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393ad6582d
Summary: In TorchScript and C++ extensions we currently advocate a mix of `torch::` and `at::` namespace usage. In the C++ frontend I had instead exported all symbols from `at::` and some from `c10::` into the `torch::` namespace. This is far, far easier for users to understand, and also avoid bugs around creating tensors vs. variables. The same should from now on be true for the TorchScript C++ API (for running and loading models) and all C++ extensions. Note that since we're just talking about typedefs, this change does not break any existing code. Once this lands I will update stuff in `pytorch/tutorials` too. zdevito ezyang gchanan Pull Request resolved: https://github.com/pytorch/pytorch/pull/13523 Differential Revision: D12942787 Pulled By: goldsborough fbshipit-source-id: 76058936bd8707b33d9e5bbc2d0705fc3d820763
37 lines
874 B
C++
37 lines
874 B
C++
#include <torch/nn/modules/linear.h>
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#include <torch/types.h>
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#include <torch/utils.h>
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#include <cmath>
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#include <cstdint>
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namespace torch {
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namespace nn {
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LinearOptions::LinearOptions(int64_t in, int64_t out) : in_(in), out_(out) {}
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LinearImpl::LinearImpl(LinearOptions options) : options(std::move(options)) {
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reset();
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}
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void LinearImpl::reset() {
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weight =
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register_parameter("weight", torch::empty({options.out_, options.in_}));
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if (options.with_bias_) {
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bias = register_parameter("bias", torch::empty(options.out_));
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}
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const auto stdv = 1.0 / std::sqrt(weight.size(1));
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NoGradGuard no_grad;
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for (auto& p : parameters()) {
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p->uniform_(-stdv, stdv);
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}
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}
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Tensor LinearImpl::forward(Tensor input) {
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AT_ASSERT(!options.with_bias_ || bias.defined());
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return torch::linear(input, weight, bias);
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}
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} // namespace nn
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} // namespace torch
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