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103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
from tilelang import tvm as tvm
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import tilelang.testing
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from tilelang.cache import cached
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import tilelang.language as T
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def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
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"""
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Defines a matrix multiplication primitive function using tilelang.
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This function constructs a tilelang primitive function for matrix multiplication,
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optimized for execution on hardware accelerators. It utilizes shared memory and
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fragment memory for performance.
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Args:
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M (int): Number of rows in matrix A and C.
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N (int): Number of columns in matrix B and C.
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K (int): Number of columns in matrix A and rows in matrix B.
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block_M (int): Block size for M dimension in shared memory and fragment.
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block_N (int): Block size for N dimension in shared memory and fragment.
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block_K (int): Block size for K dimension in shared memory.
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dtype (str, optional): Data type for input matrices A and B, and output C. Defaults to "float16".
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accum_dtype (str, optional): Accumulation data type for internal computations. Defaults to "float".
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Returns:
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T.PrimFunc: A tilelang primitive function representing the matrix multiplication.
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"""
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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), 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)
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B_shared = T.alloc_shared((block_K, block_N), 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=3):
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T.copy(A[by * block_M, k * block_K], A_shared)
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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)
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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_cache_matmul():
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"""
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Demonstrates the usage of the cached matrix multiplication kernel.
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This function defines a reference PyTorch matrix multiplication,
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creates a cached kernel from the tilelang matmul function,
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runs the kernel with random input tensors, compares the output with the reference,
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and prints the CUDA kernel source code.
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"""
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def ref_program(A, B):
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"""
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Reference PyTorch matrix multiplication for comparison.
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"""
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import torch
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.half) # Assuming dtype="float16" in matmul
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return C
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func = matmul(1024, 1024, 1024, 128, 128, 32)
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kernel = cached(func, [2], execution_backend="cython")
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import torch
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a = torch.randn(1024, 1024).cuda().half()
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b = torch.randn(1024, 1024).cuda().half()
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c = kernel(a, b)
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print("\nOutput from Cached Kernel:")
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print(c)
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ref_c = ref_program(a, b)
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print("\nReference PyTorch Output:")
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print(ref_c)
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torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
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print("\nOutputs are close (within tolerance).")
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# Get CUDA Source
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print("\nCUDA Kernel Source:")
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print(kernel.get_kernel_source())
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def test_cache_matmul_f16f16f16_nn():
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"""
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Test function for cached matrix multiplication (float16 inputs, float16 output, no transpose).
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"""
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run_cache_matmul()
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if __name__ == "__main__":
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tilelang.testing.main()
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