Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,444 Bytes
e4df51f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 |
import torch
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
try:
from flash_attn import flash_attn_varlen_func
FLASH_ATTN_AVALIABLE = True
except:
FLASH_ATTN_AVALIABLE = False
def apply_rotary_emb(
x: torch.Tensor,
freqs_cis,
use_real = True,
use_real_unbind_dim = -1,
):
"""
Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting
tensors contain rotary embeddings and are returned as real tensors.
Args:
x (`torch.Tensor`):
Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply
freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([B, S, D], [B, S, D],)
Returns:
Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
"""
if use_real:
B, H, S, D = x.size()
cos, sin = freqs_cis[..., 0], freqs_cis[..., 1]
cos = cos.unsqueeze(1)
sin = sin.unsqueeze(1)
cos, sin = cos.to(x.device), sin.to(x.device)
if use_real_unbind_dim == -1:
# Used for flux, cogvideox, hunyuan-dit
x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]
x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
elif use_real_unbind_dim == -2:
# Used for Stable Audio
x_real, x_imag = x.reshape(*x.shape[:-1], 2, -1).unbind(-2) # [B, S, H, D//2]
x_rotated = torch.cat([-x_imag, x_real], dim=-1)
else:
raise ValueError(f"`use_real_unbind_dim={use_real_unbind_dim}` but should be -1 or -2.")
out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
return out
else:
# used for lumina
x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
freqs_cis = freqs_cis.unsqueeze(2)
x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
return x_out.type_as(x)
class FluxAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
image_rotary_emb=None,
lens=None,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(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)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
# supporting sequence length
q_lens = lens.clone() if lens is not None else torch.LongTensor([query.shape[2]] * batch_size).to(query.device)
k_lens = lens.clone() if lens is not None else torch.LongTensor([key.shape[2]] * batch_size).to(key.device)
# hacked: shared attention
txt_len = 512
context_key = [
torch.cat([key[0], key[1, :, txt_len:]], dim=1).permute(1, 0, 2),
key[1].permute(1, 0, 2)
]
context_value = [
torch.cat([value[0], value[1, :, txt_len:]], dim=1).permute(1, 0, 2),
value[1].permute(1, 0, 2)
]
k_lens = torch.LongTensor([k.size(0) for k in context_key]).to(query.device)
key = pad_sequence(context_key, batch_first=True).permute(0, 2, 1, 3)
value = pad_sequence(context_value, batch_first=True).permute(0, 2, 1, 3)
# core attention
if FLASH_ATTN_AVALIABLE:
query = query.permute(0, 2, 1, 3) # batch, sequence, num_head, head_dim
key = key.permute(0, 2, 1, 3)
value = value.permute(0, 2, 1, 3)
query = torch.cat([u[:l] for u, l in zip(query, q_lens)], dim=0)
key = torch.cat([u[:l] for u, l in zip(key, k_lens)], dim=0)
value = torch.cat([u[:l] for u, l in zip(value, k_lens)], dim=0)
cu_seqlens_q = F.pad(q_lens.cumsum(dim=0), (1, 0)).to(torch.int32)
cu_seqlens_k = F.pad(k_lens.cumsum(dim=0), (1, 0)).to(torch.int32)
max_seqlen_q = torch.max(q_lens).item()
max_seqlen_k = torch.max(k_lens).item()
hidden_states = flash_attn_varlen_func(query, key, value, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k)
hidden_states = pad_sequence([
hidden_states[start: end]
for start, end in zip(cu_seqlens_q[:-1], cu_seqlens_q[1:])
], batch_first=True)
hidden_states = hidden_states.reshape(batch_size, -1, attn.heads * head_dim)
else:
attn_mask = torch.zeros((query.size(0), 1, query.size(2), key.size(2)), dtype=torch.bool).to(query)
for i, (q_len, k_len) in enumerate(zip(q_lens, k_lens)):
attn_mask[i, :, :q_len, :k_len] = True
hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_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)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states
|