squall/flash-attention
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
main
flash-attention/tests/test_flash_attn_ck.py
rocking e2182cc21d
Support page kvcache in AMD ROCm (#1198) 
* Integrate ck branch of ck_tile/fa_bwd_opt

* Assume dq and q share the same stride

* update ck

* Integrate more stride of dq_acc

* Revert fwd dropout

* Fix paremeter order

* Integrate ck with more stride

* update the limit of hdim of bwd

* Check argument

* Add test_flash_attn_causal

* Support unpad lse

* Add  test_flash_attn_varlen_causal, test_flash_attn_race_condition, test_flash_attn_bwd_overflow, test_flash_attn_bwd_transpose, test_flash_attn_bwd_varlen_overflow, test_flash_attn_deterministic, test_flash_attn_varlen_deterministic

* Fix stride and Kn0

* Fix CK sync issue

* Fix typo

* Update CK for changing of fmha_fwd_args

* Add kvcache tmp

* Add kvcache

* Fix comment

* Sync behavior with ck

* Update CK to develop

* remove large test case

* Add kvcache test

* Fix page_block_size in arg

* Minor fix

* Fix stride error

* Update seqlen of kvcache before splitkv

* Fix compile error

* Fix bug of hdim is not 8x

* Fit ck arg

* support adaptive num_splits

* add more tests

* Refine test tolerance

* update CK

* Move override_num_splits_if_necessary into cpp

* update ck

* Update ck

* Support different flag for different version of hip

* remove coerce-illegal, becasue this is not required in FA

* Update ck to fix xcratch memory

* Add coerce-illegal in some version

* Add compile flag for rtn rounding

* remove redundant init

* Using env var to switch rounding mode

* update ck
2024-09-15 23:17:28 -07:00

1621 lines
59 KiB
Python
Raw Permalink Blame History

import math
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from flash_attn import (
flash_attn_func,
flash_attn_kvpacked_func,
flash_attn_qkvpacked_func,
flash_attn_varlen_func,
flash_attn_varlen_kvpacked_func,
flash_attn_varlen_qkvpacked_func,
flash_attn_with_kvcache,
)
from test_flash_attn import (
attn_bias_from_alibi_slopes,
convert_flash_attn_S_to_softmax,
generate_qkv,
generate_random_padding_mask,
_generate_block_kvcache,
attention_ref,
attention_kvpacked_ref,
attention_qkvpacked_ref,
)
from flash_attn.layers.rotary import apply_rotary_emb
def is_bwd_hdim_supported(d):
return d <= 256
def ck_randval_to_dropout_mask(randval, p):
# If p = 0.3, randval in 255 * (0.7, 1.0] will be dropout
# randval in 255 * [0, 0.7] will be kept
# If return dropout_mask >=0, value will be kept
return math.floor(255.0 * (1 - p)) - randval.to(torch.float32)
def pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens_q, seqlen_q_rounded, seqlen_k_rounded):
""" pad + rearrange [nheads, total_q, max_seqlen_k] into [b, nheads, seqlen_q_rounded, seqlen_k_rounded]
Arguments:
S_dmask: (nheads, total_q, max_seqlen_k)
cu_seqlens_q: (b + 1)
Output:
S_dmask: (b, nheads, seqlen_q_rounded, seqlen_k_rounded)
"""
batch_size = cu_seqlens_q.numel() - 1
seqlens_q = torch.roll(cu_seqlens_q, shifts = -1) - cu_seqlens_q
seqlens_q = seqlens_q[0:batch_size].tolist()
S_dmask = torch.split(S_dmask, seqlens_q, dim=1)
# [(nheads, seqlen_q0, max_seqlen_k), (nheads, seqlen_q1, max_seqlen_k), ..., (nheads, seqlen_qb, max_seqlen_k)]
masks = ()
for mask in S_dmask:
# (nheads, seqlen_qi, max_seqlen_k) -> (nheads, seqlen_q_rounded, seqlen_k_rounded)
mask = F.pad(mask, (0, seqlen_k_rounded - mask.shape[2], 0, seqlen_q_rounded - mask.shape[1], 0, 0)).unsqueeze(1)
masks = masks + (mask, )
S_dmask = torch.cat(masks, dim=1)
S_dmask = S_dmask.transpose(0, 1)
return S_dmask
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("deterministic", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("seqlen", [97, 128, 200, 384, 768, 1024, 1025, 2048])
@pytest.mark.parametrize("dropout_p", [0.0, 0.17])
def test_flash_attn_qkvpacked(seqlen, d, dropout_p, causal, local, alibi, deterministic, dtype):
if d > 256:
pytest.skip()
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 4
nheads = 9
window_size = (-1, -1) if not local else torch.randint(0, seqlen, (2,))
qkv = torch.randn(
batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True
)
if alibi:
alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen, seqlen, causal=causal)
else:
alibi_slopes, attn_bias = None, None
out, lse, S_dmask = flash_attn_qkvpacked_func(
qkv,
dropout_p,
causal=causal,
window_size=window_size,
alibi_slopes=alibi_slopes,
deterministic=deterministic,
return_attn_probs=True,
)
if dropout_p > 0.0:
# TODO - move to c++ mha_varlen_fwd()
S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p)
S_dmask_converted = convert_flash_attn_S_to_softmax(
S_dmask,
seqlen,
seqlen,
None,
None,
d,
dropout_p > 0.0,
causal=causal,
window_size=window_size,
)
dropout_mask = S_dmask_converted >= 0
# CK does not return P. Hence, we don't test the attn here.
else:
dropout_mask = None
out_ref, attn_ref = attention_qkvpacked_ref(
qkv, None, attn_bias, dropout_p, dropout_mask, causal=causal, window_size=window_size
)
out_pt, attn_pt = attention_qkvpacked_ref(
qkv,
None,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
upcast=False,
reorder_ops=True,
)
print(f"Output max diff: {(out - out_ref).abs().max().item()}")
print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
# Check that FlashAttention's numerical error is at most twice the numerical error
# of a Pytorch implementation.
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
g = torch.randn_like(out)
if is_bwd_hdim_supported(d):
(dqkv,) = torch.autograd.grad(out, qkv, g)
(dqkv_ref,) = torch.autograd.grad(out_ref, qkv, g)
(dqkv_pt,) = torch.autograd.grad(out_pt, qkv, g)
print(f"dQ max diff: {(dqkv[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}")
print(f"dK max diff: {(dqkv[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}")
print(f"dV max diff: {(dqkv[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}")
print(f"dQKV mean diff: {(dqkv - dqkv_ref).abs().mean().item()}")
print(f"dQ Pytorch max diff: {(dqkv_pt[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}")
print(f"dK Pytorch max diff: {(dqkv_pt[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}")
print(f"dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}")
print(f"dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}")
# TODO - use 10 times to check, wait for ck to fix bwd precision issue
assert (dqkv - dqkv_ref).abs().max().item() <= 10 * (dqkv_pt - dqkv_ref).abs().max().item()
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("deterministic", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("seqlen", [97, 128, 200, 257, 384, 512, 768, 1025, 2048])
@pytest.mark.parametrize("dropout_p", [0, 0.17])
def test_flash_attn_varlen_qkvpacked(seqlen, d, dropout_p, causal, local, alibi, deterministic, dtype):
if d > 256:
pytest.skip()
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 5
nheads = 6
window_size = (-1, -1) if not local else torch.randint(0, seqlen, (2,))
qkv = torch.randn(
batch_size, seqlen, 3, nheads, d, device=device, dtype=dtype, requires_grad=True
)
key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode="random")
# key_padding_mask = generate_random_padding_mask(seqlen, batch_size, device, mode='full')
if alibi:
alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
attn_bias = attn_bias_from_alibi_slopes(
alibi_slopes, seqlen, seqlen, key_padding_mask, key_padding_mask, causal=causal
)
else:
alibi_slopes, attn_bias = None, None
qkv_unpad, cu_seqlens, max_seqlen, qkv, output_pad_fn, dqkv_pad_fn = generate_qkv(
*qkv.unbind(dim=2), key_padding_mask, key_padding_mask, qkvpacked=True
)
out_unpad, sm_lse, S_dmask = flash_attn_varlen_qkvpacked_func(
qkv_unpad,
cu_seqlens,
max_seqlen,
dropout_p,
causal=causal,
window_size=window_size,
alibi_slopes=alibi_slopes,
deterministic=deterministic,
return_attn_probs=True,
)
out = output_pad_fn(out_unpad)
if dropout_p > 0.0:
# TODO - move to c++ mha_varlen_fwd()
S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p)
S_dmask = pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens, seqlen, seqlen)
S_dmask_converted = convert_flash_attn_S_to_softmax(
S_dmask,
seqlen,
seqlen,
key_padding_mask,
key_padding_mask,
d,
dropout_p > 0.0,
causal=causal,
window_size=window_size,
)
dropout_mask = S_dmask_converted >= 0
# CK does not return P. Hence, we don't test the attn here.
else:
dropout_mask = None
out_ref, attn_ref = attention_qkvpacked_ref(
qkv,
key_padding_mask,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
)
out_pt, attn_pt = attention_qkvpacked_ref(
qkv,
key_padding_mask,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
upcast=False,
reorder_ops=True,
)
print(f"Output max diff: {(out - out_ref).abs().max().item()}")
print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
# Check that FlashAttention's numerical error is at most twice the numerical error
# of a Pytorch implementation.
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
g = torch.randn_like(out)
if is_bwd_hdim_supported(d):
(dqkv_unpad,) = torch.autograd.grad(out, qkv_unpad, g)
dqkv = dqkv_pad_fn(dqkv_unpad)
(dqkv_ref,) = torch.autograd.grad(out_ref, qkv, g)
(dqkv_pt,) = torch.autograd.grad(out_pt, qkv, g)
print(f"dQ max diff: {(dqkv[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}")
print(f"dK max diff: {(dqkv[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}")
print(f"dV max diff: {(dqkv[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}")
print(f"dQKV mean diff: {(dqkv - dqkv_ref).abs().mean().item()}")
print(f"dQ Pytorch max diff: {(dqkv_pt[:, :, 0] - dqkv_ref[:, :, 0]).abs().max().item()}")
print(f"dK Pytorch max diff: {(dqkv_pt[:, :, 1] - dqkv_ref[:, :, 1]).abs().max().item()}")
print(f"dV Pytorch max diff: {(dqkv_pt[:, :, 2] - dqkv_ref[:, :, 2]).abs().max().item()}")
print(f"dQKV Pytorch mean diff: {(dqkv_pt - dqkv_ref).abs().mean().item()}")
# TODO - use 10 times to check, wait for ck to fix bwd precision issue
assert (dqkv - dqkv_ref).abs().max().item() <= 10 * (dqkv_pt - dqkv_ref).abs().max().item()
@pytest.mark.parametrize("kvpacked", [True, False])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
@pytest.mark.parametrize("deterministic", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize(
"seqlen_q,seqlen_k",
[
(113, 203),
(128, 217),
(113, 211),
(108, 256),
(256, 512),
(512, 256),
(1024, 1024),
(1023, 1024),
(1024, 1023),
(2048, 2048),
],
)
@pytest.mark.parametrize("dropout_p", [0.0, 0.17])
def test_flash_attn_output(
seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked
):
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 4
nheads = 9
nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
assert nheads % nheads_k == 0
window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
if kvpacked:
kv = torch.randn(
batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True
)
else:
k = torch.randn(
batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True
)
v = torch.randn(
batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True
)
if alibi:
alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen_q, seqlen_k, causal=causal)
else:
alibi_slopes, attn_bias = None, None
if kvpacked:
out, lse, S_dmask = flash_attn_kvpacked_func(
q,
kv,
dropout_p,
causal=causal,
window_size=window_size,
alibi_slopes=alibi_slopes,
deterministic=deterministic,
return_attn_probs=True,
)
else:
out, lse, S_dmask = flash_attn_func(
q,
k,
v,
dropout_p,
causal=causal,
window_size=window_size,
alibi_slopes=alibi_slopes,
deterministic=deterministic,
return_attn_probs=True,
)
if dropout_p > 0.0:
# TODO - move to c++ mha_varlen_fwd()
S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p)
S_dmask_converted = convert_flash_attn_S_to_softmax(
S_dmask,
seqlen_q,
seqlen_k,
None,
None,
d,
dropout_p > 0.0,
causal=causal,
window_size=window_size,
)
dropout_mask = S_dmask_converted >= 0
if kvpacked:
kv_rep = repeat(kv, "b s two h d -> b s two (h g) d", g=nheads // nheads_k)
k_rep, v_rep = kv_rep.unbind(dim=2)
else:
k_rep = repeat(k, "b s h d -> b s (h g) d", g=nheads // nheads_k)
v_rep = repeat(v, "b s h d -> b s (h g) d", g=nheads // nheads_k)
# CK does not return P. Hence, we don't test the attn here.
else:
dropout_mask = None
if kvpacked:
out_ref, attn_ref = attention_kvpacked_ref(
q,
kv,
None,
None,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
)
out_pt, attn_pt = attention_kvpacked_ref(
q,
kv,
None,
None,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
upcast=False,
reorder_ops=True,
)
else:
out_ref, attn_ref = attention_ref(
q,
k,
v,
None,
None,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
)
out_pt, attn_pt = attention_ref(
q,
k,
v,
None,
None,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
upcast=False,
reorder_ops=True,
)
print(f"Output max diff: {(out - out_ref).abs().max().item()}")
print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
# Check that FlashAttention's numerical error is at most twice the numerical error
# of a Pytorch implementation.
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
g = torch.randn_like(out)
if is_bwd_hdim_supported(d):
if kvpacked:
(
dq,
dkv,
) = torch.autograd.grad(out, (q, kv), g)
dk, dv = dkv.unbind(2)
(
dq_ref,
dkv_ref,
) = torch.autograd.grad(out_ref, (q, kv), g)
dk_ref, dv_ref = dkv_ref.unbind(2)
(
dq_pt,
dkv_pt,
) = torch.autograd.grad(out_pt, (q, kv), g)
dk_pt, dv_pt = dkv_pt.unbind(2)
else:
(
dq,
dk,
dv,
) = torch.autograd.grad(out, (q, k, v), g)
(
dq_ref,
dk_ref,
dv_ref,
) = torch.autograd.grad(out_ref, (q, k, v), g)
(
dq_pt,
dk_pt,
dv_pt,
) = torch.autograd.grad(out_pt, (q, k, v), g)
print(f"dQ max diff: {(dq - dq_ref).abs().max().item()}")
print(f"dK max diff: {(dk - dk_ref).abs().max().item()}")
print(f"dV max diff: {(dv - dv_ref).abs().max().item()}")
print(f"dQ mean diff: {(dq - dq_ref).abs().mean().item()}")
print(f"dK mean diff: {(dk - dk_ref).abs().mean().item()}")
print(f"dV mean diff: {(dv - dv_ref).abs().mean().item()}")
print(f"dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}")
print(f"dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}")
print(f"dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}")
print(f"dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}")
print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}")
print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}")
# TODO - use 10 times to check, wait for ck to fix bwd precision issue
assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item()
assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item()
assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item()
@pytest.mark.parametrize("kvpacked", [True, False])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
@pytest.mark.parametrize("deterministic", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize(
"seqlen_q,seqlen_k",
[
(1, 147),
(113, 203),
(128, 217),
(113, 211),
(108, 256),
(256, 512),
(512, 256),
(1024, 1024),
(1023, 1024),
(1024, 1023),
(2048, 2048),
],
)
@pytest.mark.parametrize("dropout_p", [0.0, 0.17])
def test_flash_attn_varlen_output(
seqlen_q, seqlen_k, d, dropout_p, causal, local, alibi, deterministic, mha_type, dtype, kvpacked
):
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 4
nheads = 9
nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
assert nheads % nheads_k == 0
window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
if kvpacked:
kv = torch.randn(
batch_size, seqlen_k, 2, nheads_k, d, device=device, dtype=dtype, requires_grad=True
)
else:
k = torch.randn(
batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True
)
v = torch.randn(
batch_size, seqlen_k, nheads_k, d, device=device, dtype=dtype, requires_grad=True
)
query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random")
key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random")
# key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode='full')
if alibi:
alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
attn_bias = attn_bias_from_alibi_slopes(
alibi_slopes, seqlen_q, seqlen_k, query_padding_mask, key_padding_mask, causal=causal
)
else:
alibi_slopes, attn_bias = None, None
if kvpacked:
(
q_unpad,
kv_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
q,
kv,
output_pad_fn,
dq_pad_fn,
dkv_pad_fn,
) = generate_qkv(q, *kv.unbind(dim=2), query_padding_mask, key_padding_mask, kvpacked=True)
out_unpad, sm_lse, S_dmask = flash_attn_varlen_kvpacked_func(
q_unpad,
kv_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
causal=causal,
window_size=window_size,
alibi_slopes=alibi_slopes,
deterministic=deterministic,
return_attn_probs=True,
)
else:
(
q_unpad,
k_unpad,
v_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
q,
k,
v,
output_pad_fn,
dq_pad_fn,
dk_pad_fn,
) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False)
out_unpad, sm_lse, S_dmask = flash_attn_varlen_func(
q_unpad,
k_unpad,
v_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p,
causal=causal,
window_size=window_size,
alibi_slopes=alibi_slopes,
deterministic=deterministic,
return_attn_probs=True,
)
out = output_pad_fn(out_unpad)
if dropout_p > 0.0:
# TODO - move to c++ mha_varlen_fwd()
S_dmask = ck_randval_to_dropout_mask(S_dmask, dropout_p)
S_dmask = pad_rearrange_dropout_mask_hts_to_bhss(S_dmask, cu_seqlens_q, seqlen_q, seqlen_k)
S_dmask_converted = convert_flash_attn_S_to_softmax(
S_dmask,
seqlen_q,
seqlen_k,
query_padding_mask,
key_padding_mask,
d,
dropout_p > 0.0,
causal=causal,
window_size=window_size,
)
dropout_mask = S_dmask_converted >= 0
if kvpacked:
kv_rep = repeat(kv, "b s two h d -> b s two (h g) d", g=nheads // nheads_k)
k_rep, v_rep = kv_rep.unbind(dim=2)
else:
k_rep = repeat(k, "b s h d -> b s (h g) d", g=nheads // nheads_k)
v_rep = repeat(v, "b s h d -> b s (h g) d", g=nheads // nheads_k)
# CK does not return P. Hence, we don't test the attn here.
else:
dropout_mask = None
if kvpacked:
out_ref, attn_ref = attention_kvpacked_ref(
q,
kv,
query_padding_mask,
key_padding_mask,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
)
out_pt, attn_pt = attention_kvpacked_ref(
q,
kv,
query_padding_mask,
key_padding_mask,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
upcast=False,
reorder_ops=True,
)
else:
out_ref, attn_ref = attention_ref(
q,
k,
v,
query_padding_mask,
key_padding_mask,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
)
out_pt, attn_pt = attention_ref(
q,
k,
v,
query_padding_mask,
key_padding_mask,
attn_bias,
dropout_p,
dropout_mask,
causal=causal,
window_size=window_size,
upcast=False,
reorder_ops=True,
)
print(f"Output max diff: {(out - out_ref).abs().max().item()}")
print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
# Check that FlashAttention's numerical error is at most 4 times the numerical error
# of a Pytorch implementation.
assert (out - out_ref).abs().max().item() <= 4 * (out_pt - out_ref).abs().max().item()
g = torch.randn_like(out)
if is_bwd_hdim_supported(d):
if kvpacked:
(
dq_unpad,
dkv_unpad,
) = torch.autograd.grad(out, (q_unpad, kv_unpad), g)
dk, dv = dkv_pad_fn(dkv_unpad).unbind(2)
(
dq_ref,
dkv_ref,
) = torch.autograd.grad(out_ref, (q, kv), g)
dk_ref, dv_ref = dkv_ref.unbind(2)
(
dq_pt,
dkv_pt,
) = torch.autograd.grad(out_pt, (q, kv), g)
dk_pt, dv_pt = dkv_pt.unbind(2)
else:
(
dq_unpad,
dk_unpad,
dv_unpad,
) = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g)
dk = dk_pad_fn(dk_unpad)
dv = dk_pad_fn(dv_unpad)
(
dq_ref,
dk_ref,
dv_ref,
) = torch.autograd.grad(out_ref, (q, k, v), g)
(
dq_pt,
dk_pt,
dv_pt,
) = torch.autograd.grad(out_pt, (q, k, v), g)
dq = dq_pad_fn(dq_unpad)
print(f"dQ max diff: {(dq - dq_ref).abs().max().item()}")
print(f"dK max diff: {(dk - dk_ref).abs().max().item()}")
print(f"dV max diff: {(dv - dv_ref).abs().max().item()}")
print(f"dQ mean diff: {(dq - dq_ref).abs().mean().item()}")
print(f"dK mean diff: {(dk - dk_ref).abs().mean().item()}")
print(f"dV mean diff: {(dv - dv_ref).abs().mean().item()}")
print(f"dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}")
print(f"dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}")
print(f"dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}")
print(f"dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}")
print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}")
print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}")
# TODO - use 10 times to check, wait for ck to fix bwd precision issue
assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item()
assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item()
assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item()
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("swap_sq_sk", [False, True])
@pytest.mark.parametrize(
"seqlen_q,seqlen_k",
[
# (1, 239),
(3, 799),
(127, 512),
(127, 513),
(113, 203),
(128, 217),
(113, 211),
(108, 256),
(256, 512),
(1023, 1024),
],
)
def test_flash_attn_causal(seqlen_q, seqlen_k, swap_sq_sk, d, local, dtype):
if max(seqlen_q, seqlen_k) >= 2048:
pytest.skip()
if swap_sq_sk:
seqlen_q, seqlen_k = seqlen_k, seqlen_q
device = "cuda"
causal = True
# set seed
torch.random.manual_seed(0)
batch_size = 8
nheads = 9
window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size)
out_ref, attn_ref = attention_ref(
q, k, v, None, None, None, 0.0, None, causal=causal, window_size=window_size
)
out_pt, attn_pt = attention_ref(
q,
k,
v,
None,
None,
None,
0.0,
None,
causal=causal,
window_size=window_size,
upcast=False,
reorder_ops=True,
)
print(f"Output max diff: {(out - out_ref).abs().max().item()}")
print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
# Check that FlashAttention's numerical error is at most 4 times the numerical error
# of a Pytorch implementation.
assert (out - out_ref).abs().max().item() <= 4 * (out_pt - out_ref).abs().max().item() + 1e-5
g = torch.randn_like(out)
if is_bwd_hdim_supported(d):
do_o = (g.float() * out.float()).sum(-1)
(
dq,
dk,
dv,
) = torch.autograd.grad(out, (q, k, v), g)
(
dq_ref,
dk_ref,
dv_ref,
) = torch.autograd.grad(out_ref, (q, k, v), g)
(
dq_pt,
dk_pt,
dv_pt,
) = torch.autograd.grad(out_pt, (q, k, v), g)
print(f"dQ max diff: {(dq - dq_ref).abs().max().item()}")
print(f"dK max diff: {(dk - dk_ref).abs().max().item()}")
print(f"dV max diff: {(dv - dv_ref).abs().max().item()}")
print(f"dQ mean diff: {(dq - dq_ref).abs().mean().item()}")
print(f"dK mean diff: {(dk - dk_ref).abs().mean().item()}")
print(f"dV mean diff: {(dv - dv_ref).abs().mean().item()}")
print(f"dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}")
print(f"dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}")
print(f"dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}")
print(f"dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}")
print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}")
print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}")
# TODO - use 10 times to check, wait for ck to fix bwd precision issue
assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() + 1e-4
assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() + 1e-4
assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() + 1e-4
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("swap_sq_sk", [False, True])
@pytest.mark.parametrize(
"seqlen_q,seqlen_k",
[
# (1, 239),
(3, 799),
(127, 512),
(127, 513),
(113, 203),
(128, 217),
(113, 211),
(108, 256),
(256, 512),
(1023, 1024),
],
)
# TODO: Support paged_kv_block
# @pytest.mark.parametrize("paged_kv_block_size", [None, 256, 512])
@pytest.mark.parametrize("paged_kv_block_size", [None])
def test_flash_attn_varlen_causal(
seqlen_q, seqlen_k, swap_sq_sk, d, local, paged_kv_block_size, dtype
):
if max(seqlen_q, seqlen_k) >= 2048:
pytest.skip()
if swap_sq_sk:
seqlen_q, seqlen_k = seqlen_k, seqlen_q
device = "cuda"
causal = True
# set seed
torch.random.manual_seed(0)
batch_size = 8
nheads = 9
window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
if paged_kv_block_size is None:
k = torch.randn(
batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True
)
v = torch.randn(
batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True
)
block_table = None
else:
k, v, block_table, k_cache_paged, v_cache_paged, num_blocks = _generate_block_kvcache(
seqlen_k, paged_kv_block_size, batch_size, nheads, d, device, dtype
)
query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random")
key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random")
(
q_unpad,
k_unpad,
v_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
q,
k,
v,
output_pad_fn,
dq_pad_fn,
dk_pad_fn,
) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False)
out_unpad = flash_attn_varlen_func(
q_unpad,
k_unpad if paged_kv_block_size is None else k_cache_paged,
v_unpad if paged_kv_block_size is None else v_cache_paged,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
0.0,
causal=causal,
window_size=window_size,
block_table=block_table,
)
out = output_pad_fn(out_unpad)
out_ref, attn_ref = attention_ref(
q,
k,
v,
query_padding_mask,
key_padding_mask,
None,
0.0,
None,
causal=causal,
window_size=window_size,
)
out_pt, attn_pt = attention_ref(
q,
k,
v,
query_padding_mask,
key_padding_mask,
None,
0.0,
None,
causal=causal,
window_size=window_size,
upcast=False,
reorder_ops=True,
)
print(f"Output max diff: {(out - out_ref).abs().max().item()}")
print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
# Check that FlashAttention's numerical error is at most twice the numerical error
# of a Pytorch implementation.
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item() + 1e-5
g = torch.randn_like(out)
if is_bwd_hdim_supported(d):
do_o = (g.float() * out.float()).sum(-1)
test_backward = block_table is None
if test_backward:
(
dq_unpad,
dk_unpad,
dv_unpad,
) = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g)
dq = dq_pad_fn(dq_unpad)
dk = dk_pad_fn(dk_unpad)
dv = dk_pad_fn(dv_unpad)
(
dq_ref,
dk_ref,
dv_ref,
) = torch.autograd.grad(out_ref, (q, k, v), g)
(
dq_pt,
dk_pt,
dv_pt,
) = torch.autograd.grad(out_pt, (q, k, v), g)
print(f"dQ max diff: {(dq - dq_ref).abs().max().item()}")
print(f"dK max diff: {(dk - dk_ref).abs().max().item()}")
print(f"dV max diff: {(dv - dv_ref).abs().max().item()}")
print(f"dQ mean diff: {(dq - dq_ref).abs().mean().item()}")
print(f"dK mean diff: {(dk - dk_ref).abs().mean().item()}")
print(f"dV mean diff: {(dv - dv_ref).abs().mean().item()}")
print(f"dQ Pytorch max diff: {(dq_pt - dq_ref).abs().max().item()}")
print(f"dK Pytorch max diff: {(dk_pt - dk_ref).abs().max().item()}")
print(f"dV Pytorch max diff: {(dv_pt - dv_ref).abs().max().item()}")
print(f"dQ Pytorch mean diff: {(dq_pt - dq_ref).abs().mean().item()}")
print(f"dK Pytorch mean diff: {(dk_pt - dk_ref).abs().mean().item()}")
print(f"dV Pytorch mean diff: {(dv_pt - dv_ref).abs().mean().item()}")
if test_backward:
# TODO - use 10 times to check, wait for ck to fix bwd precision issue
assert (dq - dq_ref).abs().max().item() <= 10 * (dq_pt - dq_ref).abs().max().item() + 1e-5
assert (dk - dk_ref).abs().max().item() <= 10 * (dk_pt - dk_ref).abs().max().item() + 1e-5
assert (dv - dv_ref).abs().max().item() <= 10 * (dv_pt - dv_ref).abs().max().item() + 1e-5
# TODO - support splitkv
# def test_flash_attn_splitkv
# TODO - Support has_leftpad
@pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("num_splits", [1, 0])
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
@pytest.mark.parametrize("new_kv", [False, True])
@pytest.mark.parametrize("alibi", [False, True])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False])
@pytest.mark.parametrize("rotary_interleaved", [False, True])
@pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0])
@pytest.mark.parametrize("paged_kv_block_size", [None, 256])
@pytest.mark.parametrize("has_leftpad", [False])
@pytest.mark.parametrize("has_batch_idx", [False, True])
@pytest.mark.parametrize("d", [32, 59, 64, 80, 128, 256])
@pytest.mark.parametrize(
"seqlen_q,seqlen_k",
[
(1, 128),
(1, 339),
(3, 1024),
(64, 800),
(64, 256),
(3, 799),
(64, 2048),
(16, 20000),
(1, 128 * 1024),
(16, 128 * 1024),
(128, 128),
],
)
def test_flash_attn_kvcache(
seqlen_q,
seqlen_k,
d,
has_batch_idx,
has_leftpad,
paged_kv_block_size,
rotary_fraction,
rotary_interleaved,
seqlen_new_eq_seqlen_q,
causal,
local,
alibi,
new_kv,
mha_type,
num_splits,
dtype,
):
if seqlen_q > seqlen_k and new_kv:
pytest.skip()
if not new_kv and rotary_fraction > 0.0:
pytest.skip()
if has_batch_idx and paged_kv_block_size is not None:
pytest.skip()
if has_leftpad and paged_kv_block_size is not None:
pytest.skip()
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 1
batch_size_cache = batch_size if not has_batch_idx else batch_size * 2
nheads = 6
# rotary_dim must be a multiple of 16, and must be <= d
rotary_dim = math.floor(int(rotary_fraction * d) / 16) * 16
nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
assert nheads % nheads_k == 0
window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
seqlen_new = seqlen_q if seqlen_new_eq_seqlen_q else torch.randint(1, seqlen_q + 1, (1,)).item()
if new_kv:
k = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
v = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
else:
k, v = None, None
if paged_kv_block_size is None:
k_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
v_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
block_table = None
else:
(
k_cache,
v_cache,
block_table,
k_cache_paged,
v_cache_paged,
num_blocks,
) = _generate_block_kvcache(
seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype
)
cache_seqlens = torch.randint(
0 if new_kv else 1,
# If we don't use seqlen_q in the case of causal and rotary, cos/sin won't be long enough
(
(seqlen_k - (seqlen_q if (causal or local) and rotary_dim > 1 else seqlen_new) + 1)
if new_kv
else (seqlen_k + 1)
),
(batch_size,),
dtype=torch.int32,
device=device,
)
if has_leftpad:
cache_leftpad = torch.cat([torch.randint(0, cache_seqlens[i].item(), (1,), dtype=torch.int32, device=device)
if cache_seqlens[i].item() > 0 else torch.zeros(1, dtype=torch.int32, device=device)
for i in range(batch_size)])
else:
cache_leftpad = None
arange = rearrange(torch.arange(seqlen_k, device=device), "s -> 1 s")
cache_seqlens_expanded = rearrange(cache_seqlens, "b -> b 1")
key_padding_mask = arange < cache_seqlens_expanded + (seqlen_new if new_kv else 0)
if has_leftpad:
key_padding_mask = torch.logical_and(
key_padding_mask, arange >= cache_leftpad.unsqueeze(-1).expand(-1, seqlen_k)
)
if has_batch_idx:
cache_batch_idx = torch.randperm(batch_size_cache, dtype=torch.int32, device=device)[
:batch_size
]
else:
cache_batch_idx = None
if alibi:
alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
attn_bias = attn_bias_from_alibi_slopes(
alibi_slopes, seqlen_q, seqlen_k, None, key_padding_mask, causal=causal, key_leftpad=cache_leftpad
)
else:
alibi_slopes, attn_bias = None, None
# cache_seqlens = torch.tensor([64], dtype=torch.int32, device=device)
if rotary_dim > 0:
angle = (
torch.rand(
seqlen_k if paged_kv_block_size is None else num_blocks * paged_kv_block_size,
rotary_dim // 2,
device=device,
)
* 2
* math.pi
)
cos = torch.cos(angle).to(dtype=dtype)
sin = torch.sin(angle).to(dtype=dtype)
if causal or local:
q_ro = apply_rotary_emb(
q, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
)
else:
q_ro = rearrange(
apply_rotary_emb(
rearrange(q, "b s h d -> b 1 (s h) d"),
cos,
sin,
seqlen_offsets=cache_seqlens,
interleaved=rotary_interleaved,
),
"b 1 (s h) d -> b s h d",
s=seqlen_q,
)
# q_ro = q
k_ro = apply_rotary_emb(
k, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
)
else:
cos, sin = None, None
q_ro, k_ro = q, k
# k_cache[:, 64:] = -1
k_cache_ref = (
k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
).clone()
v_cache_ref = (
v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
).clone()
if new_kv:
update_mask = torch.logical_and(
cache_seqlens_expanded <= arange, arange < cache_seqlens_expanded + seqlen_new
)
k_cache_ref[update_mask] = rearrange(k_ro, "b s ... -> (b s) ...")
v_cache_ref[update_mask] = rearrange(v, "b s ... -> (b s) ...")
k_cache_rep = repeat(k_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
v_cache_rep = repeat(v_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
out = flash_attn_with_kvcache(
q,
k_cache if paged_kv_block_size is None else k_cache_paged,
v_cache if paged_kv_block_size is None else v_cache_paged,
k,
v,
rotary_cos=cos,
rotary_sin=sin,
cache_seqlens=cache_seqlens,
cache_batch_idx=cache_batch_idx,
cache_leftpad=cache_leftpad,
block_table=block_table,
causal=causal,
window_size=window_size,
rotary_interleaved=rotary_interleaved,
alibi_slopes=alibi_slopes,
num_splits=num_splits,
)
# out = flash_attn_with_kvcache(
# q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=causal, window_size=window_size
# )
# out = flash_attn_with_kvcache(q, k_cache, v_cache, causal=causal, window_size=window_size)
# qk = torch.einsum("bqhd,bkhd->bhqk", q, k_cache_ref)
# m = qk.amax(-1, keepdim=True)
# s_tmp = torch.exp((qk - m) / math.sqrt(d))
# o1 = torch.einsum('bhst,bthd->bshd', s_tmp, v_cache_ref)
# lse_ref = torch.logsumexp(qk / math.sqrt(d), -1)
# probs = torch.softmax(qk, dim=-1)
out_ref, _ = attention_ref(
q_ro,
k_cache_rep,
v_cache_rep,
None,
key_padding_mask,
attn_bias,
0.0,
None,
causal=causal,
window_size=window_size,
key_leftpad=cache_leftpad,
)
out_pt, _ = attention_ref(
q_ro,
k_cache_rep,
v_cache_rep,
None,
key_padding_mask,
attn_bias,
0.0,
None,
causal=causal,
window_size=window_size,
upcast=False,
reorder_ops=True,
key_leftpad=cache_leftpad,
)
print(f"Output max diff: {(out - out_ref).abs().max().item()}")
print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
# Check that FlashAttention's numerical error is at most twice the numerical error
# of a Pytorch implementation.
if new_kv:
if paged_kv_block_size is None:
k_cache_select = (
k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
)
v_cache_select = (
v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
)
else:
k_cache_select = rearrange(
k_cache_paged[block_table.to(dtype=torch.long).flatten()],
"(b nblocks) block_size ... -> b (nblocks block_size) ...",
b=batch_size,
)[:, :seqlen_k]
v_cache_select = rearrange(
v_cache_paged[block_table.to(dtype=torch.long).flatten()],
"(b nblocks) block_size ... -> b (nblocks block_size) ...",
b=batch_size,
)[:, :seqlen_k]
assert torch.allclose(k_cache_select, k_cache_ref, rtol=1e-3, atol=1e-3)
assert torch.equal(v_cache_select, v_cache_ref)
# mult = 3 if f16, bf16 need 4
mult = 4 if not alibi else 5
assert (out - out_ref).abs().max().item() <= mult * (out_pt - out_ref).abs().max().item() + 1e-5
@pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize(
"seqlen_q,seqlen_k",
[
(1, 239),
(239, 1),
(3, 799),
(799, 3),
(1024, 128),
(97, 97),
(128, 128),
(200, 200),
(256, 256),
(257, 257),
(384, 384),
(512, 512),
(768, 768),
# (1024, 1024),
],
)
@pytest.mark.parametrize("dropout_p", [0.0, 0.17])
def test_flash_attn_race_condition(seqlen_q, seqlen_k, d, dropout_p, causal, dtype):
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 60 # Sometimes we need large batch size for the race conditions to trigger
nheads = 4
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
torch.random.manual_seed(42)
out0, lse0, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True)
g = torch.randn_like(out0)
if dropout_p == 0 and is_bwd_hdim_supported(d):
(
dq0,
dk0,
dv0,
) = torch.autograd.grad(out0, (q, k, v), g)
# Numerical error if we just do any arithmetic on dq
dq_atol = 2 * ((dq0 + 0.3 - 0.3) - dq0).abs().max().item()
for i in range(250):
torch.random.manual_seed(42)
out, lse, _ = flash_attn_func(q, k, v, dropout_p, causal=causal, return_attn_probs=True)
assert torch.equal(out, out0)
assert torch.equal(lse, lse0)
if dropout_p == 0:
(
dq,
dk,
dv,
) = torch.autograd.grad(out, (q, k, v), g)
dq_equal = torch.allclose(dq, dq0, atol=dq_atol)
if not dq_equal:
print(f"Iter {i}, {dq_atol = }, dQ max diff: {(dq - dq0).abs().max().item()}")
assert torch.equal(dv, dv0)
assert torch.equal(dk, dk0)
assert dq_equal
@pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [16, 32, 64])
@pytest.mark.parametrize("seqlen", [1, 2, 5, 17, 128])
def test_flash_attn_bwd_overflow(seqlen, d, causal, dtype):
"""We previously had a bug where not masking elements beyond seqlen_k caused NaN in dQ,
in the case where seqlen % 128 != 0.
"""
# TODO - 1 or 2 might fail, need to check
if seqlen == 1 or seqlen == 2:
pytest.skip()
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 2
nheads = 5
q = torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 5
k, v = [
torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda") * 3
for _ in range(2)
]
q.requires_grad_(True)
k.requires_grad_(True)
v.requires_grad_(True)
out = flash_attn_func(q, k, v, causal=causal)
g = torch.randn_like(out)
out.backward(g)
q_pt = q.detach().clone().requires_grad_(True)
k_pt = k.detach().clone().requires_grad_(True)
v_pt = v.detach().clone().requires_grad_(True)
out_pt, _ = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True)
out_pt.backward(g)
q_ref = q.detach().clone().requires_grad_(True)
k_ref = k.detach().clone().requires_grad_(True)
v_ref = v.detach().clone().requires_grad_(True)
out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal)
out_ref.backward(g)
print(f"dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}")
print(f"dK max diff: {(k.grad - k_ref.grad).abs().max().item()}")
print(f"dV max diff: {(v.grad - v_ref.grad).abs().max().item()}")
print(f"dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}")
print(f"dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}")
print(f"dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}")
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
assert (q.grad - q_ref.grad).abs().max().item() <= 5 * (
q_pt.grad - q_ref.grad
).abs().max().item() + 1e-3
assert (k.grad - k_ref.grad).abs().max().item() <= 5 * (
k_pt.grad - k_ref.grad
).abs().max().item() + 1e-3
assert (v.grad - v_ref.grad).abs().max().item() <= 5 * (
v_pt.grad - v_ref.grad
).abs().max().item() + 1e-3
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [64, 128])
@pytest.mark.parametrize("seqlen", [97, 128, 200, 256])
def test_flash_attn_bwd_transpose(seqlen, d, causal, dtype):
"""We previously had a bug where we were using the wrong strides of dout, which shows up
when dout is not contiguous.
"""
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 5
nheads = 2
q, k, v = [
torch.randn([batch_size, seqlen, nheads, d], dtype=dtype, device="cuda", requires_grad=True)
for _ in range(3)
]
out = rearrange(flash_attn_func(q, k, v, causal=causal), "b s ... -> s b ...")
# So g is not contiguous
g = torch.randn(seqlen, 2 * batch_size, nheads, d, dtype=dtype, device="cuda")[:, ::2]
out.backward(g)
q_pt = q.detach().clone().requires_grad_(True)
k_pt = k.detach().clone().requires_grad_(True)
v_pt = v.detach().clone().requires_grad_(True)
out_pt, attn_pt = attention_ref(q_pt, k_pt, v_pt, causal=causal, upcast=False, reorder_ops=True)
out_pt = rearrange(out_pt, "b s ... -> s b ...")
out_pt.backward(g)
q_ref = q.detach().clone().requires_grad_(True)
k_ref = k.detach().clone().requires_grad_(True)
v_ref = v.detach().clone().requires_grad_(True)
out_ref, attn_ref = attention_ref(q_ref, k_ref, v_ref, causal=causal)
out_ref = rearrange(out_ref, "b s ... -> s b ...")
out_ref.backward(g)
print(f"dQ max diff: {(q.grad - q_ref.grad).abs().max().item()}")
print(f"dK max diff: {(k.grad - k_ref.grad).abs().max().item()}")
print(f"dV max diff: {(v.grad - v_ref.grad).abs().max().item()}")
print(f"dQ Pytorch max diff: {(q_pt.grad - q_ref.grad).abs().max().item()}")
print(f"dK Pytorch max diff: {(k_pt.grad - k_ref.grad).abs().max().item()}")
print(f"dV Pytorch max diff: {(v_pt.grad - v_ref.grad).abs().max().item()}")
assert (out - out_ref).abs().max().item() <= 2 * (out_pt - out_ref).abs().max().item()
assert (q.grad - q_ref.grad).abs().max().item() <= 2 * (
q_pt.grad - q_ref.grad
).abs().max().item()
assert (k.grad - k_ref.grad).abs().max().item() <= 2 * (
k_pt.grad - k_ref.grad
).abs().max().item()
assert (v.grad - v_ref.grad).abs().max().item() <= 2 * (
v_pt.grad - v_ref.grad
).abs().max().item()
@pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [16, 32, 64])
def test_flash_attn_bwd_varlen_overflow(d, causal, dtype):
"""We previously had a bug where not masking elements beyond seqlen_k caused NaN in dQ,
in the case where seqlen % 128 != 0 or varlen.
"""
device = "cuda"
# set seed
torch.random.manual_seed(0)
nheads = 5
q_cuseqlen = torch.tensor([0, 76, 110, 256], device=device, dtype=torch.int32)
k_cuseqlen = torch.tensor([0, 1, 2, 3], device=device, dtype=torch.int32)
Mq = 256
Mk = 3
q = torch.randn([Mq, nheads, d], dtype=dtype, device=device) * 3
k, v = [torch.randn([Mk, nheads, d], dtype=dtype, device=device) * 3 for _ in range(2)]
q.requires_grad_(True)
k.requires_grad_(True)
v.requires_grad_(True)
out = flash_attn_varlen_func(q, k, v, q_cuseqlen, k_cuseqlen, Mq, Mk, causal=causal)
g = torch.randn_like(out)
out.backward(g)
assert not q.grad.isnan().any()
assert not k.grad.isnan().any()
assert not v.grad.isnan().any()
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("swap_sq_sk", [False, True])
@pytest.mark.parametrize(
"seqlen_q,seqlen_k",
[
(1, 239),
(3, 799),
(127, 512),
(127, 513),
(113, 203),
(128, 217),
(113, 211),
(108, 256),
(256, 512),
(1023, 1024),
],
)
def test_flash_attn_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype):
if (
max(seqlen_q, seqlen_k) >= 2048
and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
):
pytest.skip() # Reference implementation OOM
if swap_sq_sk:
seqlen_q, seqlen_k = seqlen_k, seqlen_q
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 4
nheads = 9
window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
out = flash_attn_func(q, k, v, 0.0, causal=causal, window_size=window_size, deterministic=True)
g = torch.randn_like(out)
dq0, dk0, dv0 = torch.autograd.grad(out, (q, k, v), g, retain_graph=True)
for _ in range(50):
dq, dk, dv = torch.autograd.grad(out, (q, k, v), g, retain_graph=True)
assert torch.equal(dv, dv0)
assert torch.equal(dk, dk0)
assert torch.equal(dq, dq0)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("local", [False, True])
@pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("d", [32, 40, 59, 64, 80, 96, 111, 128, 160, 192, 224, 256])
@pytest.mark.parametrize("swap_sq_sk", [False, True])
@pytest.mark.parametrize(
"seqlen_q,seqlen_k",
[
(1, 239),
(3, 799),
(127, 512),
(127, 513),
(113, 203),
(128, 217),
(113, 211),
(108, 256),
(256, 512),
(1023, 1024),
],
)
def test_flash_attn_varlen_deterministic(seqlen_q, seqlen_k, swap_sq_sk, d, causal, local, dtype):
if (
max(seqlen_q, seqlen_k) >= 2048
and torch.cuda.get_device_properties("cuda").total_memory <= 16 * 2**30
):
pytest.skip() # Reference implementation OOM
if swap_sq_sk:
seqlen_q, seqlen_k = seqlen_k, seqlen_q
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 2
nheads = 9
window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype, requires_grad=True)
k = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
v = torch.randn(batch_size, seqlen_k, nheads, d, device=device, dtype=dtype, requires_grad=True)
query_padding_mask = generate_random_padding_mask(seqlen_q, batch_size, device, mode="random")
key_padding_mask = generate_random_padding_mask(seqlen_k, batch_size, device, mode="random")
(
q_unpad,
k_unpad,
v_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
q,
k,
v,
output_pad_fn,
dq_pad_fn,
dk_pad_fn,
) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False)
out = flash_attn_varlen_func(
q_unpad,
k_unpad,
v_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
0.0,
causal=causal,
window_size=window_size,
deterministic=True,
)
g = torch.randn_like(out)
dq0, dk0, dv0 = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True)
for _ in range(50):
dq, dk, dv = torch.autograd.grad(out, (q_unpad, k_unpad, v_unpad), g, retain_graph=True)
assert torch.equal(dv, dv0)
assert torch.equal(dk, dk0)
assert torch.equal(dq, dq0)
Reference in New Issue View Git Blame Copy Permalink