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Fix nullptr math & clean segment_csr_cpu.cpp #292

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Fix nullptr math & clean segment_csr_cpu.cpp #292

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32 changes: 17 additions & 15 deletions csrc/cpu/segment_csr_cpu.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ segment_csr_cpu(torch::Tensor src, torch::Tensor indptr,
sizes[i] = src.size(i);
indptr = indptr.expand(sizes);

auto dim = indptr.dim() - 1;
const auto dim = indptr.dim() - 1;

src = src.contiguous();

Expand Down Expand Up @@ -50,38 +50,40 @@ segment_csr_cpu(torch::Tensor src, torch::Tensor indptr,
return std::make_tuple(out, arg_out);
}

auto N = out.size(dim) * (indptr.numel() / indptr.size(-1));
auto K = out.numel() / N;
auto E = src.size(dim);
const auto N = out.size(dim) * (indptr.numel() / indptr.size(-1));
const auto K = out.numel() / N;
const auto E = src.size(dim);

auto indptr_info = getTensorInfo<int64_t>(indptr);
auto stride = indptr_info.strides[indptr_info.dims - 1];
const auto indptr_info = getTensorInfo<int64_t>(indptr);
const auto stride = indptr_info.strides[indptr_info.dims - 1];
std::vector<int64_t> args(K);
AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, src.scalar_type(), "_", [&] {
auto src_data = src.data_ptr<scalar_t>();
const auto src_data = src.data_ptr<scalar_t>();
auto out_data = out.data_ptr<scalar_t>();

std::vector<scalar_t> vals(K);
int64_t row_start, row_end;
AT_DISPATCH_REDUCTION_TYPES(reduce, [&] {
for (auto n = 0; n < N; n++) {
auto offset = IndexPtrToOffset<int64_t>::get(n, indptr_info);
row_start = indptr_info.data[offset];
row_end = indptr_info.data[offset + stride];
const auto offset1 = IndexPtrToOffset<int64_t>::get(n, indptr_info);
const auto row_start = indptr_info.data[offset1];
const auto row_end = indptr_info.data[offset1 + stride];

offset = (n / (indptr.size(-1) - 1)) * E * K;
const auto offset2 = (n / (indptr.size(-1) - 1)) * E * K;
for (auto k = 0; k < K; k++)
vals[k] = Reducer<scalar_t, REDUCE>::init();

for (auto e = row_start; e < row_end; e++)
for (auto k = 0; k < K; k++)
Reducer<scalar_t, REDUCE>::update(
&vals[k], src_data[offset + e * K + k], &args[k], e);
&vals[k], src_data[offset2 + e * K + k], &args[k], e);

for (auto k = 0; k < K; k++)
Reducer<scalar_t, REDUCE>::write(out_data + n * K + k, vals[k],
if (reduce2REDUCE.at(reduce) == MIN || reduce2REDUCE.at(reduce) == MAX) {
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@rusty1s rusty1s May 4, 2022

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It looks like we skip writing to the destination here for any other reduction (which is likely unintented - see test failures).

for (auto k = 0; k < K; k++){
Reducer<scalar_t, REDUCE>::write(out_data + n * K + k, vals[k],
arg_out_data + n * K + k, args[k],
row_end - row_start);
}
}
}
});
});
Expand Down