mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-23 07:00:29 +08:00
Tong WU
0c7e74196e
* [Cleanup] Remove `tilelang.disable_cache()` calls from example scripts * lint * lint
85 lines
2.8 KiB
Python
85 lines
2.8 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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from tilelang.autotuner import AutoTuner
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def ref_program(x, y):
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return x + y
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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(
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(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 get_configs(M, N):
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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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def get_best_config(M, N):
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def kernel(block_M=None, block_N=None, threads=None):
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return elementwise_add(M, N, block_M, block_N, "float32", "float32", threads)
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autotuner = AutoTuner.from_kernel(
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kernel=kernel, configs=get_configs(M, N)).set_compile_args(
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out_idx=[-1],
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target="cuda",
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).set_profile_args(
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supply_type=tilelang.TensorSupplyType.Auto,
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ref_prog=ref_program,
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skip_check=False,
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)
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return autotuner.run(warmup=3, rep=20)
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def 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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M, N = args.m, args.n
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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 args.use_autotune:
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result = get_best_config(M, N)
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kernel = result.kernel
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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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main()
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