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[Bug] Fix incorrect assertion in FA4 and add UT. (#11182)

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This commit is contained in:
Lifu Huang
2025-10-06 14:58:39 -07:00
committed by GitHub
parent 73ea484af1
commit 748f86f3de
2 changed files with 576 additions and 5 deletions
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5
sgl-kernel/python/sgl_kernel/flash_attn.py
View File

@@ -161,10 +161,7 @@ def flash_attn_with_kvcache(
k is None and v is None
), "FA4 does not support updating KV cache in-place."
assert (
rotary_cos is None
and rotary_sin is None
and rotary_interleaved is None
and rotary_seqlens is None
rotary_cos is None and rotary_sin is None and rotary_seqlens is None
), "FA4 does not support rotary embedding."
assert (
cache_batch_idx is None and cache_leftpad is None

576
sgl-kernel/tests/test_flash_attention_4.py
View File

@@ -10,10 +10,11 @@ import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from sgl_kernel.flash_attn import flash_attn_varlen_func
from sgl_kernel.flash_attn import flash_attn_varlen_func, flash_attn_with_kvcache
from utils import is_hopper
flash_attn_varlen_func = partial(flash_attn_varlen_func, ver=4)
flash_attn_with_kvcache = partial(flash_attn_with_kvcache, ver=4)
def unpad_input(hidden_states, attention_mask, unused_mask=None):
@@ -873,5 +874,578 @@ def test_flash_attn_varlen_output(
).abs().max().item() + dv_atol
@pytest.mark.skipif(
is_hopper(),
reason="skip on hopper",
)
# @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float8_e4m3fn])
@pytest.mark.parametrize("dtype", [torch.bfloat16])
# @pytest.mark.parametrize("dtype", [torch.float8_e4m3fn])
@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
# @pytest.mark.parametrize("mha_type", ["mha"])
@pytest.mark.parametrize("has_learnable_sink", [False, True])
# @pytest.mark.parametrize("has_learnable_sink", [False])
# @pytest.mark.parametrize("new_kv", [False, True])
@pytest.mark.parametrize("new_kv", [False])
@pytest.mark.parametrize("local", [False, True])
# @pytest.mark.parametrize("local", [False])
# @pytest.mark.parametrize("causal", [False, True])
@pytest.mark.parametrize("causal", [True])
# @pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False])
@pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [False])
# @pytest.mark.parametrize("has_rotary_seqlens", [False, True])
@pytest.mark.parametrize("has_rotary_seqlens", [False])
# @pytest.mark.parametrize("rotary_interleaved", [False, True])
@pytest.mark.parametrize("rotary_interleaved", [True])
# @pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0])
@pytest.mark.parametrize("rotary_fraction", [0.0])
# @pytest.mark.parametrize("page_size", [None] + ([1, 4, 128]))
@pytest.mark.parametrize("page_size", [None, 128])
# @pytest.mark.parametrize("page_size", [128])
# @pytest.mark.parametrize("has_leftpad", [False, True])
@pytest.mark.parametrize("has_leftpad", [False])
# @pytest.mark.parametrize("has_batch_idx", [False, True])
@pytest.mark.parametrize("has_batch_idx", [False])
# @pytest.mark.parametrize("varlen_q", [False, True])
@pytest.mark.parametrize("varlen_q", [False])
# @pytest.mark.parametrize("d", [32, 59, 64, 80, 128, 256])
# @pytest.mark.parametrize("d", [32, 64, 96, 128, 160, 192, 224, 256])
# @pytest.mark.parametrize('d', [32, 40, 64, 80, 96, 128, 160, 192])
# @pytest.mark.parametrize('d', [56, 80])
# @pytest.mark.parametrize("d", [128])
@pytest.mark.parametrize("d", [64])
# @pytest.mark.parametrize("d", [192])
@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),
# (256, 512), # To test appending KV with more than 1 block
# (2048, 3577), # Enough tile to test persistent scheduler
],
)
# @pytest.mark.parametrize('seqlen_q,seqlen_k', [(256, 128)])
def test_flash_attn_kvcache(
seqlen_q,
seqlen_k,
d,
varlen_q,
has_batch_idx,
has_leftpad,
page_size,
rotary_fraction,
rotary_interleaved,
has_rotary_seqlens,
seqlen_new_eq_seqlen_q,
causal,
local,
new_kv,
has_learnable_sink,
mha_type,
dtype,
):
if page_size is not None and seqlen_k % page_size != 0:
pytest.skip()
if seqlen_q > seqlen_k and new_kv:
pytest.skip()
if not new_kv and rotary_fraction > 0.0:
pytest.skip()
if rotary_fraction == 0.0 and has_rotary_seqlens:
pytest.skip()
device = "cuda"
# set seed
torch.random.manual_seed(0)
batch_size = 5
# batch_size = 1
batch_size_cache = batch_size if not has_batch_idx else batch_size * 2
nheads = 6
# nheads = 1
# 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
dtype_ref = torch.bfloat16 if dtype == torch.float8_e4m3fn else dtype
# dv_vals = [128, d] if d > 128 and d <= 192 else ([256, 512, d] if d <= 64 else [d])
dv_vals = [d]
if dtype == torch.float8_e4m3fn:
dv_vals = [d]
# attention_chunk_vals = [torch.randint(1, seqlen_k * 2, (1,)).item(), 0] if (causal or local) else [0]
attention_chunk_vals = [0]
for dv, attention_chunk in itertools.product(dv_vals, attention_chunk_vals):
# has_qv = d == 64 and dv >= 256
has_qv = False
q = (
torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype_ref)
.to(dtype)
.to(dtype_ref)
)
if has_qv:
qv = (
torch.randn(
batch_size, seqlen_q, nheads, dv, device=device, dtype=dtype_ref
)
.to(dtype)
.to(dtype_ref)
)
else:
qv = None
if varlen_q:
query_padding_mask = generate_random_padding_mask(
seqlen_q, batch_size, device, mode="random"
)
q_unpad, indices_q, cu_seqlens_q, max_seqlen_q, *rest = unpad_input(
q, query_padding_mask
)
output_pad_fn = lambda output_unpad: pad_input(
output_unpad, indices_q, batch_size, seqlen_q
)
qv_unpad = (
rearrange(qv, "b s ... -> (b s) ...")[indices_q] if has_qv else None
)
else:
query_padding_mask = None
q_unpad = q
qv_unpad = qv
cu_seqlens_q, max_seqlen_q = None, None
# Put window_size after QKV randn so that window_size changes from test to test
window_size = (
(None, None) if not local else torch.randint(0, seqlen_k, (2,)).tolist()
)
if has_learnable_sink:
learnable_sink = torch.randn(nheads, dtype=torch.bfloat16, device=device)
else:
learnable_sink = None
seqlen_new = (
seqlen_q
if seqlen_new_eq_seqlen_q
else torch.randint(1, seqlen_q + 1, (1,)).item()
)
cu_seqlens_k_new = None
key_new_padding_mask = None
if new_kv:
k = (
torch.randn(
batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype_ref
)
.to(dtype)
.to(dtype_ref)
)
v = (
torch.randn(
batch_size, seqlen_new, nheads_k, dv, device=device, dtype=dtype_ref
)
.to(dtype)
.to(dtype_ref)
)
if varlen_q: # k & v are also varlen
key_new_padding_mask = generate_random_padding_mask(
seqlen_new, batch_size, device, mode="random"
)
k_unpad, indices_k, cu_seqlens_k_new, *rest = unpad_input(
k, key_new_padding_mask
)
v_unpad, *rest = unpad_input(v, key_new_padding_mask)
else:
k_unpad, v_unpad = k, v
else:
k, v, k_unpad, v_unpad = None, None, None, None
if page_size is None:
k_cache = (
torch.randn(
batch_size_cache,
seqlen_k,
nheads_k,
d,
device=device,
dtype=dtype_ref,
)
.to(dtype)
.to(dtype_ref)
)
v_cache = (
torch.randn(
batch_size_cache,
seqlen_k,
nheads_k,
dv,
device=device,
dtype=dtype_ref,
)
.to(dtype)
.to(dtype_ref)
)
page_table = None
else:
(
k_cache,
v_cache,
page_table,
k_cache_paged,
v_cache_paged,
num_blocks,
) = _generate_block_kvcache(
seqlen_k,
page_size,
batch_size_cache,
nheads_k,
d,
dv,
device,
dtype,
dtype_ref,
)
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
if has_batch_idx:
cache_batch_idx = torch.randperm(
batch_size_cache, dtype=torch.int32, device=device
)[:batch_size]
else:
cache_batch_idx = None
arange = rearrange(torch.arange(seqlen_k, device=device), "s -> 1 s")
cache_seqlens_expanded = rearrange(cache_seqlens, "b -> b 1")
if not new_kv:
key_padding_mask = arange < cache_seqlens_expanded
else:
k_new_seqlens = (
key_new_padding_mask.sum(-1, keepdims=True) if varlen_q else seqlen_new
)
key_padding_mask = arange < cache_seqlens_expanded + k_new_seqlens
if has_leftpad:
key_padding_mask = torch.logical_and(
key_padding_mask,
arange >= cache_leftpad.unsqueeze(-1).expand(-1, seqlen_k),
)
# cache_seqlens = torch.tensor([64], dtype=torch.int32, device=device)
rotary_seqlens = cache_seqlens if not has_rotary_seqlens else cache_seqlens // 2
if rotary_dim > 0:
angle = (
torch.rand(
seqlen_k if page_size is None else num_blocks * page_size,
rotary_dim // 2,
device=device,
)
* 2
* math.pi
)
cos = torch.cos(angle).to(dtype=dtype_ref).to(dtype).to(dtype_ref)
sin = torch.sin(angle).to(dtype=dtype_ref).to(dtype).to(dtype_ref)
if causal or local:
q_ro = apply_rotary_emb(
q,
cos,
sin,
seqlen_offsets=rotary_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=rotary_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=rotary_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]
).clone()
v_cache_ref = (
v_cache if not has_batch_idx else v_cache[cache_batch_idx]
).clone()
if new_kv:
update_mask = torch.logical_and(
cache_seqlens_expanded <= arange,
arange < cache_seqlens_expanded + k_new_seqlens,
)
k_to_update = rearrange(k_ro, "b s ... -> (b s) ...")
v_to_update = rearrange(v, "b s ... -> (b s) ...")
if varlen_q:
k_to_update = k_to_update[indices_k]
v_to_update = v_to_update[indices_k]
k_cache_ref[update_mask] = k_to_update
v_cache_ref[update_mask] = v_to_update
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_ref, _ = attention_ref(
q_ro,
k_cache_rep,
v_cache_rep,
query_padding_mask,
key_padding_mask,
causal=causal,
qv=qv,
window_size=window_size,
learnable_sink=learnable_sink,
attention_chunk=attention_chunk,
key_leftpad=cache_leftpad,
)
out_pt, _ = attention_ref(
q_ro,
k_cache_rep,
v_cache_rep,
query_padding_mask,
key_padding_mask,
causal=causal,
qv=qv,
window_size=window_size,
learnable_sink=learnable_sink,
attention_chunk=attention_chunk,
upcast=False,
reorder_ops=True,
key_leftpad=cache_leftpad,
intermediate_dtype=dtype if dtype == torch.float8_e4m3fn else None,
)
q = q.to(dtype)
q_unpad = q_unpad.to(dtype) if varlen_q else None
k_cache = k_cache.to(dtype)
v_cache = v_cache.to(dtype)
k_cache_paged = k_cache_paged.to(dtype) if page_size is not None else None
v_cache_paged = v_cache_paged.to(dtype) if page_size is not None else None
k = k.to(dtype) if k is not None else None
v = v.to(dtype) if v is not None else None
k_unpad = k_unpad.to(dtype) if k_unpad is not None else None
v_unpad = v_unpad.to(dtype) if v_unpad is not None else None
qv = qv.to(dtype) if qv is not None else None
qv_unpad = qv_unpad.to(dtype) if (varlen_q and qv is not None) else None
cos = cos.to(dtype) if cos is not None else None
sin = sin.to(dtype) if sin is not None else None
k_cache_saved = k_cache.clone() if page_size is None else k_cache_paged.clone()
v_cache_saved = v_cache.clone() if page_size is None else v_cache_paged.clone()
# num_splits_vals = [1, 0]
num_splits_vals = [1]
# precompute_metadata_vals = [False, True]
precompute_metadata_vals = [False]
for num_splits, precompute_metadata in itertools.product(
num_splits_vals, precompute_metadata_vals
):
# if precompute_metadata:
# scheduler_metadata = get_scheduler_metadata(
# batch_size, max_seqlen_q if varlen_q else seqlen_q, seqlen_k, nheads, nheads_k, d,
# cache_seqlens, q.dtype, headdim_v=dv, cu_seqlens_q=cu_seqlens_q,
# cu_seqlens_k_new=cu_seqlens_k_new, cache_leftpad=cache_leftpad,
# max_seqlen_k_new=seqlen_new, page_size=page_size,
# causal=causal, window_size=window_size, attention_chunk=attention_chunk,
# num_splits=num_splits
# )
# else:
# scheduler_metadata = None
scheduler_metadata = None
# Repeat to test metadata reuse
for _ in range(1 if not precompute_metadata else 2):
if page_size is None:
k_cache.copy_(k_cache_saved)
v_cache.copy_(v_cache_saved)
else:
k_cache_paged.copy_(k_cache_saved)
v_cache_paged.copy_(v_cache_saved)
# out, lse, *rest = flash_attn_with_kvcache(
out, lse, *rest = flash_attn_with_kvcache(
q if not varlen_q else q_unpad,
k_cache if page_size is None else k_cache_paged,
v_cache if page_size is None else v_cache_paged,
# k if not new_kv or not varlen_q else k_unpad,
# v if not new_kv or not varlen_q else v_unpad,
# qv=qv if not varlen_q else qv_unpad,
# rotary_cos=cos,
# rotary_sin=sin,
cache_seqlens=cache_seqlens,
# cache_batch_idx=cache_batch_idx,
# cache_leftpad=cache_leftpad,
page_table=page_table,
cu_seqlens_q=cu_seqlens_q,
# cu_seqlens_k_new=cu_seqlens_k_new,
# rotary_seqlens=rotary_seqlens,
causal=causal,
window_size=window_size,
sinks=learnable_sink,
# attention_chunk=attention_chunk,
# rotary_interleaved=rotary_interleaved,
# scheduler_metadata=scheduler_metadata,
# num_splits=num_splits,
return_softmax_lse=True,
)
if varlen_q:
out = output_pad_fn(out)
# 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)
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()}")
# breakpoint()
# Check that FlashAttention's numerical error is at most twice the numerical error
# of a Pytorch implementation.
if new_kv:
if page_size is None:
k_cache_select = (
k_cache.to(dtype_ref)
if not has_batch_idx
else k_cache.to(dtype_ref)[cache_batch_idx]
)
v_cache_select = (
v_cache.to(dtype_ref)
if not has_batch_idx
else v_cache.to(dtype_ref)[cache_batch_idx]
)
else:
k_cache_select = rearrange(
k_cache_paged.to(dtype_ref)[
(
page_table
if not has_batch_idx
else page_table[cache_batch_idx]
).flatten()
],
"(b nblocks) block_size ... -> b (nblocks block_size) ...",
b=batch_size,
)[:, :seqlen_k].to(dtype_ref)
v_cache_select = rearrange(
v_cache_paged.to(dtype_ref)[
(
page_table
if not has_batch_idx
else page_table[cache_batch_idx]
).flatten()
],
"(b nblocks) block_size ... -> b (nblocks block_size) ...",
b=batch_size,
)[:, :seqlen_k].to(dtype_ref)
k_cache_ref = k_cache_ref.to(dtype).to(dtype_ref)
v_cache_ref = v_cache_ref.to(dtype).to(dtype_ref)
if dtype is not torch.float8_e4m3fn:
assert torch.equal(v_cache_select, v_cache_ref)
else:
assert torch.allclose(
v_cache_select, v_cache_ref, rtol=1e-3, atol=1e-3
)
# breakpoint()
# if rotary_dim == 0 and dtype is not torch.float8_e4m3fn:
if rotary_dim == 0:
assert torch.equal(k_cache_select, k_cache_ref)
else:
# if not torch.allclose(k_cache_select, k_cache_ref, rtol=1e-3, atol=1e-3):
# breakpoint()
if dtype is not torch.float8_e4m3fn:
assert torch.allclose(
k_cache_select, k_cache_ref, rtol=1e-3, atol=1e-3
)
else:
assert torch.allclose(
k_cache_select, k_cache_ref, rtol=1e-1, atol=1e-1
)
mult = 4 if dtype == torch.float8_e4m3fn else 2
assert (out - out_ref).abs().max().item() <= mult * (
out_pt - out_ref
).abs().max().item() + 1e-5
mult_mean = 3 if dtype == torch.float8_e4m3fn else 1.5
assert (out - out_ref).abs().mean().item() <= mult_mean * (
out_pt - out_ref
).abs().mean().item()
def _generate_block_kvcache(
seqlen_k, page_size, batch_size, nheads_k, d, dv, device, dtype, dtype_ref
):
num_blocks = math.ceil(seqlen_k / page_size) * batch_size * 3
k_cache_paged = (
torch.randn(num_blocks, page_size, nheads_k, d, device=device, dtype=dtype_ref)
.to(dtype)
.to(dtype_ref)
)
v_cache_paged = (
torch.randn(num_blocks, page_size, nheads_k, dv, device=device, dtype=dtype_ref)
.to(dtype)
.to(dtype_ref)
)
page_table = rearrange(
torch.randperm(num_blocks, dtype=torch.int32, device=device),
"(b nblocks) -> b nblocks",
b=batch_size,
)
k_cache = rearrange(
k_cache_paged[page_table.flatten()],
"(b nblocks) block_size ... -> b (nblocks block_size) ...",
b=batch_size,
)[:, :seqlen_k]
v_cache = rearrange(
v_cache_paged[page_table.flatten()],
"(b nblocks) block_size ... -> b (nblocks block_size) ...",
b=batch_size,
)[:, :seqlen_k]
return k_cache, v_cache, page_table, k_cache_paged, v_cache_paged, num_blocks
if __name__ == "__main__":
pytest.main([__file__])