdr 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() special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) n_image_features = image_features.shape[0] * image_features.shape[1] if inputs_embeds[special_image_mask].numel() != image_features.numel(): raise ValueError( 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, pixel_values: torch.FloatTensor = None, image_sizes: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[TransformersKwargs], ) -> Union[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_embeds = self.get_image_features(pixel_values, image_sizes) image_embeds = torch.cat(image_embeds, dim=0) special_image_mask = self.get_placeholder_mask( input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds ) inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_embeds) # 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): base_model_prefix = "" _tied_weights_keys = ["lm_head.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 set_decoder(self, decoder): self.model.set_decoder(decoder) def get_decoder(self): return self.model.get_decoder() # Make modules available throught conditional class for BC @property def text_model(self): return self.model.text_model @property def vqmodel(self): return self.model.vqmodel @property def vocabulary_mapping(self): return self.model.vocabulary_mapping 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, pixel_values: torch.FloatTensor = None, image_sizes: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[TransformersKwargs], ) -> Union[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 requests >>> from PIL import Image >>> model = Emu3ForConditionalGeneration.from_pretrained("BAAI/Emu3-Chat-hf", torch_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) >>> 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) >>> 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, **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, **kwargs, ) if cache_position[0] != 0: model_inputs["pixel_values"] = None return model_inputs __all__ = [ "Emu3ForConditionalGeneration", "Emu3ForCausalLM", "Emu3TextModel", "Emu3PreTrainedModel", "Emu3VQVAE", "Emu3Model", ]