mirror of
https://github.com/zebrajr/pytorch.git
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041b4431b2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/55163 Test Plan: Sandcastle Reviewed By: ngimel Differential Revision: D27448156 fbshipit-source-id: 585da57d4de91c692b6360d65f7b8a66deb0f8c1
109 lines
3.3 KiB
C++
109 lines
3.3 KiB
C++
#include <torch/csrc/utils/tensor_apply.h>
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#include <ATen/TensorUtils.h>
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#include <ATen/ExpandUtils.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/utils/python_numbers.h>
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#include <torch/csrc/utils/python_scalars.h>
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using namespace at;
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namespace torch { namespace utils {
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struct StridedData {
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StridedData(const Tensor & tensor)
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: data(tensor.data_ptr())
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, strides(tensor.strides())
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, elementSize(tensor.element_size()) {}
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void* data;
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IntArrayRef strides;
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int64_t elementSize;
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void step(int dim) {
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data = (char*)data + (strides[dim] * elementSize);
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}
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};
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template<size_t N>
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static void recursive_apply(IntArrayRef sizes, ScalarType scalarType, int64_t dim,
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PyObject* fn, std::array<StridedData, N> strided_data) {
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int64_t ndim = sizes.size();
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if (dim == ndim) {
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auto args = THPObjectPtr(PyTuple_New(N));
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if (!args) throw python_error();
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for(const auto i : c10::irange(N)) {
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PyObject* arg = load_scalar(strided_data[i].data, scalarType);
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if (!arg) throw python_error();
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PyTuple_SET_ITEM(args.get(), i, arg);
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}
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auto ret = THPObjectPtr(PyObject_CallObject(fn, args.get()));
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if (!ret) throw python_error();
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store_scalar(strided_data[0].data, scalarType, ret.get());
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return;
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}
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auto n = sizes[dim];
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for (int64_t i = 0; i < n; i++) {
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recursive_apply(sizes, scalarType, dim + 1, fn, strided_data);
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for (auto& td : strided_data) {
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td.step(dim);
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}
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}
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}
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Tensor & apply_(Tensor & self, PyObject* fn) {
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if (self.is_meta()) {
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return self; // Just skip
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}
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if (!self.device().is_cpu()) {
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throw TypeError("apply_ is only implemented on CPU tensors");
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}
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auto scalarType = self.scalar_type();
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recursive_apply<1>(self.sizes(), scalarType, 0, fn, {{ self }});
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return self;
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}
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Tensor & map_(Tensor & self, const Tensor & other_, PyObject* fn) {
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if (!other_.options().type_equal(self.options())) {
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throw TypeError("map_: expected %s for 'other' (got %s)",
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self.toString().c_str(), other_.toString().c_str());
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}
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if (self.is_meta()) {
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return self; // Just skip
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}
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if (!self.device().is_cpu()) {
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throw TypeError("map_ is only implemented on CPU tensors");
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}
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Tensor other;
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std::tie(other) = expand_inplace(self, other_, "map_");
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auto scalarType = self.scalar_type();
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recursive_apply<2>(self.sizes(), scalarType, 0, fn, {{ self, other }});
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return self;
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}
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Tensor & map2_(Tensor & self, const Tensor & x_, const Tensor & y_, PyObject* fn) {
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if (!x_.options().type_equal(self.options())) {
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throw TypeError("map2_: expected %s for argument 'x' (got %s)",
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self.toString().c_str(), x_.toString().c_str());
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}
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if (!y_.options().type_equal(self.options())) {
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throw TypeError("map2_: expected %s for argument 'y' (got %s)",
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self.toString().c_str(), y_.toString().c_str());
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}
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if (self.is_meta()) {
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return self; // Just skip
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}
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if (!self.device().is_cpu() || !x_.device().is_cpu() || !y_.device().is_cpu()) {
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throw TypeError("map2_ is only implemented on CPU tensors");
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}
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Tensor other1, other2;
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std::tie(other1, other2) = expand_inplace(self, x_, y_, "map2_");
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auto scalarType = self.scalar_type();
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recursive_apply<3>(self.sizes(), scalarType, 0, fn, {{ self, other1, other2 }});
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return self;
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}
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}} // namespace torch::utils
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