[EXAMPLES][TUTORIAL] Changed to new triton.kernel API
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Philippe Tillet
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c33d6d15f5
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4ccd78f1a6
@@ -16,16 +16,14 @@ class _dot(torch.autograd.Function):
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// accumulator
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float c[TM, TN] = 0;
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//pointers
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// pointers
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TYPE* pa[TM, TK] = A + rk[newaxis, :] * 1 + rm[:, newaxis] * lda;
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TYPE* pb[TK, TN] = B + rk[:, newaxis] * ldb + rn[newaxis, :] * 1;
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for(int k=K; k>0; k-=TK) {
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TYPE a[TM, TK] = *pa;
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TYPE b[TK, TN] = *pb;
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c += a @ b;
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pa = pa + TK * 1;
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pb = pb + TK * ldb;
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}
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@@ -40,32 +38,35 @@ class _dot(torch.autograd.Function):
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c = _dot._call(a,b)
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return c
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kernel = dict()
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@staticmethod
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def _call(a, b):
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# shapes
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M, K = a.shape
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K, N = b.shape
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# leading dimension
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lda = K
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ldb = N
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ldc = N
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dtype = a.dtype
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# create kernel if necessary
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if dtype not in _dot.kernel:
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defines = {
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'TYPE' : dtype,
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'TM' : [64, 128],
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'TN' : [64, 128],
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'TK' : [8, 16],
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}
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_dot.kernel[dtype] = triton.kernel(_dot.src, num_warps=[2, 4], defines=defines)
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kernel = _dot.kernel[dtype]
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# allocate output
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c = triton.empty([M,N], dtype=dtype)
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grid = lambda opt: [triton.cdiv(M, opt.d('TM')), triton.cdiv(N, opt.d('TN'))]
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defines= {
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'TYPE' : dtype,
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'TM' : [32,64,128],
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'TN' : [32,64,128],
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'TK' : [8],
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}
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_dot.kernel = triton.kernel(_dot.src, defines=defines)
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_dot.kernel(a, b, c, M, N, K, lda, ldb, ldc,
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grid=grid, num_warps=4, defines=defines)
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# enqueue
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grid = lambda opt: [triton.cdiv(M, opt.d('TM')),
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triton.cdiv(N, opt.d('TN'))]
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kernel(a, b, c, M, N, K, lda, ldb, ldc, grid=grid)
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return c
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@@ -81,4 +82,4 @@ b = torch.rand((K, N)).cuda()
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zc = torch.matmul(a,b)
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zc_ = dot(a,b)
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print(torch.allclose(zc, zc_))
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print(torch.allclose(zc, zc_))
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