mirror of
https://github.com/tile-ai/tilelang.git
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Yichen YanandLeiWang1999
10baa27ce7
* enable tvm-ffi for metal * copy metal codegen from tvm * Fix * upd * Compile host runtime with torch's mps * upd * add current device/hsot code * Pass basic test * fmt * add comment * no need to change device codegen * cleanup * add torch to cache key for metal * remove hardcoded compile options * Add `MarkHostMetalContextMutator` * update codegen * update and cleanup * tvm_ffi for metal by default * upd * cleanup * fix * fix * test * fix * fix test * fix * test * fix * upd * upd * fix * upd * add PrintLine helper in c codegen * use tvm's hook * Use latest tvm * test install wheels again * limit ccache to 200 MB * refactor * lint fix * resolve comments * fix --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
84 lines
2.4 KiB
Python
84 lines
2.4 KiB
Python
import tilelang
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from tilelang import tvm as tvm
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import tilelang.testing
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import tilelang.language as T
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import torch
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@tilelang.jit
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def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float32, accum_dtype=T.float32):
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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, scope="shared")
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B_shared = T.alloc_shared((block_K, block_N), dtype, scope="shared")
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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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T.copy(A[by * block_M, ko * block_K], A_shared, coalesced_width=2)
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T.copy(B[ko * block_K, bx * block_N], B_shared, coalesced_width=2)
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for i, j in T.Parallel(block_M, block_N):
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for k in T.Serial(block_K):
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C_local[i, j] += A_shared[i, k] * B_shared[k, j]
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T.copy(C_local, C[by * block_M, bx * block_N], coalesced_width=2)
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return gemm
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def assert_gemm(
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M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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dtype=T.float32,
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accum_dtype=T.float32,
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atol=1e-8,
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):
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jit_kernel = matmul(M, N, K, block_M, block_N, block_K, dtype=dtype, accum_dtype=accum_dtype)
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torch_dtype = dtype.as_torch()
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a, b = None, None
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if "int" in dtype:
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a = torch.randint(100, (M, K), dtype=torch_dtype, device="mps")
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b = torch.randint(100, (K, N), dtype=torch_dtype, device="mps")
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else:
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a = torch.randn(M, K, dtype=torch_dtype, device="mps")
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b = torch.randn(K, N, dtype=torch_dtype, device="mps")
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c = torch.zeros(M, N, dtype=torch_dtype, device="mps")
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jit_kernel(a, b, c)
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assert torch.allclose(a @ b, c, atol=atol)
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assert jit_kernel.kernel_source is not None
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@tilelang.testing.requires_metal
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def test_gemm_float32():
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assert_gemm(1024, 1024, 1024, 16, 16, 16)
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@tilelang.testing.requires_metal
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def test_gemm_float16():
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assert_gemm(1024, 1024, 1024, 16, 16, 16, dtype=T.float16, atol=1)
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@tilelang.testing.requires_metal
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def test_gemm_int32():
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assert_gemm(1024, 1024, 1024, 16, 16, 16, dtype=T.int32, atol=1)
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if __name__ == "__main__":
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if torch.mps.is_available():
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tilelang.testing.main()
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