mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-22 06:50:25 +08:00
Wenhao Xie
50e789dd14
* [Feature] Support None type as input for T.ptr and T.Tensor * lint * lint * lint * lint fix
117 lines
3.9 KiB
Python
117 lines
3.9 KiB
Python
import torch
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from tilelang import tvm as tvm
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import tilelang.testing
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import tilelang as tl
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import tilelang.language as T
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from tilelang.utils import map_torch_type
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@tl.jit
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def ptr_null_test(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_ptr: T.ptr,
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b_ptr: T.ptr,
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c_ptr: T.ptr,
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bias_ptr: T.ptr,
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m: T.int32,
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n: T.int32,
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k: T.int32,
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with_bias: T.bool,
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):
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A = T.make_tensor(a_ptr, (m, k), dtype)
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B = T.make_tensor(b_ptr, (k, n), dtype)
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C = T.make_tensor(c_ptr, (m, n), accum_dtype)
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Bias = T.make_tensor(bias_ptr, (n), accum_dtype)
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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=3):
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# Copy tile of A
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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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if with_bias:
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for i, j in T.Parallel(block_M, block_N):
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C_local[i, j] += Bias[bx * block_N + j]
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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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@tl.jit
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def tensor_null_test(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((K, N), dtype),
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C: T.Tensor((M, N), accum_dtype),
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Bias: T.Tensor((N), accum_dtype),
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with_bias: T.bool,
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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=3):
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# Copy tile of A
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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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if with_bias:
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for i, j in T.Parallel(block_M, block_N):
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C_local[i, j] += Bias[bx * block_N + j]
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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_test(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
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func = ptr_null_test(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
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a = torch.randn(M, K, device="cuda", dtype=map_torch_type(dtype))
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b = torch.randn(N, K, device="cuda", dtype=map_torch_type(dtype))
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c = torch.zeros(M, N, device="cuda", dtype=map_torch_type(accum_dtype))
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d = torch.randn(N, device="cuda", dtype=map_torch_type(accum_dtype))
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func(a, b, c, None, M, N, K, False)
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ref_no_bias = (a @ b.T).to(map_torch_type(accum_dtype))
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ref_with_bias = ref_no_bias + d
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torch.testing.assert_close(c, ref_no_bias, atol=1e-2, rtol=1e-2)
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func(a, b, c, d, M, N, K, True)
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torch.testing.assert_close(c, ref_with_bias, atol=1e-2, rtol=1e-2)
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func = tensor_null_test(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
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func(a, b, c, None, False)
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torch.testing.assert_close(c, ref_no_bias, atol=1e-2, rtol=1e-2)
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func(a, b, c, d, True)
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torch.testing.assert_close(c, ref_with_bias, atol=1e-2, rtol=1e-2)
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def test_nullptr():
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run_test(1024, 1024, 1024, 128, 128, 32)
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if __name__ == "__main__":
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tilelang.testing.main()
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