Update resampler.py
fix
This commit is contained in:
smthemex
+131
-541
@@ -1,568 +1,158 @@
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# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
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# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
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# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
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import math
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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 einops import rearrange
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from einops.layers.torch import Rearrange
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class AttnProcessor(nn.Module):
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r"""
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Default processor for performing attention-related computations.
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"""
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# FFN
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def FeedForward(dim, mult=4):
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inner_dim = int(dim * mult)
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return nn.Sequential(
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nn.LayerNorm(dim),
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nn.Linear(dim, inner_dim, bias=False),
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nn.GELU(),
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nn.Linear(inner_dim, dim, bias=False),
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)
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def reshape_tensor(x, heads):
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bs, length, width = x.shape
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# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
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x = x.view(bs, length, heads, -1)
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# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
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x = x.transpose(1, 2)
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# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
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x = x.reshape(bs, heads, length, -1)
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return x
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class PerceiverAttention(nn.Module):
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def __init__(self, *, dim, dim_head=64, heads=8):
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super().__init__()
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self.scale = dim_head**-0.5
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self.dim_head = dim_head
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self.heads = heads
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inner_dim = dim_head * heads
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self.norm1 = nn.LayerNorm(dim)
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self.norm2 = nn.LayerNorm(dim)
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self.to_q = nn.Linear(dim, inner_dim, bias=False)
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self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
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self.to_out = nn.Linear(inner_dim, dim, bias=False)
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def forward(self, x, latents):
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"""
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Args:
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x (torch.Tensor): image features
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shape (b, n1, D)
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latent (torch.Tensor): latent features
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shape (b, n2, D)
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"""
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x = self.norm1(x)
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latents = self.norm2(latents)
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b, l, _ = latents.shape
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q = self.to_q(latents)
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kv_input = torch.cat((x, latents), dim=-2)
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k, v = self.to_kv(kv_input).chunk(2, dim=-1)
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q = reshape_tensor(q, self.heads)
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k = reshape_tensor(k, self.heads)
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v = reshape_tensor(v, self.heads)
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# attention
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scale = 1 / math.sqrt(math.sqrt(self.dim_head))
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weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
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weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
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out = weight @ v
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out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
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return self.to_out(out)
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class Resampler(nn.Module):
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def __init__(
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self,
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hidden_size=None,
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cross_attention_dim=None,
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dim=1024,
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depth=8,
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dim_head=64,
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heads=16,
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num_queries=8,
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embedding_dim=768,
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output_dim=1024,
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ff_mult=4,
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max_seq_len: int = 257, # CLIP tokens + CLS token
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apply_pos_emb: bool = False,
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num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence
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):
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super().__init__()
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self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
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def __call__(
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self,
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attn,
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hidden_states,
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encoder_hidden_states=None,
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attention_mask=None,
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temb=None,
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*args,
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**kwargs,
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):
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residual = hidden_states
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self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
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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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self.proj_in = nn.Linear(embedding_dim, dim)
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input_ndim = hidden_states.ndim
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self.proj_out = nn.Linear(dim, output_dim)
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self.norm_out = nn.LayerNorm(output_dim)
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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(batch_size, channel, height * width).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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)
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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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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(1, 2)
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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(encoder_hidden_states)
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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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query = attn.head_to_batch_dim(query)
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key = attn.head_to_batch_dim(key)
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value = attn.head_to_batch_dim(value)
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attention_probs = attn.get_attention_scores(query, key, attention_mask)
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hidden_states = torch.bmm(attention_probs, value)
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hidden_states = attn.batch_to_head_dim(hidden_states)
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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(batch_size, channel, height, width)
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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 IPAttnProcessor(nn.Module):
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r"""
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Attention processor for IP-Adapater.
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Args:
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hidden_size (`int`):
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The hidden size of the attention layer.
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cross_attention_dim (`int`):
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The number of channels in the `encoder_hidden_states`.
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scale (`float`, defaults to 1.0):
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the weight scale of image prompt.
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num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
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The context length of the image features.
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"""
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def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
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super().__init__()
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self.hidden_size = hidden_size
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self.cross_attention_dim = cross_attention_dim
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self.scale = scale
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self.num_tokens = num_tokens
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self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
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self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
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def __call__(
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self,
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attn,
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hidden_states,
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encoder_hidden_states=None,
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attention_mask=None,
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temb=None,
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*args,
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**kwargs,
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):
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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(batch_size, channel, height * width).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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)
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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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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(1, 2)
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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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else:
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# get encoder_hidden_states, ip_hidden_states
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end_pos = encoder_hidden_states.shape[1] - self.num_tokens
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encoder_hidden_states, ip_hidden_states = (
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encoder_hidden_states[:, :end_pos, :],
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encoder_hidden_states[:, end_pos:, :],
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self.to_latents_from_mean_pooled_seq = (
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nn.Sequential(
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nn.LayerNorm(dim),
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nn.Linear(dim, dim * num_latents_mean_pooled),
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Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
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)
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if attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
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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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query = attn.head_to_batch_dim(query)
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key = attn.head_to_batch_dim(key)
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value = attn.head_to_batch_dim(value)
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attention_probs = attn.get_attention_scores(query, key, attention_mask)
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hidden_states = torch.bmm(attention_probs, value)
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hidden_states = attn.batch_to_head_dim(hidden_states)
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# for ip-adapter
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ip_key = self.to_k_ip(ip_hidden_states)
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ip_value = self.to_v_ip(ip_hidden_states)
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ip_key = attn.head_to_batch_dim(ip_key)
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ip_value = attn.head_to_batch_dim(ip_value)
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ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
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self.attn_map = ip_attention_probs
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ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
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ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
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hidden_states = hidden_states + self.scale * ip_hidden_states
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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(batch_size, channel, height, width)
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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 AttnProcessor2_0(torch.nn.Module):
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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).
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"""
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def __init__(
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self,
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hidden_size=None,
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cross_attention_dim=None,
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):
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super().__init__()
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
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def __call__(
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self,
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attn,
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hidden_states,
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encoder_hidden_states=None,
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attention_mask=None,
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temb=None,
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*args,
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**kwargs,
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):
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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(batch_size, channel, height * width).transpose(1, 2)
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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if num_latents_mean_pooled > 0
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else None
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)
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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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(batch_size, attn.heads, -1, attention_mask.shape[-1])
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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(1, 2)
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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(encoder_hidden_states)
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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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inner_dim = key.shape[-1]
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head_dim = inner_dim // attn.heads
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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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# 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(batch_size, -1, attn.heads * head_dim)
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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(batch_size, channel, height, width)
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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 IPAttnProcessor2_0(torch.nn.Module):
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r"""
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Attention processor for IP-Adapater for PyTorch 2.0.
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Args:
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hidden_size (`int`):
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The hidden size of the attention layer.
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cross_attention_dim (`int`):
|
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The number of channels in the `encoder_hidden_states`.
|
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scale (`float`, defaults to 1.0):
|
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the weight scale of image prompt.
|
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num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
|
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The context length of the image features.
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"""
|
||||
|
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def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
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super().__init__()
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|
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
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self.hidden_size = hidden_size
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self.cross_attention_dim = cross_attention_dim
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self.scale = scale
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self.num_tokens = num_tokens
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self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
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self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
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def __call__(
|
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self,
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attn,
|
||||
hidden_states,
|
||||
encoder_hidden_states=None,
|
||||
attention_mask=None,
|
||||
temb=None,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
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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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||||
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input_ndim = hidden_states.ndim
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||||
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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(batch_size, channel, height * width).transpose(1, 2)
|
||||
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||||
batch_size, sequence_length, _ = (
|
||||
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
||||
)
|
||||
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if attention_mask is not None:
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attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
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||||
# scaled_dot_product_attention expects attention_mask shape to be
|
||||
# (batch, heads, source_length, target_length)
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attention_mask = attention_mask.view(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(1, 2)
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||||
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||||
query = attn.to_q(hidden_states)
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||||
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||||
if encoder_hidden_states is None:
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||||
encoder_hidden_states = hidden_states
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||||
else:
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||||
# get encoder_hidden_states, ip_hidden_states
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||||
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
||||
encoder_hidden_states, ip_hidden_states = (
|
||||
encoder_hidden_states[:, :end_pos, :],
|
||||
encoder_hidden_states[:, end_pos:, :],
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(
|
||||
nn.ModuleList(
|
||||
[
|
||||
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
|
||||
FeedForward(dim=dim, mult=ff_mult),
|
||||
]
|
||||
)
|
||||
)
|
||||
if attn.norm_cross:
|
||||
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
||||
|
||||
key = attn.to_k(encoder_hidden_states)
|
||||
value = attn.to_v(encoder_hidden_states)
|
||||
def forward(self, x):
|
||||
if self.pos_emb is not None:
|
||||
n, device = x.shape[1], x.device
|
||||
pos_emb = self.pos_emb(torch.arange(n, device=device))
|
||||
x = x + pos_emb
|
||||
|
||||
inner_dim = key.shape[-1]
|
||||
head_dim = inner_dim // attn.heads
|
||||
latents = self.latents.repeat(x.size(0), 1, 1)
|
||||
|
||||
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
x = self.proj_in(x)
|
||||
|
||||
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
if self.to_latents_from_mean_pooled_seq:
|
||||
meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
|
||||
meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
|
||||
latents = torch.cat((meanpooled_latents, latents), dim=-2)
|
||||
|
||||
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
||||
# TODO: add support for attn.scale when we move to Torch 2.1
|
||||
hidden_states = F.scaled_dot_product_attention(
|
||||
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
||||
)
|
||||
for attn, ff in self.layers:
|
||||
latents = attn(x, latents) + latents
|
||||
latents = ff(latents) + latents
|
||||
|
||||
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
||||
hidden_states = hidden_states.to(query.dtype)
|
||||
|
||||
# for ip-adapter
|
||||
ip_key = self.to_k_ip(ip_hidden_states)
|
||||
ip_value = self.to_v_ip(ip_hidden_states)
|
||||
|
||||
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
|
||||
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
||||
# TODO: add support for attn.scale when we move to Torch 2.1
|
||||
ip_hidden_states = F.scaled_dot_product_attention(
|
||||
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
|
||||
)
|
||||
with torch.no_grad():
|
||||
self.attn_map = query @ ip_key.transpose(-2, -1).softmax(dim=-1)
|
||||
#print(self.attn_map.shape)
|
||||
|
||||
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
||||
ip_hidden_states = ip_hidden_states.to(query.dtype)
|
||||
|
||||
hidden_states = hidden_states + self.scale * ip_hidden_states
|
||||
|
||||
# linear proj
|
||||
hidden_states = attn.to_out[0](hidden_states)
|
||||
# dropout
|
||||
hidden_states = attn.to_out[1](hidden_states)
|
||||
|
||||
if input_ndim == 4:
|
||||
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
||||
|
||||
if attn.residual_connection:
|
||||
hidden_states = hidden_states + residual
|
||||
|
||||
hidden_states = hidden_states / attn.rescale_output_factor
|
||||
|
||||
return hidden_states
|
||||
latents = self.proj_out(latents)
|
||||
return self.norm_out(latents)
|
||||
|
||||
|
||||
## for controlnet
|
||||
class CNAttnProcessor:
|
||||
r"""
|
||||
Default processor for performing attention-related computations.
|
||||
"""
|
||||
def masked_mean(t, *, dim, mask=None):
|
||||
if mask is None:
|
||||
return t.mean(dim=dim)
|
||||
|
||||
def __init__(self, num_tokens=4):
|
||||
self.num_tokens = num_tokens
|
||||
denom = mask.sum(dim=dim, keepdim=True)
|
||||
mask = rearrange(mask, "b n -> b n 1")
|
||||
masked_t = t.masked_fill(~mask, 0.0)
|
||||
|
||||
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None, *args, **kwargs,):
|
||||
residual = hidden_states
|
||||
|
||||
if attn.spatial_norm is not None:
|
||||
hidden_states = attn.spatial_norm(hidden_states, temb)
|
||||
|
||||
input_ndim = hidden_states.ndim
|
||||
|
||||
if input_ndim == 4:
|
||||
batch_size, channel, height, width = hidden_states.shape
|
||||
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
||||
|
||||
batch_size, sequence_length, _ = (
|
||||
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
||||
)
|
||||
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
||||
|
||||
if attn.group_norm is not None:
|
||||
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
||||
|
||||
query = attn.to_q(hidden_states)
|
||||
|
||||
if encoder_hidden_states is None:
|
||||
encoder_hidden_states = hidden_states
|
||||
else:
|
||||
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
||||
encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text
|
||||
if attn.norm_cross:
|
||||
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
||||
|
||||
key = attn.to_k(encoder_hidden_states)
|
||||
value = attn.to_v(encoder_hidden_states)
|
||||
|
||||
query = attn.head_to_batch_dim(query)
|
||||
key = attn.head_to_batch_dim(key)
|
||||
value = attn.head_to_batch_dim(value)
|
||||
|
||||
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
||||
hidden_states = torch.bmm(attention_probs, value)
|
||||
hidden_states = attn.batch_to_head_dim(hidden_states)
|
||||
|
||||
# linear proj
|
||||
hidden_states = attn.to_out[0](hidden_states)
|
||||
# dropout
|
||||
hidden_states = attn.to_out[1](hidden_states)
|
||||
|
||||
if input_ndim == 4:
|
||||
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
||||
|
||||
if attn.residual_connection:
|
||||
hidden_states = hidden_states + residual
|
||||
|
||||
hidden_states = hidden_states / attn.rescale_output_factor
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class CNAttnProcessor2_0:
|
||||
r"""
|
||||
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
|
||||
"""
|
||||
|
||||
def __init__(self, num_tokens=4):
|
||||
if not hasattr(F, "scaled_dot_product_attention"):
|
||||
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
||||
self.num_tokens = num_tokens
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
attn,
|
||||
hidden_states,
|
||||
encoder_hidden_states=None,
|
||||
attention_mask=None,
|
||||
temb=None,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
residual = hidden_states
|
||||
|
||||
if attn.spatial_norm is not None:
|
||||
hidden_states = attn.spatial_norm(hidden_states, temb)
|
||||
|
||||
input_ndim = hidden_states.ndim
|
||||
|
||||
if input_ndim == 4:
|
||||
batch_size, channel, height, width = hidden_states.shape
|
||||
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
||||
|
||||
batch_size, sequence_length, _ = (
|
||||
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
||||
# scaled_dot_product_attention expects attention_mask shape to be
|
||||
# (batch, heads, source_length, target_length)
|
||||
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
||||
|
||||
if attn.group_norm is not None:
|
||||
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
||||
|
||||
query = attn.to_q(hidden_states)
|
||||
|
||||
if encoder_hidden_states is None:
|
||||
encoder_hidden_states = hidden_states
|
||||
else:
|
||||
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
||||
encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text
|
||||
if attn.norm_cross:
|
||||
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
||||
|
||||
key = attn.to_k(encoder_hidden_states)
|
||||
value = attn.to_v(encoder_hidden_states)
|
||||
|
||||
inner_dim = key.shape[-1]
|
||||
head_dim = inner_dim // attn.heads
|
||||
|
||||
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
|
||||
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
|
||||
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
||||
# TODO: add support for attn.scale when we move to Torch 2.1
|
||||
hidden_states = F.scaled_dot_product_attention(
|
||||
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
||||
)
|
||||
|
||||
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
||||
hidden_states = hidden_states.to(query.dtype)
|
||||
|
||||
# linear proj
|
||||
hidden_states = attn.to_out[0](hidden_states)
|
||||
# dropout
|
||||
hidden_states = attn.to_out[1](hidden_states)
|
||||
|
||||
if input_ndim == 4:
|
||||
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
||||
|
||||
if attn.residual_connection:
|
||||
hidden_states = hidden_states + residual
|
||||
|
||||
hidden_states = hidden_states / attn.rescale_output_factor
|
||||
|
||||
return hidden_states
|
||||
return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)
|
||||
|
||||
Reference in New Issue
Block a user