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Kill THCUNN (#63429)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63429 Test Plan: Imported from OSS Reviewed By: mruberry Differential Revision: D30441308 Pulled By: ngimel fbshipit-source-id: 3ae342a2f8d5c7f8827b637c4055c5d1b0a1be26
This commit is contained in:
Peter Bell
Facebook GitHub Bot
parent
db1b27fa8d
commit
560cd88195
22 changed files with 2 additions and 376 deletions
18
BUILD.bazel
18
BUILD.bazel
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@ -409,21 +409,6 @@ filegroup(
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],
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)
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filegroup(
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name = "thcunn_srcs_cu",
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srcs = [
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"aten/src/THCUNN/BCECriterion.cu.cc",
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"aten/src/THCUNN/ELU.cu.cc",
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"aten/src/THCUNN/HardTanh.cu.cc",
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"aten/src/THCUNN/LeakyReLU.cu.cc",
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"aten/src/THCUNN/MultiMarginCriterion.cu.cc",
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"aten/src/THCUNN/SoftMarginCriterion.cu.cc",
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"aten/src/THCUNN/SoftPlus.cu.cc",
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"aten/src/THCUNN/SoftShrink.cu.cc",
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"aten/src/THCUNN/Tanh.cu.cc",
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],
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)
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filegroup(
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name = "aten_srcs_cu",
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srcs = [
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@ -573,8 +558,6 @@ cc_library(
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"aten/src/THC/**/*.cpp",
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"aten/src/THC/*.cuh",
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"aten/src/THC/generic/*.cu.cc",
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"aten/src/THCUNN/*.cuh",
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"aten/src/THCUNN/generic/*.cu.cc",
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],
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exclude = [
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"aten/src/ATen/Config.h",
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@ -716,7 +699,6 @@ cu_library(
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srcs = [
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":aten_srcs_cu",
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":thc_srcs_cu",
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":thcunn_srcs_cu",
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],
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copts = ATEN_COPTS + torch_cuda_half_options,
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visibility = ["//visibility:public"],
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@ -242,8 +242,7 @@ into the repo directory.
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* [aten](aten) - C++ tensor library for PyTorch (no autograd support)
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* [src](aten/src) - [README](aten/src/README.md)
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* [TH](aten/src/TH)
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[THC](aten/src/THC)
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[THCUNN](aten/src/THCUNN) - Legacy library code from the original
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[THC](aten/src/THC) - Legacy library code from the original
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Torch. Try not to add things here; we're slowly porting these to
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[native](aten/src/ATen/native).
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* generic - Contains actual implementations of operators,
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@ -126,7 +126,7 @@ We hope you never spend hours debugging your code because of bad stack traces or
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PyTorch has minimal framework overhead. We integrate acceleration libraries
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such as [Intel MKL](https://software.intel.com/mkl) and NVIDIA ([cuDNN](https://developer.nvidia.com/cudnn), [NCCL](https://developer.nvidia.com/nccl)) to maximize speed.
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At the core, its CPU and GPU Tensor and neural network backends
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(TH, THC, THNN, THCUNN) are mature and have been tested for years.
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are mature and have been tested for years.
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Hence, PyTorch is quite fast – whether you run small or large neural networks.
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@ -80,21 +80,14 @@ if(USE_ROCM)
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# ATen proper)
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set(AT_CUDA_ENABLED 1)
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add_subdirectory(src/THH)
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add_subdirectory(src/THHUNN)
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message("ROCm is enabled.")
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elseif(USE_CUDA)
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set(AT_CUDA_ENABLED 1)
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add_subdirectory(src/THC)
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add_subdirectory(src/THCUNN)
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else()
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message("disabling CUDA because USE_CUDA is set false")
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set(AT_CUDA_ENABLED 0)
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endif()
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if(NOT USE_CUDA)
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# we still parse THCUNN even if cuda is disabled to make sure to
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# install it
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install(FILES src/THCUNN/generic/THCUNN.h DESTINATION "${ATEN_INSTALL_INCLUDE_SUBDIR}/THCUNN/generic")
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endif()
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if(NOT USE_NNPACK)
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set(AT_NNPACK_ENABLED 0)
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@ -282,7 +282,6 @@ bool geometry_is_contiguous(IntArrayRef sizes, IntArrayRef strides) {
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return contig_if_nonempty;
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}
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// Correspond to THCUNN_check_dim_size/THNN_check_dim_size
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void check_dim_size(
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const Tensor& tensor,
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int64_t dim,
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@ -144,7 +144,6 @@ TORCH_API void* maybe_data_ptr(const TensorArg& tensor);
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// on whether a subgeometry is contiguous.
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TORCH_API bool geometry_is_contiguous(IntArrayRef sizes, IntArrayRef strides);
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// Correspond to THCUNN_check_dim_size/THNN_check_dim_size
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TORCH_API void check_dim_size(
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const Tensor& tensor,
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int64_t dim,
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@ -7,7 +7,6 @@
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#include <ATen/ExpandUtils.h>
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#include <THC/THC.h>
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#include <THC/THCTensor.hpp>
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#include <THCUNN/THCUNN.h>
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#undef THNN_
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#undef THCIndexTensor_
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#include <ATen/DeviceGuard.h>
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@ -2,9 +2,6 @@
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#include <ATen/ATen.h>
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// Contents of this file are copied from THCUNN/common.h for the ease of porting
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// THCUNN functions into ATen.
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namespace at { namespace cuda { namespace detail {
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// CUDA: grid stride looping
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@ -7,7 +7,6 @@ multiple variants of the library, summarized here:
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* TH = TorcH
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* THC = TorcH Cuda
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* THCS = TorcH Cuda Sparse (now defunct)
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* THCUNN = TorcH CUda Neural Network (see cunn)
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* THNN = TorcH Neural Network (now defunct)
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* THS = TorcH Sparse (now defunct)
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@ -1,10 +0,0 @@
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set(ATen_CUDA_SRCS ${ATen_CUDA_SRCS}
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PARENT_SCOPE)
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set(ATen_CUDA_INCLUDE ${ATen_CUDA_INCLUDE}
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"${CMAKE_CURRENT_SOURCE_DIR}"
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PARENT_SCOPE)
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install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
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DESTINATION ${ATEN_INSTALL_INCLUDE_SUBDIR}
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FILES_MATCHING PATTERN "*.h" PATTERN "*.cuh")
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@ -1,26 +0,0 @@
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# THCUNN
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THCUNN is a library that gathers nn's C implementations of neural network modules. It's entirely free of Lua dependency and therefore can be used in any application that has a C FFI. Please note that it only contains quite low level functions; most users will want to use ATen, which provides a C++ wrapper around these functions.
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Looking to add an implementation? Consider writing an ATen native function
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instead! See [../ATen/native](../ATen/native).
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## Links
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* [API reference](doc/api_reference.md)
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* [Style guidelines](doc/style_guidelines.md)
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## API
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THCUNN is a purely functional library. It provides 2-3 functions for each module, that perform the most important operations:
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* **updateOutput** - applies the module to an input
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* **updateGradInput** - accepts gradient w.r.t. output and previous module input, and computes a gradient w.r.t. that input
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* **accGradParameters** - *(optional, only modules with parameters)* accepts gradient w.r.t. output and previous module input, and computes gradient w.r.t. the parameters
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For information on argument types please check the [API reference](doc/api_reference.md).
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## Developer docs
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* [Style guidelines](doc/style_guidelines.md)
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@ -1,43 +0,0 @@
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// Based on the simpleTempltes CUDA example
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#ifndef THCUNN_SHAREDMEM_H
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#define THCUNN_SHAREDMEM_H
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template <typename T>
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struct SharedMem {
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__device__ T *getPointer()
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{
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extern __device__ void error(void);
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error();
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return NULL;
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}
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};
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template <>
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struct SharedMem<half>
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{
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__device__ half *getPointer() {
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extern __shared__ half s_half[];
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return s_half;
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}
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};
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template <>
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struct SharedMem<float>
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{
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__device__ float *getPointer() {
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extern __shared__ float s_float[];
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return s_float;
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}
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};
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template <>
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struct SharedMem<double>
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{
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__device__ double *getPointer() {
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extern __shared__ double s_double[];
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return s_double;
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}
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};
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#endif
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@ -1,38 +0,0 @@
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#ifndef THC_HALF_AUTO_NUMERICS_INC
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#define THC_HALF_AUTO_NUMERICS_INC
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#include <TH/THHalf.h>
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#include <THC/THCNumerics.cuh>
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// WARNING: THCNumerics is being deprecated. Read the comments and function usage
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// in THCNumerics to learn about the deprecation
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//
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// Half numerics functions defined as free functions, so cunn code can be
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// written generically, i.e. without excessive calling of THCNumerics<THHalf> functions.
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// these functions should move to THCNumerics
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inline __host__ __device__ THHalf fmaxType(THHalf x, THHalf y) {
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return THCNumerics<THHalf>::ge(x, y) ? x : y;
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}
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inline __host__ __device__ float fmaxType(float x, THHalf y) {
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return fmaxf(x, ScalarConvert<THHalf, float>::to(y));
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}
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inline __host__ __device__ float fmaxType(float x, float y) {
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return fmaxf(x, y);
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}
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inline __host__ __device__ double fmaxType(double x, double y) {
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return fmax(x, y);
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}
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// arithmetic functions
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inline __host__ __device__ THHalf pow(THHalf a, THHalf b) {
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return THCNumerics<THHalf>::pow(a, b);
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}
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#endif
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@ -1,13 +0,0 @@
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#include <THC/THC.h>
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#define THCIndexTensor THCudaLongTensor
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#define THCIndexTensor_(NAME) THCudaLongTensor_ ## NAME
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typedef int64_t THCIndex_t;
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#define THNN_(NAME) TH_CONCAT_3(THNN_, CReal, NAME)
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#include <THCUNN/generic/THCUNN.h>
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#include <THC/THCGenerateFloatTypes.h>
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#include <THCUNN/generic/THCUNN.h>
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#include <THC/THCGenerateBFloat16Type.h>
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@ -1,83 +0,0 @@
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#ifndef THCUNN_COMMON_H
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#define THCUNN_COMMON_H
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#define THCUNN_assertSameGPU(...) THAssertMsg(THCTensor_(checkGPU)(__VA_ARGS__), \
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"Some of weight/gradient/input tensors are located on different GPUs. Please move them to a single one.")
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// Use 1024 threads per block, which requires cuda sm_2x or above
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const int CUDA_NUM_THREADS = 1024;
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// CUDA: number of blocks for threads.
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inline int GET_BLOCKS(const int64_t N)
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{
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// Round up division for positive number
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auto block_num = N / CUDA_NUM_THREADS + (N % CUDA_NUM_THREADS == 0 ? 0 : 1);
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constexpr int64_t max_int = std::numeric_limits<int>::max();
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THAssertMsg(block_num <= max_int, "Can't schedule too many blocks on CUDA device");
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return static_cast<int>(block_num);
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}
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#define THCUNN_resizeAs_indices(STATE, I1, I2) \
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if (!I1->sizes().equals(I2->sizes())) \
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{ \
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THCudaLongTensor_resizeAs(STATE, I1, I2); \
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}
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#define THCUNN_check_shape(STATE, I1, I2) \
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if (I1 != NULL && I2 != NULL && !THCTensor_(isSameSizeAs)(STATE, I1, I2)) \
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{ \
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THCDescBuff s1 = THCTensor_(sizeDesc)(STATE, I1); \
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THCDescBuff s2 = THCTensor_(sizeDesc)(STATE, I2); \
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THError(#I1 " and " #I2 " shapes do not match: " \
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#I1 " %s, " #I2 " %s", s1.str, s2.str); \
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}
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#define THCUNN_check_shape_indices(STATE, I1, I2) \
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if (!I1->sizes().equals(I2->sizes())) \
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{ \
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THCDescBuff s1 = THCIndexTensor_(sizeDesc)(STATE, I1); \
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THCDescBuff s2 = THCTensor_(sizeDesc)(STATE, I2); \
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THError(#I1 " and " #I2 " shapes do not match: " \
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#I1 " %s, " #I2 " %s", s1.str, s2.str); \
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}
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#define THCUNN_check_nElement(STATE, I1, I2) \
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if (I1 != NULL && I2 != NULL ) { \
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ptrdiff_t n1 = THCTensor_(nElement)(STATE, I1); \
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ptrdiff_t n2 = THCTensor_(nElement)(STATE, I2); \
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if (n1 != n2) \
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{ \
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THCDescBuff s1 = THCTensor_(sizeDesc)(state, I1); \
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THCDescBuff s2 = THCTensor_(sizeDesc)(state, I2); \
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THError(#I1 " and " #I2 " have different number of elements: " \
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#I1 "%s has %ld elements, while " \
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#I2 "%s has %ld elements", s1.str, n1, s2.str, n2); \
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} \
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}
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#define THCUNN_check_dim_size(STATE, T, DIM, DIM_SIZE, SIZE) \
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if (THCTensor_(nDimensionLegacyNoScalars)(STATE, T) != DIM || \
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THCTensor_(sizeLegacyNoScalars)(STATE, T, DIM_SIZE) != SIZE) { \
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THCDescBuff s1 = THCTensor_(sizeDesc)(state, T); \
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THError("Need " #T " of dimension %d and " #T ".size[%d] == %d" \
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" but got " #T " to be of shape: %s", DIM, DIM_SIZE, SIZE, s1.str); \
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}
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#define THCUNN_check_dim_size_indices(STATE, T, DIM, DIM_SIZE, SIZE) \
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if (THCIndexTensor_(nDimensionLegacyNoScalars)(STATE, T) != DIM || \
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THCIndexTensor_(sizeLegacyNoScalars)(STATE, T, DIM_SIZE) != SIZE) { \
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THCDescBuff s1 = THCIndexTensor_(sizeDesc)(state, T); \
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THError("Need " #T " of dimension %d and " #T ".size[%d] == %d" \
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" but got " #T " to be of shape: %s", DIM, DIM_SIZE, SIZE, s1.str); \
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}
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#define THCUNN_argCheck(STATE, COND, ARG, T, FORMAT) \
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if (!(COND)) { \
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THCDescBuff s1 = THCTensor_(sizeDesc)(state, T); \
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THArgCheck(COND, ARG, FORMAT, s1.str); \
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}
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#endif
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@ -1,26 +0,0 @@
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# API docs
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This document describes the conventions behind the THCUNN API.
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### The API
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All functions provided by THCUNN are stored in `aten/src/THCUNN/generic/THCUNN.h`.
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Look at this file.
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### Note on function names
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Please remember, that because C doesn't support function overloading, functions taking different tensor types have different names. So e.g. for an Abs module, there are actually two updateOutput functions:
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* `void THNN_FloatAbs_updateOutput(...)`
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* `void THNN_DoubleAbs_updateOutput(...)`
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In these docs such function will be referred to as `void THCUNN_Abs_updateOutput(...)`, and it's up to developer to add a type prefix. `real` is an alias for that type.
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### Argument types
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Some arguments have additional tags placed in square brackets in their header declarations:
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* **[OUT]** - This is the output argument. It will be reshaped if needed.
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* **[OPTIONAL]** - This argument is optional and can be safely set to NULL
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* **[BUFFER]** - A buffer. `updateGradInput` and `accGradParameters` should get the same buffers that were used in `updateOutput` call.
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* **[MODIFIED]** - Some functions accept an `inplace` flag. If set to true, this argument might be modified (in addition to the output).
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@ -1,64 +0,0 @@
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## API design guidelines
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Functions should return `void`.
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All functions should accept arguments in the following order. `...` represent any module-specific parameters or buffers, disregarding whether they are used for writing or reading. Arguments in `...` below should be ordered like this:
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```
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[weight], [bias], [any buffers], [additional arguments], [optional arguments]
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```
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### Modules
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```
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updateOutput: state, input, output, ...
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updateGradInput: state, input, gradOutput, gradInput, ...
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accGradParameters: state, input, gradOutput, [gradWeight], [gradBias], ...
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```
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e.g.
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```C
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void THNN_(ClassNLLCriterion_updateGradInput)(
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THCState *state,
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THCTensor *input,
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THCIndexTensor *target,
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THCTensor *gradOutput,
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THCTensor *gradInput,
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int64_t reduction,
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THCTensor *weights,
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THCTensor *total_weight,
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int64_t ignore_index)
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```
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### Criterions
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```
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updateOutput: state, input, target, output, ...
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updateGradInput: state, input, target, gradInput, ...
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```
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e.g.
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|
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```C
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void THNN_(ClassNLLCriterion_updateOutput)(
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THCState *state,
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THCTensor *input,
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THCIndexTensor *target,
|
||||
THCTensor *output,
|
||||
int64_t reduction,
|
||||
THCTensor *weights,
|
||||
THCTensor *total_weight,
|
||||
int64_t ignore_index)
|
||||
```
|
||||
|
||||
## Code style guide
|
||||
|
||||
```C
|
||||
void THNN_(GatedLinear_updateOutput)(
|
||||
THCState *state,
|
||||
THCTensor *input,
|
||||
THCTensor *output,
|
||||
int dim)
|
||||
//<- 10 ->
|
||||
```
|
||||
|
||||
All arguments should start on a new line after function name, and they should be indented using 10 spaces.
|
||||
|
||||
Use 2 spaces for block indentation.
|
||||
|
|
@ -1,29 +0,0 @@
|
|||
#ifndef THC_GENERIC_FILE
|
||||
#define THC_GENERIC_FILE "THCUNN/generic/THCUNN.h"
|
||||
#else
|
||||
|
||||
#include <ATen/core/Reduction.h>
|
||||
#include <ATen/Generator.h>
|
||||
|
||||
TORCH_CUDA_CU_API void THNN_(MultiMarginCriterion_updateOutput)(
|
||||
THCState* state,
|
||||
THCTensor* input,
|
||||
THCIndexTensor* target,
|
||||
THCTensor* output,
|
||||
int64_t reduction,
|
||||
int p,
|
||||
THCTensor* weights, // [OPTIONAL]
|
||||
accreal margin);
|
||||
|
||||
TORCH_CUDA_CU_API void THNN_(MultiMarginCriterion_updateGradInput)(
|
||||
THCState* state,
|
||||
THCTensor* input,
|
||||
THCIndexTensor* target,
|
||||
THCTensor* gradOutput,
|
||||
THCTensor* gradInput,
|
||||
int64_t reduction,
|
||||
int p,
|
||||
THCTensor* weights, // [OPTIONAL]
|
||||
accreal margin);
|
||||
|
||||
#endif
|
||||
2
setup.py
2
setup.py
|
|
@ -1028,8 +1028,6 @@ if __name__ == '__main__':
|
|||
'include/THC/*.cuh',
|
||||
'include/THC/*.h*',
|
||||
'include/THC/generic/*.h',
|
||||
'include/THCUNN/*.cuh',
|
||||
'include/THCUNN/generic/*.h',
|
||||
'include/THH/*.cuh',
|
||||
'include/THH/*.h*',
|
||||
'include/THH/generic/*.h',
|
||||
|
|
|
|||
|
|
@ -15,10 +15,6 @@ Modern infrastructure:
|
|||
to import arbitrary Python files in a script, without having to add
|
||||
them to the PYTHONPATH first.
|
||||
|
||||
Legacy infrastructure (we should kill this):
|
||||
* [cwrap](cwrap) - Implementation of legacy code generation for THNN/THCUNN.
|
||||
This is used by nnwrap.
|
||||
|
||||
Build system pieces:
|
||||
|
||||
* [setup_helpers](setup_helpers) - Helper code for searching for
|
||||
|
|
|
|||
|
|
@ -81,12 +81,10 @@ includes = [
|
|||
"aten/src/ATen/native/sparse/cuda/*",
|
||||
"aten/src/ATen/native/quantized/cuda/*",
|
||||
"aten/src/THC/*",
|
||||
"aten/src/THCUNN/*",
|
||||
"aten/src/ATen/test/*",
|
||||
# CMakeLists.txt isn't processed by default, but there are a few
|
||||
# we do want to handle, so explicitly specify them
|
||||
"aten/src/THC/CMakeLists.txt",
|
||||
"aten/src/THCUNN/CMakeLists.txt",
|
||||
"torch/*",
|
||||
"tools/autograd/templates/python_variable_methods.cpp",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -750,7 +750,6 @@ def preprocessor(
|
|||
or f.startswith("ATen/native/quantized/cuda")
|
||||
or f.startswith("ATen/native/sparse/cuda")
|
||||
or f.startswith("THC/")
|
||||
or f.startswith("THCUNN/")
|
||||
or (f.startswith("THC") and not f.startswith("THCP"))
|
||||
):
|
||||
return templ.format(get_hip_file_path(m.group(1), is_pytorch_extension))
|
||||
|
|
|
|||
Loading…
Reference in a new issue