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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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117
tests/generators.py
View File

@@ -1,7 +1,7 @@
import enum
import random
import torch
from typing import Generator, Tuple, List
from typing import Generator, List
from deep_gemm.utils import (
align, ceil_div,
@@ -11,7 +11,6 @@ from deep_gemm.utils import (
class KernelType(enum.Enum):
# For SM100 GEMMs
Kernel1D1D = 0
Kernel1D2D = 1
KernelNoSF = 2
@@ -48,62 +47,87 @@ def get_ue8m0_usage(kernel_type: KernelType) -> bool:
return kernel_type.is_1d1d()
def get_kernel_types(use_bf16: bool = False) -> tuple:
if use_bf16:
def get_kernel_types(dtype: torch.dtype) -> tuple:
if dtype == torch.bfloat16:
return (KernelType.KernelNoSF, )
return (KernelType.Kernel1D2D, ) if get_arch_major() == 9 else (KernelType.Kernel1D1D, KernelType.Kernel1D2D)
# TODO: SM100 1D2D kernels are going to be deprecated
# But if you want to test it, please use:
# `(KernelType.Kernel1D2D, ) if get_arch_major() == 9 else (KernelType.Kernel1D1D, KernelType.Kernel1D2D)`
return (KernelType.Kernel1D2D, ) if get_arch_major() == 9 else (KernelType.Kernel1D1D, )
def get_out_dtype() -> tuple:
return (torch.bfloat16, ) if get_arch_major() == 9 else (torch.bfloat16, torch.float)
def get_major_ab(allow_a_mn_major: bool, allow_b_mn_major: bool) -> Generator:
for major_a in (MajorTypeAB.KMajor, MajorTypeAB.MNMajor):
for major_b in (MajorTypeAB.KMajor, MajorTypeAB.MNMajor):
if major_a.is_mn_major() and not allow_a_mn_major:
continue
if major_b.is_mn_major() and not allow_b_mn_major:
continue
yield major_a, major_b
def get_major_ab(freeze_a: bool) -> tuple:
# TODO: test other major-ness for SM90 BF16 GEMMs
if get_arch_major() == 9:
return ((MajorTypeAB.KMajor, MajorTypeAB.KMajor), )
if freeze_a:
return (MajorTypeAB.KMajor, MajorTypeAB.KMajor), (MajorTypeAB.KMajor, MajorTypeAB.MNMajor)
return (MajorTypeAB.KMajor, MajorTypeAB.KMajor), (MajorTypeAB.KMajor, MajorTypeAB.MNMajor), \
(MajorTypeAB.MNMajor, MajorTypeAB.KMajor), (MajorTypeAB.MNMajor, MajorTypeAB.MNMajor)
def enumerate_normal(dtype: torch.dtype) -> Generator:
assert dtype in (torch.float8_e4m3fn, torch.bfloat16)
fp32_output_nk = [(256, 7168), (129280, 7168)]
bf16_output_nk = [(2112, 7168), (576, 7168), (24576, 1536), (32768, 512), (7168, 16384), (4096, 7168), (7168, 2048)]
m_fwd_list, m_bwd_list = [128, 4096], [4096, ]
nk_list = bf16_output_nk
# Only BF16 GEMM needs FP32 outputs
if dtype == torch.bfloat16:
nk_list += fp32_output_nk
for kernel_type in get_kernel_types(dtype):
# Forward
for m in m_fwd_list:
for n, k in nk_list:
out_dtype = torch.float if (n, k) in fp32_output_nk else torch.bfloat16
yield kernel_type, m, n, k, MajorTypeAB.KMajor, MajorTypeAB.KMajor, False, out_dtype
# TODO: support BF16 SM90 MN-major kernels
if dtype == torch.bfloat16 and get_arch_major() == 9:
continue
# Backward
for m in m_bwd_list:
for n, k in nk_list:
override_major = MajorTypeAB.MNMajor
override_kernel_type = kernel_type
if get_arch_major() == 9 and dtype == torch.float8_e4m3fn:
override_major = MajorTypeAB.KMajor
override_kernel_type = KernelType.Kernel1D1D
yield kernel_type, m, k, n, MajorTypeAB.KMajor, override_major, False, torch.bfloat16 # Dgrad
yield override_kernel_type, n, m, k, override_major, override_major, True, torch.float # Wgrad
yield override_kernel_type, n, m, k, override_major, override_major, False, torch.bfloat16 # Wgrad
def enumerate_normal(use_bf16: bool = False) -> Generator:
for kernel_type in get_kernel_types(use_bf16):
for m in (128, 4096):
for n, k in [(2112, 7168), (24576, 1536), (32768, 512), (7168, 16384), (4096, 7168), (7168, 2048)]:
for major_a, major_b in get_major_ab(False):
for out_dtype in get_out_dtype():
for accumulate in (False, ) if out_dtype == torch.bfloat16 or kernel_type.is_1d2d() else (False, True):
yield kernel_type, m, n, k, major_a, major_b, accumulate, out_dtype
def enumerate_m_grouped_contiguous(use_bf16: bool = False) -> Generator:
for kernel_type in get_kernel_types(use_bf16):
def enumerate_m_grouped_contiguous(dtype: torch.dtype) -> Generator:
for kernel_type in get_kernel_types(dtype):
for num_groups, expected_m_per_group, n, k in ((4, 8192, 4096, 7168), (4, 8192, 7168, 2048), (8, 4096, 4096, 7168), (8, 4096, 7168, 2048)):
for major_a, major_b in get_major_ab(True):
for major_a, major_b in get_major_ab(False, get_arch_major() > 9):
yield kernel_type, num_groups, expected_m_per_group, n, k, major_a, major_b
def enumerate_m_grouped_masked() -> Generator:
def enumerate_m_grouped_masked(dtype: torch.dtype) -> Generator:
max_m = 4096
for kernel_type in get_kernel_types():
for kernel_type in get_kernel_types(dtype):
for num_groups, m in ((1, 1024), (2, 512), (4, 256)):
for n, k in ((4096, 7168), (7168, 2048), ):
yield kernel_type, num_groups, max_m, m, n, k
def enumerate_k_grouped_contiguous():
# TODO: support SM90 kernels
if get_arch_major() == 9:
return []
# Only K-major is supported for SM90
major_a, major_b = (MajorTypeAB.KMajor, MajorTypeAB.KMajor) if get_arch_major() == 9 \
else (MajorTypeAB.MNMajor, MajorTypeAB.MNMajor)
# Must with FP32 accumulation and 1D1D kernels
for num_groups, m, n, expected_k_per_group in (( 4, 4096, 7168, 8192), ( 4, 7168, 2048, 8192), # EP64
( 8, 4096, 7168, 4096), ( 8, 7168, 2048, 4096), # EP32
(16, 4096, 7168, 2048), (16, 7168, 2048, 2048)): # EP16
ks = [align(int(expected_k_per_group * random.uniform(0.7, 1.3)), get_mk_alignment_for_contiguous_layout()) for _ in range(num_groups)]
yield num_groups, m, n, ks, expected_k_per_group
yield num_groups, m, n, major_a, major_b, ks, expected_k_per_group
def enumerate_sf_layout():
@@ -134,6 +158,7 @@ def enumerate_transpose():
def generate_normal(m: int, n: int, k: int,
major_a: MajorTypeAB, major_b: MajorTypeAB,
accumulate: bool, out_dtype: torch.dtype,
kernel_type: KernelType,
use_ue8m0: bool = False, use_bf16: bool = False):
a = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
b = torch.randn((n, k), device='cuda', dtype=torch.bfloat16)
@@ -147,7 +172,9 @@ def generate_normal(m: int, n: int, k: int,
b = b if major_b.is_k_major() else b.T.contiguous().T
return a, b, c, d, ref_d
a_fp8, b_fp8 = per_token_cast_to_fp8(a, use_ue8m0=use_ue8m0), per_block_cast_to_fp8(b, use_ue8m0=use_ue8m0)
a_fp8 = per_token_cast_to_fp8(a, use_ue8m0=use_ue8m0)
b_fp8 = per_token_cast_to_fp8(b, use_ue8m0=use_ue8m0) if kernel_type.is_1d1d() and accumulate \
else per_block_cast_to_fp8(b, use_ue8m0=use_ue8m0)
a_fp8 = a_fp8 if major_a.is_k_major() else (a_fp8[0].T.contiguous().T, a_fp8[1])
b_fp8 = b_fp8 if major_b.is_k_major() else (b_fp8[0].T.contiguous().T, b_fp8[1])
return a_fp8, b_fp8, c, d, ref_d
@@ -214,7 +241,7 @@ def generate_m_grouped_masked(num_groups: int, max_m: int, expected_m_per_group:
return a_fp8, b_fp8, masked_m, d, ref_d
def generate_k_grouped_contiguous(num_groups: int, m: int, n: int, ks: List[int], use_ue8m0: bool):
def generate_k_grouped_contiguous(num_groups: int, m: int, n: int, major_a: MajorTypeAB, major_b: MajorTypeAB, ks: List[int], use_ue8m0: bool):
assert get_mk_alignment_for_contiguous_layout() % 128 == 0
k = sum(ks)
@@ -232,4 +259,20 @@ def generate_k_grouped_contiguous(num_groups: int, m: int, n: int, ks: List[int]
a_fp8 = per_channel_cast_to_fp8(a, use_ue8m0=use_ue8m0)
b_fp8 = per_channel_cast_to_fp8(b, use_ue8m0=use_ue8m0)
# Transpose for K Major A/B
if (major_a, major_b) == (MajorTypeAB.KMajor, MajorTypeAB.KMajor):
a, sfa = a_fp8
b, sfb = b_fp8
new_a = torch.empty((sum(ks) * m, ), dtype=a.dtype, device=a.device)
new_b = torch.empty((sum(ks) * n, ), dtype=b.dtype, device=b.device)
prefix = 0
for K in ks:
new_a[prefix * m : (prefix + K) * m] = a[prefix : prefix + K, ].T.flatten()
new_b[prefix * n : (prefix + K) * n] = b[prefix : prefix + K, ].T.flatten()
prefix += K
a_fp8, b_fp8 = (new_a, sfa.T), (new_b, sfb.T)
else:
assert (major_a, major_b) == (MajorTypeAB.MNMajor, MajorTypeAB.MNMajor)
return k, a_fp8, b_fp8, c, d, ref_d

64
tests/test_attention.py Normal file
View File

@@ -0,0 +1,64 @@
import random
import torch
from typing import Tuple
import deep_gemm
from deep_gemm.testing import bench_kineto, calc_diff, count_bytes
from deep_gemm.utils import ceil_div, per_custom_dims_cast_to_fp8
from generators import get_arch_major, generate_normal, get_ue8m0_usage, get_kernel_types, MajorTypeAB
def apply_skip_head_mid(d: torch.Tensor, head_splits: Tuple[int, int, int]):
left, mid, right = head_splits
m, n = d.shape
assert n % (left + right) == 0
num_heads = n // (left + right)
# Split and insert padding tensor
d = d.view(m, num_heads, -1)
d_left = d[:, :, :left]
d_right = d[:, :, -right:]
d_mid = torch.zeros((m, num_heads, mid), dtype=d.dtype, device=d.device)
return torch.cat([d_left, d_mid, d_right], dim=2).view(m, -1)
def test_gemm_skip_head_mid() -> None:
print('Testing GEMM skip head mid:')
head_splits = (128, 64, 128)
major_a, major_b = MajorTypeAB.KMajor, MajorTypeAB.KMajor
out_dtype, accumulate = torch.bfloat16, False
for kernel_type in get_kernel_types(dtype=torch.float8_e4m3fn):
for m in (128, 4096):
for n, k in [(32768, 512), (8192, 512)]:
kernel_opt = f'1D1D' if kernel_type.is_1d1d() else '1D2D'
use_ue8m0 = get_ue8m0_usage(kernel_type)
disable_ue8m0_cast = not use_ue8m0
a, b, _, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_ue8m0=use_ue8m0)
d = apply_skip_head_mid(d, head_splits)
ref_d = apply_skip_head_mid(ref_d, head_splits)
deep_gemm.fp8_gemm_nt_skip_head_mid(a, b, d, head_splits, disable_ue8m0_cast=disable_ue8m0_cast)
diff = calc_diff(d, ref_d)
assert diff < 0.001, f'{m=}, {n=}, {k=}, {kernel_opt}, {diff:.5f}'
t = bench_kineto(lambda: deep_gemm.fp8_gemm_nt_skip_head_mid(a, b, d, head_splits, disable_ue8m0_cast=disable_ue8m0_cast),
'fp8_gemm', suppress_kineto_output=True)
print(f' > Perf (m={m:5}, n={n:5}, k={k:5}, {kernel_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')
print()
if __name__ == '__main__':
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.manual_seed(0)
random.seed(0)
test_gemm_skip_head_mid()

58
tests/test_bf16.py
View File

@@ -7,6 +7,7 @@ from deep_gemm.testing import (
calc_diff, count_bytes
)
from generators import (
get_arch_major,
enumerate_normal, enumerate_m_grouped_contiguous, enumerate_m_grouped_masked, generate_normal,
generate_m_grouped_contiguous, generate_m_grouped_masked
)
@@ -14,14 +15,18 @@ from generators import (
def test_gemm() -> None:
print('Testing GEMM:')
for _, m, n, k, major_a, major_b, accumulate, out_dtype in enumerate_normal(use_bf16=True):
for kernel_type, m, n, k, major_a, major_b, accumulate, out_dtype in enumerate_normal(torch.bfloat16):
# TODO: support accumulation for SM90 BF16 GEMM
if get_arch_major() == 9 and accumulate:
continue
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'
acc_opt = f'acc={int(accumulate)}'
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_bf16=True)
a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_bf16=True)
func_name = f'bf16_gemm_{major_opt.lower() if test_alias else "nt"}'
if test_alias:
a = a if major_a.is_k_major() else a.T
@@ -31,28 +36,22 @@ def test_gemm() -> None:
diff = calc_diff(d, ref_d)
assert diff < 0.0001, (f'{m=}, {n=}, {k=}, {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_bf16=True)
a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_bf16=True)
cublas_t = 0
t = bench_kineto(lambda: deep_gemm.bf16_gemm_nt(a, b, d, c=c), 'bf16_gemm', suppress_kineto_output=True)
if accumulate == 0 and out_dtype == torch.bfloat16:
# noinspection PyBroadException
try:
cublas_t = bench_kineto(lambda: a @ b.T, 'nvjet', suppress_kineto_output=True)
except Exception:
pass
print(f' > Perf (m={m:5}, n={n:5}, k={k:5}, layout={major_opt}, {out_opt}, {acc_opt}): '
f'{t * 1e6:4.0f} us | '
cublas_t, split_k_t = bench_kineto(lambda: deep_gemm.cublaslt_gemm_nt(a, b, d, c=c), ('nvjet', 'reduce'), suppress_kineto_output=True)
print(f' > Perf (m={m:6}, n={n:6}, k={k:6}, layout={major_opt}, {out_opt}, {acc_opt}): '
f'{t * 1e6:5.0f} 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 | '
f'{cublas_t / t:.2f}x cuBLAS')
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 _, num_groups, expected_m_per_group, n, k, major_a, major_b in enumerate_m_grouped_contiguous(use_bf16=True):
for _, num_groups, expected_m_per_group, n, k, major_a, major_b in enumerate_m_grouped_contiguous(torch.bfloat16):
major_opt = 'N' if major_a.is_k_major() else 'T'
major_opt += 'T' if major_b.is_k_major() else 'N'
@@ -85,7 +84,7 @@ 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 _, num_groups, max_m, expected_m_per_group, n, k in enumerate_m_grouped_masked():
for _, num_groups, max_m, expected_m_per_group, n, k in enumerate_m_grouped_masked(torch.bfloat16):
# Test correctness
for i in range(10):
a, b, masked_m, d, ref_d = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k, use_bf16=True)
@@ -111,6 +110,27 @@ def test_m_grouped_gemm_masked() -> None:
print()
def test_cublaslt_gemm() -> None:
print('Testing cuBLASLt GEMM:')
for kernel_type, m, n, k, major_a, major_b, accumulate, out_dtype in enumerate_normal(dtype=torch.bfloat16):
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'
acc_opt = f'acc={int(accumulate)}'
a, b, c, d, ref_d = generate_normal(m, n, k, major_a, major_b, accumulate, out_dtype, kernel_type, use_bf16=True)
deep_gemm.cublaslt_gemm_nt(a, b, d, c=c)
diff = calc_diff(d, ref_d)
assert diff < 5e-7, f'{diff=}, ({m=}, {n=}, {k=}, {major_opt=}, {accumulate=}, {out_dtype=})'
t = bench_kineto(lambda: deep_gemm.cublaslt_gemm_nt(a, b, d, c=c), 'nvjet', suppress_kineto_output=True,)
print(f' > Perf (m={m:6}, n={n:6}, k={k:6}, layout={major_opt}, {out_opt}, {acc_opt}): '
f'{t * 1e6:5.0f} 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')
print()
if __name__ == '__main__':
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
@@ -121,5 +141,9 @@ if __name__ == '__main__':
print(f' > {deep_gemm.__path__}\n')
test_gemm()
test_m_grouped_gemm_contiguous()
test_m_grouped_gemm_masked()
# TODO: support SM100
if get_arch_major() == 9:
test_m_grouped_gemm_contiguous()
test_m_grouped_gemm_masked()
test_cublaslt_gemm()

85
tests/test_einsum.py Normal file
View File

@@ -0,0 +1,85 @@
import random
import torch
import deep_gemm
from deep_gemm.testing import (
bench, bench_kineto,
calc_diff, count_bytes
)
def test_bmk_bnk_mn() -> None:
print('Testing "bmk, bnk -> mn":')
for s in (129, 4096, 8192):
for m, n, k in [(128, 384, 128), (256, 256, 256), (384, 128, 384)]:
for dtype in (torch.float, torch.bfloat16):
a = torch.randn((s, m, k), dtype=torch.bfloat16, device='cuda')
b = torch.randn((s, n, k), dtype=torch.bfloat16, device='cuda')
d = torch.randn((m, n), dtype=dtype, device='cuda')
c = d if dtype == torch.float else None
# Test correctness
ref_d = (c if dtype == torch.float else 0) + torch.bmm(a.float(), b.float().mT).sum(0)
deep_gemm.einsum('bmk,bnk->mn', a, b, d, c=c)
assert calc_diff(d, ref_d) < 1e-5
t = bench_kineto(lambda: deep_gemm.einsum('bmk,bnk->mn', a, b, d, c=c), 'bmn_bnk_mn_gemm_impl', suppress_kineto_output=True)
print(f' > Perf (b={s:4.0f}, {m=}, {n=}, {k=}, {"FP32" if dtype == torch.float else "BF16"}): ',
f'{t * 1e6:4.0f} us | '
f'{2 * s * m * n * k / t / 1e12:4.0f} TFLOPS | '
f'{(count_bytes(a, b) + (d.numel() * 4)) / 1e9 / t:4.0f} GB/s')
print()
def test_bhr_hdr_bhd():
print('Testing "bhr, hdr -> bhd":')
for b in (128, 4096, 8192):
for h, r, d in [(128, 512, 128)]:
x = torch.randn((b, h, r), device='cuda', dtype=torch.bfloat16)
fy = torch.randn((h, d, r + 128), device='cuda', dtype=torch.bfloat16)
y = fy[:, :, :r]
ref_z = torch.einsum('bhr,hdr->bhd', x, y)
z = torch.empty((b, h, d), device='cuda', dtype=torch.bfloat16)
deep_gemm.einsum('bhr,hdr->bhd', x, y, z)
assert calc_diff(z, ref_z) < 1e-10
t = bench_kineto(lambda: deep_gemm.einsum('bhr,hdr->bhd', x, y, z), 'nvjet', suppress_kineto_output=True)
print(f' > Perf ({b=:4.0f}, {h=}, {r=}, {d=}): ',
f'{t * 1e6:4.0f} us | '
f'{2 * b * h * r * d / t / 1e12:.0f} TFLOPS | '
f'{count_bytes((x, y, z)) / t / 1e9:.0f} GB/s')
print()
def test_bhd_hdr_bhr():
print('Testing "bhd, hdr -> bhr":')
for b in (128, 4096, 8192):
for h, r, d in [(128, 512, 128)]:
x = torch.randn((b, h, d), device='cuda', dtype=torch.bfloat16)
fy = torch.randn((h, d, r + 128), device='cuda', dtype=torch.bfloat16)
y = fy[:, :, :r]
ref_z = torch.einsum('bhd,hdr->bhr', x, y)
z = torch.empty((b, h, r), device='cuda', dtype=torch.bfloat16)
deep_gemm.einsum('bhd,hdr->bhr', x, y, z)
assert calc_diff(z, ref_z) < 1e-10
t = bench_kineto(lambda: deep_gemm.einsum('bhd,hdr->bhr', x, y, z), 'nvjet', suppress_kineto_output=True)
print(f' > Perf ({b=:4.0f}, {h=}, {r=}, {d=}): ',
f'{t * 1e6:4.0f} us | '
f'{2 * b * h * r * d / t / 1e12:.0f} TFLOPS | '
f'{count_bytes((x, y, z)) / t / 1e9:.0f} GB/s')
print()
if __name__ == '__main__':
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.manual_seed(0)
random.seed(0)
print('Library path:')
print(f' > {deep_gemm.__path__}\n')
test_bmk_bnk_mn()
test_bhr_hdr_bhd()
test_bhd_hdr_bhr()

60
tests/test_fp8.py
View File

@@ -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}): '