mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-22 06:50:25 +08:00
Yuqi DongandLei Wang
4ca6c13117
* [CI]:Reduce test shapes to avoid OOM errors during CI. * rabbit * Increase number of processes for pytest from 2 to 4 --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>
112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
import tilelang
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import tilelang.language as T
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import tilelang.testing
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from tilelang import tvm as tvm
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@tilelang.jit(pass_configs={"tl.disable_dynamic_tail_split": True, "tl.dynamic_alignment": 8})
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def matmul_dynamic_mnk(
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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accum_dtype,
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num_stages,
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threads,
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):
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M = tvm.te.var("m")
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N = tvm.te.var("n")
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K = tvm.te.var("k")
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A_shape = (K, M) if trans_A else (M, K)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
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B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
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@T.prim_func
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def dynamic_matmul(
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A: T.Tensor(A_shape, in_dtype),
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B: T.Tensor(B_shape, in_dtype),
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C: T.Tensor((M, N), out_dtype),
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):
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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(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_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=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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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, trans_A, trans_B)
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T.copy(C_local, C[by * block_M, bx * block_N])
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return dynamic_matmul
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def matmul_dynamic(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype,
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accum_dtype, num_stages, threads):
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print(
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f"M: {M}, N: {N}, K: {K}, block_M: {block_M}, block_N: {block_N}, block_K: {block_K}, trans_A: {trans_A}, trans_B: {trans_B}, in_dtype: {in_dtype}, out_dtype: {out_dtype}, accum_dtype: {accum_dtype}, num_stages: {num_stages}, threads: {threads}"
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)
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kernel = matmul_dynamic_mnk(block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype,
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accum_dtype, num_stages, threads)
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import torch
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if trans_A:
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A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
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if trans_B:
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B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype))
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kernel(A, B, C)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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# Get Reference Result
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ref_c = ref_program(A, B)
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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print("Kernel output matches PyTorch reference.")
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profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal)
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latency = profiler.do_bench(input_tensors=[A, B, C])
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print(f"Latency: {latency} ms")
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def main(M=16384, N=16384, K=16384):
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block_M, block_N, block_K = 128, 128, 32
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trans_A, trans_B = False, False
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in_dtype, out_dtype = "float16", "float16"
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accum_dtype = "float32"
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num_stages = 3
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threads = 128
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matmul_dynamic(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype,
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accum_dtype, num_stages, threads)
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if __name__ == "__main__":
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main()
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