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Support TMA multicast on B with m_grouped_gemm_contiguous. (#88)

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yukuai26
2025-04-21 09:43:17 +08:00
committed by GitHub
parent 83aa960b9b
commit 891f35adf5
5 changed files with 74 additions and 31 deletions
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61
tests/test_core.py
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@@ -4,6 +4,7 @@ from typing import Tuple
import deep_gemm
from deep_gemm import bench_kineto, calc_diff, ceil_div, get_col_major_tma_aligned_tensor
from deep_gemm.jit_kernels.utils import get_m_alignment_for_contiguous_layout
def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -38,7 +39,38 @@ def construct(m: int, k: int, n: int) -> \
return x_fp8, y_fp8, out, ref_out
def construct_grouped(num_groups: int, m: int, k: int, n: int, is_masked: bool) -> \
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 = m_aligned * random.randint(int(expected_m_per_group * 0.7) // m_aligned, int(expected_m_per_group * 1.3) // m_aligned)
m += group_m
group_m_list.append(group_m)
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)
out = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
ref_out = torch.randn((m, n), device='cuda', dtype=torch.bfloat16)
start = 0
for i, group_m in enumerate(group_m_list):
end = start + group_m
m_indices[start:end] = i
ref_out[start:end] = x[start:end] @ y[i].t()
start = end
assert m % 4 == 0, f'TMA alignment error: {m}'
x_fp8 = per_token_cast_to_fp8(x)
y_fp8 = (torch.empty_like(y, dtype=torch.float8_e4m3fn), torch.empty((num_groups, (n + 127) // 128, k // 128), device='cuda', dtype=torch.float))
for i in range(num_groups):
y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i])
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)
y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)
@@ -52,11 +84,6 @@ def construct_grouped(num_groups: int, m: int, k: int, n: int, is_masked: bool)
x_fp8[0][i], x_fp8[1][i] = per_token_cast_to_fp8(x[i])
y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i])
# For non-masked input, we must merge the group and M dims
if not is_masked:
x_fp8 = (x_fp8[0].view(-1, k), per_token_cast_to_fp8(x.view(-1, k))[1])
out, ref_out = out.view(-1, n), ref_out.view(-1, n)
# 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
@@ -88,28 +115,24 @@ def test_gemm() -> None:
def test_m_grouped_gemm_contiguous() -> None:
print('Testing grouped contiguous GEMM:')
for num_groups, m, k, n in ((4, 8192, 7168, 4096), (4, 8192, 2048, 7168), (8, 4096, 7168, 4096), (8, 4096, 2048, 7168)):
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
x_fp8, y_fp8, out, ref_out = construct_grouped(num_groups, m, k, n, is_masked=False)
m_indices = torch.arange(0, num_groups, device='cuda', dtype=torch.int)
m_indices = m_indices.unsqueeze(-1).expand(num_groups, m).contiguous().view(-1)
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)
diff = calc_diff(out, ref_out)
assert diff < 0.001, f'm={m * num_groups}, {k=}, {n=}, {diff:.5f}'
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_grouped(num_groups, m, k, n, is_masked=False)
m_indices = torch.arange(0, num_groups, device='cuda', dtype=torch.int)
m_indices = m_indices.unsqueeze(-1).expand(num_groups, m).contiguous().view(-1)
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)
print(f' > Performance ({num_groups=}, m_per_group={m:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
f'throughput: {2 * num_groups * m * n * k / t / 1e12:4.0f} TFLOPS, '
f'{(num_groups * (m * k + k * n + m * n * 2)) / 1e9 / t:4.0f} GB/s')
print(f' > Performance ({num_groups=}, m={m:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
f'throughput: {2 * m * n * k / t / 1e12:4.0f} TFLOPS, '
f'{(m * k + num_groups * k * n + m * n * 2) / 1e9 / t:4.0f} GB/s')
print()
@@ -121,7 +144,7 @@ def test_m_grouped_gemm_masked() -> None:
# Test correctness
masked_m_candidates = list(filter(lambda candidate: candidate <= m, (64, 128, 192, 256, 320, 384)))
for i in range(10):
x_fp8, y_fp8, out, ref_out = construct_grouped(num_groups, m, k, n, is_masked=True)
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.choice(masked_m_candidates)
@@ -132,7 +155,7 @@ def test_m_grouped_gemm_masked() -> None:
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_grouped(num_groups, m, k, n, is_masked=True)
x_fp8, y_fp8, out, ref_out = construct_masked_grouped(num_groups, m, k, n)
masked_m = torch.ones((num_groups, ), device='cuda', dtype=torch.int) * m
# noinspection PyShadowingNames