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f62bc01dfe
Summary: I got some tensor->variable conversion exceptions from `torch/csrc/autograd/variable.h`, which used the `TORCH_ASSERTM` macros instead of `AT_CHECK`, so they didn't have backtraces. This was such a substantial loss for debugability that I decided to update the whole codebase to use the backtrace-enabled ATen macros instead of `TORCH_ASSERT` and `JIT_ASSERT`, the latter having been an alias of the former. ezyang apaszke zdevito Pull Request resolved: https://github.com/pytorch/pytorch/pull/9575 Differential Revision: D8924566 Pulled By: goldsborough fbshipit-source-id: 7a4013b13eec9dbf024cef94cf49fca72f61d441
51 lines
1.3 KiB
C++
51 lines
1.3 KiB
C++
#pragma once
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#include "torch/csrc/utils/functional.h"
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#include <ATen/ATen.h>
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#include <utility>
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namespace torch { namespace utils {
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inline at::Tensor flatten_dense_tensors(at::TensorList tensors) {
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static auto flatten = [](const at::Tensor &t) { return t.contiguous().view({-1}); };
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if (tensors.size() == 1)
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return flatten(tensors[0]);
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return at::cat(fmap(tensors, flatten));
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}
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inline std::vector<at::Tensor> unflatten_dense_tensors(const at::Tensor& flat, at::TensorList tensors) {
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std::vector<at::Tensor> outputs;
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outputs.reserve(tensors.size());
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size_t offset = 0;
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for (const auto & tensor : tensors) {
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auto numel = tensor.numel();
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outputs.push_back(flat.narrow(0, offset, numel).view(tensor.sizes()));
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offset += numel;
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}
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return outputs;
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}
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struct TensorGroup {
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std::vector<at::Tensor> tensors;
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size_t size = 0;
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at::Type& type() {
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AT_ASSERT(!tensors.empty());
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return tensors[0].type();
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}
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};
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std::vector<TensorGroup> take_tensors(at::TensorList tensors, size_t size_limit);
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void reorder_tensors_like(std::vector<at::Tensor>& tensors, at::TensorList order);
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std::pair<at::Tensor, at::Tensor> flatten_sparse_tensors(at::TensorList tensors);
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std::vector<at::Tensor> unflatten_sparse_tensors(
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const at::Tensor& flat_indices,
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const at::Tensor& flat_values,
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at::TensorList tensors);
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}}
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