469 lines
18 KiB
Python
469 lines
18 KiB
Python
from typing import Callable, List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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from diffusers.models.attention_processor import Attention
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from diffusers.utils import logging
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from diffusers.utils.import_utils import is_torch_npu_available, is_xformers_available
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from diffusers.utils.torch_utils import is_torch_version, maybe_allow_in_graph
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from einops import rearrange
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from torch import nn
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class TripoSGAttnProcessor2_0:
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r"""
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Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
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used in the TripoSG model. It applies a s normalization layer and rotary embedding on query and key vector.
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"""
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def __init__(self):
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError(
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"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
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)
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def __call__(
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self,
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attn: Attention,
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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temb: Optional[torch.Tensor] = None,
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image_rotary_emb: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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from diffusers.models.embeddings import apply_rotary_emb
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residual = hidden_states
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if attn.spatial_norm is not None:
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hidden_states = attn.spatial_norm(hidden_states, temb)
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input_ndim = hidden_states.ndim
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if input_ndim == 4:
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.view(
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batch_size, channel, height * width
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).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape
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if encoder_hidden_states is None
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else encoder_hidden_states.shape
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)
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(
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attention_mask, sequence_length, batch_size
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)
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# scaled_dot_product_attention expects attention_mask shape to be
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# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.view(
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batch_size, attn.heads, -1, attention_mask.shape[-1]
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)
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if attn.group_norm is not None:
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
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1, 2
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)
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query = attn.to_q(hidden_states)
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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elif attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(
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encoder_hidden_states
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)
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key = attn.to_k(encoder_hidden_states)
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value = attn.to_v(encoder_hidden_states)
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# NOTE that pre-trained models split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim)
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# instead of .view(..., 3, attn.heads, dim). So we need to re-split here.
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if not attn.is_cross_attention:
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qkv = torch.cat((query, key, value), dim=-1)
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split_size = qkv.shape[-1] // attn.heads // 3
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qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
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query, key, value = torch.split(qkv, split_size, dim=-1)
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else:
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kv = torch.cat((key, value), dim=-1)
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split_size = kv.shape[-1] // attn.heads // 2
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kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
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key, value = torch.split(kv, split_size, dim=-1)
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head_dim = key.shape[-1]
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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if attn.norm_q is not None:
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query = attn.norm_q(query)
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if attn.norm_k is not None:
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key = attn.norm_k(key)
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# Apply RoPE if needed
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if image_rotary_emb is not None:
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query = apply_rotary_emb(query, image_rotary_emb)
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if not attn.is_cross_attention:
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key = apply_rotary_emb(key, image_rotary_emb)
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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# TODO: add support for attn.scale when we move to Torch 2.1
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hidden_states = F.scaled_dot_product_attention(
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).reshape(
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batch_size, -1, attn.heads * head_dim
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)
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hidden_states = hidden_states.to(query.dtype)
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# linear proj
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hidden_states = attn.to_out[0](hidden_states)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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if input_ndim == 4:
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hidden_states = hidden_states.transpose(-1, -2).reshape(
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batch_size, channel, height, width
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)
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if attn.residual_connection:
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hidden_states = hidden_states + residual
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hidden_states = hidden_states / attn.rescale_output_factor
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return hidden_states
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class FusedTripoSGAttnProcessor2_0:
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r"""
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Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0) with fused
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projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on
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query and key vector.
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"""
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def __init__(self):
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError(
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"FusedTripoSGAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
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)
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def __call__(
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self,
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attn: Attention,
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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temb: Optional[torch.Tensor] = None,
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image_rotary_emb: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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from diffusers.models.embeddings import apply_rotary_emb
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residual = hidden_states
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if attn.spatial_norm is not None:
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hidden_states = attn.spatial_norm(hidden_states, temb)
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input_ndim = hidden_states.ndim
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if input_ndim == 4:
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.view(
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batch_size, channel, height * width
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).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape
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if encoder_hidden_states is None
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else encoder_hidden_states.shape
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)
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(
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attention_mask, sequence_length, batch_size
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)
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# scaled_dot_product_attention expects attention_mask shape to be
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# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.view(
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batch_size, attn.heads, -1, attention_mask.shape[-1]
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)
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if attn.group_norm is not None:
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
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1, 2
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)
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# NOTE that pre-trained split heads first, then split qkv
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if encoder_hidden_states is None:
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qkv = attn.to_qkv(hidden_states)
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split_size = qkv.shape[-1] // attn.heads // 3
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qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
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query, key, value = torch.split(qkv, split_size, dim=-1)
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else:
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if attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(
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encoder_hidden_states
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)
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query = attn.to_q(hidden_states)
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kv = attn.to_kv(encoder_hidden_states)
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split_size = kv.shape[-1] // attn.heads // 2
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kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
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key, value = torch.split(kv, split_size, dim=-1)
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head_dim = key.shape[-1]
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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if attn.norm_q is not None:
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query = attn.norm_q(query)
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if attn.norm_k is not None:
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key = attn.norm_k(key)
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# Apply RoPE if needed
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if image_rotary_emb is not None:
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query = apply_rotary_emb(query, image_rotary_emb)
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if not attn.is_cross_attention:
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key = apply_rotary_emb(key, image_rotary_emb)
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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# TODO: add support for attn.scale when we move to Torch 2.1
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hidden_states = F.scaled_dot_product_attention(
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query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
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)
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hidden_states = hidden_states.transpose(1, 2).reshape(
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batch_size, -1, attn.heads * head_dim
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)
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hidden_states = hidden_states.to(query.dtype)
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# linear proj
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hidden_states = attn.to_out[0](hidden_states)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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if input_ndim == 4:
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hidden_states = hidden_states.transpose(-1, -2).reshape(
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batch_size, channel, height, width
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)
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if attn.residual_connection:
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hidden_states = hidden_states + residual
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hidden_states = hidden_states / attn.rescale_output_factor
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return hidden_states
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class MIAttnProcessor2_0:
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r"""
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Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
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used in the MIDI model. It applies a normalization layer and rotary embedding on query and key vector.
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"""
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def __init__(self, use_mi: bool = True):
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError(
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"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
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)
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self.use_mi = use_mi
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def __call__(
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self,
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attn: Attention,
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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temb: Optional[torch.Tensor] = None,
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image_rotary_emb: Optional[torch.Tensor] = None,
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num_instances: Optional[Union[int, torch.IntTensor]] = None,
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num_instances_per_batch: Optional[int] = None,
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) -> torch.Tensor:
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from diffusers.models.embeddings import apply_rotary_emb
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residual = hidden_states
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if attn.spatial_norm is not None:
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hidden_states = attn.spatial_norm(hidden_states, temb)
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input_ndim = hidden_states.ndim
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if input_ndim == 4:
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.view(
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batch_size, channel, height * width
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).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape
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if encoder_hidden_states is None
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else encoder_hidden_states.shape
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)
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(
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attention_mask, sequence_length, batch_size
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)
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# scaled_dot_product_attention expects attention_mask shape to be
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# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.view(
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batch_size, attn.heads, -1, attention_mask.shape[-1]
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)
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if attn.group_norm is not None:
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hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
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1, 2
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)
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query = attn.to_q(hidden_states)
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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elif attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(
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encoder_hidden_states
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)
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key = attn.to_k(encoder_hidden_states)
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value = attn.to_v(encoder_hidden_states)
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# NOTE that pre-trained models split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim)
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# instead of .view(..., 3, attn.heads, dim). So we need to re-split here.
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if not attn.is_cross_attention:
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qkv = torch.cat((query, key, value), dim=-1)
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split_size = qkv.shape[-1] // attn.heads // 3
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qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
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query, key, value = torch.split(qkv, split_size, dim=-1)
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else:
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kv = torch.cat((key, value), dim=-1)
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split_size = kv.shape[-1] // attn.heads // 2
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kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
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key, value = torch.split(kv, split_size, dim=-1)
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head_dim = key.shape[-1]
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query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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if attn.norm_q is not None:
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query = attn.norm_q(query)
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if attn.norm_k is not None:
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key = attn.norm_k(key)
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# Apply RoPE if needed
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if image_rotary_emb is not None:
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query = apply_rotary_emb(query, image_rotary_emb)
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if not attn.is_cross_attention:
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key = apply_rotary_emb(key, image_rotary_emb)
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if not self.use_mi:
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hidden_states = F.scaled_dot_product_attention(
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query,
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key,
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value,
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attn_mask=attention_mask,
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dropout_p=0.0,
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is_causal=False,
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)
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elif num_instances is not None and num_instances_per_batch is None:
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# for inference
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key = rearrange(
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key, "(b ni) h nt c -> b h (ni nt) c", ni=num_instances
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).repeat_interleave(num_instances, dim=0)
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value = rearrange(
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value, "(b ni) h nt c -> b h (ni nt) c", ni=num_instances
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).repeat_interleave(num_instances, dim=0)
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# the output of sdp = (batch, num_heads, seq_len, head_dim)
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hidden_states = F.scaled_dot_product_attention(
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query,
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key,
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value,
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dropout_p=0.0,
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is_causal=False,
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)
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elif num_instances is not None and num_instances_per_batch is not None:
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# for training (the same batch size is required)
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# process multi-instance attention among the first `num_instances` samples
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# and do object self-attention on the left samples in per batch
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patch_hidden_states = []
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start_idx = 0
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while start_idx < batch_size: # for classifier-free guidance
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for num in num_instances:
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# Multi-object self-attention
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query_ = query[start_idx : start_idx + num]
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key_ = rearrange(
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key[start_idx : start_idx + num],
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"(b ni) h nt c -> b h (ni nt) c",
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ni=num,
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).repeat_interleave(num, dim=0)
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value_ = rearrange(
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value[start_idx : start_idx + num],
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"(b ni) h nt c -> b h (ni nt) c",
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ni=num,
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).repeat_interleave(num, dim=0)
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patch_hidden_states.append(
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F.scaled_dot_product_attention(
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query_,
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key_,
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value_,
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dropout_p=0.0,
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is_causal=False,
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)
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)
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# Single-object self-attention for padding and regularization
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query_ = query[
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start_idx + num : start_idx + num_instances_per_batch
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]
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key_ = key[start_idx + num : start_idx + num_instances_per_batch]
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value_ = value[
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start_idx + num : start_idx + num_instances_per_batch
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]
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if query_.shape[0] > 0:
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patch_hidden_states.append(
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F.scaled_dot_product_attention(
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query_,
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key_,
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value_,
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dropout_p=0.0,
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is_causal=False,
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)
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)
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start_idx += num_instances_per_batch
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hidden_states = torch.cat(patch_hidden_states, dim=0)
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hidden_states = hidden_states.transpose(1, 2).reshape(
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batch_size, -1, attn.heads * head_dim
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)
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hidden_states = hidden_states.to(query.dtype)
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# linear proj
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hidden_states = attn.to_out[0](hidden_states)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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if input_ndim == 4:
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hidden_states = hidden_states.transpose(-1, -2).reshape(
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batch_size, channel, height, width
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)
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if attn.residual_connection:
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hidden_states = hidden_states + residual
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hidden_states = hidden_states / attn.rescale_output_factor
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return hidden_states
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