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https://github.com/tile-ai/tilelang.git
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Cunxiao NiandLeiWang1999
91d5ef5480
* [Profiler]Adds CUPTI profiler support * format * rafactor cupti profiler * format * rafactor * rafactor * fix lint * fix lint * refactor * add profiler tests --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
import tilelang
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import tilelang.language as T
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@tilelang.jit(out_idx=[-1])
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def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
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@T.prim_func
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def gemm(
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A: T.Tensor((M, K), dtype),
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B: T.Tensor((K, N), dtype),
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C: T.Tensor((M, N), dtype),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_K, block_N), dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
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T.copy(A[by * block_M, k * block_K], A_shared)
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local)
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T.copy(C_local, C[by * block_M, bx * block_N])
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return gemm
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def test_profiler():
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kernel = matmul(1024, 1024, 1024, 128, 128, 32)
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import torch
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a = torch.randn(1024, 1024).cuda().half()
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b = torch.randn(1024, 1024).cuda().half()
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c = kernel(a, b)
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ref_c = a @ b
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torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
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# benchmark
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profiler = kernel.get_profiler()
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# use cupti backend
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cupti_latency = profiler.do_bench(backend="cupti")
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# use event backend
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event_latency = profiler.do_bench(backend="event")
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print(f"cupti Latency: {cupti_latency}ms")
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print(f"event Latency: {event_latency}ms")
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if __name__ == "__main__":
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tilelang.testing.main()
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