Files
tilelang/testing/python/profiler/test_tilelang_profiler.py
T
Lei WangandZhiwen Mo f8dc61c02c test: reduce CI runtime for slow Python suites (#1932)
* test: trim CI runtime for slow Python suites

* remove useless test

* Update TVM submodule to latest commit and remove manual device assert test from CI. Replace with a call to tilelang.testing.main().

* lint fix

* fix

* revert test for eager jit

---------

Co-authored-by: Zhiwen Mo <zm125@ic.ac.uk>
2026-03-18 13:08:30 +08:00

57 lines
1.6 KiB
Python

import tilelang
import tilelang.language as T
import pytest
@tilelang.jit(out_idx=[-1])
def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
@T.prim_func
def gemm(
A: T.Tensor((M, K), dtype),
B: T.Tensor((K, N), dtype),
C: T.Tensor((M, N), dtype),
):
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
A_shared = T.alloc_shared((block_M, block_K), dtype)
B_shared = T.alloc_shared((block_K, block_N), dtype)
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
T.clear(C_local)
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
T.copy(A[by * block_M, k * block_K], A_shared)
T.copy(B[k * block_K, bx * block_N], B_shared)
T.gemm(A_shared, B_shared, C_local)
T.copy(C_local, C[by * block_M, bx * block_N])
return gemm
@pytest.mark.perf
def test_profiler():
kernel = matmul(1024, 1024, 1024, 128, 128, 32)
import torch
a = torch.randn(1024, 1024).cuda().half()
b = torch.randn(1024, 1024).cuda().half()
c = kernel(a, b)
ref_c = a @ b
torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
# benchmark
profiler = kernel.get_profiler()
# use cupti backend
cupti_latency = profiler.do_bench(backend="cupti")
# use event backend
event_latency = profiler.do_bench(backend="event")
print(f"cupti Latency: {cupti_latency}ms")
print(f"event Latency: {event_latency}ms")
if __name__ == "__main__":
tilelang.testing.main()