Spaces:
Runtime error
Runtime error
File size: 16,039 Bytes
8a09a62 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 |
import torch
import torch.nn as nn
from typing import Tuple, Union, Optional
try:
import flash_attn
if hasattr(flash_attn, '__version__') and int(flash_attn.__version__[0]) == 2:
from flash_attn.flash_attn_interface import flash_attn_kvpacked_func
from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention
else:
from flash_attn.flash_attn_interface import flash_attn_unpadded_kvpacked_func
from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention
except Exception as e:
print(f'flash_attn import failed: {e}')
def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False):
"""
Reshape frequency tensor for broadcasting it with another tensor.
This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
for the purpose of broadcasting the frequency tensor during element-wise operations.
Args:
freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped.
x (torch.Tensor): Target tensor for broadcasting compatibility.
head_first (bool): head dimension first (except batch dim) or not.
Returns:
torch.Tensor: Reshaped frequency tensor.
Raises:
AssertionError: If the frequency tensor doesn't match the expected shape.
AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions.
"""
ndim = x.ndim
assert 0 <= 1 < ndim
if isinstance(freqs_cis, tuple):
# freqs_cis: (cos, sin) in real space
if head_first:
assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
else:
assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
else:
# freqs_cis: values in complex space
if head_first:
assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
else:
assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
return freqs_cis.view(*shape)
def rotate_half(x):
x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]
return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
def apply_rotary_emb(
xq: torch.Tensor,
xk: Optional[torch.Tensor],
freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
head_first: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Apply rotary embeddings to input tensors using the given frequency tensor.
This function applies rotary embeddings to the given query 'xq' and key 'xk' 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:
xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D]
xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D]
freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials.
head_first (bool): head dimension first (except batch dim) or not.
Returns:
Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
"""
xk_out = None
if isinstance(freqs_cis, tuple):
cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D]
cos, sin = cos.to(xq.device), sin.to(xq.device)
xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq)
if xk is not None:
xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk)
else:
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2]
freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2]
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
if xk is not None:
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2]
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
return xq_out, xk_out
class FlashSelfMHAModified(nn.Module):
"""
Use QK Normalization.
"""
def __init__(self,
dim,
num_heads,
qkv_bias=True,
qk_norm=False,
attn_drop=0.0,
proj_drop=0.0,
device=None,
dtype=None,
norm_layer=nn.LayerNorm,
):
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.dim = dim
self.num_heads = num_heads
assert self.dim % num_heads == 0, "self.kdim must be divisible by num_heads"
self.head_dim = self.dim // num_heads
assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
self.Wqkv = nn.Linear(dim, 3 * dim, bias=qkv_bias, **factory_kwargs)
# TODO: eps should be 1 / 65530 if using fp16
self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
self.inner_attn = FlashSelfAttention(attention_dropout=attn_drop)
self.out_proj = nn.Linear(dim, dim, bias=qkv_bias, **factory_kwargs)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, freqs_cis_img=None):
"""
Parameters
----------
x: torch.Tensor
(batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim)
freqs_cis_img: torch.Tensor
(batch, hidden_dim // 2), RoPE for image
"""
b, s, d = x.shape
qkv = self.Wqkv(x)
qkv = qkv.view(b, s, 3, self.num_heads, self.head_dim) # [b, s, 3, h, d]
q, k, v = qkv.unbind(dim=2) # [b, s, h, d]
q = self.q_norm(q).half() # [b, s, h, d]
k = self.k_norm(k).half()
# Apply RoPE if needed
if freqs_cis_img is not None:
qq, kk = apply_rotary_emb(q, k, freqs_cis_img)
assert qq.shape == q.shape and kk.shape == k.shape, f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}'
q, k = qq, kk
qkv = torch.stack([q, k, v], dim=2) # [b, s, 3, h, d]
context = self.inner_attn(qkv)
out = self.out_proj(context.view(b, s, d))
out = self.proj_drop(out)
out_tuple = (out,)
return out_tuple
class FlashCrossMHAModified(nn.Module):
"""
Use QK Normalization.
"""
def __init__(self,
qdim,
kdim,
num_heads,
qkv_bias=True,
qk_norm=False,
attn_drop=0.0,
proj_drop=0.0,
device=None,
dtype=None,
norm_layer=nn.LayerNorm,
):
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.qdim = qdim
self.kdim = kdim
self.num_heads = num_heads
assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads"
self.head_dim = self.qdim // num_heads
assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
self.scale = self.head_dim ** -0.5
self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs)
# TODO: eps should be 1 / 65530 if using fp16
self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
self.inner_attn = FlashCrossAttention(attention_dropout=attn_drop)
self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, y, freqs_cis_img=None):
"""
Parameters
----------
x: torch.Tensor
(batch, seqlen1, hidden_dim) (where hidden_dim = num_heads * head_dim)
y: torch.Tensor
(batch, seqlen2, hidden_dim2)
freqs_cis_img: torch.Tensor
(batch, hidden_dim // num_heads), RoPE for image
"""
b, s1, _ = x.shape # [b, s1, D]
_, s2, _ = y.shape # [b, s2, 1024]
q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d]
kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d]
k, v = kv.unbind(dim=2) # [b, s2, h, d]
q = self.q_norm(q).half() # [b, s1, h, d]
k = self.k_norm(k).half() # [b, s2, h, d]
# Apply RoPE if needed
if freqs_cis_img is not None:
qq, _ = apply_rotary_emb(q, None, freqs_cis_img)
assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}'
q = qq # [b, s1, h, d]
kv = torch.stack([k, v], dim=2) # [b, s1, 2, h, d]
context = self.inner_attn(q, kv) # [b, s1, h, d]
context = context.view(b, s1, -1) # [b, s1, D]
out = self.out_proj(context)
out = self.proj_drop(out)
out_tuple = (out,)
return out_tuple
class CrossAttention(nn.Module):
"""
Use QK Normalization.
"""
def __init__(self,
qdim,
kdim,
num_heads,
qkv_bias=True,
qk_norm=False,
attn_drop=0.0,
proj_drop=0.0,
device=None,
dtype=None,
norm_layer=nn.LayerNorm,
):
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.qdim = qdim
self.kdim = kdim
self.num_heads = num_heads
assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads"
self.head_dim = self.qdim // num_heads
assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
self.scale = self.head_dim ** -0.5
self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs)
# TODO: eps should be 1 / 65530 if using fp16
self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, y, freqs_cis_img=None):
"""
Parameters
----------
x: torch.Tensor
(batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim)
y: torch.Tensor
(batch, seqlen2, hidden_dim2)
freqs_cis_img: torch.Tensor
(batch, hidden_dim // 2), RoPE for image
"""
b, s1, c = x.shape # [b, s1, D]
_, s2, c = y.shape # [b, s2, 1024]
q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d]
kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d]
k, v = kv.unbind(dim=2) # [b, s, h, d]
q = self.q_norm(q)
k = self.k_norm(k)
# Apply RoPE if needed
if freqs_cis_img is not None:
qq, _ = apply_rotary_emb(q, None, freqs_cis_img)
assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}'
q = qq
q = q * self.scale
q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C
k = k.permute(0, 2, 3, 1).contiguous() # k -> B, L2, H, C - B, H, C, L2
attn = q @ k # attn -> B, H, L1, L2
attn = attn.softmax(dim=-1) # attn -> B, H, L1, L2
attn = self.attn_drop(attn)
x = attn @ v.transpose(-2, -3) # v -> B, L2, H, C - B, H, L2, C x-> B, H, L1, C
context = x.transpose(1, 2) # context -> B, H, L1, C - B, L1, H, C
context = context.contiguous().view(b, s1, -1)
out = self.out_proj(context) # context.reshape - B, L1, -1
out = self.proj_drop(out)
out_tuple = (out,)
return out_tuple
class Attention(nn.Module):
"""
We rename some layer names to align with flash attention
"""
def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0.,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.dim = dim
self.num_heads = num_heads
assert self.dim % num_heads == 0, 'dim should be divisible by num_heads'
self.head_dim = self.dim // num_heads
# This assertion is aligned with flash attention
assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
self.scale = self.head_dim ** -0.5
# qkv --> Wqkv
self.Wqkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
# TODO: eps should be 1 / 65530 if using fp16
self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.out_proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, freqs_cis_img=None):
B, N, C = x.shape
qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d]
q, k, v = qkv.unbind(0) # [b, h, s, d]
q = self.q_norm(q) # [b, h, s, d]
k = self.k_norm(k) # [b, h, s, d]
# Apply RoPE if needed
if freqs_cis_img is not None:
qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True)
assert qq.shape == q.shape and kk.shape == k.shape, \
f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}'
q, k = qq, kk
q = q * self.scale
attn = q @ k.transpose(-2, -1) # [b, h, s, d] @ [b, h, d, s]
attn = attn.softmax(dim=-1) # [b, h, s, s]
attn = self.attn_drop(attn)
x = attn @ v # [b, h, s, d]
x = x.transpose(1, 2).reshape(B, N, C) # [b, s, h, d]
x = self.out_proj(x)
x = self.proj_drop(x)
out_tuple = (x,)
return out_tuple
|