mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-22 06:50:25 +08:00
Lei Wang
25a50f1a37
* [Refactor] Update JIT kernel functions and streamline GEMM tests * Renamed and refactored matmul and run_gemm functions to matmul_kernel_jit and run_gemm_kernel_jit for clarity. * Removed redundant JIT decorator from the matmul function, ensuring it is applied only to the kernel function. * Updated test function names to reflect changes in the kernel functions, enhancing consistency and readability. * Cleaned up commented-out code and unnecessary imports to improve overall code quality. * Update main function call in GEMM test to use tilelang testing framework * Update README and example scripts to include JIT decorator comments * Added comments in README.md and various example scripts to indicate the use of the @tilelang.jit decorator for returning torch functions. * Removed redundant comments that previously instructed to add the decorator, streamlining the documentation and improving clarity. * Update GEMM test parameters for improved performance * Set num_stages to 0 and adjusted matrix dimensions in test functions to enhance performance and consistency across GEMM tests in test_tilelang_kernel_gemm.py.
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
import tilelang
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import tilelang.language as T
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# add decorator @tilelang.jit if you want to return a torch function
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# @tilelang.jit
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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 main(
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A: T.Tensor((M, K), dtype),
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B: T.Tensor((N, K), dtype),
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C: T.Tensor((M, N), dtype),
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):
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# Initialize Kernel Context
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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_N, block_K), 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 ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=0):
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# Copy tile of A
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# This is a sugar syntax for parallelized copy
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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.clear(A_shared)
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# Demonstrate parallelized copy from global to shared for B
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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# Perform a tile-level GEMM on the shared buffers
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# Currently we dispatch to the cute/hip on Nvidia/AMD GPUs
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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# Copy result back to global memory
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T.copy(C_local, C[by * block_M, bx * block_N])
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return main
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def run_matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
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program = matmul(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
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kernel = tilelang.compile(
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program, out_idx=[2], target="cuda", pass_configs={"tl.disable_tma_lower": True})
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import torch
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from tilelang.utils import map_torch_type
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a = torch.randn((M, K), dtype=map_torch_type(dtype)).cuda()
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b = torch.randn((N, K), dtype=map_torch_type(dtype)).cuda()
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c = kernel(a, b)
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assert torch.allclose(c, torch.zeros_like(c))
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def test_matmul():
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run_matmul(1024, 1024, 1024, 128, 128, 32)
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if __name__ == "__main__":
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test_matmul()
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