1622 lines
62 KiB
Python
1622 lines
62 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/emu3/modular_emu3.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_emu3.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# 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 functools import cached_property
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from typing import Callable, Optional, Union
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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 ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...integrations import use_kernel_forward_from_hub
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from ...masking_utils import create_causal_mask
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
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from ...utils.generic import check_model_inputs
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from .configuration_emu3 import Emu3Config, Emu3TextConfig, Emu3VQVAEConfig
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class Emu3Attention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: Emu3Config, layer_idx: int):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.attention_dropout = config.attention_dropout
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self.is_causal = True
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
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)
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, torch.Tensor]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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@use_kernel_forward_from_hub("RMSNorm")
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class Emu3RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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Emu3RMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class Emu3MLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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class Emu3DecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: Emu3Config, layer_idx: int):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = Emu3Attention(config=config, layer_idx=layer_idx)
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self.mlp = Emu3MLP(config)
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self.input_layernorm = Emu3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = Emu3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
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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_value=past_key_value,
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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(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=2, padding=0)
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def forward(self, hidden_states):
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# no asymmetric padding in torch conv, must do it ourselves
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hidden_states = F.pad(hidden_states, pad=(0, 1, 0, 1), mode="constant", value=0)
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hidden_states = self.conv(hidden_states)
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return hidden_states
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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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|
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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):
|
|
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("<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)
|
|
|
|
|
|
@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
|
|
|
|
@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)
|
|
|
|
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
|
|
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
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",
|
|
]
|