File size: 26,365 Bytes
ae81e0f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 |
"""
Subquadratic attention combining sliding window and linear attentions
- Using "standard" sliding windows
- Didactically computes outputs with n^2 attention weights for now
- Copied + adapted from linear_window_attention_tk.py for single-file reference
For each layer:
- We first compute (softmax) attention over sliding windows
- We then compute standard linear attention to "fill in" the earlier parts
- We combine to model the entire sequence
"""
from typing import List, Tuple, Optional, Callable
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.cache_utils import Cache
try:
from transformers.modeling_flash_attention_utils import _flash_attention_forward
except ModuleNotFoundError:
_flash_attention_forward = None # Transformers v4.36
# Causal linear attention dot product CUDA kernel from fast-transformers
from csrc import causal_dot_product
from src.model.rotary import apply_rotary_pos_emb
from .linear_attention import (
LolcatsLinearAttention, LinearAttentionState,
softmax_attention
)
# ----------------------
# Sliding window helpers
# ----------------------
def get_masks(window_size: int, q_len: int, k_len: int,
device: torch.device) -> tuple[torch.Tensor]:
"""
Return masks for softmax and linear attention terms
-> 1 is include, 0 is ignore
"""
kwargs = {'device': device, 'dtype': int}
causal_mask = torch.ones((q_len, k_len), **kwargs).tril(max(k_len - q_len, 0))
linear_mask = torch.ones((q_len, k_len), **kwargs).tril(max(k_len - q_len, 0) - window_size)
window_mask = causal_mask - linear_mask
# Return softmax mask (window), linear attention mask
# -> shapes broadcast over (b, h, q_len, k_len)
return window_mask[None, None, ...], linear_mask[None, None, ...]
def hybrid_attention_quadratic(q: torch.Tensor, k: torch.Tensor,
f_q: torch.Tensor, f_k: torch.Tensor,
v: torch.Tensor,
window_factor: torch.Tensor,
linear_factor: torch.Tensor,
window_size: int,
kv_state: torch.Tensor = None,
k_state: torch.Tensor = None,
eps: float = 1e-12,
mask_value: float=-1e8):
"""
Hybrid attention combining sliding window and linear attentions
"""
mask_window, mask_linear = get_masks(window_size, q.shape[-2], k.shape[-2], q.device)
# 1. Sliding window (softmax attention)
a_sm = torch.einsum('bhmd,bhnd->bhmn', q.float(), k.float()) * (k.shape[-1] ** -0.5)
a_sm = a_sm.masked_fill(~mask_window.bool(), mask_value)
# torch.softmax(a_sm, dim=-1), but we account for the max when combining
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factor * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
# 2. Under window (linear attention)
a_ln = torch.einsum('bhmd,bhnd->bhmn', f_q.float(), f_k.float())
a_ln = linear_factor * a_ln.masked_fill(~mask_linear.bool(), 0)
sum_ln = a_ln.sum(dim=-1, keepdim=True)
# 3. Combine
a = ((a_sm + a_ln) / (sum_sm + sum_ln)).to(q.dtype) # Save attention weights
# Allow outputs to also depend on prior kv_state and k_state
y = torch.einsum('bhmn,bhnd->bhmd', a_sm + a_ln, v.float())
if kv_state is not None: # Combine with prior kv_state and k_state
y += linear_factor * torch.einsum('bhld,bhdf->bhlf', f_q.float(), kv_state.float())
sum_ln += linear_factor * torch.einsum(
'bhld,bhnd->bhl', f_q.float(), k_state.float())[..., None]
y = (y / (sum_sm + sum_ln)).to(q.dtype)
return y, a # attention weights only for the last chunk
# ------------------------------
# Hybrid window attention linear
# ------------------------------
def under_window_linear_attention(f_q: torch.Tensor, f_k: torch.Tensor, v: torch.Tensor,
window_size: int, linear_factor: float, eps: float=1e-12):
"""Compute hybrid window attention dot product with linear complexity in q_len"""
dtype = f_q.dtype
w = window_size
f_k = F.pad(f_k, (0, 0, w, 0), value=0)[:, :, :-w, :]
v = F.pad(v, (0, 0, w, 0), value=0)[:, :, :-w, :]
qkv = linear_factor * causal_dot_product(f_q.contiguous().to(dtype=torch.float32),
f_k.contiguous().to(dtype=torch.float32),
v.contiguous().to(dtype=torch.float32)).to(dtype=dtype)
sum_f_k = f_k.float().cumsum(dim=2).to(dtype=dtype)
sum_qk = linear_factor * torch.einsum("bhld,bhld->bhl", f_q, sum_f_k)[..., None]
sum_qk[sum_qk == 0] += eps
return qkv, sum_qk
def sliding_window_softmax_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
window_size: int, window_factor: float, mask_value: float=-1e8):
"""
Compute sliding window softmax attention without materializing
O(seq_len^2) attention weights
"""
d = q.shape[-1]
# Compute windows for keys
window_kwargs = {'dimension': 2, 'size': window_size, 'step': 1}
k = F.pad(k, (0, 0, window_size - 1, 0), value=0).unfold(**window_kwargs)
v = F.pad(v, (0, 0, window_size - 1, 0), value=0).unfold(**window_kwargs)
# Compute windowed_softmax(qk); causal in its construction
a_sm = torch.einsum('bhld,bhldw->bhlw', q, k) * (d ** -0.5)
a_sm[a_sm == 0] = -torch.finfo(q.dtype).max # heuristic for zeroing out padding above
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factor * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
return torch.einsum('bhlw,bhldw->bhld', a_sm, v), sum_sm
# return torch.einsum('bhlw,bhldw->bhld', torch.softmax(qk, dim=-1), v)
def hybrid_attention_linear(q: torch.Tensor, k: torch.Tensor,
f_q: torch.Tensor, f_k: torch.Tensor,
v: torch.Tensor,
window_factor: torch.Tensor = None,
linear_factor: torch.Tensor = None,
window_size: int = 64,
kv_state: torch.Tensor = None,
k_state: torch.Tensor = None,
eps: float = 1e-12,
mask_value: float=-1e8):
"""
Alternative hybrid attention combining sliding window and linear attentions
-> Uses O(n) memory if n is sequence length by padding and unfolding windows
"""
window_kwargs = {'dimension': 2, 'size': window_size, 'step': 1}
# 1. Sliding window (softmax attention)
with torch.no_grad():
qkv_sm, sum_qk_sm = sliding_window_softmax_attention(q, k, v, window_size, window_factor, mask_value)
# 2. Under window (linear attention)
qkv_ln, sum_qk_ln = under_window_linear_attention(f_q, f_k, v, window_size, linear_factor, eps)
# 3. Combine
y = (qkv_sm + qkv_ln) / (sum_qk_sm + sum_qk_ln)
return y, None
# ---------------------
# Attention layer class
# ---------------------
class LolcatsLinearSlidingWindowAttention(LolcatsLinearAttention):
"""
Lolcats attention combining sliding window and linear attention
"""
def __init__(self,
window_size: int = 64,
decode_window_size: int = None,
affine_attention_factors: bool = False,
init_window_factor: float = 0,
train_window_factor: bool = True,
state_grad_enabled: bool = False,
**kwargs):
self.window_size = window_size
self.decode_window_size = (
decode_window_size if decode_window_size is not None else window_size
)
self.window_kwargs = {'dimension': 2, 'size': window_size, 'step': 1}
super().__init__(**kwargs)
# Determine how we compute attentions
self.linear_attention = hybrid_attention_linear
self.attention_type = 'lolcats_llama_window_sw'
# Learnable factor for combining attentions
self.affine_attention_factors = affine_attention_factors
device, dtype = self.q_proj.weight.device, self.q_proj.weight.dtype
if train_window_factor:
self.window_factors = nn.Parameter(
init_window_factor * torch.ones(1, self.num_heads, 1, 1, device=device, dtype=dtype))
else:
self.register_buffer(
"window_factors", init_window_factor * torch.ones(1, self.num_heads, 1, 1, device=device, dtype=dtype)
)
# Whether we use original flash attention 2 inference (use during attention transfer)
self.base_inference = False
self.state_grad_enabled = state_grad_enabled
def forward(self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""
Forward pass with the option to compute attention weights multiple ways
if self.train_attention is True
-> Consistent with HuggingFace Transformers for easy use with their pretrained models
"""
b, l, _ = hidden_states.size()
if self.train_attention and self.base_inference:
with torch.no_grad():
_y_true = flash_attention_2(self, # self.base_attn,
hidden_states=hidden_states,
attention_mask=None,
position_ids=position_ids,
past_key_value=None,
output_attentions=False,
use_cache=False)[0]
# _y_true.shape is (batch_size, seq_len, num_heads, head_dim)
y_true = _y_true.reshape(b, l, -1).contiguous()
y_true = self.o_proj(y_true)
# layer_io = (hidden_states, _y_true) # hack
layer_io = (hidden_states.cpu(), _y_true.cpu()) # hack
return y_true, layer_io, None
else:
q, k, v, kv_seq_len = self.process_qkv(hidden_states, attention_mask,
position_ids, past_key_value)
f_q, f_k = self.feature_map_q(q), self.feature_map_k(k) # Have to do after repeat for grouped-query attn if we use same fmap
attn_weights = None
# attention_mask = None # For now this is always True
if past_key_value is None: # Regular training
window_factors = F.sigmoid(self.window_factors)
linear_factors = 1 - window_factors if self.affine_attention_factors else 1
y_true, a_pred = self.linear_attention(q, k, f_q, f_k, v,
window_factors, linear_factors,
window_size=self.window_size)
attn_weights = a_pred
else:
past_key_value.window_size = self.decode_window_size
if f_q.shape[2] == 1 and kv_seq_len > 1 and not self.training: # Generating
assert use_cache is True
_kv = past_key_value.update_for_decoding(k, v, self.layer_idx,
self.feature_map_k,
dtype=q.dtype)
k_cache, v_cache, f_kv_state, f_k_state = _kv
# Sliding window + linear attention decode
window_factors = F.sigmoid(self.window_factors)
linear_factors = 1 - window_factors if self.affine_attention_factors else 1
# Softmax attention terms
a_sm = torch.einsum('bhmd,bhnd->bhmn', q.float(), k_cache.float()) * (k.shape[-1] ** -0.5)
a_sm_max = torch.amax(a_sm, dim=-1, keepdim=True)
a_sm = window_factors * torch.exp(a_sm - a_sm_max)
sum_sm = a_sm.sum(dim=-1, keepdim=True)
# Combine with linear attention terms
y_true = (torch.einsum('bhmn,bhnd->bhmd', a_sm, v_cache.float())
+ linear_factors * torch.einsum('bhlf,bhfd->bhld', f_q.float(), f_kv_state.float()))
sum_ln = linear_factors * torch.einsum(
'bhlf,bhnf->bhl', f_q.float(), f_k_state.float())[..., None]
y_true = (y_true / (sum_sm + sum_ln)).to(q.dtype)
else: # Stateful training
try:
kv_state = past_key_value.kv_states[self.layer_idx]
k_state = past_key_value.k_states[self.layer_idx]
except IndexError:
kv_state, k_state = None, None
window_factors = F.sigmoid(self.window_factors)
linear_factors = 1 - window_factors if self.affine_attention_factors else 1
y_true, _ = self.linear_attention(q, k, f_q, f_k, v,
window_factors, linear_factors,
window_size=self.window_size,
kv_state=kv_state,
k_state=k_state)
# Save and update KV cache and states
# past_key_value.update(k, v.detach(), self.layer_idx,
# fmap_key_states=f_k.detach(),
# accumulate_in_fp32=True)
past_key_value.update(k, v, self.layer_idx,
fmap_key_states=f_k,
accumulate_in_fp32=True)
# Concatenate heads and apply output projection
_y_true = y_true.transpose(1, 2).contiguous()
y_true = self.o_proj(_y_true.view(b, l, self.hidden_size))
if self.train_attention:
attn_weights = _y_true # flash_attn outputs are shape (b, l, h, d)
return y_true, attn_weights, past_key_value
class LinearAttentionSlidingWindowCache(LinearAttentionState):
"""
Class for `past_key_values`
-> Alternative to KV cache; here we only maintain a "KV state" and "K state"
-> Modified from transformers.cache_utils.DynamicCache (v4.36)
"""
def __init__(self, window_size: int = 64) -> None:
super().__init__()
self._seen_tokens = 0 # should be `self.seen_tokens` in Transformers v4.36
self._seen_tokens_by_layer: List[int] = []
self.kv_states: List[torch.Tensor] = []
self.k_states: List[torch.Tensor] = []
# Account for sliding windows
self.decode_kv_states: List[torch.Tensor] = []
self.decode_k_states: List[torch.Tensor] = []
self.k_cache: List[torch.Tensor] = []
self.v_cache: List[torch.Tensor] = []
self.window_size = window_size
def update(self, key_states: torch.Tensor, value_states: torch.Tensor,
layer_idx: Optional[int] = None, cache_kwargs: Optional[any] = None,
accumulate_in_fp32: bool = False,
fmap_key_states: torch.Tensor = None, # should not be None
grad_enabled: bool = False,
**kwargs: any,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Update KV, K states; and KV cache during training
- For decoding, use `self.decode_kv_states` to keep track of KV states
up to sliding window terms
- For (chunked) training, use `self.kv_states` to keep track of KV states
up to end of sequence
- Likewise for `self.decode_k_states` and `self.k_states`
"""
with torch.set_grad_enabled(grad_enabled):
if layer_idx == 0:
self._seen_tokens += key_states.shape[-2]
dtype = key_states.dtype
if accumulate_in_fp32:
# key_states = key_states.float()
fmap_key_states = fmap_key_states.float()
value_states = value_states.float()
# Decoding KV state (KV terms up to last window_size)
decode_kv_state = torch.einsum(
'bhlf,bhld->bhfd', fmap_key_states[:, :, :-self.window_size], value_states[:, :, :-self.window_size]
)
# KV state
kv_state = decode_kv_state + torch.einsum(
'bhlf,bhld->bhfd', fmap_key_states[:, :, -self.window_size:], value_states[:, :, -self.window_size:]
)
# shape is b, h, 1, f; note the 1
decode_k_state = fmap_key_states[:, :, :-self.window_size].sum(dim=-2, keepdim=True)
k_state = (decode_k_state + fmap_key_states[:, :, -self.window_size:].sum(dim=-2, keepdim=True))
# Update the cache
if len(self.k_states) <= layer_idx: # Initializing kv and k states
self.kv_states.append(kv_state.to(dtype))
self.k_states.append(k_state.to(dtype))
self.decode_kv_states.append(decode_kv_state.to(dtype))
self.decode_k_states.append(decode_k_state.to(dtype))
self.k_cache.append(key_states[:, :, -self.window_size:, :])
self.v_cache.append(value_states[:, :, -self.window_size:, :].to(dtype))
# self._seen_tokens_by_layer[layer_idx].append(key_states.shape[-2])
else:
# Update kv and k states recurrently
kv_state = (self.kv_states[layer_idx].to(kv_state.dtype) + kv_state).to(dtype)
k_state = (self.k_states[layer_idx].to(kv_state.dtype) + k_state).to(dtype)
self.kv_states[layer_idx] = kv_state
self.k_states[layer_idx] = k_state
decode_kv_state = (self.decode_kv_states[layer_idx].to(kv_state.dtype)
+ decode_kv_state).to(dtype)
decode_k_state = (self.decode_k_states[layer_idx].to(kv_state.dtype)
+ decode_k_state).to(dtype)
self.decode_kv_states[layer_idx] = decode_kv_state
self.decode_k_states[layer_idx] = decode_k_state
self.k_cache[layer_idx] = key_states[:, :, -self.window_size:, :]
self.v_cache[layer_idx] = value_states[:, :, -self.window_size:, :]
self._seen_tokens_by_layer[layer_idx] += key_states.shape[-2]
return self.kv_states[layer_idx], self.k_states[layer_idx]
def update_for_decoding(self, keys: torch.Tensor, values: torch.Tensor,
layer_idx: int, feature_map_k: Callable, dtype: torch.dtype):
"""
Update the decoding KV and K states, and KV cache, during decodeing
"""
with torch.no_grad():
k_cache = self.k_cache[layer_idx]
v_cache = self.v_cache[layer_idx]
if k_cache.shape[-2] < self.window_size: # build window-size cache
self.k_cache[layer_idx] = torch.cat([k_cache, keys], dim=-2)
self.v_cache[layer_idx] = torch.cat([v_cache, values], dim=-2)
else:
# MZ 6/3: handle short inputs; zero-out padding when initial k.shape[2] < self.window_size
# if k_cache[:, :, :1, :].sum() == 0: # heuristic for zeroing out padding in cache
# f_k_state = torch.zeros(k_cache[:, :, :1, :].shape, dtype=dtype, device=k_cache.device)
# else:
# f_k_state = feature_map_k(k_cache[:, :, :1, :])
# -> MZ (later): above only relevant if we zero-pad in our hybrid attention computation
k_state = feature_map_k(k_cache[:, :, :1, :])
v_state = v_cache[:, :, :1, :]
kv_state = torch.einsum('bhlf,bhld->bhfd', k_state.float(), v_state.float()).to(dtype) # b, h, f, d
self.decode_kv_states[layer_idx] += kv_state
self.decode_k_states[layer_idx] += k_state
self.k_cache[layer_idx] = torch.cat([k_cache[:, :, 1:, :], keys], dim=-2)
self.v_cache[layer_idx] = torch.cat([v_cache[:, :, 1:, :], values], dim=-2)
if layer_idx == 0:
self._seen_tokens += keys.shape[-2]
self._seen_tokens_by_layer[layer_idx] += keys.shape[-2]
return (self.k_cache[layer_idx], self.v_cache[layer_idx],
self.decode_kv_states[layer_idx], self.decode_k_states[layer_idx])
# -----------------
# Flash Attention 2
# -----------------
def flash_attention_2(self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""
Wrapper for LlamaFlashAttention2
Copied and modified from HF Transformers v4.36 and v4.43 implementations
- (4.43) https://github.com/huggingface/transformers/blob/868d36d29ec132deeaaf8571b25b6a1b911d0145/src/transformers/models/llama/modeling_llama.py#L402
- (4.36) https://github.com/huggingface/transformers/blob/a7cab3c283312b8d4de5df3bbe719971e24f4281/src/transformers/models/llama/modeling_llama.py#L456
"""
output_attentions = False
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
try: # As in Transformers v4.36
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(key_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
except: # As in Transformers v4.39
cos, sin = self.rotary_emb(key_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
if getattr(self, '_flash_attention_forward', False):
attn_output = self._flash_attention_forward(
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate,
is_causal=True,
)
else:
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=0, # dropout_rate,
sliding_window=getattr(self, "sliding_window", None),
use_top_left_mask=self._flash_attn_uses_top_left_mask,
is_causal=True,
)
return attn_output, past_key_value
|