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https://github.com/tile-ai/tilelang.git
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senlyu163
f4f87f468d
* Remove JIT decorator from elementwise_add function in examples * fix kernel compilation without autotune * Refactor main function to accept parameters and update tests for autotune option * Refactor autotune test function for morden style
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
import argparse
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import itertools
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import torch
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import tilelang
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import tilelang.language as T
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def ref_program(x, y):
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return x + y
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def get_configs():
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block_M = [64, 128, 256]
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block_N = [64, 128, 256]
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threads = [64, 128, 256]
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configs = list(itertools.product(block_M, block_N, threads))
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return [{"block_M": bm, "block_N": bn, "threads": th} for bm, bn, th in configs]
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@tilelang.autotune(configs=get_configs())
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@tilelang.jit(out_idx=[-1])
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def elementwise_add(M, N, block_M, block_N, in_dtype, out_dtype, threads):
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@T.prim_func
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def elem_add(A: T.Tensor((M, N), in_dtype), B: T.Tensor((M, N), in_dtype), C: T.Tensor((M, N), out_dtype)):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_N), in_dtype)
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B_shared = T.alloc_shared((block_M, block_N), in_dtype)
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C_local = T.alloc_fragment((block_M, block_N), out_dtype)
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C_shared = T.alloc_shared((block_M, block_N), out_dtype)
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T.copy(A[by * block_M, bx * block_N], A_shared)
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T.copy(B[by * block_M, bx * block_N], B_shared)
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for local_y, local_x in T.Parallel(block_M, block_N):
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C_local[local_y, local_x] = A_shared[local_y, local_x] + B_shared[local_y, local_x]
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T.copy(C_local, C_shared)
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T.copy(C_shared, C[by * block_M, bx * block_N])
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return elem_add
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def main(M=1024, N=1024, use_autotune=False):
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a = torch.randn(M, N, dtype=torch.float32, device="cuda")
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b = torch.randn(M, N, dtype=torch.float32, device="cuda")
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if use_autotune:
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kernel = elementwise_add(M, N, in_dtype="float32", out_dtype="float32")
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else:
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# Default config
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config = {"block_M": 32, "block_N": 32, "threads": 128}
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kernel = elementwise_add(M, N, **config, in_dtype="float32", out_dtype="float32")
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out = kernel(a, b)
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torch.testing.assert_close(out, ref_program(a, b), rtol=1e-2, atol=1e-2)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--m", type=int, default=1024)
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parser.add_argument("--n", type=int, default=1024)
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parser.add_argument("--use_autotune", action="store_true", default=False)
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args, _ = parser.parse_known_args()
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main(args.m, args.n, args.use_autotune)
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