68 lines
1.6 KiB
Python
68 lines
1.6 KiB
Python
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import triton
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import triton.language as tl
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import torch
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@triton.jit
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def matmul_kernel(
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A,
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B,
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C,
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M: tl.constexpr,
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N: tl.constexpr,
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K: tl.constexpr,
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BLOCK_SIZE_M: tl.constexpr,
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BLOCK_SIZE_N: tl.constexpr,
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BLOCK_SIZE_K: tl.constexpr,
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):
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# 获取当前块的索引
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pid_m = tl.program_id(0)
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pid_n = tl.program_id(1)
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# 计算当前块的起始和结束索引
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range_m = lambda: range(pid_m * BLOCK_SIZE_M, (pid_m + 1) * BLOCK_SIZE_M)
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range_n = lambda: range(pid_n * BLOCK_SIZE_N, (pid_n + 1) * BLOCK_SIZE_N)
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range_k = lambda: range(0, K)
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# 初始化结果矩阵块
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acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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# 计算矩阵乘法
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for k in range_k():
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a = tl.load(A + range_m()[:, None] * K + k)
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b = tl.load(B + k * N + range_n()[None, :])
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acc += tl.dot(a, b)
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# 将结果写回输出矩阵
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tl.store(C + range_m()[:, None] * N + range_n()[None, :], acc)
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def matmul(a, b):
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# 获取矩阵的维度
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M, K = a.shape
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K, N = b.shape
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# 创建输出矩阵
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c = torch.empty((M, N), device=a.device, dtype=a.dtype)
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# 定义块大小
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BLOCK_SIZE_M = 16
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BLOCK_SIZE_N = 16
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BLOCK_SIZE_K = 16
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# 计算网格大小
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grid = (triton.cdiv(M, BLOCK_SIZE_M), triton.cdiv(N, BLOCK_SIZE_N))
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# 启动内核
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matmul_kernel[grid](a, b, c, M, N, K, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K)
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return c
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# 示例使用
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a = torch.randn(1024, 1024, device="cuda")
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b = torch.randn(1024, 1024, device="cuda")
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c = matmul(a, b)
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print(c)
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