biondizzle/DeepGEMM
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Make various updates and fixes (#198)

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This commit is contained in:
Ray Wang
2025-09-25 16:19:07 +08:00
committed by GitHub
parent 79f48ee15a
commit 3f71de7aa9
45 changed files with 3281 additions and 1060 deletions
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60
tests/test_fp8.py
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@@ -10,7 +10,7 @@ from deep_gemm.testing import (
)
from generators import (
KernelType, get_ue8m0_usage,
KernelType, get_arch_major, get_ue8m0_usage,
enumerate_normal, enumerate_m_grouped_contiguous, enumerate_m_grouped_masked, enumerate_k_grouped_contiguous,
generate_normal, generate_m_grouped_contiguous, generate_m_grouped_masked, generate_k_grouped_contiguous
)
@@ -18,7 +18,7 @@ from generators import (
def test_gemm() -> None:
print('Testing GEMM:')
for kernel_type, m, n, k, major_a, major_b, accumulate, out_dtype in enumerate_normal():
for kernel_type, m, n, k, major_a, major_b, accumulate, out_dtype in enumerate_normal(torch.float8_e4m3fn):
major_opt = 'N' if major_a.is_k_major() else 'T'
major_opt += 'T' if major_b.is_k_major() else 'N'
out_opt = 'FP32' if out_dtype == torch.float else 'BF16'
@@ -26,42 +26,35 @@ def test_gemm() -> None:
kernel_opt = f'1D1D' if kernel_type.is_1d1d() else '1D2D'
use_ue8m0 = get_ue8m0_usage(kernel_type)
disable_ue8m0_cast = not use_ue8m0
recipe = (1, 1, 128) if kernel_type.is_1d1d() and accumulate else None
for test_alias in (False, True):
a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, use_ue8m0=use_ue8m0)
a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_ue8m0=use_ue8m0)
func_name = f'fp8_gemm_{major_opt.lower() if test_alias else "nt"}'
if test_alias:
a = a if major_a.is_k_major() else (a[0].T, a[1].T)
b = b if major_b.is_k_major() else (b[0].T, b[1].T)
assert a[0].is_contiguous() and b[0].is_contiguous()
getattr(deep_gemm, func_name)(a, b, d, c=c, disable_ue8m0_cast=disable_ue8m0_cast)
getattr(deep_gemm, func_name)(a, b, d, c=c, disable_ue8m0_cast=disable_ue8m0_cast, recipe=recipe)
diff = calc_diff(d, ref_d)
assert diff < 0.001, (f'{m=}, {n=}, {k=}, {kernel_opt}, {major_opt=}, {accumulate=}, {out_dtype=}, '
f'{diff:.5f}, alias={test_alias}')
a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, use_ue8m0=use_ue8m0)
# Test launch overhead
launch_start_t = time.time_ns()
deep_gemm.fp8_gemm_nt(a, b, d, c=c, disable_ue8m0_cast=disable_ue8m0_cast)
launch_end_t = time.time_ns()
torch.cuda.synchronize()
# noinspection PyShadowingNames
def test_func():
deep_gemm.fp8_gemm_nt(a, b, d, c=c, disable_ue8m0_cast=disable_ue8m0_cast)
t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
print(f' > Perf (m={m:5}, n={n:5}, k={k:5}, {kernel_opt}, layout={major_opt}, {out_opt}, {acc_opt}): '
f'launch {(launch_end_t - launch_start_t) / 1e3:4.0f} us | {t * 1e6:4.0f} us | '
f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | '
f'{(count_bytes(a, b, d) + count_bytes(c) * int(accumulate)) / 1e9 / t:4.0f} GB/s')
a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_ue8m0=use_ue8m0)
t = bench_kineto(lambda: deep_gemm.fp8_gemm_nt(a, b, d, c=c, disable_ue8m0_cast=disable_ue8m0_cast, recipe=recipe),
'fp8_gemm', suppress_kineto_output=True)
cublas_t, split_k_t = bench_kineto(lambda: deep_gemm.cublaslt_gemm_nt(a[0], b[0], d, c=c), ('nvjet', 'reduce'), suppress_kineto_output=True)
print(f' > Perf (m={m:6}, n={n:6}, k={k:6}, {kernel_opt}, layout={major_opt}, {out_opt}, {acc_opt}): '
f'{t * 1e6:4.0f} us | {2 * m * n * k / t / 1e12:4.0f} TFLOPS | '
f'{(count_bytes(a, b, d) + count_bytes(c) * int(accumulate)) / 1e9 / t:4.0f} GB/s | '
f'{(cublas_t + split_k_t) / t:.2f}x cuBLAS')
print()
def test_m_grouped_gemm_contiguous() -> None:
print('Testing m-grouped contiguous GEMM:')
for kernel_type, num_groups, expected_m_per_group, n, k, major_a, major_b in enumerate_m_grouped_contiguous():
for kernel_type, num_groups, expected_m_per_group, n, k, major_a, major_b in enumerate_m_grouped_contiguous(dtype=torch.float8_e4m3fn):
major_opt = 'N' if major_a.is_k_major() else 'T'
major_opt += 'T' if major_b.is_k_major() else 'N'
kernel_opt = f'1D1D' if kernel_type.is_1d1d() else '1D2D'
@@ -86,7 +79,7 @@ def test_m_grouped_gemm_contiguous() -> None:
deep_gemm.m_grouped_fp8_gemm_nt_contiguous(a, b, d, m_indices, disable_ue8m0_cast=disable_ue8m0_cast)
t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
print(f' > Perf ({num_groups=}, m={m:5}, n={n:5}, k={k:5}, {kernel_opt}, layout={major_opt}): '
print(f' > Perf ({num_groups=}, m={m:5}, n={n:6}, k={k:5}, {kernel_opt}, layout={major_opt}): '
f'{t * 1e6:4.0f} us | '
f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | '
f'{count_bytes(a, b, d) / 1e9 / t:4.0f} GB/s')
@@ -97,7 +90,7 @@ def test_m_grouped_gemm_masked() -> None:
print('Testing m-grouped masked GEMM:')
# TODO: when the actual `m` is greater than `expected_m_per_group`, efficiency may significantly decrease.
for kernel_type, num_groups, max_m, expected_m_per_group, n, k in enumerate_m_grouped_masked():
for kernel_type, num_groups, max_m, expected_m_per_group, n, k in enumerate_m_grouped_masked(torch.float8_e4m3fn):
kernel_opt = f'1D1D' if kernel_type.is_1d1d() else '1D2D'
use_ue8m0 = get_ue8m0_usage(kernel_type)
disable_ue8m0_cast = not use_ue8m0
@@ -130,26 +123,31 @@ def test_m_grouped_gemm_masked() -> None:
def test_k_grouped_gemm_contiguous() -> None:
print('Testing k-grouped contiguous GEMM:')
for num_groups, m, n, ks, expected_k_per_group in enumerate_k_grouped_contiguous():
k_grouped_fp8_gemm_contiguous = deep_gemm.k_grouped_fp8_gemm_nt_contiguous if get_arch_major() == 9 \
else deep_gemm.k_grouped_fp8_gemm_tn_contiguous
for num_groups, m, n, major_a, major_b, ks, expected_k_per_group in enumerate_k_grouped_contiguous():
use_ue8m0 = get_ue8m0_usage(KernelType.Kernel1D1D)
for test_empty_groups in (False, True):
new_ks = copy.deepcopy(ks)
if test_empty_groups:
if test_empty_groups and len(ks) > 1:
new_ks[random.randint(0, num_groups - 1)] = 0
k, a, b, c, d, ref_d = generate_k_grouped_contiguous(num_groups, m, n, new_ks, use_ue8m0=use_ue8m0)
k, a, b, c, d, ref_d = generate_k_grouped_contiguous(num_groups, m, n, major_a, major_b, new_ks, use_ue8m0=use_ue8m0)
new_ks_tensor = torch.tensor(new_ks, dtype=torch.int, device='cuda')
deep_gemm.k_grouped_fp8_gemm_tn_contiguous(a, b, d, new_ks, new_ks_tensor, c=c)
diff = calc_diff(d, ref_d)
assert diff < 0.001, f'{m=}, {n=}, {k=}, {i=}, {diff:.5f}'
k_grouped_fp8_gemm_contiguous(a, b, d, new_ks, new_ks_tensor, c)
do_check = True
if do_check:
diff = calc_diff(d, ref_d)
assert diff < 0.001, f'{m=}, {n=}, {k=}, {ks=}, {diff:.5f}'
# Test performance
k, a, b, c, d, ref_d = generate_k_grouped_contiguous(num_groups, m, n, ks, use_ue8m0=use_ue8m0)
k, a, b, c, d, ref_d = generate_k_grouped_contiguous(num_groups, m, n, major_a, major_b, ks, use_ue8m0=use_ue8m0)
ks_tensor = torch.tensor(ks, dtype=torch.int, device='cuda')
# noinspection PyShadowingNames
def test_func():
deep_gemm.k_grouped_fp8_gemm_tn_contiguous(a, b, d, ks, ks_tensor, c=c)
k_grouped_fp8_gemm_contiguous(a, b, d, ks, ks_tensor, c)
t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
print(f' > Perf ({num_groups=:2}, m={m:5}, n={n:5}, k={k:5}): '