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Only accept continguous tensors in TopK for cuda #9441

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f0c8f46
Only accept continguous tensors in TopK for cuda
t-vi Jul 14, 2018
66c4c71
use contiguous tensor instead of failing
t-vi Jul 14, 2018
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14 changes: 9 additions & 5 deletions aten/src/THC/generic/THCTensorTopK.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -5,21 +5,23 @@
THC_API void THCTensor_(topk)(THCState* state,
THCTensor *topK,
THCudaLongTensor *indices,
THCTensor *input,
THCTensor *input_,
int64_t k, int dim, int dir, int sorted) {
THAssert(topK != NULL && indices != NULL && input != NULL);
THCAssertSameGPU(THCTensor_(checkGPU)(state, 3, topK, indices, input));
THAssert(topK != NULL && indices != NULL && input_ != NULL);
THCAssertSameGPU(THCTensor_(checkGPU)(state, 3, topK, indices, input_));
THArgCheck(THCTensor_(_nDimension)(state, topK) <= MAX_CUTORCH_DIMS, 2, CUTORCH_DIM_WARNING);
int64_t dims = THCudaLongTensor__nDimension(state, indices);
THArgCheck(dims <= MAX_CUTORCH_DIMS, 3, CUTORCH_DIM_WARNING);
int numDims = THCTensor_(_nDimension)(state, input);
int numDims = THCTensor_(_nDimension)(state, input_);
THArgCheck(numDims <= MAX_CUTORCH_DIMS, 4, CUTORCH_DIM_WARNING);

THArgCheck(dim >= 0 && dim < numDims, 6, "dim not in range");

int64_t sliceSize = THCTensor_(size)(state, input, dim);
int64_t sliceSize = THCTensor_(size)(state, input_, dim);
THArgCheck(k > 0 && k <= sliceSize, 5, "k not in range for dimension");

THCTensor *input = THCTensor_(newContiguous)(state, input_);

// Build the output size, which is the dim being selected set to
// size k
THLongStorage* topKSize = THCTensor_(newSizeOf)(state, input);
Expand Down Expand Up @@ -155,6 +157,8 @@ THC_API void THCTensor_(topk)(THCState* state,
}
}

THCudaLongTensor_free(state, input);

THCudaCheck(cudaGetLastError());
}

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8 changes: 8 additions & 0 deletions test/test_torch.py
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Expand Up @@ -3194,6 +3194,14 @@ def test_topk_arguments(self):
# Make sure True isn't mistakenly taken as the 2nd dimension (interpreted as 1)
self.assertRaises(TypeError, lambda: q.topk(4, True))

@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_topk_noncontiguous_gpu(self):
t = torch.randn(20, device="cuda")[::2]
top1, idx1 = t.topk(5)
top2, idx2 = t.contiguous().topk(5)
self.assertEqual(top1, top2)
self.assertEqual(idx1, idx2)

def test_kthvalue(self):
SIZE = 50
x = torch.rand(SIZE, SIZE, SIZE)
Expand Down