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Refactor tests

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This commit is contained in:
Chenggang Zhao 2025-05-27 11:32:05 +08:00
parent c4e31d121b
commit 1169f83c36
1 changed files with 33 additions and 46 deletions
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79
tests/test_core.py
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@ -49,13 +49,10 @@ def construct(m: int, k: int, n: int) -> \
def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k: int, n: int) -> \
Tuple[int, Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
m = 0
m_aligned = get_m_alignment_for_contiguous_layout()
group_m_list = []
for i in range(num_groups):
group_m = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3))
m += ceil_div(group_m, m_aligned) * m_aligned
group_m_list.append(group_m)
alignment = get_m_alignment_for_contiguous_layout()
group_ms = [int(expected_m_per_group * random.uniform(0.7, 1.3))]
m = sum([ceil_div(x, alignment) * alignment for x in group_ms])
x = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)
m_indices = torch.empty(m, device='cuda', dtype=torch.int32)
@ -63,13 +60,14 @@ def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k:
ref_out = torch.randn((m, n), device='cuda', dtype=torch.bfloat16)
start = 0
for i, group_m in enumerate(group_m_list):
for i, group_m in enumerate(group_ms):
actual_end = start + group_m
aligned_end = start + ceil_div(group_m, m_aligned) * m_aligned
aligned_end = start + ceil_div(group_m, alignment) * alignment
m_indices[start:actual_end] = i
m_indices[actual_end:aligned_end] = -1
ref_out[start:aligned_end] = x[start:aligned_end] @ y[i].t()
start = aligned_end
ref_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_out), ref_out)
assert m % 4 == 0, f'TMA alignment error: {m}'
x_fp8 = per_token_cast_to_fp8(x)
@ -80,15 +78,15 @@ def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k:
return m, x_fp8, y_fp8, m_indices, out, ref_out
def construct_masked_grouped(num_groups: int, m: int, k: int, n: int) -> \
Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
x = torch.randn((num_groups, m, k), device='cuda', dtype=torch.bfloat16)
def construct_masked_grouped(num_groups: int, max_m: int, expected_m_per_group: int, k: int, n: int) -> \
Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
x = torch.randn((num_groups, max_m, k), device='cuda', dtype=torch.bfloat16)
y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)
out = torch.empty((num_groups, m, n), device='cuda', dtype=torch.bfloat16)
out = torch.empty((num_groups, max_m, n), device='cuda', dtype=torch.bfloat16)
ref_out = torch.einsum('gmk,gnk->gmn', x, y)
assert m % 4 == 0, f'TMA alignment error: {m}'
x_fp8 = (torch.empty_like(x, dtype=torch.float8_e4m3fn), torch.empty((num_groups, m, k // 128), device='cuda', dtype=torch.float))
assert max_m % 4 == 0, f'TMA alignment error: {max_m}'
x_fp8 = (torch.empty_like(x, dtype=torch.float8_e4m3fn), torch.empty((num_groups, max_m, k // 128), device='cuda', dtype=torch.float))
y_fp8 = (torch.empty_like(y, dtype=torch.float8_e4m3fn), torch.empty((num_groups, ceil_div(n, 128), k // 128), device='cuda', dtype=torch.float))
for i in range(num_groups):
x_fp8[0][i], x_fp8[1][i] = per_token_cast_to_fp8(x[i])
@ -96,7 +94,12 @@ def construct_masked_grouped(num_groups: int, m: int, k: int, n: int) -> \
# Transpose earlier so that the testing will not trigger transposing kernels
x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1]))
return x_fp8, y_fp8, out, ref_out
# Construct mask
masked_m = torch.empty((num_groups, ), device='cuda', dtype=torch.int)
for j in range(num_groups):
masked_m[j] = int(expected_m_per_group * random.uniform(0.7, 1.3))
return x_fp8, y_fp8, masked_m, out, ref_out
def construct_wgrad(m: int, k: int, n: int) -> \
@ -172,9 +175,6 @@ def test_gemm() -> None:
diff = calc_diff(out, ref_out)
assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}'
# Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2)
x_fp8, y_fp8, out, ref_out = construct(m, k, n)
# noinspection PyShadowingNames
def test_func():
deep_gemm.gemm_fp8_fp8_bf16_nt(x_fp8, y_fp8, out)
@ -190,63 +190,50 @@ def test_m_grouped_gemm_contiguous() -> None:
print('Testing grouped contiguous GEMM:')
for num_groups, expected_m_per_group, k, n in ((4, 8192, 7168, 4096), (4, 8192, 2048, 7168), (8, 4096, 7168, 4096), (8, 4096, 2048, 7168)):
# TODO: make a stronger test
# NOTES: we should mask the unfilled part before calculating difference
m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n)
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(out), out)
ref_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_out), ref_out)
diff = calc_diff(out, ref_out)
assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}'
# Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2)
m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n)
# noinspection PyShadowingNames
def test_func():
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
sum_m = (m_indices != -1).sum().item()
valid_m = (m_indices != -1).sum().item()
print(f' > Performance ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
f'throughput: {2 * sum_m * n * k / t / 1e12:4.0f} TFLOPS, '
f'{(sum_m * k + num_groups * k * n + sum_m * n * 2) / 1e9 / t:4.0f} GB/s')
f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
print()
def test_m_grouped_gemm_masked() -> None:
print('Testing grouped masked GEMM:')
m = 4096
max_m = 4096
for num_groups, expected_m_per_group in ((1, 1024), (2, 512), (4, 256)):
for k, n in ((7168, 4096), (2048, 7168), ):
# Test correctness
for i in range(10):
x_fp8, y_fp8, out, ref_out = construct_masked_grouped(num_groups, m, k, n)
masked_m = torch.empty((num_groups, ), device='cuda', dtype=torch.int)
for j in range(num_groups):
masked_m[j] = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3))
expected_m = min(int(masked_m.float().mean()) + 1, m)
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m)
x_fp8, y_fp8, masked_m, out, ref_out = construct_masked_grouped(num_groups, max_m, expected_m_per_group, k, n)
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group)
for j in range(num_groups):
diff = calc_diff(out[j, :masked_m[j].item()], ref_out[j, :masked_m[j].item()])
assert diff < 0.001, f'{m=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
# Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2)
x_fp8, y_fp8, out, ref_out = construct_masked_grouped(num_groups, m, k, n)
for j in range(num_groups):
masked_m[j] = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3))
expected_m = min(int(masked_m.float().mean()) + 1, m)
sum_m = masked_m.sum().item()
assert diff < 0.001, f'{max_m=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
# noinspection PyShadowingNames
def test_func():
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m)
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group)
# Test performance with fixed shapes
# noinspection PyUnboundLocalVariable
valid_m = masked_m.sum().item()
t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
print(f' > Performance ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
f'throughput: {2 * sum_m * n * k / t / 1e12:4.0f} TFLOPS, '
f'{(sum_m * k + num_groups * k * n + sum_m * n * 2) / 1e9 / t:4.0f} GB/s')
f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
print()
@ -320,4 +307,4 @@ if __name__ == '__main__':
test_m_grouped_gemm_masked()
test_wgrad_gemm()
test_k_grouped_wgrad_gemm()
test_k_grouped_wgrad_gemm()