chenchenghao/DeepGEMM
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Multiple updates and refactorings (#231)

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This commit is contained in:
Ray Wang
2025-11-21 17:49:47 +08:00
committed by GitHub
parent bb4424aad4
commit 38f8ef73a4
80 changed files with 3767 additions and 2103 deletions
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tests/test_bf16.py
+48 -14
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@@ -1,5 +1,7 @@
import torch
import copy
import numpy as np
import random
import torch
import deep_gemm
from deep_gemm.testing import (
@@ -8,13 +10,14 @@ from deep_gemm.testing import (
)
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
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
)
def test_gemm() -> None:
print('Testing GEMM:')
scores = []
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:
@@ -34,18 +37,20 @@ def test_gemm() -> None:
assert a.is_contiguous() and b.is_contiguous()
getattr(deep_gemm, func_name)(a, b, d, c=c)
diff = calc_diff(d, ref_d)
assert diff < 0.0001, (f'{m=}, {n=}, {k=}, {major_opt=}, {accumulate=}, {out_dtype=}, '
assert diff < 1e-5, (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, kernel_type, use_bf16=True)
t = bench_kineto(lambda: deep_gemm.bf16_gemm_nt(a, b, d, c=c), 'bf16_gemm', suppress_kineto_output=True)
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'{t * 1e6:7.1f} 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 + split_k_t) / t:.2f}x cuBLAS')
print()
if cublas_t > 0:
scores.append((cublas_t + split_k_t) / t)
print(f"Average speedup over cuBLASLt: {float(np.prod(scores)) ** (1.0 / len(scores)):.3f}x\n")
def test_m_grouped_gemm_contiguous() -> None:
@@ -65,7 +70,7 @@ def test_m_grouped_gemm_contiguous() -> None:
getattr(deep_gemm, func_name)(a, b, d, m_indices)
d = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(d), d)
diff = calc_diff(d, ref_d)
assert diff < 0.001, f'{m=}, {n=}, {k=}, {major_opt}, {kernel_opt}, {diff:.5f}, alias={test_alias}'
assert diff < 1e-5, f'{m=}, {n=}, {k=}, {major_opt}, {diff:.5f}, alias={test_alias}'
m, a, b, m_indices, d, ref_d = generate_m_grouped_contiguous(num_groups, expected_m_per_group, n, k, major_a, major_b, use_bf16=True)
# noinspection PyShadowingNames
@@ -91,7 +96,7 @@ def test_m_grouped_gemm_masked() -> None:
deep_gemm.m_grouped_bf16_gemm_nt_masked(a, b, d, masked_m, expected_m_per_group)
for j in range(num_groups):
diff = calc_diff(d[j, :masked_m[j].item()], ref_d[j, :masked_m[j].item()])
assert diff < 0.001, f'{m=}, {n=}, {k=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
assert diff < 1e-5, f'{max_m=}, {n=}, {k=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
# Construct full cases
a, b, masked_m, d, ref_d = generate_m_grouped_masked(num_groups, max_m, expected_m_per_group, n, k, use_bf16=True)
@@ -110,6 +115,37 @@ def test_m_grouped_gemm_masked() -> None:
print()
def test_k_grouped_gemm_contiguous() -> None:
print('Testing k-grouped contiguous GEMM:')
for num_groups, m, n, major_a, major_b, ks, expected_k_per_group in enumerate_k_grouped_contiguous(torch.bfloat16):
for test_empty_groups in (False, True):
new_ks = copy.deepcopy(ks)
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, major_a, major_b, new_ks, use_bf16=True)
new_ks_tensor = torch.tensor(new_ks, dtype=torch.int, device='cuda')
deep_gemm.k_grouped_bf16_gemm_tn_contiguous(a, b, d, new_ks, new_ks_tensor, c)
diff = calc_diff(d, ref_d)
assert diff < 1e-5, f'{m=}, {n=}, {k=}, {ks=}, {diff:.7f}'
# Test performance
k, a, b, c, d, ref_d = generate_k_grouped_contiguous(num_groups, m, n, major_a, major_b, ks, use_bf16=True)
ks_tensor = torch.tensor(ks, dtype=torch.int, device='cuda')
# noinspection PyShadowingNames
def test_func():
deep_gemm.k_grouped_bf16_gemm_tn_contiguous(a, b, d, ks, ks_tensor, c)
t = bench_kineto(test_func, 'bf16_gemm', suppress_kineto_output=True)
print(f' > Perf ({num_groups=:2}, m={m:5}, n={n:5}, k={k:5}): '
f'{t * 1e6:4.0f} us | '
f'{2 * m * n * k / t / 1e12:4.0f} TFLOPS | '
f'{count_bytes(a, b, c, d) / 1e9 / t:4.0f} GB/s')
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):
@@ -121,7 +157,7 @@ def test_cublaslt_gemm() -> None:
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=})'
assert diff < 6e-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}): '
@@ -132,18 +168,16 @@ def test_cublaslt_gemm() -> None:
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_gemm()
# TODO: support SM100
if get_arch_major() == 9:
if get_arch_major() >= 9:
test_gemm()
test_m_grouped_gemm_contiguous()
test_m_grouped_gemm_masked()
test_k_grouped_gemm_contiguous()
test_cublaslt_gemm()