421 lines
16 KiB
Python
421 lines
16 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.models.embeddings import apply_rotary_emb
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from diffusers.utils import logging
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from einops import rearrange
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class FlashTripoSGAttnProcessor2_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 Tripo2DiT 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, topk=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.topk = topk
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def qkv(self, attn, q, k, v, attn_mask, dropout_p, is_causal):
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if k.shape[-2] == 3072:
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topk = 1024
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elif k.shape[-2] == 512:
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topk = 256
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else:
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topk = k.shape[-2] // 3
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if self.topk is True:
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q1 = q[:, :, ::100, :]
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sim = q1 @ k.transpose(-1, -2)
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sim = torch.mean(sim, -2)
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topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
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topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
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v0 = torch.gather(v, dim=-2, index=topk_ind)
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k0 = torch.gather(k, dim=-2, index=topk_ind)
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out = F.scaled_dot_product_attention(q, k0, v0)
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elif self.topk is False:
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out = F.scaled_dot_product_attention(q, k, v)
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else:
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idx, counts = self.topk
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start = 0
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outs = []
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for grid_coord, count in zip(idx, counts):
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end = start + count
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q_chunk = q[:, :, start:end, :]
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q1 = q_chunk[:, :, ::50, :]
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sim = q1 @ k.transpose(-1, -2)
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sim = torch.mean(sim, -2)
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topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
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topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
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v0 = torch.gather(v, dim=-2, index=topk_ind)
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k0 = torch.gather(k, dim=-2, index=topk_ind)
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out = F.scaled_dot_product_attention(q_chunk, k0, v0)
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outs.append(out)
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start += count
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out = torch.cat(outs, dim=-2)
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self.topk = False
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return out
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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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**kwargs,
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) -> torch.Tensor:
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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 tripo2 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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# flashvdm topk
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hidden_states = self.qkv(attn, query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False)
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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 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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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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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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