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#include "segment_csr_cpu.h"
#include "index_info.h"
#include "reducer.h"
#include "utils.h"
#include <ATen/OpMathType.h>
std::tuple<torch::Tensor, torch::optional<torch::Tensor>>
segment_csr_cpu(torch::Tensor src, torch::Tensor indptr,
torch::optional<torch::Tensor> optional_out,
std::string reduce) {
CHECK_CPU(src);
CHECK_CPU(indptr);
if (optional_out.has_value())
CHECK_CPU(optional_out.value());
CHECK_INPUT(src.dim() >= indptr.dim());
auto sizes = indptr.sizes().vec();
for (auto i = 0; i < indptr.dim() - 1; i++)
sizes[i] = src.size(i);
indptr = indptr.expand(sizes);
auto dim = indptr.dim() - 1;
src = src.contiguous();
torch::Tensor out;
if (optional_out.has_value()) {
out = optional_out.value().contiguous();
for (auto i = 0; i < out.dim(); i++)
if (i != dim)
CHECK_INPUT(src.size(i) == out.size(i));
CHECK_INPUT(src.numel() == 0 || out.size(dim) == indptr.size(dim) - 1);
} else {
sizes = src.sizes().vec();
sizes[dim] = std::max<int64_t>(indptr.size(dim) - 1, 0);
out = torch::empty(sizes, src.options());
}
torch::optional<torch::Tensor> arg_out = torch::nullopt;
int64_t *arg_out_data = nullptr;
if (reduce2REDUCE.at(reduce) == MIN || reduce2REDUCE.at(reduce) == MAX) {
arg_out = torch::full(out.sizes(), src.size(dim), indptr.options());
arg_out_data = arg_out.value().data_ptr<int64_t>();
}
if (src.numel() == 0) {
if (!optional_out.has_value())
out.fill_(0);
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);
auto indptr_info = getTensorInfo<int64_t>(indptr);
auto stride = indptr_info.strides[indptr_info.dims - 1];
std::vector<int64_t> args(K);
AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, src.scalar_type(), "segment_csr_cpu", [&] {
using opmath_t = at::opmath_type<scalar_t>;
auto src_data = src.data_ptr<scalar_t>();
auto out_data = out.data_ptr<scalar_t>();
std::vector<opmath_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];
offset = (n / (indptr.size(-1) - 1)) * E * K;
for (auto k = 0; k < K; k++)
vals[k] = Reducer<opmath_t, REDUCE>::init();
for (auto e = row_start; e < row_end; e++)
for (auto k = 0; k < K; k++)
Reducer<opmath_t, REDUCE>::update(
&vals[k], static_cast<opmath_t>(src_data[offset + e * K + k]), &args[k], e);
for (auto k = 0; k < K; k++)
Reducer<scalar_t, REDUCE>::write(out_data + n * K + k, static_cast<scalar_t>(vals[k]),
arg_out_data + n * K + k, args[k],
row_end - row_start);
}
});
});
return std::make_tuple(out, arg_out);
}
torch::Tensor gather_csr_cpu(torch::Tensor src, torch::Tensor indptr,
torch::optional<torch::Tensor> optional_out) {
CHECK_CPU(src);
CHECK_CPU(indptr);
if (optional_out.has_value())
CHECK_CPU(optional_out.value());
CHECK_INPUT(src.dim() >= indptr.dim());
auto sizes = indptr.sizes().vec();
for (auto i = 0; i < indptr.dim() - 1; i++)
sizes[i] = src.size(i);
indptr = indptr.expand(sizes);
auto dim = indptr.dim() - 1;
CHECK_INPUT(src.size(dim) == 0 || src.size(dim) == indptr.size(dim) - 1);
src = src.contiguous();
torch::Tensor out;
if (optional_out.has_value()) {
out = optional_out.value().contiguous();
for (auto i = 0; i < out.dim(); i++)
if (i != dim)
CHECK_INPUT(src.size(i) == out.size(i));
} else {
auto sizes = src.sizes().vec();
if (src.numel() > 0)
sizes[dim] = *indptr.flatten()[-1].data_ptr<int64_t>();
else
sizes[dim] = 0;
out = torch::empty(sizes, src.options());
}
if (src.numel() == 0) {
if (!optional_out.has_value())
out.fill_(0);
return out;
}
auto N = src.size(dim) * (indptr.numel() / indptr.size(-1));
auto K = src.numel() / N;
auto E = out.size(dim);
auto indptr_info = getTensorInfo<int64_t>(indptr);
auto stride = indptr_info.strides[indptr_info.dims - 1];
AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, src.scalar_type(), "gather_csr_cpu", [&] {
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;
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];
for (auto k = 0; k < K; k++)
vals[k] = src_data[n * K + k];
offset = (n / (indptr.size(-1) - 1)) * E * K;
for (auto e = row_start; e < row_end; e++)
for (auto k = 0; k < K; k++)
out_data[offset + e * K + k] = vals[k];
}
});
return out;
}
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