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AstrAI/tests/extension/test_gemm.py
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ViperEkura 1798474316 perf: rebuild decode gemm dispatch around shape-driven tile configs 
- split-K removed entirely: tiled kernel walks K in one pass, no partials/semas workspace, no memset, single launch per call
- skinny GEMM (M<=8) dispatch table replaces the hand-written switch
- shape-driven four-family table replaces plan_gemm: wide-N (n>=4096) default {16,64,64,3,128} with BM=32 at M>16; narrow-N deep-K rings {16,32,256,2,64} while the grid fits one wave, {16,32,128,2,64} past it
- narrow-N is K-serial: widening the grid measurably does nothing (BN 64->32 ties, doubled m_tiles tie, kv at 4 blocks ties q/o at 24); deeper K chunks win until 72KB smem forces one CTA per SM and past one wave the 2-wave quantization loses to BK=128
- launch-check macros in common/launch.cuh; smem opt-in for the 72KB/60KB rings
- rename kernels/bf16_*.cu to gemm.cu/swiglu.cu; module names unchanged
- Python gate: lm_head (N>32768) falls back to cuBLAS, band narrows to M<=32
- drop the stale per-op benchmark narratives; fold the live numbers into cuda_kernels.md

Benchmark: NVIDIA L20 (sm_89, 92 SMs), CUDA 12.8, bf16, L2-thrash weight rotation, per-call medians at M=16: q/o 9.5us, kv 8.6us, gate/up 33.3us, down 33.7us (down -29% vs prior default). End-to-end 1B decode (gen 128, 3 trials, tokens/s vs cuBLAS): B=1 260 vs 252, B=8 1660 vs 1446, B=16 2464 vs 2437, B=32 3620 vs 3690. Prior split-K dispatch measured B=16 2243 / B=32 3393.
2026-09-04 22:41:39 +08:00

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import pytest
import torch
import torch.nn.functional as F
from astrai.extension import bf16_gemm, is_available
GEMM_AVAILABLE = (
torch.cuda.is_available()
and is_available("bf16_gemm")
and torch.cuda.get_device_capability() >= (8, 0)
)
skip_no_gemm = pytest.mark.skipif(
not GEMM_AVAILABLE,
reason="BF16 GEMM requires a built kernel and compute capability 8.0+",
)
def _assert_close_fp64(actual, x, weight, bias=None):
"""Compare bf16 kernel output vs fp64-exact with ulp-scaled tolerance.
Avoids false failures from cuBLAS default bf16 split-K partial reduction
(which can introduce ~2 ulp diffs on near-tie rounding at long K). Used
for tiled-path tests (M > 8, K >= 4096) where the bf16 accumulation tie
pattern may differ from cuBLAS's."""
exact = x.double() @ weight.double().T
if bias is not None:
exact = exact + bias.double()
ulp = (exact.abs() * 2**-9).clamp(min=2**-9)
max_ulp = ((actual.double() - exact).abs() / ulp).max().item()
assert max_ulp < 8, (
f"max_ulp={max_ulp:.1f} exceeds 8 (exact fp32 accumulation should stay within ~2 ulps)"
)
@skip_no_gemm
@pytest.mark.parametrize(
"n,k",
[(256, 1536), (1536, 1536), (6912, 1536), (1536, 6912), (100000, 1536)],
)
def test_bf16_gemm_matches_linear_shape_families(n, k):
torch.manual_seed(17)
x = torch.randn(k, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
actual = bf16_gemm(x, weight)
expected = F.linear(x, weight)
assert actual.shape == (n,)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
@skip_no_gemm
@pytest.mark.parametrize("m", [2, 3, 4, 5, 6, 7, 8])
@pytest.mark.parametrize("n,k", [(256, 1536), (1536, 1536), (1536, 6912)])
def test_bf16_gemm_matches_small_decode_batches(m, n, k):
torch.manual_seed(19 + m)
x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
actual = bf16_gemm(x, weight)
expected = F.linear(x, weight)
assert actual.shape == (m, n)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.5)
@skip_no_gemm
@pytest.mark.parametrize("m", [2, 4])
@pytest.mark.parametrize(
"n,k",
[
(1024, 4096),
(4096, 4096),
(11008, 4096),
(4096, 11008),
(14336, 4096),
(4096, 14336),
(5120, 5120),
(13824, 5120),
(5120, 13824),
(16384, 4096),
(4096, 16384),
(512, 3584),
(3584, 3584),
(18944, 3584),
(3584, 18944),
(1024, 8192),
(8192, 8192),
(28672, 8192),
(8192, 28672),
(2048, 2048),
(8192, 2048),
(2048, 8192),
],
)
def test_bf16_gemm_matches_common_transformer_shapes(m, n, k):
torch.manual_seed(2026 + m + n + k)
x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
weight = torch.empty(n, k, device="cuda", dtype=torch.bfloat16)
weight.normal_(mean=0.0, std=0.02)
actual = bf16_gemm(x, weight)
expected = F.linear(x, weight)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
@skip_no_gemm
@pytest.mark.parametrize(
"m,n,k",
[
(1, 8192, 2048),
(8, 4096, 11008),
(8, 512, 3584),
(8, 1024, 8192),
(8, 2048, 8192),
],
)
def test_bf16_gemm_matches_m8_edge_bands(m, n, k):
torch.manual_seed(2026 + m + n + k)
x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
weight = torch.empty(n, k, device="cuda", dtype=torch.bfloat16)
weight.normal_(mean=0.0, std=0.02)
actual = bf16_gemm(x, weight)
expected = F.linear(x, weight)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
@skip_no_gemm
def test_bf16_gemm_preserves_singleton_batch_and_fuses_bias():
torch.manual_seed(23)
x = torch.randn(1, 1536, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(1536, 1536, device="cuda", dtype=torch.bfloat16)
bias = torch.randn(1536, device="cuda", dtype=torch.bfloat16)
actual = bf16_gemm(x, weight, bias)
expected = F.linear(x, weight, bias)
assert actual.shape == (1, 1536)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
@skip_no_gemm
def test_bf16_gemm_small_batch_fuses_bias():
torch.manual_seed(25)
x = torch.randn(4, 1536, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(256, 1536, device="cuda", dtype=torch.bfloat16)
bias = torch.randn(256, device="cuda", dtype=torch.bfloat16)
actual = bf16_gemm(x, weight, bias)
expected = F.linear(x, weight, bias)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
@skip_no_gemm
def test_bf16_gemm_uses_current_stream():
x = torch.randn(1536, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(256, 1536, device="cuda", dtype=torch.bfloat16)
torch.cuda.synchronize()
stream = torch.cuda.Stream()
with torch.cuda.stream(stream):
actual = bf16_gemm(x, weight)
expected = F.linear(x, weight)
stream.synchronize()
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
@skip_no_gemm
def test_bf16_gemm_cuda_graph_replay():
torch.manual_seed(29)
x = torch.randn(1536, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(1536, 1536, device="cuda", dtype=torch.bfloat16)
for _ in range(3):
bf16_gemm(x, weight)
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
actual = bf16_gemm(x, weight)
x.copy_(torch.randn_like(x))
graph.replay()
expected = F.linear(x, weight)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
@skip_no_gemm
@pytest.mark.parametrize("n,k", [(64, 7), (64, 12), (33, 100), (256, 1534)])
def test_bf16_gemm_handles_unaligned_k(n, k):
torch.manual_seed(29)
x = torch.randn(k, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
actual = bf16_gemm(x, weight)
expected = F.linear(x, weight)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
x3 = torch.randn(3, k, device="cuda", dtype=torch.bfloat16)
actual3 = bf16_gemm(x3, weight)
torch.testing.assert_close(actual3, F.linear(x3, weight), rtol=0.02, atol=0.5)
@skip_no_gemm
@pytest.mark.parametrize("m", [1, 2, 3, 4])
def test_bf16_gemm_accepts_complementary_misalignment(m):
"""Misaligned weight rows plus an x base chosen so the vectorized branch
is entered with a non-16B-aligned ``x`` pointer (regression: the branch
guard checked ``x + whead`` alignment but the uint4 view was rooted at
``x`` itself, faulting with a misaligned-address CUDA error)."""
torch.manual_seed(37)
n, k = 256, 1536
# offset 5 elements = +10 bytes: weight rows land at 10 % 16 (whead=3)
# and x at 10 % 16, so (x + 2*whead) % 16 == 0 selects the fast path.
big_w = torch.randn(n * k + 8, device="cuda", dtype=torch.bfloat16)
weight = big_w[5 : 5 + n * k].view(n, k)
big_x = torch.randn(m * k + 8, device="cuda", dtype=torch.bfloat16)
x = big_x[5 : 5 + m * k].view(m, k) if m > 1 else big_x[5 : 5 + k]
assert (x.data_ptr() & 15) == 10 and (weight.data_ptr() & 15) == 10
actual = bf16_gemm(x, weight)
expected = F.linear(x, weight)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.5 if m > 1 else 0.25)
@skip_no_gemm
def test_bf16_gemm_scalar_path_handles_misaligned_weight_only():
"""Weight rows misaligned while x stays 16B-aligned take the scalar-x
middle and must stay exact."""
torch.manual_seed(41)
n, k = 256, 1536
big_w = torch.randn(n * k + 8, device="cuda", dtype=torch.bfloat16)
weight = big_w[5 : 5 + n * k].view(n, k)
x = torch.randn(2, k, device="cuda", dtype=torch.bfloat16)
assert (weight.data_ptr() & 15) == 10 and (x.data_ptr() & 15) == 0
actual = bf16_gemm(x, weight)
torch.testing.assert_close(actual, F.linear(x, weight), rtol=0.02, atol=0.5)
@skip_no_gemm
def test_bf16_gemm_small_batch_cuda_graph_replay():
torch.manual_seed(31)
x = torch.randn(8, 1536, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(1536, 1536, device="cuda", dtype=torch.bfloat16)
for _ in range(3):
bf16_gemm(x, weight)
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
actual = bf16_gemm(x, weight)
x.copy_(torch.randn_like(x))
graph.replay()
expected = F.linear(x, weight)
torch.testing.assert_close(actual, expected, rtol=0.02, atol=0.25)
@skip_no_gemm
@pytest.mark.parametrize(
"make_args,error",
[
(
lambda: (
torch.randn(65, 16, device="cuda", dtype=torch.bfloat16),
torch.randn(8, 16, device="cuda", dtype=torch.bfloat16),
),
"M must",
),
(
lambda: (
torch.randn(16, device="cuda", dtype=torch.float16),
torch.randn(8, 16, device="cuda", dtype=torch.float16),
),
"bf16",
),
(
lambda: (
torch.randn(
16, device="cuda", dtype=torch.bfloat16, requires_grad=True
),
torch.randn(8, 16, device="cuda", dtype=torch.bfloat16),
),
"autograd",
),
],
)
def test_bf16_gemm_rejects_unsupported_inputs(make_args, error):
with pytest.raises(RuntimeError, match=error):
bf16_gemm(*make_args())
# ---------------------------------------------------------------------------
# Tiled path: M in (8, 64]
# ---------------------------------------------------------------------------
@skip_no_gemm
@pytest.mark.parametrize("m", [9, 12, 16, 17, 24, 32, 33, 48, 64])
@pytest.mark.parametrize("n,k", [(256, 1536), (1536, 1536), (6912, 1536), (1536, 6912)])
def test_bf16_gemm_tiled_matches_decode_batches(m, n, k):
torch.manual_seed(31 + m)
x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
weight = torch.empty(n, k, device="cuda", dtype=torch.bfloat16)
weight.normal_(mean=0.0, std=0.02)
actual = bf16_gemm(x, weight)
assert actual.shape == (m, n)
_assert_close_fp64(actual, x, weight)
@skip_no_gemm
@pytest.mark.parametrize("m", [12, 64])
def test_bf16_gemm_tiled_matches_lm_head(m):
# N=100000 fills the SMs with N tiles alone: the splits=1 epilogue.
torch.manual_seed(37 + m)
x = torch.randn(m, 1536, device="cuda", dtype=torch.bfloat16)
weight = torch.empty(100000, 1536, device="cuda", dtype=torch.bfloat16)
weight.normal_(mean=0.0, std=0.02)
actual = bf16_gemm(x, weight)
_assert_close_fp64(actual, x, weight)
@skip_no_gemm
@pytest.mark.parametrize(
"m,n,k",
[
(12, 100, 72),
(12, 96, 1536),
(12, 1632, 1536),
(64, 100, 72),
(17, 200, 8),
(33, 160, 152),
],
)
def test_bf16_gemm_tiled_handles_remainder_tiles(m, n, k):
# N not a multiple of 64 (predicated epilogue columns) and K not a
# multiple of 64 (zero-filled staging chunks).
torch.manual_seed(41 + m + n + k)
x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
actual = bf16_gemm(x, weight)
_assert_close_fp64(actual, x, weight)
@skip_no_gemm
@pytest.mark.parametrize("m,n,k", [(12, 1536, 1536), (64, 6912, 1536)])
def test_bf16_gemm_tiled_fuses_bias(m, n, k):
# (12, 1536) exercises the narrow-N deep-K config; (64, 6912) the
# wide-N default.
torch.manual_seed(43 + m)
x = torch.randn(m, k, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(n, k, device="cuda", dtype=torch.bfloat16)
bias = torch.randn(n, device="cuda", dtype=torch.bfloat16)
actual = bf16_gemm(x, weight, bias)
_assert_close_fp64(actual, x, weight, bias)
@skip_no_gemm
def test_bf16_gemm_tiled_deterministic_across_runs():
# Single-pass K accumulation with no atomics: reruns are bitwise
# identical — CUDA Graph replay relies on this.
torch.manual_seed(47)
x = torch.randn(16, 1536, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(1536, 1536, device="cuda", dtype=torch.bfloat16)
first = bf16_gemm(x, weight)
second = bf16_gemm(x, weight)
assert torch.equal(first, second)
@skip_no_gemm
def test_bf16_gemm_tiled_cuda_graph_replay():
torch.manual_seed(53)
x = torch.randn(16, 1536, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(1536, 1536, device="cuda", dtype=torch.bfloat16)
for _ in range(3):
bf16_gemm(x, weight)
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
actual = bf16_gemm(x, weight)
x.copy_(torch.randn_like(x))
graph.replay()
_assert_close_fp64(actual, x, weight)
@skip_no_gemm
def test_bf16_gemm_tiled_rejects_k_not_multiple_of_8():
x = torch.randn(12, 12, device="cuda", dtype=torch.bfloat16)
weight = torch.randn(64, 12, device="cuda", dtype=torch.bfloat16)
with pytest.raises(RuntimeError, match="multiple of 8"):
bf16_gemm(x, weight)
@skip_no_gemm
def test_bf16_gemm_tiled_rejects_misaligned_x():
# A 2-byte storage offset breaks the 16B alignment the tiled path
# stages chunks on; the M <= 8 GEMV path still accepts it.
k = 1536
storage = torch.randn(12 * k + 1, device="cuda", dtype=torch.bfloat16)
x8 = storage[1 : 1 + 8 * k].view(8, k)
x12 = storage[1 : 1 + 12 * k].view(12, k)
weight = torch.randn(1536, k, device="cuda", dtype=torch.bfloat16)
torch.testing.assert_close(
bf16_gemm(x8, weight), F.linear(x8, weight), rtol=0.02, atol=0.5
)
with pytest.raises(RuntimeError, match="16-byte"):
bf16_gemm(x12, weight)
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