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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/53583 `Scalar` takes 32 bytes due to `c10::complex<double>` requires aligning to 16 bytes. Passing Scalar by reference shows about 1% improvements on instruction count. All the changes in this commit are codemoded except for the following 4 files (which code-gen signatures): ``` tools/codegen/api/cpp.py tools/codegen/api/native.py tools/codegen/api/structured.py caffe2/contrib/aten/gen_op.py ``` # Codemode ## Main Step For the codemod part, here is the main command used: ``` fastmod --extensions h '([a-zA-Z_+]\([^)]*,?\s*)Scalar (\w+)' '${1}const Scalar& ${2}' fastmod --extensions h '([a-zA-Z_+]\([^)]*,?\s*)optional<Scalar> (\w+)' '${1}const optional<Scalar>& ${2}' fastmod --extensions cpp '([a-zA-Z_+]\([^)]*,?\s*)Scalar (\w+)' '${1}const Scalar& ${2}' fastmod --extensions cpp '([a-zA-Z_+]\([^)]*,?\s*)optional<Scalar> (\w+)' '${1}const optional<Scalar>& ${2}' ``` As you can tell, it codemods both `Scalar` and `optional<Scalar>`. Apply these commands iteratively until reaching a fix-point (since one method signature might contain multiple `Scalar` parameter). In retrospect, excluding `thrid_party` and `torch/csrc/jit` would be a good idea. (I revert it manually later, see https://github.com/pytorch/pytorch/pull/53479 as an reference). ## Pre-Step Prior to applying the main command, as some `Scalar` are presented as `at::Scalar` or `c10::Scalar`, so I codemod some of them in advance. Here is an incomplete list: ``` fastmod --extensions h '([a-zA-Z_+]\([^)]*,?\s*)at::Scalar (\w+)' '${1}const at::Scalar& ${2}' fastmod --extensions cpp '([a-zA-Z_+]\([^)]*,?\s*)at::Scalar (\w+)' '${1}const at::Scalar& ${2}' fastmod --extensions h '([a-zA-Z_+]\([^)]*,?\s*)c10::optional<Scalar> (\w+)' '${1}const c10::optional<Scalar>& ${2}' fastmod --extensions cpp '([a-zA-Z_+]\([^)]*,?\s*)c10::optional<Scalar> (\w+)' '${1}const c10::optional<Scalar>& ${2}' ``` ## Fixup There are a couple of post codemod fixup. For example, `const Scalar` will be codemoded into `const const Scalar&`. `at:Scalar` will be codemoded into `at::const Scalar&` (if `Pre-step` is not done comprehensively). Here is an incomplete list: ``` fastmod --extensions cpp 'const const Scalar' 'const Scalar' fastmod --extensions h 'const const c10::optional<Scalar>' 'const c10::optional<Scalar>' fastmod --extensions cpp 'const const c10::optional<Scalar>' 'const c10::optional<Scalar>' fastmod 'at::const Scalar&' 'const at::Scalar&' ``` ## Supplementary `cu` and `mm` files also need to be codemoded, for example: ``` fastmod --extensions cu 'at::const Scalar&' 'const at::Scalar&' fastmod --extensions mm '([a-zA-Z_+]\([^)]*,?\s*)Scalar (\w+)' '${1}const Scalar& ${2}' ``` Function pointers are not codemoded. Here is an incomplete list: ``` # Cover case: using index_fill_fn = void(*)(TensorIterator & iter, int64_t dim, int64_t self_dim_size, int64_t self_dim_stride, Scalar source); fastmod --extensions h '(void\s*\(\s*\*\s*\)\([^)]*,?\s*)Scalar (\w+)' '${1}const Scalar& ${2}' # Cover case: using softplus_fn = void (*)(TensorIterator&, Scalar, Scalar); fastmod --extensions h '(void\s*\(\s*\*\s*\)\([^)]*,?\s*)Scalar([, \)])' '${1}const Scalar&${2}' fastmod --extensions cpp '(void\s*\(\s*\*\s*\)\([^)]*,?\s*)Scalar([, \)])' '${1}const Scalar&${2}' fastmod --extensions h '(void\s*\(\s*\*\s*\)\([^)]*,?\s*)optional<Scalar>([, \)])' '${1}const optional<Scalar>&${2}' ``` Some corner cases needs to be manually fixed. ghstack-source-id: 123970306 Test Plan: Imported from OSS Reviewed By: smessmer Differential Revision: D26904445 fbshipit-source-id: 8d8a002af4b5125f153a32f03c6956be7ae5671d
126 lines
4.3 KiB
C++
126 lines
4.3 KiB
C++
#include <torch/extension.h>
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#include <torch/library.h>
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using namespace at;
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static int test_int;
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Tensor get_tensor(caffe2::TypeMeta dtype, IntArrayRef size) {
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auto tensor_impl = c10::make_intrusive<TensorImpl, UndefinedTensorImpl>(
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Storage(
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Storage::use_byte_size_t(),
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0,
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at::DataPtr(nullptr, Device(DeviceType::MSNPU, 0)),
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nullptr,
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false),
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DispatchKey::MSNPU,
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dtype);
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// This is a hack to workaround the shape checks in _convolution.
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tensor_impl->set_sizes_contiguous(size);
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return Tensor(std::move(tensor_impl));
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}
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Tensor empty_override(IntArrayRef size, c10::optional<ScalarType> dtype, c10::optional<Layout> layout, c10::optional<Device> device,
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c10::optional<bool> pin_memory, c10::optional<c10::MemoryFormat> optional_memory_format) {
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test_int = 0;
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return get_tensor(scalarTypeToTypeMeta(dtype_or_default(dtype)), size);
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}
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Tensor& add_out_override(const Tensor & a, const Tensor & b , const Scalar& c, Tensor & out) {
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test_int = 1;
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return out;
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}
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Tensor fake_convolution(
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const Tensor& input, const Tensor& weight, const c10::optional<Tensor>& bias,
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IntArrayRef stride, IntArrayRef padding, IntArrayRef dilation,
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bool transposed, IntArrayRef output_padding, int64_t groups) {
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test_int = 2;
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// Only the first 2 dimension of output shape is correct.
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return get_tensor(input.dtype(), {input.size(0), weight.size(0), input.size(2), input.size(3)});
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}
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std::tuple<Tensor,Tensor,Tensor> fake_convolution_backward(
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const Tensor & grad_output, const Tensor & input, const Tensor & weight,
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IntArrayRef stride, IntArrayRef padding,
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IntArrayRef dilation, bool transposed, IntArrayRef output_padding,
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int64_t groups, std::array<bool,3> output_mask) {
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test_int = 3;
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return std::tuple<Tensor, Tensor, Tensor>(
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get_tensor(input.dtype(), input.sizes()),
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get_tensor(weight.dtype(), weight.sizes()),
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get_tensor(input.dtype(), {}));
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}
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TORCH_LIBRARY_IMPL(aten, MSNPU, m) {
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m.impl("empty.memory_format", empty_override);
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m.impl("add.out", add_out_override);
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m.impl("convolution_overrideable", fake_convolution);
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m.impl("convolution_backward_overrideable", fake_convolution_backward);
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}
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// TODO: Extend this to exercise multi-device setting. In that case,
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// we need to add a thread local variable to track the current device.
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struct MSNPUGuardImpl final : public c10::impl::DeviceGuardImplInterface {
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static constexpr DeviceType static_type = DeviceType::MSNPU;
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MSNPUGuardImpl() {}
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MSNPUGuardImpl(DeviceType t) {
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AT_ASSERT(t == DeviceType::MSNPU);
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}
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DeviceType type() const override {
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return DeviceType::MSNPU;
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}
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Device exchangeDevice(Device d) const override {
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AT_ASSERT(d.type() == DeviceType::MSNPU);
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AT_ASSERT(d.index() == 0);
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return d;
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}
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Device getDevice() const override {
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return Device(DeviceType::MSNPU, 0);
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}
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void setDevice(Device d) const override {
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AT_ASSERT(d.type() == DeviceType::MSNPU);
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AT_ASSERT(d.index() == 0);
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}
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void uncheckedSetDevice(Device d) const noexcept override {
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}
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Stream getStream(Device d) const noexcept override {
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return Stream(Stream::DEFAULT, Device(DeviceType::MSNPU, 0));
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}
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Stream exchangeStream(Stream s) const noexcept override {
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return Stream(Stream::DEFAULT, Device(DeviceType::MSNPU, 0));
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}
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DeviceIndex deviceCount() const noexcept override {
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return 1;
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}
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// Event-related functions
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void record(void** event,
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const Stream& stream,
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const DeviceIndex device_index,
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const EventFlag flag) const override {
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TORCH_CHECK(false, "MSNPU backend doesn't support events.");
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}
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void block(
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void* event,
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const Stream& stream) const override {
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TORCH_CHECK(false, "MSNPU backend doesn't support events.");
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}
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bool queryEvent(void* event) const override {
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TORCH_CHECK(false, "MSNPU backend doesn't support events.");
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}
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void destroyEvent(
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void* event,
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const DeviceIndex device_index) const noexcept override { }
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};
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constexpr DeviceType MSNPUGuardImpl::static_type;
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C10_REGISTER_GUARD_IMPL(MSNPU, MSNPUGuardImpl);
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int get_test_int() {
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return test_int;
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("get_test_int", &get_test_int);
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}
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