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flash-attention/tests/test_flash_attn_ck.py
rocking d8f104e97a
Support AMD ROCm on FlashAttention 2 (#1010) 
* Support ck in fmha

* Add ck submodule

* Do not return lse if return_softmax == false

* Use receipt to speed up ck compile time

* Integrate new version of ck_tile

* Support dropout for mha_fwd()

* Add dropout to mha_varlen_fwd()

* Update ck to develop

* Extract padding function for dropout randval

* Extract randval transformation function

* Sync the code structure and coding style with FA

* Remove this line, c++ api will handle this.
Sync with test_flash_attn.py

* fix compile error

* Add mha_bwd

* Generate dropout seed and offset from user generator

* update CK

* Add mha_varlen_bwd

* Use same python as build flash-attn to generate ck kernel

* Fix bug of group mode fwd about returning softmax lse

* larger the test tollerance

* Add test_flash_attn_output() and test_flash_attn_varlen_output()

* Always fill softmax_lse

* Remove duplicate benchmark script, since we already implement mha_bwd

* Refine get value from tuple

* Use default parameter for stream_config

* unblock all platform

* Add comment

* refine the test code

* Refine naming

* Add unpack to namespace

* Do not hardcode the warp size 64

* Add more targets

* Add README

* Optimize mha_fwd if seqlen_q == 1

* Support get_wheel_url for rocm

* Detect rocm environment by pytorch's IS_HIP_EXTENSION

* update to lastest ck

* Add necessary compile flag

* Sync the api with upstream FA

---------

Co-authored-by: carlushuang <carlus.huang@amd.com>
Co-authored-by: Yichen Yan <wenji.yyc@alibaba-inc.com>
Co-authored-by: Po Yen Chen <PoYen.Chen@amd.com>
Co-authored-by: Yichen Yan <oraluben@outlook.com>
2024-07-22 21:34:37 -07:00

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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,
)
from test_flash_attn import (
attn_bias_from_alibi_slopes,
convert_flash_attn_S_to_softmax,
generate_qkv,
generate_random_padding_mask,
attention_ref,
attention_kvpacked_ref,
attention_qkvpacked_ref,
)
def is_bwd_hdim_supported(d):
return d <= 128 and d % 2 == 0
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 torch.floor(255.0 * (1 - p) - randval)
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])
@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 change dq type to f32
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])
@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 change dq type to f32
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])
@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 change dq type to f32
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 change dq type to f32
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()
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