mirror of
https://github.com/saymrwulf/pytorch.git
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6ecc1a4c4f
Summary: This PR suppresses clang-tidy warnings in the codebase (for now) so that we can re-enable clang-tidy checks on master. I ran this script to add the `NOLINTNEXTLINE` comments (on a devserver): ```bash python3 setup.py develop # Uses same script that's run on CI and adds the -j (parallel), -s (add comments), -k (continue if diagnostic errors are found) options python3 tools/clang_tidy.py \ -j \ -s \ -k \ -v \ --paths torch/csrc/ \ -g"-torch/csrc/jit/passes/onnx/helper.cpp" \ -g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \ -g"-torch/csrc/jit/serialization/onnx.cpp" \ -g"-torch/csrc/jit/serialization/export.cpp" \ -g"-torch/csrc/jit/serialization/import.cpp" \ -g"-torch/csrc/jit/serialization/import_legacy.cpp" \ -g"-torch/csrc/onnx/init.cpp" \ -g"-torch/csrc/cuda/nccl.*" \ -g"-torch/csrc/cuda/python_nccl.cpp" \ -g"-torch/csrc/autograd/FunctionsManual.cpp" \ -g"-torch/csrc/generic/*.cpp" \ -g"-torch/csrc/jit/codegen/cuda/runtime/*" \ -g"-torch/csrc/deploy/interpreter/interpreter.cpp" \ -g"-torch/csrc/deploy/interpreter/interpreter.h" \ -g"-torch/csrc/deploy/interpreter/interpreter_impl.h" \ -g"-torch/csrc/deploy/interpreter/test_main.cpp" ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/60649 Test Plan: Verified changes by re-running the script (without the `-s` option) and seeing no warnings/errors. Reviewed By: walterddr, janeyx99 Differential Revision: D29504258 Pulled By: 1ntEgr8 fbshipit-source-id: 78310b30ee8213b73ddb4771ad874665323e7a4e
80 lines
2.7 KiB
C++
80 lines
2.7 KiB
C++
#pragma once
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#include <ATen/core/functional.h>
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <ATen/ATen.h>
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#include <utility>
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#include <c10/core/TensorOptions.h>
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namespace torch { namespace utils {
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/// Generate an ID for a combination of tensor backend + scalar type to be used
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/// when ordering tensors ('like' tensors are grouped by pulling out their
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/// backend + scalar type, so this function combines that into a single number)
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inline size_t type_id(const at::Tensor& tensor) {
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return static_cast<size_t>(tensor.options().backend()) *
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static_cast<size_t>(at::ScalarType::NumOptions) +
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static_cast<size_t>(tensor.scalar_type());
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}
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inline at::Tensor flatten_dense_tensors(at::TensorList tensors) {
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return at::flatten_dense_tensors(tensors);
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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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return at::unflatten_dense_tensors(flat, tensors);
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}
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// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
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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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size_t type_id() {
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AT_ASSERT(!tensors.empty());
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return ::torch::utils::type_id(tensors[0]);
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}
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const at::TensorOptions options() {
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AT_ASSERT(!tensors.empty());
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return tensors[0].options();
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}
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};
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// Helper function that takes a list of tensors and splits them into tensor
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// groups by the size limit and outputs these tensor groups. If the input
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// tensors are of different tensor types, they will be split into different
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// groups as well.
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//
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// Two options of splitting provided to the user,
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//
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// Imagine the size_limit is 256 and the list of input tensors are:
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// tensor_a(fp16 - 128 bytes),
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// tensor_b(fp32 - 256 bytes),
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// tensor_c(fp16 - 128 bytes),
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//
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// when fine_grained == false:
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// The function will read the list of tensors sequentially and accumulate
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// enough tensors for each data type until the size_limit, therefore:
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// it will output: {{tensor_a, tensor_c}, {tensor_b}}
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//
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// when fine_grained == true:
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// The function will read the list of tensors sequentially and accumulate
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// enough tensors for all data types until the size_limit, and then split
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// the accumulated tensors into different groups by data types, therefore:
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// it will output: {{tensor_a}, {tensor_b}, {tensor_c}}
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TORCH_API std::vector<TensorGroup> take_tensors(
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at::TensorList tensors,
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size_t size_limit,
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bool fine_grained = false);
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TORCH_API void reorder_tensors_like(std::vector<at::Tensor>& tensors, at::TensorList order);
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TORCH_API std::pair<at::Tensor, at::Tensor> flatten_sparse_tensors(at::TensorList tensors);
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TORCH_API 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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