|
|
|
|
|
|
|
|
from typing import Optional, Sequence, Tuple, Union |
|
|
|
|
|
import torch |
|
|
import torch.nn as nn |
|
|
import os |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
from ._ops import ops as flash_attn_gpu |
|
|
|
|
|
|
|
|
|
|
|
def maybe_contiguous(x): |
|
|
return x.contiguous() if x is not None and x.stride(-1) != 1 else x |
|
|
|
|
|
|
|
|
def _get_block_size_n(device, head_dim, is_dropout, is_causal): |
|
|
|
|
|
assert head_dim <= 256 |
|
|
major, minor = torch.cuda.get_device_capability(device) |
|
|
is_sm8x = major == 8 and minor > 0 |
|
|
is_sm80 = major == 8 and minor == 0 |
|
|
is_sm90 = major == 9 and minor == 0 |
|
|
if head_dim <= 32: |
|
|
return 128 |
|
|
if head_dim <= 64: |
|
|
return 128 if not is_dropout else 64 |
|
|
elif head_dim <= 96: |
|
|
return 64 |
|
|
elif head_dim <= 128: |
|
|
if is_sm8x: |
|
|
return 64 if (not is_dropout and is_causal) else 32 |
|
|
else: |
|
|
return 64 if not is_dropout else 32 |
|
|
elif head_dim <= 192: |
|
|
return 64 |
|
|
elif head_dim <= 224: |
|
|
return 64 |
|
|
elif head_dim <= 256: |
|
|
return 64 |
|
|
|
|
|
|
|
|
def round_multiple(x, m): |
|
|
return (x + m - 1) // m * m |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if torch.__version__ >= "2.4.0": |
|
|
_torch_custom_op_wrapper = torch.library.custom_op |
|
|
_torch_register_fake_wrapper = torch.library.register_fake |
|
|
else: |
|
|
def noop_custom_op_wrapper(name, fn=None, /, *, mutates_args, device_types=None, schema=None): |
|
|
def wrap(func): |
|
|
return func |
|
|
if fn is None: |
|
|
return wrap |
|
|
return fn |
|
|
def noop_register_fake_wrapper(op, fn=None, /, *, lib=None, _stacklevel=1): |
|
|
def wrap(func): |
|
|
return func |
|
|
if fn is None: |
|
|
return wrap |
|
|
return fn |
|
|
_torch_custom_op_wrapper = noop_custom_op_wrapper |
|
|
_torch_register_fake_wrapper = noop_register_fake_wrapper |
|
|
|
|
|
|
|
|
@_torch_custom_op_wrapper("flash_attn::_flash_attn_forward", mutates_args=(), device_types="cuda") |
|
|
def _flash_attn_forward( |
|
|
q: torch.Tensor, |
|
|
k: torch.Tensor, |
|
|
v: torch.Tensor, |
|
|
dropout_p: float, |
|
|
softmax_scale: float, |
|
|
causal: bool, |
|
|
window_size_left: int, |
|
|
window_size_right: int, |
|
|
softcap: float, |
|
|
alibi_slopes: Optional[torch.Tensor], |
|
|
return_softmax: bool |
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
|
|
q, k, v = [maybe_contiguous(x) for x in (q, k, v)] |
|
|
out, softmax_lse, S_dmask, rng_state = flash_attn_gpu.fwd( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
None, |
|
|
alibi_slopes, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size_left, |
|
|
window_size_right, |
|
|
softcap, |
|
|
return_softmax, |
|
|
None, |
|
|
) |
|
|
return out, softmax_lse, S_dmask, rng_state |
|
|
|
|
|
|
|
|
@_torch_register_fake_wrapper("flash_attn::_flash_attn_forward") |
|
|
def _flash_attn_forward_fake( |
|
|
q: torch.Tensor, |
|
|
k: torch.Tensor, |
|
|
v: torch.Tensor, |
|
|
dropout_p: float, |
|
|
softmax_scale: float, |
|
|
causal: bool, |
|
|
window_size_left: int, |
|
|
window_size_right: int, |
|
|
softcap: float, |
|
|
alibi_slopes: Optional[torch.Tensor], |
|
|
return_softmax: bool |
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
|
|
q, k, v = [maybe_contiguous(x) for x in (q, k, v)] |
|
|
batch_size, seqlen_q, num_heads, head_size = q.shape |
|
|
seqlen_k = k.shape[1] |
|
|
out = torch.empty_like(q) |
|
|
softmax_lse = torch.empty((batch_size, num_heads, seqlen_q), dtype=torch.float32, device=q.device, layout=q.layout) |
|
|
p = torch.empty((0,), dtype=q.dtype, device=q.device, layout=q.layout) |
|
|
if return_softmax: |
|
|
p = torch.empty((batch_size, num_heads, round_multiple(seqlen_q, 128), round_multiple(seqlen_k, 128)), dtype=q.dtype, device=q.device, layout=q.layout) |
|
|
rng_state = torch.empty((2,), dtype=torch.int64, device=q.device) |
|
|
|
|
|
return out, softmax_lse, p, rng_state |
|
|
|
|
|
|
|
|
if torch.__version__ >= "2.4.0": |
|
|
_wrapped_flash_attn_forward = torch.ops.flash_attn._flash_attn_forward |
|
|
else: |
|
|
_wrapped_flash_attn_forward = _flash_attn_forward |
|
|
|
|
|
|
|
|
@_torch_custom_op_wrapper("flash_attn::_flash_attn_varlen_forward", mutates_args=(), device_types="cuda") |
|
|
def _flash_attn_varlen_forward( |
|
|
q: torch.Tensor, |
|
|
k: torch.Tensor, |
|
|
v: torch.Tensor, |
|
|
cu_seqlens_q: torch.Tensor, |
|
|
cu_seqlens_k: torch.Tensor, |
|
|
max_seqlen_q: int, |
|
|
max_seqlen_k: int, |
|
|
dropout_p: float, |
|
|
softmax_scale: float, |
|
|
causal: bool, |
|
|
window_size_left: int = -1, |
|
|
window_size_right: int = -1, |
|
|
softcap: float = 0.0, |
|
|
alibi_slopes: Optional[torch.Tensor] = None, |
|
|
return_softmax: bool = False, |
|
|
block_table: Optional[torch.Tensor] = None, |
|
|
leftpad_k: Optional[torch.Tensor] = None, |
|
|
seqused_k: Optional[torch.Tensor] = None, |
|
|
zero_tensors: bool = False, |
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
|
|
q, k, v = [maybe_contiguous(x) for x in (q, k, v)] |
|
|
out, softmax_lse, S_dmask, rng_state = flash_attn_gpu.varlen_fwd( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
None, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
seqused_k, |
|
|
leftpad_k, |
|
|
block_table, |
|
|
alibi_slopes, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
zero_tensors, |
|
|
causal, |
|
|
window_size_left, |
|
|
window_size_right, |
|
|
softcap, |
|
|
return_softmax, |
|
|
None, |
|
|
) |
|
|
|
|
|
|
|
|
return out, softmax_lse, S_dmask, rng_state |
|
|
|
|
|
|
|
|
@_torch_register_fake_wrapper("flash_attn::_flash_attn_varlen_forward") |
|
|
def _flash_attn_varlen_forward_fake( |
|
|
q: torch.Tensor, |
|
|
k: torch.Tensor, |
|
|
v: torch.Tensor, |
|
|
cu_seqlens_q: torch.Tensor, |
|
|
cu_seqlens_k: torch.Tensor, |
|
|
max_seqlen_q: int, |
|
|
max_seqlen_k: int, |
|
|
dropout_p: float, |
|
|
softmax_scale: float, |
|
|
causal: bool, |
|
|
window_size_left: int = -1, |
|
|
window_size_right: int = -1, |
|
|
softcap: float = 0.0, |
|
|
alibi_slopes: Optional[torch.Tensor] = None, |
|
|
return_softmax: bool = False, |
|
|
block_table: Optional[torch.Tensor] = None, |
|
|
leftpad_k: Optional[torch.Tensor] = None, |
|
|
seqused_k: Optional[torch.Tensor] = None, |
|
|
zero_tensors: bool = False, |
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: |
|
|
q, k, v = [maybe_contiguous(x) for x in (q, k, v)] |
|
|
paged_kv = block_table is not None |
|
|
batch_size = cu_seqlens_q.numel() - 1 |
|
|
total_q, num_heads, _ = q.shape |
|
|
|
|
|
out = torch.empty_like(q) |
|
|
softmax_lse = torch.empty((num_heads, total_q), dtype=torch.float32, device=q.device, layout=q.layout) |
|
|
p = torch.empty((0,), dtype=q.dtype, device=q.device, layout=q.layout) |
|
|
seqlen_q_rounded = round_multiple(max_seqlen_q, 128) |
|
|
seqlen_k_rounded = round_multiple(max_seqlen_k, 128) |
|
|
if return_softmax: |
|
|
p = torch.empty((batch_size, num_heads, seqlen_q_rounded, seqlen_k_rounded), dtype=q.dtype, device=q.device, layout=q.layout) |
|
|
rng_state = torch.empty((2,), dtype=torch.int64, device=q.device) |
|
|
return out, softmax_lse, p, rng_state |
|
|
|
|
|
|
|
|
if torch.__version__ >= "2.4.0": |
|
|
_wrapped_flash_attn_varlen_forward = torch.ops.flash_attn._flash_attn_varlen_forward |
|
|
else: |
|
|
_wrapped_flash_attn_varlen_forward = _flash_attn_varlen_forward |
|
|
|
|
|
|
|
|
@_torch_custom_op_wrapper("flash_attn::_flash_attn_backward", mutates_args=("dq", "dk", "dv"), device_types="cuda") |
|
|
def _flash_attn_backward( |
|
|
dout: torch.Tensor, |
|
|
q: torch.Tensor, |
|
|
k: torch.Tensor, |
|
|
v: torch.Tensor, |
|
|
out: torch.Tensor, |
|
|
softmax_lse: torch.Tensor, |
|
|
dq: Optional[torch.Tensor], |
|
|
dk: Optional[torch.Tensor], |
|
|
dv: Optional[torch.Tensor], |
|
|
dropout_p: float, |
|
|
softmax_scale: float, |
|
|
causal: bool, |
|
|
window_size_left: int, |
|
|
window_size_right: int, |
|
|
softcap: float, |
|
|
alibi_slopes: Optional[torch.Tensor], |
|
|
deterministic: bool, |
|
|
rng_state: Optional[torch.Tensor] = None, |
|
|
) -> torch.Tensor: |
|
|
|
|
|
dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)] |
|
|
( |
|
|
dq, |
|
|
dk, |
|
|
dv, |
|
|
softmax_d, |
|
|
) = flash_attn_gpu.bwd( |
|
|
dout, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
out, |
|
|
softmax_lse, |
|
|
dq, |
|
|
dk, |
|
|
dv, |
|
|
alibi_slopes, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size_left, |
|
|
window_size_right, |
|
|
softcap, |
|
|
deterministic, |
|
|
None, |
|
|
rng_state, |
|
|
) |
|
|
return softmax_d |
|
|
|
|
|
|
|
|
@_torch_register_fake_wrapper("flash_attn::_flash_attn_backward") |
|
|
def _flash_attn_backward_fake( |
|
|
dout: torch.Tensor, |
|
|
q: torch.Tensor, |
|
|
k: torch.Tensor, |
|
|
v: torch.Tensor, |
|
|
out: torch.Tensor, |
|
|
softmax_lse: torch.Tensor, |
|
|
dq: Optional[torch.Tensor], |
|
|
dk: Optional[torch.Tensor], |
|
|
dv: Optional[torch.Tensor], |
|
|
dropout_p: float, |
|
|
softmax_scale: float, |
|
|
causal: bool, |
|
|
window_size_left: int, |
|
|
window_size_right: int, |
|
|
softcap: float, |
|
|
alibi_slopes: Optional[torch.Tensor], |
|
|
deterministic: bool, |
|
|
rng_state: Optional[torch.Tensor] = None, |
|
|
) -> torch.Tensor: |
|
|
dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)] |
|
|
if dq is None: |
|
|
dq = torch.empty_like(q) |
|
|
if dk is None: |
|
|
dk = torch.empty_like(k) |
|
|
if dv is None: |
|
|
dv = torch.empty_like(v) |
|
|
batch_size, seqlen_q, num_heads, _ = q.shape |
|
|
softmax_d = torch.empty((batch_size, num_heads, round_multiple(seqlen_q, 128)), device=q.device, dtype=torch.float32) |
|
|
|
|
|
return softmax_d |
|
|
|
|
|
|
|
|
if torch.__version__ >= "2.4.0": |
|
|
_wrapped_flash_attn_backward = torch.ops.flash_attn._flash_attn_backward |
|
|
else: |
|
|
_wrapped_flash_attn_backward = _flash_attn_backward |
|
|
|
|
|
|
|
|
@_torch_custom_op_wrapper("flash_attn::_flash_attn_varlen_backward", mutates_args=("dq", "dk", "dv"), device_types="cuda") |
|
|
def _flash_attn_varlen_backward( |
|
|
dout: torch.Tensor, |
|
|
q: torch.Tensor, |
|
|
k: torch.Tensor, |
|
|
v: torch.Tensor, |
|
|
out: torch.Tensor, |
|
|
softmax_lse: torch.Tensor, |
|
|
dq: Optional[torch.Tensor], |
|
|
dk: Optional[torch.Tensor], |
|
|
dv: Optional[torch.Tensor], |
|
|
cu_seqlens_q: torch.Tensor, |
|
|
cu_seqlens_k: torch.Tensor, |
|
|
max_seqlen_q: int, |
|
|
max_seqlen_k: int, |
|
|
dropout_p: float, |
|
|
softmax_scale: float, |
|
|
causal: bool, |
|
|
window_size_left: int, |
|
|
window_size_right: int, |
|
|
softcap: float, |
|
|
alibi_slopes: Optional[torch.Tensor], |
|
|
deterministic: bool, |
|
|
rng_state: Optional[torch.Tensor] = None, |
|
|
zero_tensors: bool = False, |
|
|
) -> torch.Tensor: |
|
|
|
|
|
dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)] |
|
|
( |
|
|
dq, |
|
|
dk, |
|
|
dv, |
|
|
softmax_d, |
|
|
) = flash_attn_gpu.varlen_bwd( |
|
|
dout, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
out, |
|
|
softmax_lse, |
|
|
dq, |
|
|
dk, |
|
|
dv, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
alibi_slopes, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
zero_tensors, |
|
|
causal, |
|
|
window_size_left, |
|
|
window_size_right, |
|
|
softcap, |
|
|
deterministic, |
|
|
None, |
|
|
rng_state, |
|
|
) |
|
|
|
|
|
|
|
|
return softmax_d |
|
|
|
|
|
|
|
|
@_torch_register_fake_wrapper("flash_attn::_flash_attn_varlen_backward") |
|
|
def _flash_attn_varlen_backward_fake( |
|
|
dout: torch.Tensor, |
|
|
q: torch.Tensor, |
|
|
k: torch.Tensor, |
|
|
v: torch.Tensor, |
|
|
out: torch.Tensor, |
|
|
softmax_lse: torch.Tensor, |
|
|
dq: Optional[torch.Tensor], |
|
|
dk: Optional[torch.Tensor], |
|
|
dv: Optional[torch.Tensor], |
|
|
cu_seqlens_q: torch.Tensor, |
|
|
cu_seqlens_k: torch.Tensor, |
|
|
max_seqlen_q: int, |
|
|
max_seqlen_k: int, |
|
|
dropout_p: float, |
|
|
softmax_scale: float, |
|
|
causal: bool, |
|
|
window_size_left: int, |
|
|
window_size_right: int, |
|
|
softcap: float, |
|
|
alibi_slopes: Optional[torch.Tensor], |
|
|
deterministic: bool, |
|
|
rng_state: Optional[torch.Tensor] = None, |
|
|
zero_tensors: bool = False, |
|
|
) -> torch.Tensor: |
|
|
dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)] |
|
|
batch_size = cu_seqlens_q.numel() - 1 |
|
|
total_q, num_heads, _ = q.shape |
|
|
|
|
|
if dq is None: |
|
|
dq = torch.empty_like(q) |
|
|
if dk is None: |
|
|
dk = torch.empty_like(k) |
|
|
if dv is None: |
|
|
dv = torch.empty_like(v) |
|
|
softmax_d = torch.empty((num_heads, total_q + 128 * batch_size), device=q.device, dtype=torch.float32) |
|
|
|
|
|
return softmax_d |
|
|
|
|
|
|
|
|
if torch.__version__ >= "2.4.0": |
|
|
_wrapped_flash_attn_varlen_backward = torch.ops.flash_attn._flash_attn_varlen_backward |
|
|
else: |
|
|
_wrapped_flash_attn_varlen_backward = _flash_attn_varlen_backward |
|
|
|
|
|
|
|
|
class FlashAttnQKVPackedFunc(torch.autograd.Function): |
|
|
@staticmethod |
|
|
def forward( |
|
|
ctx, |
|
|
qkv, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_softmax, |
|
|
is_grad_enabled, |
|
|
): |
|
|
is_grad = is_grad_enabled and qkv.requires_grad |
|
|
if softmax_scale is None: |
|
|
softmax_scale = qkv.shape[-1] ** (-0.5) |
|
|
q, k, v = qkv[:, :, 0].detach(), qkv[:, :, 1].detach(), qkv[:, :, 2].detach() |
|
|
head_size_og = q.size(3) |
|
|
if head_size_og % 8 != 0: |
|
|
q = torch.nn.functional.pad(q, [0, 8 - head_size_og % 8]) |
|
|
k = torch.nn.functional.pad(k, [0, 8 - head_size_og % 8]) |
|
|
v = torch.nn.functional.pad(v, [0, 8 - head_size_og % 8]) |
|
|
out_padded, softmax_lse, S_dmask, rng_state = _wrapped_flash_attn_forward( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal=causal, |
|
|
window_size_left=window_size[0], |
|
|
window_size_right=window_size[1], |
|
|
softcap=softcap, |
|
|
alibi_slopes=alibi_slopes, |
|
|
return_softmax=return_softmax and dropout_p > 0, |
|
|
) |
|
|
if is_grad: |
|
|
ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state) |
|
|
ctx.dropout_p = dropout_p |
|
|
ctx.softmax_scale = softmax_scale |
|
|
ctx.causal = causal |
|
|
ctx.window_size = window_size |
|
|
ctx.softcap = softcap |
|
|
ctx.alibi_slopes = alibi_slopes |
|
|
ctx.deterministic = deterministic |
|
|
out = out_padded[..., :head_size_og] |
|
|
return out if not return_softmax else (out, softmax_lse, S_dmask) |
|
|
|
|
|
@staticmethod |
|
|
def backward(ctx, dout, *args): |
|
|
q, k, v, out, softmax_lse, rng_state = ctx.saved_tensors |
|
|
qkv_shape = q.shape[:-2] + (3, *q.shape[-2:]) |
|
|
dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device) |
|
|
head_size_og = dout.size(3) |
|
|
dout_padded = dout |
|
|
if head_size_og % 8 != 0: |
|
|
dout_padded = torch.nn.functional.pad(dout, [0, 8 - head_size_og % 8]) |
|
|
_wrapped_flash_attn_backward( |
|
|
dout_padded, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
out, |
|
|
softmax_lse, |
|
|
dqkv[:, :, 0], |
|
|
dqkv[:, :, 1], |
|
|
dqkv[:, :, 2], |
|
|
ctx.dropout_p, |
|
|
ctx.softmax_scale, |
|
|
ctx.causal, |
|
|
ctx.window_size[0], |
|
|
ctx.window_size[1], |
|
|
ctx.softcap, |
|
|
ctx.alibi_slopes, |
|
|
ctx.deterministic, |
|
|
rng_state=rng_state, |
|
|
) |
|
|
dqkv = dqkv[..., : dout.shape[-1]] |
|
|
return dqkv, None, None, None, None, None, None, None, None, None |
|
|
|
|
|
|
|
|
class FlashAttnVarlenQKVPackedFunc(torch.autograd.Function): |
|
|
@staticmethod |
|
|
def forward( |
|
|
ctx, |
|
|
qkv, |
|
|
cu_seqlens, |
|
|
max_seqlen, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_softmax, |
|
|
is_grad_enabled, |
|
|
): |
|
|
is_grad = is_grad_enabled and qkv.requires_grad |
|
|
if softmax_scale is None: |
|
|
softmax_scale = qkv.shape[-1] ** (-0.5) |
|
|
q, k, v = qkv[:, 0].detach(), qkv[:, 1].detach(), qkv[:, 2].detach() |
|
|
head_size_og = q.size(2) |
|
|
if head_size_og % 8 != 0: |
|
|
q = torch.nn.functional.pad(q, [0, 8 - head_size_og % 8]) |
|
|
k = torch.nn.functional.pad(k, [0, 8 - head_size_og % 8]) |
|
|
v = torch.nn.functional.pad(v, [0, 8 - head_size_og % 8]) |
|
|
out_padded, softmax_lse, S_dmask, rng_state = _wrapped_flash_attn_varlen_forward( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
cu_seqlens, |
|
|
cu_seqlens, |
|
|
max_seqlen, |
|
|
max_seqlen, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal=causal, |
|
|
window_size_left=window_size[0], |
|
|
window_size_right=window_size[1], |
|
|
softcap=softcap, |
|
|
alibi_slopes=alibi_slopes, |
|
|
return_softmax=return_softmax and dropout_p > 0, |
|
|
block_table=None, |
|
|
) |
|
|
if is_grad: |
|
|
ctx.save_for_backward(q, k, v, out_padded, softmax_lse, cu_seqlens, rng_state) |
|
|
ctx.dropout_p = dropout_p |
|
|
ctx.max_seqlen = max_seqlen |
|
|
ctx.softmax_scale = softmax_scale |
|
|
ctx.causal = causal |
|
|
ctx.window_size = window_size |
|
|
ctx.softcap = softcap |
|
|
ctx.alibi_slopes = alibi_slopes |
|
|
ctx.deterministic = deterministic |
|
|
out = out_padded[..., :head_size_og] |
|
|
return out if not return_softmax else (out, softmax_lse, S_dmask) |
|
|
|
|
|
@staticmethod |
|
|
def backward(ctx, dout, *args): |
|
|
q, k, v, out, softmax_lse, cu_seqlens, rng_state = ctx.saved_tensors |
|
|
qkv_shape = q.shape[:-2] + (3, *q.shape[-2:]) |
|
|
dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device) |
|
|
head_size_og = dout.size(2) |
|
|
dout_padded = dout |
|
|
if head_size_og % 8 != 0: |
|
|
dout_padded = torch.nn.functional.pad(dout, [0, 8 - head_size_og % 8]) |
|
|
_wrapped_flash_attn_varlen_backward( |
|
|
dout_padded, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
out, |
|
|
softmax_lse, |
|
|
dqkv[:, 0], |
|
|
dqkv[:, 1], |
|
|
dqkv[:, 2], |
|
|
cu_seqlens, |
|
|
cu_seqlens, |
|
|
ctx.max_seqlen, |
|
|
ctx.max_seqlen, |
|
|
ctx.dropout_p, |
|
|
ctx.softmax_scale, |
|
|
ctx.causal, |
|
|
ctx.window_size[0], |
|
|
ctx.window_size[1], |
|
|
ctx.softcap, |
|
|
ctx.alibi_slopes, |
|
|
ctx.deterministic, |
|
|
rng_state=rng_state, |
|
|
) |
|
|
dqkv = dqkv[..., : dout.shape[-1]] |
|
|
return dqkv, None, None, None, None, None, None, None, None, None, None, None |
|
|
|
|
|
|
|
|
class FlashAttnKVPackedFunc(torch.autograd.Function): |
|
|
@staticmethod |
|
|
def forward( |
|
|
ctx, |
|
|
q, |
|
|
kv, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_softmax, |
|
|
is_grad_enabled, |
|
|
): |
|
|
is_grad = is_grad_enabled and any( |
|
|
x.requires_grad for x in [q, kv] |
|
|
) |
|
|
if softmax_scale is None: |
|
|
softmax_scale = q.shape[-1] ** (-0.5) |
|
|
k, v = kv[:, :, 0].detach(), kv[:, :, 1].detach() |
|
|
head_size_og = q.size(3) |
|
|
if head_size_og % 8 != 0: |
|
|
q = torch.nn.functional.pad(q, [0, 8 - head_size_og % 8]) |
|
|
k = torch.nn.functional.pad(k, [0, 8 - head_size_og % 8]) |
|
|
v = torch.nn.functional.pad(v, [0, 8 - head_size_og % 8]) |
|
|
out_padded, softmax_lse, S_dmask, rng_state = _wrapped_flash_attn_forward( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal=causal, |
|
|
window_size_left=window_size[0], |
|
|
window_size_right=window_size[1], |
|
|
softcap=softcap, |
|
|
alibi_slopes=alibi_slopes, |
|
|
return_softmax=return_softmax and dropout_p > 0, |
|
|
) |
|
|
if is_grad: |
|
|
ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state) |
|
|
ctx.dropout_p = dropout_p |
|
|
ctx.softmax_scale = softmax_scale |
|
|
ctx.causal = causal |
|
|
ctx.window_size = window_size |
|
|
ctx.softcap = softcap |
|
|
ctx.alibi_slopes = alibi_slopes |
|
|
ctx.deterministic = deterministic |
|
|
out = out_padded[..., :head_size_og] |
|
|
return out if not return_softmax else (out, softmax_lse, S_dmask) |
|
|
|
|
|
@staticmethod |
|
|
def backward(ctx, dout, *args): |
|
|
q, k, v, out, softmax_lse, rng_state = ctx.saved_tensors |
|
|
dq = torch.empty_like(q) |
|
|
kv_shape = k.shape[:-2] + (2, *k.shape[-2:]) |
|
|
dkv = torch.empty(kv_shape, dtype=k.dtype, device=k.device) |
|
|
head_size_og = dout.size(3) |
|
|
dout_padded = dout |
|
|
if head_size_og % 8 != 0: |
|
|
dout_padded = torch.nn.functional.pad(dout, [0, 8 - head_size_og % 8]) |
|
|
_wrapped_flash_attn_backward( |
|
|
dout_padded, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
out, |
|
|
softmax_lse, |
|
|
dq, |
|
|
dkv[:, :, 0], |
|
|
dkv[:, :, 1], |
|
|
ctx.dropout_p, |
|
|
ctx.softmax_scale, |
|
|
ctx.causal, |
|
|
ctx.window_size[0], |
|
|
ctx.window_size[1], |
|
|
ctx.softcap, |
|
|
ctx.alibi_slopes, |
|
|
ctx.deterministic, |
|
|
rng_state=rng_state, |
|
|
) |
|
|
dq = dq[..., : dout.shape[-1]] |
|
|
dkv = dkv[..., : dout.shape[-1]] |
|
|
return dq, dkv, None, None, None, None, None, None, None, None, None |
|
|
|
|
|
|
|
|
class FlashAttnVarlenKVPackedFunc(torch.autograd.Function): |
|
|
@staticmethod |
|
|
def forward( |
|
|
ctx, |
|
|
q, |
|
|
kv, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_softmax, |
|
|
is_grad_enabled, |
|
|
): |
|
|
is_grad = is_grad_enabled and any( |
|
|
x.requires_grad for x in [q, kv] |
|
|
) |
|
|
if softmax_scale is None: |
|
|
softmax_scale = q.shape[-1] ** (-0.5) |
|
|
k, v = kv[:, 0].detach(), kv[:, 1].detach() |
|
|
head_size_og = q.size(2) |
|
|
if head_size_og % 8 != 0: |
|
|
q = torch.nn.functional.pad(q, [0, 8 - head_size_og % 8]) |
|
|
k = torch.nn.functional.pad(k, [0, 8 - head_size_og % 8]) |
|
|
v = torch.nn.functional.pad(v, [0, 8 - head_size_og % 8]) |
|
|
out_padded, softmax_lse, S_dmask, rng_state = _wrapped_flash_attn_varlen_forward( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal=causal, |
|
|
window_size_left=window_size[0], |
|
|
window_size_right=window_size[1], |
|
|
softcap=softcap, |
|
|
alibi_slopes=alibi_slopes, |
|
|
return_softmax=return_softmax and dropout_p > 0, |
|
|
block_table=None, |
|
|
) |
|
|
if is_grad: |
|
|
ctx.save_for_backward( |
|
|
q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state |
|
|
) |
|
|
ctx.dropout_p = dropout_p |
|
|
ctx.max_seqlen_q = max_seqlen_q |
|
|
ctx.max_seqlen_k = max_seqlen_k |
|
|
ctx.softmax_scale = softmax_scale |
|
|
ctx.causal = causal |
|
|
ctx.window_size = window_size |
|
|
ctx.softcap = softcap |
|
|
ctx.alibi_slopes = alibi_slopes |
|
|
ctx.deterministic = deterministic |
|
|
out = out_padded[..., :head_size_og] |
|
|
return out if not return_softmax else (out, softmax_lse, S_dmask) |
|
|
|
|
|
@staticmethod |
|
|
def backward(ctx, dout, *args): |
|
|
q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state = ctx.saved_tensors |
|
|
dq = torch.empty_like(q) |
|
|
kv_shape = k.shape[:-2] + (2, *k.shape[-2:]) |
|
|
dkv = torch.empty(kv_shape, dtype=k.dtype, device=k.device) |
|
|
head_size_og = dout.size(2) |
|
|
dout_padded = dout |
|
|
if head_size_og % 8 != 0: |
|
|
dout_padded = torch.nn.functional.pad(dout, [0, 8 - head_size_og % 8]) |
|
|
_wrapped_flash_attn_varlen_backward( |
|
|
dout_padded, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
out, |
|
|
softmax_lse, |
|
|
dq, |
|
|
dkv[:, 0], |
|
|
dkv[:, 1], |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
ctx.max_seqlen_q, |
|
|
ctx.max_seqlen_k, |
|
|
ctx.dropout_p, |
|
|
ctx.softmax_scale, |
|
|
ctx.causal, |
|
|
ctx.window_size[0], |
|
|
ctx.window_size[1], |
|
|
ctx.softcap, |
|
|
ctx.alibi_slopes, |
|
|
ctx.deterministic, |
|
|
rng_state=rng_state, |
|
|
) |
|
|
dq = dq[..., : dout.shape[-1]] |
|
|
dkv = dkv[..., : dout.shape[-1]] |
|
|
return dq, dkv, None, None, None, None, None, None, None, None, None, None, None, None, None |
|
|
|
|
|
|
|
|
class FlashAttnFunc(torch.autograd.Function): |
|
|
@staticmethod |
|
|
def forward( |
|
|
ctx, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_softmax, |
|
|
is_grad_enabled, |
|
|
): |
|
|
is_grad = is_grad_enabled and any( |
|
|
x.requires_grad for x in [q, k, v] |
|
|
) |
|
|
if softmax_scale is None: |
|
|
softmax_scale = q.shape[-1] ** (-0.5) |
|
|
head_size_og = q.size(3) |
|
|
if head_size_og % 8 != 0: |
|
|
q = torch.nn.functional.pad(q, [0, 8 - head_size_og % 8]) |
|
|
k = torch.nn.functional.pad(k, [0, 8 - head_size_og % 8]) |
|
|
v = torch.nn.functional.pad(v, [0, 8 - head_size_og % 8]) |
|
|
out_padded, softmax_lse, S_dmask, rng_state = _wrapped_flash_attn_forward( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal=causal, |
|
|
window_size_left=window_size[0], |
|
|
window_size_right=window_size[1], |
|
|
softcap=softcap, |
|
|
alibi_slopes=alibi_slopes, |
|
|
return_softmax=return_softmax and dropout_p > 0, |
|
|
) |
|
|
if is_grad: |
|
|
ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state) |
|
|
ctx.dropout_p = dropout_p |
|
|
ctx.softmax_scale = softmax_scale |
|
|
ctx.causal = causal |
|
|
ctx.window_size = window_size |
|
|
ctx.softcap = softcap |
|
|
ctx.alibi_slopes = alibi_slopes |
|
|
ctx.deterministic = deterministic |
|
|
out = out_padded[..., :head_size_og] |
|
|
return out if not return_softmax else (out, softmax_lse, S_dmask) |
|
|
|
|
|
@staticmethod |
|
|
def backward(ctx, dout, *args): |
|
|
q, k, v, out, softmax_lse, rng_state = ctx.saved_tensors |
|
|
dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v) |
|
|
head_size_og = dout.size(3) |
|
|
dout_padded = dout |
|
|
if head_size_og % 8 != 0: |
|
|
dout_padded = torch.nn.functional.pad(dout, [0, 8 - head_size_og % 8]) |
|
|
_wrapped_flash_attn_backward( |
|
|
dout_padded, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
out, |
|
|
softmax_lse, |
|
|
dq, |
|
|
dk, |
|
|
dv, |
|
|
ctx.dropout_p, |
|
|
ctx.softmax_scale, |
|
|
ctx.causal, |
|
|
ctx.window_size[0], |
|
|
ctx.window_size[1], |
|
|
ctx.softcap, |
|
|
ctx.alibi_slopes, |
|
|
ctx.deterministic, |
|
|
rng_state=rng_state, |
|
|
) |
|
|
dq = dq[..., : dout.shape[-1]] |
|
|
dk = dk[..., : dout.shape[-1]] |
|
|
dv = dv[..., : dout.shape[-1]] |
|
|
return dq, dk, dv, None, None, None, None, None, None, None, None, None |
|
|
|
|
|
|
|
|
class FlashAttnVarlenFunc(torch.autograd.Function): |
|
|
@staticmethod |
|
|
def forward( |
|
|
ctx, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_softmax, |
|
|
block_table, |
|
|
is_grad_enabled, |
|
|
): |
|
|
is_grad = is_grad_enabled and any( |
|
|
x.requires_grad for x in [q, k, v] |
|
|
) |
|
|
if softmax_scale is None: |
|
|
softmax_scale = q.shape[-1] ** (-0.5) |
|
|
head_size_og = q.size(2) |
|
|
if head_size_og % 8 != 0: |
|
|
q = torch.nn.functional.pad(q, [0, 8 - head_size_og % 8]) |
|
|
k = torch.nn.functional.pad(k, [0, 8 - head_size_og % 8]) |
|
|
v = torch.nn.functional.pad(v, [0, 8 - head_size_og % 8]) |
|
|
out_padded, softmax_lse, S_dmask, rng_state = _wrapped_flash_attn_varlen_forward( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal=causal, |
|
|
window_size_left=window_size[0], |
|
|
window_size_right=window_size[1], |
|
|
softcap=softcap, |
|
|
alibi_slopes=alibi_slopes, |
|
|
return_softmax=return_softmax and dropout_p > 0, |
|
|
block_table=block_table, |
|
|
) |
|
|
if is_grad: |
|
|
ctx.save_for_backward( |
|
|
q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state |
|
|
) |
|
|
ctx.dropout_p = dropout_p |
|
|
ctx.max_seqlen_q = max_seqlen_q |
|
|
ctx.max_seqlen_k = max_seqlen_k |
|
|
ctx.softmax_scale = softmax_scale |
|
|
ctx.causal = causal |
|
|
ctx.window_size = window_size |
|
|
ctx.softcap = softcap |
|
|
ctx.alibi_slopes = alibi_slopes |
|
|
ctx.deterministic = deterministic |
|
|
|
|
|
out = out_padded[..., :head_size_og] |
|
|
return out if not return_softmax else (out, softmax_lse, S_dmask) |
|
|
|
|
|
@staticmethod |
|
|
def backward(ctx, dout, *args): |
|
|
q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state = ctx.saved_tensors |
|
|
dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v) |
|
|
head_size_og = dout.size(2) |
|
|
dout_padded = dout |
|
|
if head_size_og % 8 != 0: |
|
|
dout_padded = torch.nn.functional.pad(dout, [0, 8 - head_size_og % 8]) |
|
|
_wrapped_flash_attn_varlen_backward( |
|
|
dout_padded, |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
out, |
|
|
softmax_lse, |
|
|
dq, |
|
|
dk, |
|
|
dv, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
ctx.max_seqlen_q, |
|
|
ctx.max_seqlen_k, |
|
|
ctx.dropout_p, |
|
|
ctx.softmax_scale, |
|
|
ctx.causal, |
|
|
ctx.window_size[0], |
|
|
ctx.window_size[1], |
|
|
ctx.softcap, |
|
|
ctx.alibi_slopes, |
|
|
ctx.deterministic, |
|
|
rng_state=rng_state, |
|
|
) |
|
|
dq = dq[..., : dout.shape[-1]] |
|
|
dk = dk[..., : dout.shape[-1]] |
|
|
dv = dv[..., : dout.shape[-1]] |
|
|
return dq, dk, dv, None, None, None, None, None, None, None, None, None, None, None, None, None, None |
|
|
|
|
|
|
|
|
def flash_attn_qkvpacked_func( |
|
|
qkv, |
|
|
dropout_p=0.0, |
|
|
softmax_scale=None, |
|
|
causal=False, |
|
|
window_size=(-1, -1), |
|
|
softcap=0.0, |
|
|
alibi_slopes=None, |
|
|
deterministic=False, |
|
|
return_attn_probs=False, |
|
|
): |
|
|
"""dropout_p should be set to 0.0 during evaluation |
|
|
If Q, K, V are already stacked into 1 tensor, this function will be faster than |
|
|
calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation |
|
|
of the gradients of Q, K, V. |
|
|
For multi-query and grouped-query attention (MQA/GQA), please see |
|
|
flash_attn_kvpacked_func and flash_attn_func. |
|
|
|
|
|
If window_size != (-1, -1), implements sliding window local attention. Query at position i |
|
|
will only attend to keys between [i - window_size[0], i + window_size[1]] inclusive. |
|
|
|
|
|
Arguments: |
|
|
qkv: (batch_size, seqlen, 3, nheads, headdim) |
|
|
dropout_p: float. Dropout probability. |
|
|
softmax_scale: float. The scaling of QK^T before applying softmax. |
|
|
Default to 1 / sqrt(headdim). |
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). |
|
|
window_size: (left, right). If not (-1, -1), implements sliding window local attention. |
|
|
softcap: float. Anything > 0 activates softcapping attention. |
|
|
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of (-alibi_slope * |i - j|) is added to |
|
|
the attention score of query i and key j. |
|
|
deterministic: bool. Whether to use the deterministic implementation of the backward pass, |
|
|
which is slightly slower and uses more memory. The forward pass is always deterministic. |
|
|
return_attn_probs: bool. Whether to return the attention probabilities. This option is for |
|
|
testing only. The returned probabilities are not guaranteed to be correct |
|
|
(they might not have the right scaling). |
|
|
Return: |
|
|
out: (batch_size, seqlen, nheads, headdim). |
|
|
softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The |
|
|
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax |
|
|
normalization factor). |
|
|
S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). |
|
|
The output of softmax (possibly with different scaling). It also encodes the dropout |
|
|
pattern (negative means that location was dropped, nonnegative means it was kept). |
|
|
""" |
|
|
return FlashAttnQKVPackedFunc.apply( |
|
|
qkv, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_attn_probs, |
|
|
torch.is_grad_enabled(), |
|
|
) |
|
|
|
|
|
|
|
|
def flash_attn_kvpacked_func( |
|
|
q, |
|
|
kv, |
|
|
dropout_p=0.0, |
|
|
softmax_scale=None, |
|
|
causal=False, |
|
|
window_size=(-1, -1), |
|
|
softcap=0.0, |
|
|
alibi_slopes=None, |
|
|
deterministic=False, |
|
|
return_attn_probs=False, |
|
|
): |
|
|
"""dropout_p should be set to 0.0 during evaluation |
|
|
If K, V are already stacked into 1 tensor, this function will be faster than |
|
|
calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation |
|
|
of the gradients of K, V. |
|
|
Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads |
|
|
than Q. Note that the number of heads in Q must be divisible by the number of heads in KV. |
|
|
For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head |
|
|
0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V. |
|
|
|
|
|
If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix. |
|
|
For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is: |
|
|
1 1 1 1 0 |
|
|
1 1 1 1 1 |
|
|
If seqlen_q = 5 and seqlen_k = 2, the causal mask is: |
|
|
0 0 |
|
|
0 0 |
|
|
0 0 |
|
|
1 0 |
|
|
1 1 |
|
|
If the row of the mask is all zero, the output will be zero. |
|
|
|
|
|
If window_size != (-1, -1), implements sliding window local attention. Query at position i |
|
|
will only attend to keys between |
|
|
[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive. |
|
|
|
|
|
Arguments: |
|
|
q: (batch_size, seqlen, nheads, headdim) |
|
|
kv: (batch_size, seqlen, 2, nheads_k, headdim) |
|
|
dropout_p: float. Dropout probability. |
|
|
softmax_scale: float. The scaling of QK^T before applying softmax. |
|
|
Default to 1 / sqrt(headdim). |
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). |
|
|
window_size: (left, right). If not (-1, -1), implements sliding window local attention. |
|
|
softcap: float. Anything > 0 activates softcapping attention. |
|
|
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of |
|
|
(-alibi_slope * |i + seqlen_k - seqlen_q - j|) |
|
|
is added to the attention score of query i and key j. |
|
|
deterministic: bool. Whether to use the deterministic implementation of the backward pass, |
|
|
which is slightly slower and uses more memory. The forward pass is always deterministic. |
|
|
return_attn_probs: bool. Whether to return the attention probabilities. This option is for |
|
|
testing only. The returned probabilities are not guaranteed to be correct |
|
|
(they might not have the right scaling). |
|
|
Return: |
|
|
out: (batch_size, seqlen, nheads, headdim). |
|
|
softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The |
|
|
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax |
|
|
normalization factor). |
|
|
S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). |
|
|
The output of softmax (possibly with different scaling). It also encodes the dropout |
|
|
pattern (negative means that location was dropped, nonnegative means it was kept). |
|
|
""" |
|
|
return FlashAttnKVPackedFunc.apply( |
|
|
q, |
|
|
kv, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_attn_probs, |
|
|
torch.is_grad_enabled(), |
|
|
) |
|
|
|
|
|
|
|
|
def flash_attn_func( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
dropout_p=0.0, |
|
|
softmax_scale=None, |
|
|
causal=False, |
|
|
window_size=(-1, -1), |
|
|
softcap=0.0, |
|
|
alibi_slopes=None, |
|
|
deterministic=False, |
|
|
return_attn_probs=False, |
|
|
): |
|
|
"""dropout_p should be set to 0.0 during evaluation |
|
|
Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads |
|
|
than Q. Note that the number of heads in Q must be divisible by the number of heads in KV. |
|
|
For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head |
|
|
0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V. |
|
|
|
|
|
If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix. |
|
|
For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is: |
|
|
1 1 1 1 0 |
|
|
1 1 1 1 1 |
|
|
If seqlen_q = 5 and seqlen_k = 2, the causal mask is: |
|
|
0 0 |
|
|
0 0 |
|
|
0 0 |
|
|
1 0 |
|
|
1 1 |
|
|
If the row of the mask is all zero, the output will be zero. |
|
|
|
|
|
If window_size != (-1, -1), implements sliding window local attention. Query at position i |
|
|
will only attend to keys between |
|
|
[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive. |
|
|
|
|
|
Arguments: |
|
|
q: (batch_size, seqlen, nheads, headdim) |
|
|
k: (batch_size, seqlen, nheads_k, headdim) |
|
|
v: (batch_size, seqlen, nheads_k, headdim) |
|
|
dropout_p: float. Dropout probability. |
|
|
softmax_scale: float. The scaling of QK^T before applying softmax. |
|
|
Default to 1 / sqrt(headdim). |
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). |
|
|
window_size: (left, right). If not (-1, -1), implements sliding window local attention. |
|
|
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of |
|
|
(-alibi_slope * |i + seqlen_k - seqlen_q - j|) |
|
|
is added to the attention score of query i and key j. |
|
|
deterministic: bool. Whether to use the deterministic implementation of the backward pass, |
|
|
which is slightly slower and uses more memory. The forward pass is always deterministic. |
|
|
return_attn_probs: bool. Whether to return the attention probabilities. This option is for |
|
|
testing only. The returned probabilities are not guaranteed to be correct |
|
|
(they might not have the right scaling). |
|
|
Return: |
|
|
out: (batch_size, seqlen, nheads, headdim). |
|
|
softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The |
|
|
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax |
|
|
normalization factor). |
|
|
S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). |
|
|
The output of softmax (possibly with different scaling). It also encodes the dropout |
|
|
pattern (negative means that location was dropped, nonnegative means it was kept). |
|
|
""" |
|
|
return FlashAttnFunc.apply( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_attn_probs, |
|
|
torch.is_grad_enabled(), |
|
|
) |
|
|
|
|
|
|
|
|
def flash_attn_varlen_qkvpacked_func( |
|
|
qkv, |
|
|
cu_seqlens, |
|
|
max_seqlen, |
|
|
dropout_p=0.0, |
|
|
softmax_scale=None, |
|
|
causal=False, |
|
|
window_size=(-1, -1), |
|
|
softcap=0.0, |
|
|
alibi_slopes=None, |
|
|
deterministic=False, |
|
|
return_attn_probs=False, |
|
|
): |
|
|
"""dropout_p should be set to 0.0 during evaluation |
|
|
If Q, K, V are already stacked into 1 tensor, this function will be faster than |
|
|
calling flash_attn_varlen_func on Q, K, V since the backward pass avoids explicit concatenation |
|
|
of the gradients of Q, K, V. |
|
|
For multi-query and grouped-query attention (MQA/GQA), please see |
|
|
flash_attn_varlen_kvpacked_func and flash_attn_varlen_func. |
|
|
|
|
|
If window_size != (-1, -1), implements sliding window local attention. Query at position i |
|
|
will only attend to keys between [i - window_size[0], i + window_size[1]] inclusive. |
|
|
|
|
|
Arguments: |
|
|
qkv: (total, 3, nheads, headdim), where total = total number of tokens in the batch. |
|
|
cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths |
|
|
of the sequences in the batch, used to index into qkv. |
|
|
max_seqlen: int. Maximum sequence length in the batch. |
|
|
dropout_p: float. Dropout probability. |
|
|
softmax_scale: float. The scaling of QK^T before applying softmax. |
|
|
Default to 1 / sqrt(headdim). |
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). |
|
|
window_size: (left, right). If not (-1, -1), implements sliding window local attention. |
|
|
softcap: float. Anything > 0 activates softcapping attention. |
|
|
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of (-alibi_slope * |i - j|) |
|
|
is added to the attention score of query i and key j. |
|
|
deterministic: bool. Whether to use the deterministic implementation of the backward pass, |
|
|
which is slightly slower and uses more memory. The forward pass is always deterministic. |
|
|
return_attn_probs: bool. Whether to return the attention probabilities. This option is for |
|
|
testing only. The returned probabilities are not guaranteed to be correct |
|
|
(they might not have the right scaling). |
|
|
Return: |
|
|
out: (total, nheads, headdim). |
|
|
softmax_lse [optional, if return_attn_probs=True]: (nheads, total_q_seqlen). The |
|
|
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax |
|
|
normalization factor). |
|
|
S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). |
|
|
The output of softmax (possibly with different scaling). It also encodes the dropout |
|
|
pattern (negative means that location was dropped, nonnegative means it was kept). |
|
|
""" |
|
|
return FlashAttnVarlenQKVPackedFunc.apply( |
|
|
qkv, |
|
|
cu_seqlens, |
|
|
max_seqlen, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_attn_probs, |
|
|
torch.is_grad_enabled(), |
|
|
) |
|
|
|
|
|
|
|
|
def flash_attn_varlen_kvpacked_func( |
|
|
q, |
|
|
kv, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p=0.0, |
|
|
softmax_scale=None, |
|
|
causal=False, |
|
|
window_size=(-1, -1), |
|
|
softcap=0.0, |
|
|
alibi_slopes=None, |
|
|
deterministic=False, |
|
|
return_attn_probs=False, |
|
|
): |
|
|
"""dropout_p should be set to 0.0 during evaluation |
|
|
If K, V are already stacked into 1 tensor, this function will be faster than |
|
|
calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation |
|
|
of the gradients of K, V. |
|
|
Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads |
|
|
than Q. Note that the number of heads in Q must be divisible by the number of heads in KV. |
|
|
For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head |
|
|
0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V. |
|
|
|
|
|
If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix. |
|
|
For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is: |
|
|
1 1 1 1 0 |
|
|
1 1 1 1 1 |
|
|
If seqlen_q = 5 and seqlen_k = 2, the causal mask is: |
|
|
0 0 |
|
|
0 0 |
|
|
0 0 |
|
|
1 0 |
|
|
1 1 |
|
|
If the row of the mask is all zero, the output will be zero. |
|
|
|
|
|
If window_size != (-1, -1), implements sliding window local attention. Query at position i |
|
|
will only attend to keys between |
|
|
[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive. |
|
|
|
|
|
Arguments: |
|
|
q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch. |
|
|
kv: (total_k, 2, nheads_k, headdim), where total_k = total number of key tokens in the batch. |
|
|
cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths |
|
|
of the sequences in the batch, used to index into q. |
|
|
cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths |
|
|
of the sequences in the batch, used to index into kv. |
|
|
max_seqlen_q: int. Maximum query sequence length in the batch. |
|
|
max_seqlen_k: int. Maximum key sequence length in the batch. |
|
|
dropout_p: float. Dropout probability. |
|
|
softmax_scale: float. The scaling of QK^T before applying softmax. |
|
|
Default to 1 / sqrt(headdim). |
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). |
|
|
window_size: (left, right). If not (-1, -1), implements sliding window local attention. |
|
|
softcap: float. Anything > 0 activates softcapping attention. |
|
|
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of |
|
|
(-alibi_slope * |i + seqlen_k - seqlen_q - j|) |
|
|
is added to the attention score of query i and key j. |
|
|
deterministic: bool. Whether to use the deterministic implementation of the backward pass, |
|
|
which is slightly slower and uses more memory. The forward pass is always deterministic. |
|
|
return_attn_probs: bool. Whether to return the attention probabilities. This option is for |
|
|
testing only. The returned probabilities are not guaranteed to be correct |
|
|
(they might not have the right scaling). |
|
|
Return: |
|
|
out: (total, nheads, headdim). |
|
|
softmax_lse [optional, if return_attn_probs=True]: (nheads, total_q_seqlen). The |
|
|
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax |
|
|
normalization factor). |
|
|
S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). |
|
|
The output of softmax (possibly with different scaling). It also encodes the dropout |
|
|
pattern (negative means that location was dropped, nonnegative means it was kept). |
|
|
""" |
|
|
return FlashAttnVarlenKVPackedFunc.apply( |
|
|
q, |
|
|
kv, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_attn_probs, |
|
|
torch.is_grad_enabled(), |
|
|
) |
|
|
|
|
|
|
|
|
def flash_attn_varlen_func( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p=0.0, |
|
|
softmax_scale=None, |
|
|
causal=False, |
|
|
window_size=(-1, -1), |
|
|
softcap=0.0, |
|
|
alibi_slopes=None, |
|
|
deterministic=False, |
|
|
return_attn_probs=False, |
|
|
block_table=None, |
|
|
): |
|
|
"""dropout_p should be set to 0.0 during evaluation |
|
|
Supports multi-query and grouped-query attention (MQA/GQA) by passing in K, V with fewer heads |
|
|
than Q. Note that the number of heads in Q must be divisible by the number of heads in KV. |
|
|
For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head |
|
|
0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V. |
|
|
|
|
|
If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix. |
|
|
For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is: |
|
|
1 1 1 1 0 |
|
|
1 1 1 1 1 |
|
|
If seqlen_q = 5 and seqlen_k = 2, the causal mask is: |
|
|
0 0 |
|
|
0 0 |
|
|
0 0 |
|
|
1 0 |
|
|
1 1 |
|
|
If the row of the mask is all zero, the output will be zero. |
|
|
|
|
|
If window_size != (-1, -1), implements sliding window local attention. Query at position i |
|
|
will only attend to keys between |
|
|
[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive. |
|
|
|
|
|
Arguments: |
|
|
q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch. |
|
|
k: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch. |
|
|
v: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch. |
|
|
cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths |
|
|
of the sequences in the batch, used to index into q. |
|
|
cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths |
|
|
of the sequences in the batch, used to index into kv. |
|
|
max_seqlen_q: int. Maximum query sequence length in the batch. |
|
|
max_seqlen_k: int. Maximum key sequence length in the batch. |
|
|
dropout_p: float. Dropout probability. |
|
|
softmax_scale: float. The scaling of QK^T before applying softmax. |
|
|
Default to 1 / sqrt(headdim). |
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). |
|
|
window_size: (left, right). If not (-1, -1), implements sliding window local attention. |
|
|
softcap: float. Anything > 0 activates softcapping attention. |
|
|
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of |
|
|
(-alibi_slope * |i + seqlen_k - seqlen_q - j|) |
|
|
is added to the attention score of query i and key j. |
|
|
deterministic: bool. Whether to use the deterministic implementation of the backward pass, |
|
|
which is slightly slower and uses more memory. The forward pass is always deterministic. |
|
|
return_attn_probs: bool. Whether to return the attention probabilities. This option is for |
|
|
testing only. The returned probabilities are not guaranteed to be correct |
|
|
(they might not have the right scaling). |
|
|
Return: |
|
|
out: (total, nheads, headdim). |
|
|
softmax_lse [optional, if return_attn_probs=True]: (nheads, total_q_seqlen). The |
|
|
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax |
|
|
normalization factor). |
|
|
S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen). |
|
|
The output of softmax (possibly with different scaling). It also encodes the dropout |
|
|
pattern (negative means that location was dropped, nonnegative means it was kept). |
|
|
""" |
|
|
return FlashAttnVarlenFunc.apply( |
|
|
q, |
|
|
k, |
|
|
v, |
|
|
cu_seqlens_q, |
|
|
cu_seqlens_k, |
|
|
max_seqlen_q, |
|
|
max_seqlen_k, |
|
|
dropout_p, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size, |
|
|
softcap, |
|
|
alibi_slopes, |
|
|
deterministic, |
|
|
return_attn_probs, |
|
|
block_table, |
|
|
torch.is_grad_enabled(), |
|
|
) |
|
|
|
|
|
|
|
|
def flash_attn_with_kvcache( |
|
|
q, |
|
|
k_cache, |
|
|
v_cache, |
|
|
k=None, |
|
|
v=None, |
|
|
rotary_cos=None, |
|
|
rotary_sin=None, |
|
|
cache_seqlens: Optional[Union[(int, torch.Tensor)]] = None, |
|
|
cache_batch_idx: Optional[torch.Tensor] = None, |
|
|
cache_leftpad: Optional[torch.Tensor] = None, |
|
|
block_table: Optional[torch.Tensor] = None, |
|
|
softmax_scale=None, |
|
|
causal=False, |
|
|
window_size=(-1, -1), |
|
|
softcap=0.0, |
|
|
rotary_interleaved=True, |
|
|
alibi_slopes=None, |
|
|
num_splits=0, |
|
|
return_softmax_lse=False, |
|
|
): |
|
|
""" |
|
|
If k and v are not None, k_cache and v_cache will be updated *inplace* with the new values from |
|
|
k and v. This is useful for incremental decoding: you can pass in the cached keys/values from |
|
|
the previous step, and update them with the new keys/values from the current step, and do |
|
|
attention with the updated cache, all in 1 kernel. |
|
|
|
|
|
If you pass in k / v, you must make sure that the cache is large enough to hold the new values. |
|
|
For example, the KV cache could be pre-allocated with the max sequence length, and you can use |
|
|
cache_seqlens to keep track of the current sequence lengths of each sequence in the batch. |
|
|
|
|
|
Also apply rotary embedding if rotary_cos and rotary_sin are passed in. The key @k will be |
|
|
rotated by rotary_cos and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc. |
|
|
If causal or local (i.e., window_size != (-1, -1)), the query @q will be rotated by rotary_cos |
|
|
and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc. |
|
|
If not causal and not local, the query @q will be rotated by rotary_cos and rotary_sin at |
|
|
indices cache_seqlens only (i.e. we consider all tokens in @q to be at position cache_seqlens). |
|
|
|
|
|
See tests/test_flash_attn.py::test_flash_attn_kvcache for examples of how to use this function. |
|
|
|
|
|
Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads |
|
|
than Q. Note that the number of heads in Q must be divisible by the number of heads in KV. |
|
|
For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head |
|
|
0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V. |
|
|
|
|
|
If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix. |
|
|
For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is: |
|
|
1 1 1 1 0 |
|
|
1 1 1 1 1 |
|
|
If seqlen_q = 5 and seqlen_k = 2, the causal mask is: |
|
|
0 0 |
|
|
0 0 |
|
|
0 0 |
|
|
1 0 |
|
|
1 1 |
|
|
If the row of the mask is all zero, the output will be zero. |
|
|
|
|
|
If window_size != (-1, -1), implements sliding window local attention. Query at position i |
|
|
will only attend to keys between |
|
|
[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive. |
|
|
|
|
|
Note: Does not support backward pass. |
|
|
|
|
|
Arguments: |
|
|
q: (batch_size, seqlen, nheads, headdim) |
|
|
k_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no block_table, |
|
|
or (num_blocks, page_block_size, nheads_k, headdim) if there's a block_table (i.e. paged KV cache) |
|
|
page_block_size must be a multiple of 256. |
|
|
v_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no block_table, |
|
|
or (num_blocks, page_block_size, nheads_k, headdim) if there's a block_table (i.e. paged KV cache) |
|
|
k [optional]: (batch_size, seqlen_new, nheads_k, headdim). If not None, we concatenate |
|
|
k with k_cache, starting at the indices specified by cache_seqlens. |
|
|
v [optional]: (batch_size, seqlen_new, nheads_k, headdim). Similar to k. |
|
|
rotary_cos [optional]: (seqlen_ro, rotary_dim / 2). If not None, we apply rotary embedding |
|
|
to k and q. Only applicable if k and v are passed in. rotary_dim must be divisible by 16. |
|
|
rotary_sin [optional]: (seqlen_ro, rotary_dim / 2). Similar to rotary_cos. |
|
|
cache_seqlens: int, or (batch_size,), dtype torch.int32. The sequence lengths of the |
|
|
KV cache. |
|
|
cache_batch_idx: (batch_size,), dtype torch.int32. The indices used to index into the KV cache. |
|
|
If None, we assume that the batch indices are [0, 1, 2, ..., batch_size - 1]. |
|
|
If the indices are not distinct, and k and v are provided, the values updated in the cache |
|
|
might come from any of the duplicate indices. |
|
|
cache_leftpad: (batch_size,), dtype torch.int32. The index that the KV cache starts. If None, assume 0. |
|
|
block_table [optional]: (batch_size, max_num_blocks_per_seq), dtype torch.int32. |
|
|
softmax_scale: float. The scaling of QK^T before applying softmax. |
|
|
Default to 1 / sqrt(headdim). |
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling). |
|
|
window_size: (left, right). If not (-1, -1), implements sliding window local attention. |
|
|
softcap: float. Anything > 0 activates softcapping attention. |
|
|
rotary_interleaved: bool. Only applicable if rotary_cos and rotary_sin are passed in. |
|
|
If True, rotary embedding will combine dimensions 0 & 1, 2 & 3, etc. If False, |
|
|
rotary embedding will combine dimensions 0 & rotary_dim / 2, 1 & rotary_dim / 2 + 1 |
|
|
(i.e. GPT-NeoX style). |
|
|
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of |
|
|
(-alibi_slope * |i + seqlen_k - seqlen_q - j|) |
|
|
is added to the attention score of query i and key j. |
|
|
num_splits: int. If > 1, split the key/value into this many chunks along the sequence. |
|
|
If num_splits == 1, we don't split the key/value. If num_splits == 0, we use a heuristic |
|
|
to automatically determine the number of splits. |
|
|
Don't change this unless you know what you are doing. |
|
|
return_softmax_lse: bool. Whether to return the logsumexp of the attention scores. |
|
|
|
|
|
Return: |
|
|
out: (batch_size, seqlen, nheads, headdim). |
|
|
softmax_lse [optional, if return_softmax_lse=True]: (batch_size, nheads, seqlen). The |
|
|
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax |
|
|
normalization factor). |
|
|
""" |
|
|
assert k_cache.stride(-1) == 1, "k_cache must have contiguous last dimension" |
|
|
assert v_cache.stride(-1) == 1, "v_cache must have contiguous last dimension" |
|
|
q, k, v = [maybe_contiguous(x) for x in (q, k, v)] |
|
|
if softmax_scale is None: |
|
|
softmax_scale = q.shape[-1] ** (-0.5) |
|
|
if cache_seqlens is not None and isinstance(cache_seqlens, int): |
|
|
cache_seqlens = torch.full( |
|
|
(k_cache.shape[0],), cache_seqlens, dtype=torch.int32, device=k_cache.device |
|
|
) |
|
|
cache_seqlens = maybe_contiguous(cache_seqlens) |
|
|
cache_batch_idx = maybe_contiguous(cache_batch_idx) |
|
|
block_table = maybe_contiguous(block_table) |
|
|
out, softmax_lse = flash_attn_gpu.fwd_kvcache( |
|
|
q, |
|
|
k_cache, |
|
|
v_cache, |
|
|
k, |
|
|
v, |
|
|
cache_seqlens, |
|
|
rotary_cos, |
|
|
rotary_sin, |
|
|
cache_batch_idx, |
|
|
cache_leftpad, |
|
|
block_table, |
|
|
alibi_slopes, |
|
|
None, |
|
|
softmax_scale, |
|
|
causal, |
|
|
window_size[0], |
|
|
window_size[1], |
|
|
softcap, |
|
|
rotary_interleaved, |
|
|
num_splits, |
|
|
) |
|
|
return (out, softmax_lse) if return_softmax_lse else out |
|
|
|
