diff --git a/.editorconfig b/.editorconfig deleted file mode 100644 index e69de29..0000000 diff --git a/.vscode_shared/BenHimes/settings.json b/.vscode_shared/BenHimes/settings.json index 5c7d1d8..4f7567d 100644 --- a/.vscode_shared/BenHimes/settings.json +++ b/.vscode_shared/BenHimes/settings.json @@ -74,7 +74,17 @@ "semaphore": "cpp", "stop_token": "cpp", "complex": "cpp", - "unordered_set": "cpp" + "unordered_set": "cpp", + "__config": "cpp", + "target": "cpp", + "ios": "cpp", + "__pragma_push": "cpp", + "__locale": "cpp", + "__bit_reference": "cpp", + "__functional_base": "cpp", + "__node_handle": "cpp", + "__memory": "cpp", + "locale": "cpp" }, "C_Cpp.clang_format_path": "/usr/bin/clang-format-14", "editor.formatOnSave": true, diff --git a/.vscode_shared/BenHimes/tasks.json b/.vscode_shared/BenHimes/tasks.json index bc2e846..7063d6d 100644 --- a/.vscode_shared/BenHimes/tasks.json +++ b/.vscode_shared/BenHimes/tasks.json @@ -14,7 +14,12 @@ { "label": "Book_build", "type": "shell", - "command": "${HOME}/.FastFFTDocs/bin/jupyter-book build --all ./docs && firefox ./docs/_build/html/index.html" + "command": "${HOME}/.FastFFTDocs/bin/jupyter-book build --all ./docs && firefox ./docs/_build/html/index.html", + "problemMatcher": [], + "group": { + "kind": "build", + "isDefault": true + } }, { "label": "Book_publish", diff --git a/build/Makefile b/build/Makefile index 6c46363..86def23 100644 --- a/build/Makefile +++ b/build/Makefile @@ -2,7 +2,6 @@ NVCC=nvcc # TODO: test with gcc and clang NVCC_FLAGS=-ccbin=g++ -t 8 - # TODO: static may need to be added later or independently just for benchmarks, while libs to link through python are probably going to need to be dynamic. NVCC_FLAGS+=--cudart=static NVCC_FLAGS+=--default-stream per-thread -m64 -O3 --use_fast_math --extra-device-vectorization --extended-lambda --Wext-lambda-captures-this -std=c++17 -Xptxas --warn-on-local-memory-usage,--warn-on-spills, --generate-line-info -Xcompiler=-std=c++17 @@ -11,6 +10,9 @@ NVCC_FLAGS+=--expt-relaxed-constexpr # For testing, particularly until the cufftdx::BlockDim() operator is enabled NVCC_FLAGS+=-DCUFFTDX_DISABLE_RUNTIME_ASSERTS +# Don't explicitly instantiate the 3d versions unless requested +NVCC_FLAGS+=-DFastFFT_3d_instantiation + # Gencode arguments, only supporting Volta or newer # SMS ?= 70 75 80 86 # In initial dev, only compile for 70 or 86 depending on which workstation I'm on, b/c it is faster. @@ -38,7 +40,7 @@ $(info $$CUFFTDX_INCLUDE_DIR is [${CUFFTDX_INCLUDE_DIR}]) CUDA_BIN_DIR=$(shell dirname `which $(NVCC)`) CUDA_INCLUDE_DIR=$(CUDA_BIN_DIR)/../include - +# TODO: Changing the debug flags will not force re compilation DEBUG_FLAGS := # Debug level determines various asserts and print macros defined in FastFFT.cuh These should only be set when building tests and developing. ifeq (${FFT_DEBUG_LEVEL},) @@ -79,6 +81,10 @@ endif ifeq (${FastFFT_sync_checking},1) # If HEAVYERRORCHECKING_FFT is not already asked for, then add it anytime debug_stage < 8 (partial FFTs) DEBUG_FLAGS+=-DHEAVYERRORCHECKING_FFT +else + ifeq (${debug_level}, 4) + DEBUG_FLAGS+=-DHEAVYERRORCHECKING_FFT + endif endif @@ -89,6 +95,7 @@ EXTERNAL_LIBS= -lfftw3f -lcufft_static -lculibos -lcudart_static -lrt TEST_BUILD_DIR=tests TEST_SRC_DIR=../src/tests # Get all the test source files and remove cu extension +# TODO: selective targets TEST_TARGETS=$(patsubst %.cu,$(TEST_BUILD_DIR)/%,$(notdir $(wildcard $(TEST_SRC_DIR)/*.cu))) TEST_DEPS=$(wildcard $(TEST_SRC_DIR)/*.cuh) diff --git a/docs/_docs/MS/manuscript.md b/docs/_docs/MS/manuscript.md index 5b38b3c..18936d3 100644 --- a/docs/_docs/MS/manuscript.md +++ b/docs/_docs/MS/manuscript.md @@ -7,28 +7,37 @@ ## Abstract -The Fast Fourier transform (FFT) is one of the most widely used and heavily optimized digital signal processing algorithms. Many of the image processing algorithms used in cryo-Electron Microscopy rely on the FFT to accelerate convolution operations central to image alignment and also in reconstruction algorithms that operate most accurately in Fourier space. FFT libraries like FFTW and cuFFT provide routines for highly-optimized general purpose multi-dimensional FFTs; however, they overlook several use-cases where only a subset of the input or output points are required. We demonstrate that algorithms based on the transform decomposition approach are well suited to the memory hierarchy of modern GPUs by implementing them in CUDA/C++ using the cufftdx header-only library. The results have practical implications, accelerating several key image processing algorithms by factors of 3-10x over those built using Nvidia’s general purpose FFT library cuFFT. These include movie-frame alignment, image resampling via Fourier cropping, 2d and 3d template matching, and subtomogram averaging and alignment. +The Fast Fourier transform (FFT) is one of the most widely used and heavily optimized digital signal processing algorithms. Many of the image processing algorithms used in cryo-Electron Microscopy rely on the FFT to accelerate convolution operations central to image alignment and also in reconstruction algorithms that operate most accurately in Fourier space. FFT libraries like FFTW and cuFFT provide routines for highly-optimized general purpose multi-dimensional FFTs; however, their generality comes at a cost of performance in several use-cases where only a subset of the input or output points are required. We demonstrate that algorithms based on the transform decomposition approach are well suited to the memory hierarchy of modern GPUs by implementing them in CUDA/C++ using the cufftdx header-only library. The results have practical implications, accelerating several key image processing algorithms by factors of 3-10x over those built using Nvidia’s general purpose FFT library cuFFT. These include movie-frame alignment, image resampling via Fourier cropping, 2d and 3d template matching, 3d reconstruction, and subtomogram averaging and alignment. ## Introduction -The Discrete Fourier Transform (DFT) and linear filtering, *e.g.* convolution, are among the most common operations in digital signal processing. It is therefore assumed that the reader has a basic familiarity with Fourier Analysis and it's applications in their respective fields; we will focus here on digital image processing for convenience. For a detailed introduction to the reader is referred to the free book by Smith {cite:p}`smith_mathematics_2008`. There are many ways to implement the computation of the DFT, the most simple of which can be understood as a matrix multiplication, which scales in computational complexity as O(n^2). The Fast Fourier Transform (FFT) implements some level of recursion, as described below, which in turn reduces the computational complexity to O(nlog(n)). This reduction in number of operations leads to both an increase in the speed, reduced roundoff error, as well as a reduced memory footprint. The goal of many popular FFT library's is to provide optimal routines for FFT's of any size that are also adapted to the vagarys of the specific computer hardware they are being run on ⚠️. Most papers describing FFT algorithms focus on computational complexity from the perspective of the number of arithmetic operations ⚠️, however, cache coherency and limited on chip memory ultimately reduce the efficiency of large FFTs (⚠️ TODO: Intro figure 1). For certain applications, particularly research that requires high performance computing resources, this generality is not strictly needed, and substantial financial and environmental resources could be minimized by using specialezed hardware or software. (Add something here about asics and fpgas ⚠️) By incorporating prior information about specific image processing algorithms, we present optimized two and three dimensional FFT routines that blah blah blahh⚠️. +The Discrete Fourier Transform (DFT) and linear filtering, *e.g.* convolution, are among the most common operations in digital signal processing. It is therefore assumed that the reader has a basic familiarity with Fourier Analysis and it's applications in their respective fields; we will focus here on digital image processing for convenience. For a detailed introduction to the reader is referred to the free book by Smith {cite:p}`smith_mathematics_2008`. There are many ways to implement the computation of the DFT, the most simple of which can be understood as a matrix multiplication, which scales in computational complexity as O(n^2). In order improve the performance of the DFT, several algorithms have been devised, that collectively may be called the Fast Fourier Transform (FFT). All of these implement some level of recursion, as described below, which in turn reduces the computational complexity to O(nlog(n)). The FFT additionally improves performance by reducing the total memory footprint of the calculation, allowing for better utilization of high-performance, low capacity memory cache. In addition to increasing computational speed, this reduction in the total number of floating point operations also leads to reduced roundoff error, making the end result more accurate. The goal of many popular FFT library's is to provide optimal routines for FFT's of any size that are also adapted to the vagarys of the specific computer hardware they are being run on ⚠️. Most papers describing FFT algorithms focus on computational complexity from the perspective of the number of arithmetic operations ⚠️, however, cache coherency and limited on chip memory ultimately reduce the efficiency of large FFTs (⚠️ TODO: Intro figure 1). For certain applications, particularly research that requires high performance computing resources, some generality may be exchanged for even more performance, resulting in substantial reduction of financial, and environmental, resources. At the extreme end of this tradeoff between generality and performance are hardware build specifically for a single algorithm, (Add something here about asics and fpgas and ANTON ⚠️) By incorporating prior information about specific image processing algorithms, we present optimized two and three dimensional FFT routines that blah blah blahh⚠️. ### Background +#### Examples from cryo-EM + +⚠️ I think it makes sense to lead with the specific use cases we will be having to test and implement the desired improvements, from the perspective of cryo-EM. From there, providing a few examples of parallels, probably in CNNs etc and only then going into specifics about the implementation. + #### The discrete Fourier Transform -The Fourier Transform is a mathematical operation that converts an input function into a dual space; for example a function of time into a function of frequency. These dual spaces are sometimes referred to as position and momentum space, real and Fourier space, image space and k-space etc. Given that a "real-space" function can be complex valued, we will use the position and momentum space nomenclature to avoid ambiguity. +The Fourier Transform is a mathematical operation that maps an input function into a dual space; for example a function of time into a function of frequency. These dual spaces are sometimes referred to as position and momentum space, real and Fourier space, image space and k-space etc. Given that a "real-space" function can be complex valued, we will use the position and momentum space nomenclature to avoid ambiguity. -creatinThe discrete Fourier Transform (DFT) extends the operation of the Fourier Transform to a band-limited sequence of evenly spaced samples of a continuous function. In one dimension, it is defined for a sequence of N samples $x(n)$ as: Throughout the text we use lower/upper case to refer to position/momemtum space variables. +The discrete Fourier Transform (DFT) extends the operation of the Fourier Transform to a band-limited sequence of evenly spaced samples of a continuous function. In one dimension, it is defined for a sequence of N samples $x(n)$ as: % This produces a labelled eqution in jupyter book that will at least render the math in vscode preview, just without the label. -$$ X(k) = \sum_{n=0}^{N-1} x(n) \exp\left( -2\pi i k n \right) $$ (dft-1d-equation) +$$ X(k) = \sum_{n=0}^{N-1} x(n) \exp\left( \frac{-2\pi i k}{N}n \right) $$ (dft-1d-equation) + +```{note} +*Throughout the text we use lower/upper case to refer to position/momemtum space variables.* +``` + The DFT is fully seperable when calculated with respect to a Cartesian coordintate system. For example, for an M x N array, the DFT is defined as: -$$ X(k_m,k_n) = \sum_{m=0}^{M-1} \left[ \sum_{n=0}^{N-1} x(m,n) \exp\left( -2\pi i k_n n \right) \right] \exp\left( -2\pi i k_m m \right) $$ (dft-2d-equation) +$$ X(k_m,k_n) = \sum_{m=0}^{M-1} \left[ \sum_{n=0}^{N-1} x(m,n) \exp\left( \frac{-2\pi i k_n}{N} n \right) \right] \exp\left( \frac{-2\pi i k_m}{M} m \right) $$ (dft-2d-equation) -From this equation, it should be clear in the most simple case the 2D DFT can be calculated by first calculating the 1D FFT for each column and then each row, resulting in $ M \times N $ 1D DFTs. This seperability extends to higher dimensions, and is what permits us to exploit regions of the input that are known to be zero. +From this equation, it should be clear in the most simple cases the 2D DFT can be calculated by first calculating the 1D FFT for each column and then each row, resulting in $ M \times N $ 1D DFTs. This seperability extends to higher dimensions, and is what permits us to exploit regions of the input that are known to be zero. In addition to being seperable, the DFT has several other important properties: @@ -82,9 +91,6 @@ The simplest approach to avoiding redundant calculations and memory transfers in ### The DFT and FFT -Fast Fourier Transform (FFT) is a mathematical operation that transforms a function $ X(n) $ from the real-valued plane into the complex-valued plane. The function $ f(x) $ is a function of $ x $ and is often a function of the real-valued signal $ x $ or a function of the complex-valued signal $ x + i\cdot y $. The FFT is an optimized algorithm for copying the discrete Fourier transform (DFT) defined as: - -( I think this paragraph is probably belonging in the introduction ) The FFT is useful in image analysis as it can help to isolate repetitive features of a given size. It is also useful in signal processing as it can be used to perform convolution operations. Multidimensional FFTs are fully seperable, such that in the simpliest case, FFTs in higher dimensions are composed of a sequence of 1D FFTs. For example, in two dimensions, the FFT of a 2D image is composed of the 1D FFTs of the rows and columns. A naive implementation is compuatationally ineffecient due to the strided memory access pattern⚠️. One solution is to transpose the data after the FFT and then transpose the result back ⚠️. This requires extra trips to and from main memory. Another solution is to decompose the 2D transform using vector-radix FFTs ⚠️. diff --git a/docs/_docs/references/class_reference.md b/docs/_docs/references/class_reference.md index 210fa60..9182718 100644 --- a/docs/_docs/references/class_reference.md +++ b/docs/_docs/references/class_reference.md @@ -1,5 +1,7 @@ # Fourier Transformer Class Reference + + ## Public Methods ## State Variables diff --git a/docs/_docs/references/development_tools.md b/docs/_docs/references/development_tools.md index 56d25e6..bda4fb4 100644 --- a/docs/_docs/references/development_tools.md +++ b/docs/_docs/references/development_tools.md @@ -101,16 +101,14 @@ FastFFT::PrintState() std::cout << std::endl; std::cout << "State Variables:\n" << std::endl; - std::cout << "is_in_memory_host_pointer " << is_in_memory_host_pointer << std::endl; - std::cout << "is_in_memory_device_pointer " << is_in_memory_device_pointer << std::endl; + // std::cerr << "is_in_memory_device_pointer " << is_in_memory_device_pointer << std::endl; // FIXME: switched to is_pointer_in_device_memory(d_ptr.buffer_1) defined in FastFFT.cuh std::cout << "is_in_buffer_memory " << is_in_buffer_memory << std::endl; std::cout << "is_fftw_padded_input " << is_fftw_padded_input << std::endl; std::cout << "is_fftw_padded_output " << is_fftw_padded_output << std::endl; - std::cout << "is_real_valued_input " << is_real_valued_input << std::endl; + std::cout << "is_real_valued_input " << IsAllowedRealType << std::endl; std::cout << "is_set_input_params " << is_set_input_params << std::endl; std::cout << "is_set_output_params " << is_set_output_params << std::endl; std::cout << "is_size_validated " << is_size_validated << std::endl; - std::cout << "is_set_input_pointer " << is_set_input_pointer << std::endl; std::cout << std::endl; std::cout << "Size variables:\n" << std::endl; @@ -125,11 +123,11 @@ FastFFT::PrintState() std::cout << std::endl; std::cout << "Misc:\n" << std::endl; - std::cout << "compute_memory_allocated " << compute_memory_allocated << std::endl; + std::cout << "compute_memory_wanted " << compute_memory_wanted << std::endl; std::cout << "memory size to copy " << memory_size_to_copy << std::endl; std::cout << "fwd_size_change_type " << SizeChangeName[fwd_size_change_type] << std::endl; std::cout << "inv_size_change_type " << SizeChangeName[inv_size_change_type] << std::endl; - std::cout << "transform stage complete " << TransformStageCompletedName[transform_stage_completed] << std::endl; + std::cout << "transform stage complete " << transform_stage_completed << std::endl; std::cout << "input_origin_type " << OriginTypeName[input_origin_type] << std::endl; std::cout << "output_origin_type " << OriginTypeName[output_origin_type] << std::endl; diff --git a/docs/_docs/references/usage.md b/docs/_docs/references/usage.md new file mode 100644 index 0000000..de1596f --- /dev/null +++ b/docs/_docs/references/usage.md @@ -0,0 +1,69 @@ +# Basic usage principles + +## Overview + +The FastFFT library is built to optimize a subset of discrete Fourier transforms, most of which involve padding/cropping, *i.e.*, size changes. While a substantial effort has been made to maintain parallels between FastFFT and other popular libraries like cuFFT and FFTW3, the ability to use variable input/output sizes and input/compute/output variable types complicates the interface to some degree. + +## cuFFT plans, a review + +Creating a plan is a fundamental process in dealing with fft libraries. We'll use the process in cuFFT for illustration. + +### Creating a plan + +In cufft, the first step to library access is to create a "handle" to a plan, *i.e.*, a pointer, which is needed for both forward and inverse transforms. Not required, *per se*, but highly recommended is to place that plan in a specific cuda stream. A cudaStream allows for fine grained control of a queue of work. All processes in the FastFFT library are placed into the cudaStreamPerThread stream, a special, non-synchronzing stream that has a unique idea for every unique host thread, permitting easy thread saftey without explicit management by using common Host thread management strategies like openMP. + +```cpp + cufftCreate(&cuda_plan_forward); + cufftCreate(&cuda_plan_inverse); + + cufftSetStream(cuda_plan_forward, cudaStreamPerThread); + cufftSetStream(cuda_plan_inverse, cudaStreamPerThread); + + // The parallel in Fast FFT would be to create an empty FourierTransformer object, e.g. + // The template arguments are: ComputeBaseType, InputType, OtherImageType, Rank + FastFFT::FourierTransformer FT; +``` + +```{note} + +The current implementation only supports float for all three stages and dimensions (rank) of 2,3. This is under active development. + + * support for input __half and __nv_bfloat16 are next to improve bandwidth + * support for input __half2 and __nv_bfloat162 and float2 follow this to enable c2c transforms. + * support for half-precision ComputeType **may** be explored, but prioritizing non-power of 2 support for ComputeType = float is a higher priority. + +Any algorithms that couple a second image to one of the intra process functors assumes the OtherImageType matched the data on the stage it is used. + * For example, the correlation functor assumes the input is a real image, and the second image is a complex image, and the output is a real image. +``` + +The next step in the cuFFT library is to actually create the plan itself, which requires informing the cuda driver of several parameters. + +* Similar to the templated declarion of the FastFFT::FourierTransformer object, the input/compute/output datatypes are all specified. + +* Batched and strided FFT's are allowed in cuFFT while in FastFFT we are currently only supporting plans for stride of 1 and individual +transforms, although using the library in a batched manner would be trivial to add. + +* Perhaps the most significant difference to note, is that in cuFFT, the dinmensionality of the transform is fixed, while in FastFFT the input and output sizes may, and likely should be different. + +```cpp + + cufftXtMakePlanMany(cuda_plan_forward, rank, fftDims, + NULL, 1, 1, CUDA_R_32F, + NULL, 1, 1, CUDA_C_32F, iBatch, &cuda_plan_worksize_forward, CUDA_C_32F); + + FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + FT.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); +``` + +```{note} +Both SetForwardFFTPlan and SetInverseFFTPlan must be called prior to using any FFT algorithms, and the buffer memory used by FastFFT is allocated automatically on the latter of the two calls. +``` + +* Create an FourierTransformer object +* Set the forward and inverse plans + * If these are not set, the ValidateDimensions function will throw an error in debug mode +* Optionally Set input and/or output pointers + * This will instruct FastFFT to read/write on relevant transforms to and from these external memory buffers + * Otherwise, data must be manually copied to/from the FastFFT buffers using the relevant methods + * FastFFT::CopyHostToDevice + * If the input/output pointers are not set and the FastFFT buffers are not allocated, the will be allocated on the first call to either of the latter two functions diff --git a/docs/_toc.yml b/docs/_toc.yml index fed816a..b1e5012 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -9,6 +9,8 @@ parts: - file: _docs/references/intro sections: - file: _docs/references/project_layout + - file: _docs/references/usage.md + - file: _docs/references/class_reference.md - file: _docs/references/coordinate_conventions - file: _docs/references/naming_conventions - file: _docs/references/coding_conventions diff --git a/include/FastFFT.cuh b/include/FastFFT.cuh index e8deafb..f296148 100644 --- a/include/FastFFT.cuh +++ b/include/FastFFT.cuh @@ -1,310 +1,23 @@ // Utilites for FastFFT.cu that we don't need the host to know about (FastFFT.h) #include "FastFFT.h" -#ifndef Fast_FFT_cuh_ -#define Fast_FFT_cuh_ +#ifndef __INCLUDE_FAST_FFT_CUH__ +#define __INCLUDE_FAST_FFT_CUH__ +#include "detail/detail.cuh" #include -#include "cufftdx/include/cufftdx.hpp" -// clang-format off - -// “This software contains source code provided by NVIDIA Corporation.” Much of it is modfied as noted at relevant function definitions. - -// When defined Turns on synchronization based checking for all FFT kernels as well as cudaErr macros -// Defined in the Makefile when DEBUG_STAGE is not equal 8 (the default, not partial transforms.) -// #define HEAVYERRORCHECKING_FFT - -// Various levels of debuging conditions and prints -// #define FFT_DEBUG_LEVEL 0 +// “This software contains source code provided by NVIDIA Corporation.” +// This is located in include/cufftdx* +// Please review the license in the cufftdx directory. // #define forceforce( type ) __nv_is_extended_device_lambda_closure_type( type ) //FIXME: change to constexpr func - - -template -constexpr inline bool IS_IKF_t( ) { - if constexpr ( std::is_final_v ) { - return true; - } - else { - return false; - } -}; - - - -#if FFT_DEBUG_LEVEL < 1 - -#define MyFFTDebugPrintWithDetails(...) -#define MyFFTDebugAssertTrue(cond, msg, ...) -#define MyFFTDebugAssertFalse(cond, msg, ...) -#define MyFFTDebugAssertTestTrue(cond, msg, ...) -#define MyFFTDebugAssertTestFalse(cond, msg, ...) - -#else -// Minimally define asserts that check state variables and setup. -#define MyFFTDebugAssertTrue(cond, msg, ...) { if ( (cond) != true ) { std::cerr << msg << std::endl << " Failed Assert at " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; exit(-1); } } -#define MyFFTDebugAssertFalse(cond, msg, ...) { if ( (cond) == true ) { std::cerr << msg << std::endl << " Failed Assert at " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; exit(-1); } } - -#endif - -#if FFT_DEBUG_LEVEL > 1 -// Turn on checkpoints in the testing functions. -#define MyFFTDebugAssertTestTrue(cond, msg, ...) { if ( (cond) != true ) { std::cerr << " Test " << msg << " FAILED!" << std::endl << " at " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl;exit(-1); } else { std::cerr << " Test " << msg << " passed!" << std::endl; }} -#define MyFFTDebugAssertTestFalse(cond, msg, ...) { if ( (cond) == true ) { std::cerr << " Test " << msg << " FAILED!" << std::endl << " at " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; exit(-1); } else { std::cerr << " Test " << msg << " passed!" << std::endl; } } - -#endif - -#if FFT_DEBUG_LEVEL == 2 -#define MyFFTDebugPrintWithDetails(...) -#endif - -#if FFT_DEBUG_LEVEL == 3 -// More verbose debug info -#define MyFFTDebugPrint(...) { std::cerr << __VA_ARGS__ << std::endl; } -#define MyFFTDebugPrintWithDetails(...) { std::cerr << __VA_ARGS__ << " From: " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; } -#endif - -#if FFT_DEBUG_LEVEL == 4 -// More verbose debug info + state info -#define MyFFTDebugPrint(...) { FastFFT::FourierTransformer::PrintState( ); std::cerr << __VA_ARGS__ << std::endl; } -#define MyFFTDebugPrintWithDetails(...) { FastFFT::FourierTransformer::PrintState( ); std::cerr << __VA_ARGS__ << " From: " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; } - -#endif - -// Always in use -#define MyFFTPrint(...) { std::cerr << __VA_ARGS__ << std::endl; } -#define MyFFTPrintWithDetails(...) { std::cerr << __VA_ARGS__ << " From: " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; } -#define MyFFTRunTimeAssertTrue(cond, msg, ...) { if ( (cond) != true ) { std::cerr << msg << std::endl << " Failed Assert at " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl;exit(-1); } } -#define MyFFTRunTimeAssertFalse(cond, msg, ...) { if ( (cond) == true ) {std::cerr << msg << std::endl << " Failed Assert at " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl;exit(-1); } } - - - -// I use the same things in cisTEM, so check for them. FIXME, get rid of defines and also find a better sharing mechanism. -#ifndef cudaErr -// Note we are using std::cerr b/c the wxWidgets apps running in cisTEM are capturing std::cout -// If I leave cudaErr blank when HEAVYERRORCHECKING_FFT is not defined, I get some reports/warnings about unused or unreferenced variables. I suspect the performance hit is very small so just leave this on. -// The real cost is in the synchronization of in pre/postcheck. -#define cudaErr(error) { auto status = static_cast(error); if ( status != cudaSuccess ) { std::cerr << cudaGetErrorString(status) << " :-> "; MyFFTPrintWithDetails(""); } }; -#endif - -#ifndef postcheck - #ifndef precheck - #ifndef HEAVYERRORCHECKING_FFT - #define postcheck - #define precheck - #else - #define postcheck { cudaErr(cudaPeekAtLastError( )); cudaError_t error = cudaStreamSynchronize(cudaStreamPerThread); cudaErr(error) } - #define precheck { cudaErr(cudaGetLastError( )) } - #endif - #endif -#endif - - -inline void checkCudaErr(cudaError_t err) { - if ( err != cudaSuccess ) { - std::cerr << cudaGetErrorString(err) << " :-> " << std::endl; - MyFFTPrintWithDetails(" "); - } -}; - -#define USEFASTSINCOS -// The __sincosf doesn't appear to be the problem with accuracy, likely just the extra additions, but it probably also is less flexible with other types. I don't see a half precision equivalent. -#ifdef USEFASTSINCOS -__device__ __forceinline__ void SINCOS(float arg, float* s, float* c) { - __sincosf(arg, s, c); -} -#else -__device__ __forceinline__ void SINCOS(float arg, float* s, float* c) { - sincos(arg, s, c); -} -#endif - namespace FastFFT { -template -bool pointer_is_in_memory_and_registered(T ptr, const char* ptr_name = nullptr) { - cudaPointerAttributes attr; - cudaErr(cudaPointerGetAttributes(&attr, ptr)); - - if ( attr.type == 1 && attr.devicePointer == attr.hostPointer ) { - return true; - } - else { - return false; - } -} - -__device__ __forceinline__ int -d_ReturnReal1DAddressFromPhysicalCoord(int3 coords, short4 img_dims) { - return ((((int)coords.z * (int)img_dims.y + coords.y) * (int)img_dims.w * 2) + (int)coords.x); -} - -static constexpr const int XZ_STRIDE = 16; - -static constexpr const int bank_size = 32; -static constexpr const int bank_padded = bank_size + 1; -static constexpr const unsigned int ubank_size = 32; -static constexpr const unsigned int ubank_padded = ubank_size + 1; - -__device__ __forceinline__ int GetSharedMemPaddedIndex(const int index) { - return (index % bank_size) + ((index / bank_size) * bank_padded); -} - -__device__ __forceinline__ int GetSharedMemPaddedIndex(const unsigned int index) { - return (index % ubank_size) + ((index / ubank_size) * ubank_padded); -} - -// Return the address of the 1D transform index 0 -static __device__ __forceinline__ unsigned int Return1DFFTAddress(const unsigned int pixel_pitch) { - return pixel_pitch * (blockIdx.y + blockIdx.z * gridDim.y); -} - -// Return the address of the 1D transform index 0. Right now testing for a stride of 2, but this could be modifiable if it works. -static __device__ __forceinline__ unsigned int Return1DFFTAddress_strided_Z(const unsigned int pixel_pitch) { - // In the current condition, threadIdx.z is either 0 || 1, and gridDim.z = size_z / 2 - // index into a 2D tile in the XZ plane, for output in the ZX transposed plane (for coalsced write.) - return pixel_pitch * (blockIdx.y + (XZ_STRIDE * blockIdx.z + threadIdx.z) * gridDim.y); -} - -// Return the address of the 1D transform index 0 -static __device__ __forceinline__ unsigned int ReturnZplane(const unsigned int NX, const unsigned int NY) { - return (blockIdx.z * NX * NY); -} - -// Return the address of the 1D transform index 0 -static __device__ __forceinline__ unsigned int Return1DFFTAddress_Z(const unsigned int NY) { - return blockIdx.y + (blockIdx.z * NY); -} - -// Return the address of the 1D transform index 0 -static __device__ __forceinline__ unsigned int Return1DFFTColumn_XYZ_transpose(const unsigned int NX) { - // NX should be size_of::value for this method. Should this be templated? - // presumably the XZ axis is alread transposed on the forward, used to index into this state. Indexs in (ZY)' plane for input, to be transposed and permuted to output.' - return NX * (XZ_STRIDE * (blockIdx.y + gridDim.y * blockIdx.z) + threadIdx.z); -} - -// Return the address of the 1D transform index 0 -static __device__ __forceinline__ unsigned int Return1DFFTAddress_XZ_transpose(const unsigned int X) { - return blockIdx.z + gridDim.z * (blockIdx.y + X * gridDim.y); -} - -// Return the address of the 1D transform index 0 -static __device__ __forceinline__ unsigned int Return1DFFTAddress_XZ_transpose_strided_Z(const unsigned int IDX) { - // return (XZ_STRIDE*blockIdx.z + (X % XZ_STRIDE)) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + (X / XZ_STRIDE) * gridDim.y ); - // (IDX % XZ_STRIDE) -> transposed x coordinate in tile - // ((blockIdx.z*XZ_STRIDE) -> tile offest in physical X (with above gives physical X out (transposed Z)) - // (XZ_STRIDE*gridDim.z) -> n elements in physical X (transposed Z) - // above * blockIdx.y -> offset in physical Y (transposed Y) - // (IDX / XZ_STRIDE) -> n elements physical Z (transposed X) - return ((IDX % XZ_STRIDE) + (blockIdx.z * XZ_STRIDE)) + (XZ_STRIDE * gridDim.z) * (blockIdx.y + (IDX / XZ_STRIDE) * gridDim.y); -} - -static __device__ __forceinline__ unsigned int Return1DFFTAddress_XZ_transpose_strided_Z(const unsigned int IDX, const unsigned int Q, const unsigned int sub_fft) { - // return (XZ_STRIDE*blockIdx.z + (X % XZ_STRIDE)) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + (X / XZ_STRIDE) * gridDim.y ); - // (IDX % XZ_STRIDE) -> transposed x coordinate in tile - // ((blockIdx.z*XZ_STRIDE) -> tile offest in physical X (with above gives physical X out (transposed Z)) - // (XZ_STRIDE*gridDim.z) -> n elements in physical X (transposed Z) - // above * blockIdx.y -> offset in physical Y (transposed Y) - // (IDX / XZ_STRIDE) -> n elements physical Z (transposed X) - return ((IDX % XZ_STRIDE) + (blockIdx.z * XZ_STRIDE)) + (XZ_STRIDE * gridDim.z) * (blockIdx.y + ((IDX / XZ_STRIDE) * Q + sub_fft) * gridDim.y); -} - -// Return the address of the 1D transform index 0 -static __device__ __forceinline__ unsigned int Return1DFFTAddress_YZ_transpose_strided_Z(const unsigned int IDX) { - // return (XZ_STRIDE*blockIdx.z + (X % XZ_STRIDE)) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + (X / XZ_STRIDE) * gridDim.y ); - return ((IDX % XZ_STRIDE) + (blockIdx.y * XZ_STRIDE)) + (gridDim.y * XZ_STRIDE) * (blockIdx.z + (IDX / XZ_STRIDE) * gridDim.z); -} - -// Return the address of the 1D transform index 0 -static __device__ __forceinline__ unsigned int Return1DFFTAddress_YZ_transpose_strided_Z(const unsigned int IDX, const unsigned int Q, const unsigned int sub_fft) { - // return (XZ_STRIDE*blockIdx.z + (X % XZ_STRIDE)) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + (X / XZ_STRIDE) * gridDim.y ); - return ((IDX % XZ_STRIDE) + (blockIdx.y * XZ_STRIDE)) + (gridDim.y * XZ_STRIDE) * (blockIdx.z + ((IDX / XZ_STRIDE) * Q + sub_fft) * gridDim.z); -} - -// Return the address of the 1D transform index 0 -static __device__ __forceinline__ unsigned int Return1DFFTColumn_XZ_to_XY( ) { - // return blockIdx.y + gridDim.y * ( blockIdx.z + gridDim.z * X); - return blockIdx.y + gridDim.y * blockIdx.z; -} - -static __device__ __forceinline__ unsigned int Return1DFFTAddress_YX_to_XY( ) { - return blockIdx.z + gridDim.z * blockIdx.y; -} - -static __device__ __forceinline__ unsigned int Return1DFFTAddress_YX( ) { - return Return1DFFTColumn_XZ_to_XY( ); -} - -// Complex a * conj b multiplication -template -static __device__ __host__ inline auto ComplexConjMulAndScale(const ComplexType a, const ComplexType b, ScalarType s) -> decltype(b) { - ComplexType c; - c.x = s * (a.x * b.x + a.y * b.y); - c.y = s * (a.y * b.x - a.x * b.y); - return c; -} - -// GetCudaDeviceArch from https://github.com/mnicely/cufft_examples/blob/master/Common/cuda_helper.h -void GetCudaDeviceProps(DeviceProps& dp); - -void CheckSharedMemory(int& memory_requested, DeviceProps& dp); -void CheckSharedMemory(unsigned int& memory_requested, DeviceProps& dp); - using namespace cufftdx; -// TODO this probably needs to depend on the size of the xform, at least small vs large. -constexpr const int elements_per_thread_16 = 4; -constexpr const int elements_per_thread_32 = 8; -constexpr const int elements_per_thread_64 = 8; -constexpr const int elements_per_thread_128 = 8; -constexpr const int elements_per_thread_256 = 8; -constexpr const int elements_per_thread_512 = 8; -constexpr const int elements_per_thread_1024 = 8; -constexpr const int elements_per_thread_2048 = 8; -constexpr const int elements_per_thread_4096 = 8; -constexpr const int elements_per_thread_8192 = 16; - -namespace KernelFunction { - -// Define an enum for different functors -// Intra Kernel Function Type -enum IKF_t { NOOP, - CONJ_MUL }; - -// Maybe a better way to check , but using keyword final to statically check for non NONE types -template -class my_functor {}; - -template -class my_functor { - public: - __device__ __forceinline__ - T - operator( )( ) { - printf("really specific NOOP\n"); - return 0; - } -}; - -template -class my_functor final { - public: - __device__ __forceinline__ - T - operator( )(float& template_fft_x, float& template_fft_y, const float& target_fft_x, const float& target_fft_y) { - // Is there a better way than declaring this variable each time? - // This is target * conj(template) - float tmp = (template_fft_x * target_fft_x + template_fft_y * target_fft_y); - template_fft_y = (template_fft_x * target_fft_y - template_fft_y * target_fft_x); - template_fft_x = tmp; - } -}; - -} // namespace KernelFunction - // constexpr const std::map elements_per_thread = { // {16, 4}, {"GPU", 15}, {"RAM", 20}, // }; @@ -337,94 +50,85 @@ C2C additionally specify direction and may specify an operation. For these kernels the XY transpose is intended for 2d transforms, while the XZ is for 3d transforms. */ -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_R2C_NONE_XY(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); + void block_fft_kernel_R2C_NONE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); // XZ_STRIDE ffts/block via threadIdx.x, notice launch bounds. Creates partial coalescing. -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_R2C_NONE_XZ(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); + void block_fft_kernel_R2C_NONE_XZ(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_R2C_INCREASE_XY(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); + void block_fft_kernel_R2C_INCREASE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); // XZ_STRIDE ffts/block via threadIdx.x, notice launch bounds. Creates partial coalescing. -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_R2C_INCREASE_XZ(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); + void block_fft_kernel_R2C_INCREASE_XZ(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); // __launch_bounds__(FFT::max_threads_per_block) we don't know this because it is threadDim.x * threadDim.z - this could be templated if it affects performance significantly -template -__global__ void block_fft_kernel_R2C_DECREASE_XY(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); +template +__global__ void block_fft_kernel_R2C_DECREASE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); ///////////// // C2C ///////////// -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_INCREASE(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); + void block_fft_kernel_C2C_INCREASE(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); // __launch_bounds__(FFT::max_threads_per_block) we don't know this because it is threadDim.x * threadDim.z - this could be templated if it affects performance significantly -template -__global__ void block_fft_kernel_C2C_DECREASE(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); - -template -__launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_WithPadding_SwapRealSpaceQuadrants(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); +template +__global__ void block_fft_kernel_C2C_DECREASE(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, - Offsets mem_offsets, int Q, typename FFT::workspace_type workspace_fwd, typename invFFT::workspace_type workspace_inv); + void block_fft_kernel_C2C_WithPadding_SwapRealSpaceQuadrants(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, + void block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE(const ExternalImage_t* __restrict__ image_to_search, const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, int Q, typename FFT::workspace_type workspace_fwd, typename invFFT::workspace_type workspace_inv, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); -template -__launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul_SwapRealSpaceQuadrants(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, - Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace_fwd, typename invFFT::workspace_type workspace_inv); - -template -__global__ void block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, +template +__global__ void block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul(const ExternalImage_t* __restrict__ image_to_search, const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace_fwd, typename invFFT::workspace_type workspace_inv); -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_NONE(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); + void block_fft_kernel_C2C_NONE(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_NONE_XZ(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); + void block_fft_kernel_C2C_NONE_XZ(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_NONE_XYZ(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); + void block_fft_kernel_C2C_NONE_XYZ(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_INCREASE_XYZ(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); + void block_fft_kernel_C2C_INCREASE_XYZ(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace); + ///////////// // C2R ///////////// -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2R_NONE(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); + void block_fft_kernel_C2R_NONE(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2R_NONE_XY(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); + void block_fft_kernel_C2R_NONE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace); // __launch_bounds__(FFT::max_threads_per_block) we don't know this because it is threadDim.x * threadDim.z - this could be templated if it affects performance significantly -template -__global__ void block_fft_kernel_C2R_DECREASE_XY(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, const float twiddle_in, const unsigned int Q, typename FFT::workspace_type workspace); +template +__global__ void block_fft_kernel_C2R_DECREASE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, const float twiddle_in, const unsigned int Q, typename FFT::workspace_type workspace); ////////////////////////////// // Thread FFT based Kernel definitions @@ -434,47 +138,174 @@ __global__ void block_fft_kernel_C2R_DECREASE_XY(const ComplexType* __restrict__ // R2C ///////////// -template -__global__ void thread_fft_kernel_R2C_decomposed(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); +template +__global__ void thread_fft_kernel_R2C_decomposed(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); -template -__global__ void thread_fft_kernel_R2C_decomposed_transposed(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); +template +__global__ void thread_fft_kernel_R2C_decomposed_transposed(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); ///////////// // C2C ///////////// -template -__global__ void thread_fft_kernel_C2C_decomposed(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); +template +__global__ void thread_fft_kernel_C2C_decomposed(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); -template -__global__ void thread_fft_kernel_C2C_decomposed_ConjMul(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); +template +__global__ void thread_fft_kernel_C2C_decomposed_ConjMul(const ExternalImage_t* __restrict__ image_to_search, const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); ///////////// // C2R ///////////// -template -__global__ void thread_fft_kernel_C2R_decomposed(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); +template +__global__ void thread_fft_kernel_C2R_decomposed(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); -template -__global__ void thread_fft_kernel_C2R_decomposed_transposed(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); +template +__global__ void thread_fft_kernel_C2R_decomposed_transposed(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q); ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // End FFT Kernels ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// -template -__global__ void clip_into_top_left_kernel(InputType* input_values, OutputType* output_values, const short4 dims); +template +__global__ void clip_into_top_left_kernel(InputType* input_values, OutputBaseType* output_values, const short4 dims); // Modified from GpuImage::ClipIntoRealKernel -template -__global__ void clip_into_real_kernel(InputType* real_values_gpu, - OutputType* other_image_real_values_gpu, - short4 dims, - short4 other_dims, - int3 wanted_coordinate_of_box_center, - OutputType wanted_padding_value); +template +__global__ void clip_into_real_kernel(InputType* real_values_gpu, + OutputBaseType* other_image_real_values_gpu, + short4 dims, + short4 other_dims, + int3 wanted_coordinate_of_box_center, + OutputBaseType wanted_padding_value); + +// TODO: This would be much cleaner if we could first go from complex_compute_t -> float 2 then do conversions +// I think since this would be a compile time decision, it would be fine, but it would be good to confirm. +template +inline __device__ SetTo_t convert_if_needed(const GetFrom_t* __restrict__ ptr, const int idx) { + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; + + // For now, we are assuming (as everywhere else) that compute precision is never double + // and may in the future be _half. But is currently only float. + // FIXME: THis should be caught earlier I think. + if constexpr ( std::is_same_v ) { + static_no_doubles( ); + } + if constexpr ( std::is_same_v ) { + static_no_half_support_yet( ); + } + + if constexpr ( std::is_same_v, complex_compute_t> || std::is_same_v, float2> ) { + if constexpr ( std::is_same_v || std::is_same_v ) { + // In this case we assume we have a real valued result, packed into the first half of the complex array + // TODO: think about cases where we may hit this block unintentionally and how to catch this + return std::move(reinterpret_cast(ptr)[idx]); + } + else if constexpr ( std::is_same_v ) { + // In this case we assume we have a real valued result, packed into the first half of the complex array + // TODO: think about cases where we may hit this block unintentionally and how to catch this + return std::move(__float2half_rn(reinterpret_cast(ptr)[idx])); + } + else if constexpr ( std::is_same_v ) { + // Note: we will eventually need a similar hase for __nv_bfloat16 + // I think I may need to strip the const news for this to work + if constexpr ( std::is_same_v ) { + return std::move(__floats2half2_rn(ptr[idx].real( ), 0.f)); + } + else { + return std::move(__floats2half2_rn(static_cast(ptr)[idx], 0.f)); + } + } + else if constexpr ( std::is_same_v, complex_compute_t> && std::is_same_v, complex_compute_t> ) { + // return std::move(static_cast(ptr)[idx]); + return std::move(ptr[idx]); + } + else if constexpr ( std::is_same_v, complex_compute_t> && std::is_same_v, float2> ) { + // return std::move(static_cast(ptr)[idx]); + return std::move(SetTo_t{ptr[idx].real( ), ptr[idx].imag( )}); + } + else if constexpr ( std::is_same_v, float2> && std::is_same_v, complex_compute_t> ) { + // return std::move(static_cast(ptr)[idx]); + return std::move(SetTo_t{ptr[idx].x, ptr[idx].y}); + } + else if constexpr ( std::is_same_v, float2> && std::is_same_v, float2> ) { + // return std::move(static_cast(ptr)[idx]); + return std::move(ptr[idx]); + } + else { + static_no_match( ); + } + } + else if constexpr ( std::is_same_v, scalar_compute_t> || std::is_same_v, float> ) { + if constexpr ( std::is_same_v || std::is_same_v ) { + // In this case we assume we have a real valued result, packed into the first half of the complex array + // TODO: think about cases where we may hit this block unintentionally and how to catch this + return std::move(static_cast(ptr)[idx]); + } + else if constexpr ( std::is_same_v ) { + // In this case we assume we have a real valued result, packed into the first half of the complex array + // TODO: think about cases where we may hit this block unintentionally and how to catch this + return std::move(__float2half_rn(static_cast(ptr)[idx])); + } + else if constexpr ( std::is_same_v ) { + // Here we assume we are reading a real value and placeing it in a complex array. Could this go sideways? + return std::move(__floats2half2_rn(static_cast(ptr)[idx], 0.f)); + } + else if constexpr ( std::is_same_v, complex_compute_t> || std::is_same_v, float2> ) { + // Here we assume we are reading a real value and placeing it in a complex array. Could this go sideways? + return std::move(SetTo_t{static_cast(ptr)[idx], 0.f}); + } + else { + static_no_match( ); + } + } + else if constexpr ( std::is_same_v, __half> ) { + if constexpr ( std::is_same_v || std::is_same_v ) { + return std::move(__half2float(ptr[idx])); + } + else if constexpr ( std::is_same_v ) { + // In this case we assume we have a real valued result, packed into the first half of the complex array + // TODO: think about cases where we may hit this block unintentionally and how to catch this + return std::move(static_cast(ptr)[idx]); + } + else if constexpr ( std::is_same_v ) { + // Here we assume we are reading a real value and placeing it in a complex array. Could this go sideways? + // FIXME: For some reason CUDART_ZERO_FP16 is not defined even with cuda_fp16.h included + return std::move(__halves2half2(static_cast(ptr)[idx], __ushort_as_half((unsigned short)0x0000U))); + } + else if constexpr ( std::is_same_v, complex_compute_t> || std::is_same_v, float2> ) { + // Here we assume we are reading a real value and placeing it in a complex array. Could this go sideways? + return std::move(SetTo_t{__half2float(static_cast(ptr)[idx]), 0.f}); + } + else { + static_no_match( ); + } + } + else if constexpr ( std::is_same_v, __half2> ) { + if constexpr ( std::is_same_v || std::is_same_v || std::is_same_v ) { + // In this case we assume we have a real valued result, packed into the first half of the complex array + // TODO: think about cases where we may hit this block unintentionally and how to catch this + return std::move(reinterpret_cast(ptr)[idx]); + } + else if constexpr ( std::is_same_v ) { + // Here we assume we are reading a real value and placeing it in a complex array. Could this go sideways? + // FIXME: For some reason CUDART_ZERO_FP16 is not defined even with cuda_fp16.h included + return std::move(static_cast(ptr)[idx]); + } + else if constexpr ( std::is_same_v, complex_compute_t> || std::is_same_v, float2> ) { + // Here we assume we are reading a real value and placeing it in a complex array. Could this go sideways? + return std::move(SetTo_t{__low2float(static_cast(ptr)[idx]), __high2float(static_cast(ptr)[idx])}); + } + else { + static_no_match( ); + } + } + else { + static_no_match( ); + } +} ////////////////////////////////////////////// // IO functions adapted from the cufftdx examples @@ -482,27 +313,30 @@ __global__ void clip_into_real_kernel(InputType* real_values_gpu, template struct io { - using complex_type = typename FFT::value_type; - using scalar_type = typename complex_type::value_type; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; + + static inline __device__ unsigned int + stride_size( ) { - static inline __device__ unsigned int stride_size( ) { return cufftdx::size_of::value / FFT::elements_per_thread; } - static inline __device__ void load_r2c(const scalar_type* input, - complex_type* thread_data) { + template + static inline __device__ void load_r2c(const data_io_t* input, + complex_compute_t* thread_data) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - thread_data[i].x = input[index]; + thread_data[i].x = convert_if_needed(input, index); thread_data[i].y = 0.0f; index += stride; } } - static inline __device__ void store_r2c(const complex_type* thread_data, - complex_type* output, - int offset) { + static inline __device__ void store_r2c(const complex_compute_t* __restrict__ thread_data, + complex_compute_t* __restrict__ output, + int offset) { const unsigned int stride = stride_size( ); unsigned int index = offset + threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { @@ -522,14 +356,14 @@ struct io { // Since we can make repeated use of the same shared memory for each sub-fft // we use this method to load into shared mem instead of directly to registers // TODO set this up for async mem load - static inline __device__ void load_shared(const complex_type* input, - complex_type* shared_input, - complex_type* thread_data, - float* twiddle_factor_args, - float twiddle_in, - int* input_map, - int* output_map, - int Q) { + static inline __device__ void load_shared(const complex_compute_t* __restrict__ input, + complex_compute_t* __restrict__ shared_input, + complex_compute_t* __restrict__ thread_data, + float* __restrict__ twiddle_factor_args, + float twiddle_in, + int* input_map, + int* __restrict__ output_map, + int Q) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { @@ -545,30 +379,31 @@ struct io { // Since we can make repeated use of the same shared memory for each sub-fft // we use this method to load into shared mem instead of directly to registers // TODO set this up for async mem load - static inline __device__ void load_shared(const complex_type* input, - complex_type* shared_input, - complex_type* thread_data, - float* twiddle_factor_args, - float twiddle_in) { + template + static inline __device__ void load_shared(const data_io_t* __restrict__ input, + complex_compute_t* __restrict__ shared_input, + complex_compute_t* __restrict__ thread_data, + float* __restrict__ twiddle_factor_args, + float twiddle_in) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { twiddle_factor_args[i] = twiddle_in * index; - thread_data[i] = input[index]; + thread_data[i] = convert_if_needed(input, index); shared_input[index] = thread_data[i]; index += stride; } } - static inline __device__ void load_shared(const complex_type* input, - complex_type* shared_input, - complex_type* thread_data, - float* twiddle_factor_args, - float twiddle_in, - int* input_map, - int* output_map, - int Q, - int number_of_elements) { + static inline __device__ void load_shared(const complex_compute_t* __restrict__ input, + complex_compute_t* __restrict__ shared_input, + complex_compute_t* __restrict__ thread_data, + float* __restrict__ twiddle_factor_args, + float twiddle_in, + int* __restrict__ input_map, + int* __restrict__ output_map, + int Q, + int number_of_elements) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { @@ -589,14 +424,15 @@ struct io { // Since we can make repeated use of the same shared memory for each sub-fft // we use this method to load into shared mem instead of directly to registers // TODO set this up for async mem load - alternatively, load to registers then copy but leave in register for firt compute - static inline __device__ void load_r2c_shared(const scalar_type* input, - scalar_type* shared_input, - complex_type* thread_data, - float* twiddle_factor_args, - float twiddle_in, - int* input_map, - int* output_map, - int Q) { + template + static inline __device__ void load_r2c_shared(const data_io_t* __restrict__ input, + scalar_compute_t* __restrict__ shared_input, + complex_compute_t* __restrict__ thread_data, + float* __restrict__ twiddle_factor_args, + float twiddle_in, + int* __restrict__ input_map, + int* __restrict__ output_map, + int Q) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { @@ -604,7 +440,7 @@ struct io { input_map[i] = index; output_map[i] = Q * index; twiddle_factor_args[i] = twiddle_in * index; - thread_data[i].x = input[index]; + thread_data[i].x = convert_if_needed(input, index); thread_data[i].y = 0.0f; shared_input[index] = thread_data[i].x; index += stride; @@ -614,38 +450,40 @@ struct io { // Since we can make repeated use of the same shared memory for each sub-fft // we use this method to load into shared mem instead of directly to registers // TODO set this up for async mem load - alternatively, load to registers then copy but leave in register for firt compute - static inline __device__ void load_r2c_shared(const scalar_type* input, - scalar_type* shared_input, - complex_type* thread_data, - float* twiddle_factor_args, - float twiddle_in) { + template + static inline __device__ void load_r2c_shared(const data_io_t* __restrict__ input, + scalar_compute_t* __restrict__ shared_input, + complex_compute_t* __restrict__ thread_data, + float* __restrict__ twiddle_factor_args, + float twiddle_in) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { twiddle_factor_args[i] = twiddle_in * index; - thread_data[i].x = input[index]; + thread_data[i].x = convert_if_needed(input, index); thread_data[i].y = 0.0f; shared_input[index] = thread_data[i].x; index += stride; } } - static inline __device__ void load_r2c_shared_and_pad(const scalar_type* input, - complex_type* shared_mem) { + template + static inline __device__ void load_r2c_shared_and_pad(const data_io_t* __restrict__ input, + complex_compute_t* __restrict__ shared_mem) { const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x + (threadIdx.z * size_of::value); + unsigned int index = threadIdx.x + (threadIdx.y * size_of::value); for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - shared_mem[GetSharedMemPaddedIndex(index)] = complex_type(input[index], 0.f); + shared_mem[GetSharedMemPaddedIndex(index)] = complex_compute_t(convert_if_needed(input, index), 0.f); index += stride; } __syncthreads( ); } - static inline __device__ void copy_from_shared(const complex_type* shared_mem, - complex_type* thread_data, - const unsigned int Q) { + static inline __device__ void copy_from_shared(const complex_compute_t* __restrict__ shared_mem, + complex_compute_t* __restrict__ thread_data, + const unsigned int Q) { const unsigned int stride = stride_size( ) * Q; // I think the Q is needed, but double check me TODO - unsigned int index = (threadIdx.x * Q) + threadIdx.z; + unsigned int index = (threadIdx.x * Q) + threadIdx.y; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { thread_data[i] = shared_mem[GetSharedMemPaddedIndex(index)]; index += stride; @@ -656,18 +494,18 @@ struct io { // Note that unlike most functions in this file, this one does not have a // const decorator on the thread mem, as we want to modify it with the twiddle factors // before reducing the full shared mem space. - static inline __device__ void reduce_block_fft(complex_type* thread_data, - complex_type* shared_mem, + static inline __device__ void reduce_block_fft(complex_compute_t* __restrict__ thread_data, + complex_compute_t* __restrict__ shared_mem, const float twiddle_in, const unsigned int Q) { const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x + (threadIdx.z * size_of::value); - complex_type twiddle; + unsigned int index = threadIdx.x + (threadIdx.y * size_of::value); + complex_compute_t twiddle; // In the first loop, all threads participate and write back to natural order in shared memory // while also updating with the full size twiddle factor. for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - // ( index * threadIdx.z) == ( k % P * n2 ) - SINCOS(twiddle_in * (index * threadIdx.z), &twiddle.y, &twiddle.x); + // ( index * threadIdx.y) == ( k % P * n2 ) + SINCOS(twiddle_in * (index * threadIdx.y), &twiddle.y, &twiddle.x); thread_data[i] *= twiddle; shared_mem[GetSharedMemPaddedIndex(index)] = thread_data[i]; @@ -679,7 +517,7 @@ struct io { // Reuse index for ( index = 2; index <= Q; index *= 2 ) { // Some threads drop out each loop - if ( threadIdx.z % index == 0 ) { + if ( threadIdx.y % index == 0 ) { for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { thread_data[i] += shared_mem[GetSharedMemPaddedIndex(threadIdx.x + (i * stride) + (index / 2 * size_of::value))]; } @@ -689,18 +527,19 @@ struct io { } } - static inline __device__ void store_r2c_reduced(const complex_type* thread_data, - complex_type* output, - const unsigned int pixel_pitch, - const unsigned int memory_limit) { - if ( threadIdx.z == 0 ) { + template + static inline __device__ void store_r2c_reduced(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + const unsigned int pixel_pitch, + const unsigned int memory_limit) { + if ( threadIdx.y == 0 ) { // Finally we write out the first size_of::values to global const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i <= FFT::elements_per_thread / 2; i++ ) { if ( index < memory_limit ) { // transposed index. - output[index * pixel_pitch + blockIdx.y] = thread_data[i]; + output[index * pixel_pitch + blockIdx.y] = convert_if_needed(thread_data, i); } index += stride; } @@ -709,25 +548,25 @@ struct io { // when using load_shared || load_r2c_shared, we need then copy from shared mem into the registers. // notice we still need the packed complex values for the xform. - static inline __device__ void copy_from_shared(const scalar_type* shared_input, - complex_type* thread_data, - int* input_map) { + static inline __device__ void copy_from_shared(const scalar_compute_t* __restrict__ shared_input, + complex_compute_t* __restrict__ thread_data, + int* input_map) { for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { thread_data[i].x = shared_input[input_map[i]]; thread_data[i].y = 0.0f; } } - static inline __device__ void copy_from_shared(const complex_type* shared_input_complex, - complex_type* thread_data, - int* input_map) { + static inline __device__ void copy_from_shared(const complex_compute_t* __restrict__ shared_input_complex, + complex_compute_t* __restrict__ thread_data, + int* __restrict__ input_map) { for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { thread_data[i] = shared_input_complex[input_map[i]]; } } - static inline __device__ void copy_from_shared(const scalar_type* shared_input, - complex_type* thread_data) { + static inline __device__ void copy_from_shared(const scalar_compute_t* __restrict__ shared_input, + complex_compute_t* __restrict__ thread_data) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { @@ -737,8 +576,8 @@ struct io { } } - static inline __device__ void copy_from_shared(const complex_type* shared_input_complex, - complex_type* thread_data) { + static inline __device__ void copy_from_shared(const complex_compute_t* __restrict__ shared_input_complex, + complex_compute_t* __restrict__ thread_data) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { @@ -747,37 +586,58 @@ struct io { } } - static inline __device__ void load_shared_and_conj_multiply(const complex_type* image_to_search, - complex_type* thread_data) { + template + static inline __device__ void load_shared_and_conj_multiply(ExternalImage_t* __restrict__ image_to_search, + complex_compute_t* __restrict__ thread_data) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; - complex_type c; - for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - c.x = (thread_data[i].x * image_to_search[index].x + thread_data[i].y * image_to_search[index].y); - c.y = (thread_data[i].y * image_to_search[index].x - thread_data[i].x * image_to_search[index].y); - // a * conj b - thread_data[i] = c; //ComplexConjMulAndScale(thread_data[i], image_to_search[index], 1.0f); - index += stride; + complex_compute_t c; + if constexpr ( std::is_same_v ) { + for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { + c.x = (thread_data[i].x * __low2float(image_to_search[index]) + thread_data[i].y * __high2float(image_to_search[index].y)); + c.y = (thread_data[i].y * __low2float(image_to_search[index]) - thread_data[i].x * __high2float(image_to_search[index].y)); + thread_data[i] = c; + index += stride; + } + } + else if constexpr ( std::is_same_v ) { + for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { + c.x = (thread_data[i].x * image_to_search[index].x + thread_data[i].y * image_to_search[index].y); + c.y = (thread_data[i].y * image_to_search[index].x - thread_data[i].x * image_to_search[index].y); + thread_data[i] = c; + index += stride; + } + } + else { + static_assert_type_name(image_to_search); } } // TODO: set user lambda to default = false, then get rid of other load_shared - template - static inline __device__ void load_shared(const complex_type* image_to_search, - complex_type* thread_data, - FunctionType intra_op_functor = nullptr) { + template + static inline __device__ void load_shared(const ExternalImage_t* __restrict__ image_to_search, + complex_compute_t* __restrict__ thread_data, + FunctionType intra_op_functor = nullptr) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; if constexpr ( IS_IKF_t( ) ) { - for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - intra_op_functor(thread_data[i].x, thread_data[i].y, image_to_search[index].x, image_to_search[index].y); //ComplexConjMulAndScale(thread_data[i], image_to_search[index], 1.0f); - index += stride; + if constexpr ( std::is_same_v ) { + for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { + intra_op_functor(thread_data[i].x, thread_data[i].y, __low2float(image_to_search[index]), __high2float(image_to_search[index])); + index += stride; + } + } + else { + for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { + intra_op_functor(thread_data[i].x, thread_data[i].y, image_to_search[index].x, image_to_search[index].y); + index += stride; + } } } else { for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { // a * conj b - thread_data[i] = thread_data[i], image_to_search[index]; //ComplexConjMulAndScale(thread_data[i], image_to_search[index], 1.0f); + thread_data[i] = thread_data[i], image_to_search[index]; index += stride; } } @@ -785,40 +645,40 @@ struct io { // Now we need send to shared mem and transpose on the way // TODO: fix bank conflicts later. - static inline __device__ void transpose_r2c_in_shared_XZ(complex_type* shared_mem, - complex_type* thread_data) { + static inline __device__ void transpose_r2c_in_shared_XZ(complex_compute_t* __restrict__ shared_mem, + complex_compute_t* __restrict__ thread_data) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { - shared_mem[threadIdx.z + index * XZ_STRIDE] = thread_data[i]; + shared_mem[threadIdx.y + index * XZ_STRIDE] = thread_data[i]; index += stride; } constexpr unsigned int threads_per_fft = cufftdx::size_of::value / FFT::elements_per_thread; constexpr unsigned int output_values_to_store = (cufftdx::size_of::value / 2) + 1; constexpr unsigned int values_left_to_store = threads_per_fft == 1 ? 1 : (output_values_to_store % threads_per_fft); if ( threadIdx.x < values_left_to_store ) { - shared_mem[threadIdx.z + index * XZ_STRIDE] = thread_data[FFT::elements_per_thread / 2]; + shared_mem[threadIdx.y + index * XZ_STRIDE] = thread_data[FFT::elements_per_thread / 2]; } __syncthreads( ); } // Now we need send to shared mem and transpose on the way // TODO: fix bank conflicts later. - static inline __device__ void transpose_in_shared_XZ(complex_type* shared_mem, - complex_type* thread_data) { + static inline __device__ void transpose_in_shared_XZ(complex_compute_t* __restrict__ shared_mem, + complex_compute_t* __restrict__ thread_data) { const unsigned int stride = io::stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - // return (XZ_STRIDE*blockIdx.z + threadIdx.z) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + X * gridDim.y ); + // return (XZ_STRIDE*blockIdx.z + threadIdx.y) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + X * gridDim.y ); // XZ_STRIDE == XZ_STRIDE - shared_mem[threadIdx.z + index * XZ_STRIDE] = thread_data[i]; + shared_mem[threadIdx.y + index * XZ_STRIDE] = thread_data[i]; index += stride; } __syncthreads( ); } - static inline __device__ void store_r2c_transposed_xz(const complex_type* thread_data, - complex_type* output) { + static inline __device__ void store_r2c_transposed_xz(const complex_compute_t* __restrict__ thread_data, + complex_compute_t* __restrict__ output) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { @@ -835,103 +695,109 @@ struct io { } // Store a transposed tile, made up of contiguous (full) FFTS - static inline __device__ void store_r2c_transposed_xz_strided_Z(const complex_type* shared_mem, - complex_type* output) { + template + static inline __device__ void store_r2c_transposed_xz_strided_Z(const complex_compute_t* __restrict__ shared_mem, + data_io_t* __restrict__ output) { const unsigned int stride = stride_size( ); constexpr unsigned int output_values_to_store = (cufftdx::size_of::value / 2) + 1; - unsigned int index = threadIdx.x + threadIdx.z * output_values_to_store; + unsigned int index = threadIdx.x + threadIdx.y * output_values_to_store; for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { - output[Return1DFFTAddress_XZ_transpose_strided_Z(index)] = shared_mem[index]; + output[Return1DFFTAddress_XZ_transpose_strided_Z(index)] = convert_if_needed(shared_mem, index); index += stride; } constexpr unsigned int threads_per_fft = cufftdx::size_of::value / FFT::elements_per_thread; constexpr unsigned int values_left_to_store = threads_per_fft == 1 ? 1 : (output_values_to_store % threads_per_fft); if ( threadIdx.x < values_left_to_store ) { - output[Return1DFFTAddress_XZ_transpose_strided_Z(index)] = shared_mem[index]; + output[Return1DFFTAddress_XZ_transpose_strided_Z(index)] = convert_if_needed(shared_mem, index); } __syncthreads( ); } // Store a transposed tile, made up of non-contiguous (strided partial) FFTS // - static inline __device__ void store_r2c_transposed_xz_strided_Z(const complex_type* shared_mem, - complex_type* output, - const unsigned int Q, - const unsigned int sub_fft) { + template + static inline __device__ void store_r2c_transposed_xz_strided_Z(const complex_compute_t* __restrict__ shared_mem, + data_io_t* __restrict__ output, + const unsigned int Q, + const unsigned int sub_fft) { const unsigned int stride = stride_size( ); constexpr unsigned int output_values_to_store = (cufftdx::size_of::value / 2) + 1; - unsigned int index = threadIdx.x + threadIdx.z * output_values_to_store; + unsigned int index = threadIdx.x + threadIdx.y * output_values_to_store; for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { - output[Return1DFFTAddress_XZ_transpose_strided_Z(index, Q, sub_fft)] = shared_mem[index]; + output[Return1DFFTAddress_XZ_transpose_strided_Z(index, Q, sub_fft)] = convert_if_needed(shared_mem, index); index += stride; } constexpr unsigned int threads_per_fft = cufftdx::size_of::value / FFT::elements_per_thread; constexpr unsigned int values_left_to_store = threads_per_fft == 1 ? 1 : (output_values_to_store % threads_per_fft); if ( threadIdx.x < values_left_to_store ) { - output[Return1DFFTAddress_XZ_transpose_strided_Z(index, Q, sub_fft)] = shared_mem[index]; + output[Return1DFFTAddress_XZ_transpose_strided_Z(index, Q, sub_fft)] = convert_if_needed(shared_mem, index); } __syncthreads( ); } - static inline __device__ void store_transposed_xz_strided_Z(const complex_type* shared_mem, - complex_type* output) { + template + static inline __device__ void store_transposed_xz_strided_Z(const complex_compute_t* __restrict__ shared_mem, + data_io_t* __restrict__ output) { const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x + threadIdx.z * cufftdx::size_of::value; + unsigned int index = threadIdx.x + threadIdx.y * cufftdx::size_of::value; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - output[Return1DFFTAddress_XZ_transpose_strided_Z(index)] = shared_mem[index]; + output[Return1DFFTAddress_XZ_transpose_strided_Z(index)] = convert_if_needed(shared_mem, index); index += stride; } __syncthreads( ); } - static inline __device__ void store_r2c_transposed_xy(const complex_type* thread_data, - complex_type* output, - int pixel_pitch) { + template + static inline __device__ void store_r2c_transposed_xy(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + int pixel_pitch) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { // output map is thread local, so output_MAP[i] gives the x-index in the non-transposed array and blockIdx.y gives the y-index - output[index * pixel_pitch + blockIdx.y] = thread_data[i]; + output[index * pixel_pitch + blockIdx.y] = convert_if_needed(thread_data, i); index += stride; } constexpr unsigned int threads_per_fft = cufftdx::size_of::value / FFT::elements_per_thread; constexpr unsigned int output_values_to_store = (cufftdx::size_of::value / 2) + 1; constexpr unsigned int values_left_to_store = threads_per_fft == 1 ? 1 : (output_values_to_store % threads_per_fft); if ( threadIdx.x < values_left_to_store ) { - output[index * pixel_pitch + blockIdx.y] = thread_data[FFT::elements_per_thread / 2]; + output[index * pixel_pitch + blockIdx.y] = convert_if_needed(thread_data, FFT::elements_per_thread / 2); } } - static inline __device__ void store_r2c_transposed_xy(const complex_type* thread_data, - complex_type* output, - int* output_MAP, - int pixel_pitch) { + template + static inline __device__ void store_r2c_transposed_xy(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + int* output_MAP, + int pixel_pitch) { const unsigned int stride = stride_size( ); for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { // output map is thread local, so output_MAP[i] gives the x-index in the non-transposed array and blockIdx.y gives the y-index - output[output_MAP[i] * pixel_pitch + blockIdx.y] = thread_data[i]; + output[output_MAP[i] * pixel_pitch + blockIdx.y] = convert_if_needed(thread_data, i); // if (blockIdx.y == 32) printf("from store transposed %i , val %f %f\n", output_MAP[i], thread_data[i].x, thread_data[i].y); } constexpr unsigned int threads_per_fft = cufftdx::size_of::value / FFT::elements_per_thread; constexpr unsigned int output_values_to_store = (cufftdx::size_of::value / 2) + 1; constexpr unsigned int values_left_to_store = threads_per_fft == 1 ? 1 : (output_values_to_store % threads_per_fft); if ( threadIdx.x < values_left_to_store ) { - output[output_MAP[FFT::elements_per_thread / 2] * pixel_pitch + blockIdx.y] = thread_data[FFT::elements_per_thread / 2]; + output[output_MAP[FFT::elements_per_thread / 2] * pixel_pitch + blockIdx.y] = convert_if_needed(thread_data, FFT::elements_per_thread / 2); } } - static inline __device__ void store_r2c_transposed_xy(const complex_type* thread_data, - complex_type* output, - int* output_MAP, - int pixel_pitch, - int memory_limit) { + template + static inline __device__ void store_r2c_transposed_xy(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + int* __restrict__ output_MAP, + int pixel_pitch, + int memory_limit) { const unsigned int stride = stride_size( ); for ( unsigned int i = 0; i <= FFT::elements_per_thread / 2; i++ ) { // output map is thread local, so output_MAP[i] gives the x-index in the non-transposed array and blockIdx.y gives the y-index // if (blockIdx.y == 1) printf("index, pitch, blcok, address %i, %i, %i, %i\n", output_MAP[i], pixel_pitch, memory_limit, output_MAP[i]*pixel_pitch + blockIdx.y); if ( output_MAP[i] < memory_limit ) - output[output_MAP[i] * pixel_pitch + blockIdx.y] = thread_data[i]; + output[output_MAP[i] * pixel_pitch + blockIdx.y] = convert_if_needed(thread_data, i); // if (blockIdx.y == 32) printf("from store transposed %i , val %f %f\n", output_MAP[i], thread_data[i].x, thread_data[i].y); } // constexpr unsigned int threads_per_fft = cufftdx::size_of::value / FFT::elements_per_thread; @@ -944,12 +810,13 @@ struct io { // } } - static inline __device__ void load_c2r(const complex_type* input, - complex_type* thread_data) { + template + static inline __device__ void load_c2r(const data_io_t* __restrict__ input, + complex_compute_t* __restrict__ thread_data) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { - thread_data[i] = input[index]; + thread_data[i] = convert_if_needed(input, index); index += stride; } constexpr unsigned int threads_per_fft = cufftdx::size_of::value / FFT::elements_per_thread; @@ -957,17 +824,18 @@ struct io { // threads_per_fft == 1 means that EPT == SIZE, so we need to load one more element constexpr unsigned int values_left_to_load = threads_per_fft == 1 ? 1 : (output_values_to_load % threads_per_fft); if ( threadIdx.x < values_left_to_load ) { - thread_data[FFT::elements_per_thread / 2] = input[index]; + thread_data[FFT::elements_per_thread / 2] = convert_if_needed(input, index); } } - static inline __device__ void load_c2r_transposed(const complex_type* input, - complex_type* thread_data, - unsigned int pixel_pitch) { + template + static inline __device__ void load_c2r_transposed(const data_io_t* __restrict__ input, + complex_compute_t* __restrict__ thread_data, + unsigned int pixel_pitch) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { - thread_data[i] = input[(pixel_pitch * index) + blockIdx.y]; + thread_data[i] = convert_if_needed(input, (pixel_pitch * index) + blockIdx.y); index += stride; } constexpr unsigned int threads_per_fft = cufftdx::size_of::value / FFT::elements_per_thread; @@ -975,15 +843,15 @@ struct io { // threads_per_fft == 1 means that EPT == SIZE, so we need to load one more element constexpr unsigned int values_left_to_load = threads_per_fft == 1 ? 1 : (output_values_to_load % threads_per_fft); if ( threadIdx.x < values_left_to_load ) { - thread_data[FFT::elements_per_thread / 2] = input[(pixel_pitch * index) + blockIdx.y]; + thread_data[FFT::elements_per_thread / 2] = convert_if_needed(input, (pixel_pitch * index) + blockIdx.y); } } - static inline __device__ void load_c2r_shared_and_pad(const complex_type* input, - complex_type* shared_mem, - const unsigned int pixel_pitch) { + static inline __device__ void load_c2r_shared_and_pad(const complex_compute_t* __restrict__ input, + complex_compute_t* __restrict__ shared_mem, + const unsigned int pixel_pitch) { const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x + (threadIdx.z * size_of::value); + unsigned int index = threadIdx.x + (threadIdx.y * size_of::value); for ( unsigned int i = 0; i < FFT::elements_per_thread / 2; i++ ) { shared_mem[GetSharedMemPaddedIndex(index)] = input[pixel_pitch * index]; index += stride; @@ -999,67 +867,59 @@ struct io { } // this may benefit from asynchronous execution - static inline __device__ void load(const complex_type* input, - complex_type* thread_data) { + template + static inline __device__ void load(const data_io_t* __restrict__ input, + complex_compute_t* __restrict__ thread_data) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - thread_data[i] = input[index]; - // if (blockIdx.y == 0) printf("block %i , val %f %f\n", index, input[index].x, input[index].y); - - index += stride; - } - } - - // this may benefit from asynchronous execution - static inline __device__ void load(const complex_type* input, - complex_type* thread_data, - int last_index_to_load) { - const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x; - for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - if ( index < last_index_to_load ) - thread_data[i] = input[index]; - else - thread_data[i] = complex_type(0.0f, 0.0f); + thread_data[i] = convert_if_needed(input, index); index += stride; } } // TODO: set pre_op_functor to default=false and get rid of other load - template - static inline __device__ void load(const complex_type* input, - complex_type* thread_data, - int last_index_to_load, - FunctionType pre_op_functor = nullptr) { + template + static inline __device__ void load(const data_io_t* __restrict__ input, + complex_compute_t* __restrict__ thread_data, + int last_index_to_load, + FunctionType pre_op_functor = nullptr) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; + // FIXME: working out how to use these functors and this is NOT what is intended if constexpr ( IS_IKF_t( ) ) { + float2 temp; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - if ( index < last_index_to_load ) - thread_data[i] = pre_op_functor(input[index]); - else - thread_data[i] = pre_op_functor(complex_type(0.0f, 0.0f)); + if ( index < last_index_to_load ) { + temp = pre_op_functor(convert_if_needed(input, index)); + thread_data[i] = convert_if_needed(&temp, 0); + } + else { + // thread_data[i] = complex_compute_t{0.0f, 0.0f}; + temp = pre_op_functor(float2{0.0f, 0.0f}); + thread_data[i] = convert_if_needed(&temp, 0); + } + index += stride; } } else { for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { if ( index < last_index_to_load ) - thread_data[i] = input[index]; + thread_data[i] = convert_if_needed(input, index); else - thread_data[i] = complex_type(0.0f, 0.0f); + thread_data[i] = complex_compute_t{0.0f, 0.0f}; index += stride; } } } - static inline __device__ void store_and_swap_quadrants(const complex_type* thread_data, - complex_type* output, - int first_negative_index) { + static inline __device__ void store_and_swap_quadrants(const complex_compute_t* __restrict__ thread_data, + complex_compute_t* __restrict__ output, + int first_negative_index) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; - complex_type phase_shift; + complex_compute_t phase_shift; int logical_y; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { // If no kernel based changes are made to source_idx, this will be the same as the original index value @@ -1074,12 +934,12 @@ struct io { } } - static inline __device__ void store_and_swap_quadrants(const complex_type* thread_data, - complex_type* output, - int* source_idx, - int first_negative_index) { + static inline __device__ void store_and_swap_quadrants(const complex_compute_t* __restrict__ thread_data, + complex_compute_t* __restrict__ output, + int* __restrict__ source_idx, + int first_negative_index) { const unsigned int stride = stride_size( ); - complex_type phase_shift; + complex_compute_t phase_shift; int logical_y; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { // If no kernel based changes are made to source_idx, this will be the same as the original index value @@ -1093,205 +953,222 @@ struct io { } } - template - static inline __device__ void store(const complex_type* thread_data, - complex_type* output, - FunctionType post_op_functor = nullptr) { + template + static inline __device__ void store(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + FunctionType post_op_functor = nullptr) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; if constexpr ( IS_IKF_t( ) ) { for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - output[index] = post_op_functor(thread_data[i]); + output[index] = post_op_functor(convert_if_needed(thread_data, i)); index += stride; } } else { for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - output[index] = thread_data[i]; + output[index] = convert_if_needed(thread_data, i); index += stride; } } } - static inline __device__ void store(const complex_type* thread_data, - complex_type* output, - const unsigned int Q, - const unsigned int sub_fft) { + template + static inline __device__ void store(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + const unsigned int Q, + const unsigned int sub_fft) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - output[index * Q + sub_fft] = thread_data[i]; + output[index * Q + sub_fft] = convert_if_needed(thread_data, i); index += stride; } } - static inline __device__ void store_Z(const complex_type* shared_mem, - complex_type* output) { + template + static inline __device__ void store_Z(const complex_compute_t* __restrict__ shared_mem, + data_io_t* __restrict__ output) { const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x + threadIdx.z * size_of::value; + unsigned int index = threadIdx.x + threadIdx.y * size_of::value; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - output[Return1DFFTAddress_YZ_transpose_strided_Z(index)] = shared_mem[index]; + output[Return1DFFTAddress_YZ_transpose_strided_Z(index)] = convert_if_needed(shared_mem, index); index += stride; } } - static inline __device__ void store_Z(const complex_type* shared_mem, - complex_type* output, - const unsigned int Q, - const unsigned int sub_fft) { + template + static inline __device__ void store_Z(const complex_compute_t* __restrict__ shared_mem, + data_io_t* __restrict__ output, + const unsigned int Q, + const unsigned int sub_fft) { const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x + threadIdx.z * size_of::value; + unsigned int index = threadIdx.x + threadIdx.y * size_of::value; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - output[Return1DFFTAddress_YZ_transpose_strided_Z(index, Q, sub_fft)] = shared_mem[index]; + output[Return1DFFTAddress_YZ_transpose_strided_Z(index, Q, sub_fft)] = convert_if_needed(shared_mem, index); index += stride; } __syncthreads( ); } - static inline __device__ void store(const complex_type* thread_data, - complex_type* output, - unsigned int memory_limit) { + template + static inline __device__ void store(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + unsigned int memory_limit) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { if ( index < memory_limit ) - output[index] = thread_data[i]; + output[index] = convert_if_needed(thread_data, i); index += stride; } } - static inline __device__ void store(const complex_type* thread_data, - complex_type* output, - int* source_idx) { + template + static inline __device__ void store(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + int* __restrict__ source_idx) { const unsigned int stride = stride_size( ); for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { // If no kernel based changes are made to source_idx, this will be the same as the original index value - output[source_idx[i]] = thread_data[i]; + output[source_idx[i]] = convert_if_needed(thread_data, i); } } - static inline __device__ void store_subset(const complex_type* thread_data, - complex_type* output, - int* source_idx) { + template + static inline __device__ void store_subset(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + int* __restrict__ source_idx) { const unsigned int stride = stride_size( ); for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { // If no kernel based changes are made to source_idx, this will be the same as the original index value if ( source_idx[i] >= 0 ) - output[source_idx[i]] = thread_data[i]; + output[source_idx[i]] = convert_if_needed(thread_data, i); } } - static inline __device__ void store_coalesced(const complex_type* shared_output, - complex_type* global_output, - int offset) { + template + static inline __device__ void store_coalesced(const complex_compute_t* __restrict__ shared_output, + data_io_t* __restrict__ global_output, + int offset) { const unsigned int stride = stride_size( ); unsigned int index = offset + threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - global_output[index] = shared_output[index]; + global_output[index] = convert_if_needed(shared_output, index); index += stride; } } - static inline __device__ void load_c2c_shared_and_pad(const complex_type* input, - complex_type* shared_mem) { + template + static inline __device__ void load_c2c_shared_and_pad(const data_io_t* __restrict__ input, + complex_compute_t* __restrict__ shared_mem) { const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x + (threadIdx.z * size_of::value); + unsigned int index = threadIdx.x + (threadIdx.y * size_of::value); for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - shared_mem[GetSharedMemPaddedIndex(index)] = input[index]; + shared_mem[GetSharedMemPaddedIndex(index)] = convert_if_needed(input, index); index += stride; } __syncthreads( ); } - static inline __device__ void store_c2c_reduced(const complex_type* thread_data, - complex_type* output) { - if ( threadIdx.z == 0 ) { + template + static inline __device__ void store_c2c_reduced(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output) { + if ( threadIdx.y == 0 ) { // Finally we write out the first size_of::values to global const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x + (threadIdx.z * size_of::value); + unsigned int index = threadIdx.x + (threadIdx.y * size_of::value); for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { if ( index < size_of::value ) { // transposed index. - output[index] = thread_data[i]; + output[index] = convert_if_needed(thread_data, i); } index += stride; } } } - static inline __device__ void store_c2r_reduced(const complex_type* thread_data, - scalar_type* output) { - if ( threadIdx.z == 0 ) { + template + static inline __device__ void store_c2r_reduced(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output) { + if ( threadIdx.y == 0 ) { // Finally we write out the first size_of::values to global const unsigned int stride = stride_size( ); - unsigned int index = threadIdx.x + (threadIdx.z * size_of::value); + unsigned int index = threadIdx.x + (threadIdx.y * size_of::value); + for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { if ( index < size_of::value ) { // transposed index. - output[index] = reinterpret_cast(thread_data)[i]; + output[index] = convert_if_needed(thread_data, i); } index += stride; } } } - static inline __device__ void store_transposed(const complex_type* thread_data, - complex_type* output, - int* output_map, - int* rotated_offset, - int memory_limit) { + template + static inline __device__ void store_transposed(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + int* __restrict__ output_map, + int* __restrict__ rotated_offset, + int memory_limit) { for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { // If no kernel based changes are made to source_idx, this will be the same as the original index value if ( output_map[i] < memory_limit ) - output[rotated_offset[1] * output_map[i] + rotated_offset[0]] = thread_data[i]; + output[rotated_offset[1] * output_map[i] + rotated_offset[0]] = convert_if_needed(thread_data, i); } } - static inline __device__ void store_c2r(const complex_type* thread_data, - scalar_type* output) { + template + static inline __device__ void store_c2r(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; + for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - output[index] = reinterpret_cast(thread_data)[i]; + output[index] = convert_if_needed(thread_data, i); index += stride; } } - static inline __device__ void store_c2r(const complex_type* thread_data, - scalar_type* output, - unsigned int memory_limit) { + template + static inline __device__ void store_c2r(const complex_compute_t* __restrict__ thread_data, + data_io_t* __restrict__ output, + unsigned int memory_limit) { const unsigned int stride = stride_size( ); unsigned int index = threadIdx.x; + for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - // TODO: does reinterpret_cast(thread_data)[i] make more sense than just thread_data[i].x?? + // TODO: does reinterpret_cast(thread_data)[i] make more sense than just thread_data[i].x?? if ( index < memory_limit ) - output[index] = reinterpret_cast(thread_data)[i]; + output[index] = convert_if_needed(thread_data, i); index += stride; } } -}; // struct io} +}; template struct io_thread { - using complex_type = typename FFT::value_type; - using scalar_type = typename complex_type::value_type; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - static inline __device__ void load_r2c(const scalar_type* input, - complex_type* thread_data, - const int stride) { + template + static inline __device__ void load_r2c(const data_io_t* __restrict__ input, + complex_compute_t* __restrict__ thread_data, + const int stride) { unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < size_of::value; i++ ) { - thread_data[i].x = input[index]; - thread_data[i].y = scalar_type(0); + thread_data[i].x = convert_if_needed(input, index); + thread_data[i].y = scalar_compute_t{ }; index += stride; } } - static inline __device__ void store_r2c(const complex_type* shared_output, - complex_type* output, - const int stride, - const int memory_limit) { + static inline __device__ void store_r2c(const complex_compute_t* __restrict__ shared_output, + complex_compute_t* __restrict__ output, + const int stride, + const int memory_limit) { // Each thread reads in the input data at stride = mem_offsets.Q unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < size_of::value / 2; i++ ) { @@ -1303,30 +1180,31 @@ struct io_thread { } } - static inline __device__ void store_r2c_transposed_xy(const complex_type* shared_output, - complex_type* output, - int stride, - int pixel_pitch, - int memory_limit) { + template + static inline __device__ void store_r2c_transposed_xy(const complex_compute_t* __restrict__ shared_output, + data_io_t* __restrict__ output, + int stride, + int pixel_pitch, + int memory_limit) { // Each thread reads in the input data at stride = mem_offsets.Q unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < size_of::value / 2; i++ ) { - output[index * pixel_pitch] = shared_output[index]; + output[index * pixel_pitch] = convert_if_needed(shared_output, index); index += stride; } if ( index < memory_limit ) { - output[index * pixel_pitch] = shared_output[index]; + output[index * pixel_pitch] = convert_if_needed(shared_output, index); } } - static inline __device__ void remap_decomposed_segments(const complex_type* thread_data, - complex_type* shared_output, - float twiddle_in, - int Q, - int memory_limit) { + static inline __device__ void remap_decomposed_segments(const complex_compute_t* __restrict__ thread_data, + complex_compute_t* __restrict__ shared_output, + float twiddle_in, + int Q, + int memory_limit) { // Unroll the first loop and initialize the shared mem. - complex_type twiddle; - int index = threadIdx.x * size_of::value; + complex_compute_t twiddle; + int index = threadIdx.x * size_of::value; twiddle_in *= threadIdx.x; // twiddle factor arg now just needs to multiplied by K = (index + i) for ( unsigned int i = 0; i < size_of::value; i++ ) { SINCOS(twiddle_in * (index + i), &twiddle.y, &twiddle.x); @@ -1351,9 +1229,9 @@ struct io_thread { __syncthreads( ); } - static inline __device__ void load_c2c(const complex_type* input, - complex_type* thread_data, - const int stride) { + static inline __device__ void load_c2c(const complex_compute_t* __restrict__ input, + complex_compute_t* __restrict__ thread_data, + const int stride) { unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < size_of::value; i++ ) { thread_data[i] = input[index]; @@ -1361,9 +1239,9 @@ struct io_thread { } } - static inline __device__ void store_c2c(const complex_type* shared_output, - complex_type* output, - const int stride) { + static inline __device__ void store_c2c(const complex_compute_t* __restrict__ shared_output, + complex_compute_t* __restrict__ output, + const int stride) { // Each thread reads in the input data at stride = mem_offsets.Q unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < size_of::value; i++ ) { @@ -1372,13 +1250,13 @@ struct io_thread { } } - static inline __device__ void remap_decomposed_segments(const complex_type* thread_data, - complex_type* shared_output, - float twiddle_in, - int Q) { + static inline __device__ void remap_decomposed_segments(const complex_compute_t* __restrict__ thread_data, + complex_compute_t* __restrict__ shared_output, + float twiddle_in, + int Q) { // Unroll the first loop and initialize the shared mem. - complex_type twiddle; - int index = threadIdx.x * size_of::value; + complex_compute_t twiddle; + int index = threadIdx.x * size_of::value; twiddle_in *= threadIdx.x; // twiddle factor arg now just needs to multiplied by K = (index + i) for ( unsigned int i = 0; i < size_of::value; i++ ) { SINCOS(twiddle_in * (index + i), &twiddle.y, &twiddle.x); @@ -1400,21 +1278,22 @@ struct io_thread { __syncthreads( ); } - static inline __device__ void load_c2r(const complex_type* input, - complex_type* thread_data, - const int stride, - const int memory_limit) { + template + static inline __device__ void load_c2r(const data_io_t* __restrict__ input, + complex_compute_t* __restrict__ thread_data, + const int stride, + const int memory_limit) { // Each thread reads in the input data at stride = mem_offsets.Q unsigned int index = threadIdx.x; unsigned int offset = 2 * memory_limit - 2; for ( unsigned int i = 0; i < size_of::value; i++ ) { if ( index < memory_limit ) { - thread_data[i] = input[index]; + thread_data[i] = convert_if_needed(input, index); } else { // assuming even dimension // FIXME shouldn't need to read in from global for an even stride - thread_data[i] = input[offset - index]; + thread_data[i] = convert_if_needed(input, offset - index); thread_data[i].y = -thread_data[i].y; // conjugate } index += stride; @@ -1422,11 +1301,11 @@ struct io_thread { } // FIXME as above - static inline __device__ void load_c2r_transposed(const complex_type* input, - complex_type* thread_data, - int stride, - int pixel_pitch, - int memory_limit) { + static inline __device__ void load_c2r_transposed(const complex_compute_t* __restrict__ input, + complex_compute_t* __restrict__ thread_data, + int stride, + int pixel_pitch, + int memory_limit) { // Each thread reads in the input data at stride = mem_offsets.Q unsigned int index = threadIdx.x; // unsigned int offset = 2*memory_limit - 2; @@ -1445,13 +1324,13 @@ struct io_thread { } } - static inline __device__ void remap_decomposed_segments_c2r(const complex_type* thread_data, - scalar_type* shared_output, - scalar_type twiddle_in, - int Q) { + static inline __device__ void remap_decomposed_segments_c2r(const complex_compute_t* __restrict__ thread_data, + scalar_compute_t* __restrict__ shared_output, + scalar_compute_t twiddle_in, + int Q) { // Unroll the first loop and initialize the shared mem. - complex_type twiddle; - int index = threadIdx.x * size_of::value; + complex_compute_t twiddle; + int index = threadIdx.x * size_of::value; twiddle_in *= threadIdx.x; // twiddle factor arg now just needs to multiplied by K = (index + i) for ( unsigned int i = 0; i < size_of::value; i++ ) { SINCOS(twiddle_in * (index + i), &twiddle.y, &twiddle.x); @@ -1472,36 +1351,47 @@ struct io_thread { __syncthreads( ); } - static inline __device__ void store_c2r(const scalar_type* shared_output, - scalar_type* output, - const int stride) { + static inline __device__ void store_c2r(const scalar_compute_t* __restrict__ shared_output, + scalar_compute_t* __restrict__ output, + const int stride) { // Each thread reads in the input data at stride = mem_offsets.Q unsigned int index = threadIdx.x; + for ( unsigned int i = 0; i < size_of::value; i++ ) { - output[index] = shared_output[index]; + output[index] = convert_if_needed(shared_output, index); index += stride; } } - static inline __device__ void load_shared_and_conj_multiply(const complex_type* image_to_search, - const complex_type* shared_mem, - complex_type* thread_data, - const int stride) { - unsigned int index = threadIdx.x; - complex_type c; - for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - c.x = (shared_mem[index].x * image_to_search[index].x + shared_mem[index].y * image_to_search[index].y); - c.y = (shared_mem[index].y * image_to_search[index].x - shared_mem[index].x * image_to_search[index].y); - // a * conj b - thread_data[i] = c; //ComplexConjMulAndScale(thread_data[i], image_to_search[index], 1.0f); - index += stride; + template + static inline __device__ void load_shared_and_conj_multiply(const ExternalImage_t* __restrict__ image_to_search, + const complex_compute_t* __restrict__ shared_mem, + complex_compute_t* __restrict__ thread_data, + const int stride) { + unsigned int index = threadIdx.x; + complex_compute_t c; + if constexpr ( std::is_same_v ) { + + for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { + c.x = (shared_mem[index].x * __low2float(image_to_search[index]) + shared_mem[index].y * __high2float(image_to_search[index])); + c.y = (shared_mem[index].y * __low2float(image_to_search[index]) - shared_mem[index].x * __high2float(image_to_search[index])); + thread_data[i] = c; + index += stride; + } } + else { + for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { + c.x = (shared_mem[index].x * image_to_search[index].x + shared_mem[index].y * image_to_search[index].y); + c.y = (shared_mem[index].y * image_to_search[index].x - shared_mem[index].x * image_to_search[index].y); + thread_data[i] = c; + index += stride; + } + } + __syncthreads( ); } }; // struct thread_io } // namespace FastFFT -// clang-format on - #endif // Fast_FFT_cuh_ diff --git a/include/FastFFT.h b/include/FastFFT.h index 2efd63d..fd38efd 100644 --- a/include/FastFFT.h +++ b/include/FastFFT.h @@ -1,35 +1,12 @@ // Insert some license stuff here -#ifndef _INCLUDE_FASTFFT_H -#define _INCLUDE_FASTFFT_H - -#include -#include -#include - -// Forward declaration so we can leave the inclusion of cuda_fp16.h to FastFFT.cu -struct __half; -struct __half2; -// #include - -#ifndef ENABLE_FastFFT // ifdef being used in cisTEM that defines these -#if __cplusplus >= 202002L -#include -using namespace std::numbers; -#else -#if __cplusplus < 201703L -#message "C++ is " __cplusplus -#error "C++17 or later required" -#else -template -// inline constexpr _Tp pi_v = _Enable_if_floating<_Tp>(3.141592653589793238462643383279502884L); -inline constexpr _Tp pi_v = 3.141592653589793238462643383279502884L; -#endif // __cplusplus require > 17 -#endif // __cplusplus 20 support -#endif // enable FastFFT - -#include "../src/fastfft/types.cuh" +#ifndef __INCLUDE_FASTFFT_H_ +#define __INCLUDE_FASTFFT_H_ +#include "detail/detail.h" + +// TODO: When recompiling a changed debug type, make has no knowledge and the -B flag must be passed. +// Save the most recent state and have make query that to determine if the -B flag is needed. // For testing/debugging it is convenient to execute and have print functions for partial transforms. // These will go directly in the kernels and also in the helper Image.cuh definitions for PrintArray. // The number refers to the number of 1d FFTs performed, @@ -38,184 +15,88 @@ inline constexpr _Tp pi_v = 3.141592653589793238462643383279502884L; // Inv 5, 6, 7 ( original y, z, x) // Defined in make by setting environmental variable FFT_DEBUG_STAGE -// #include -/* - -Some of the more relevant notes about extended lambdas. -https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#extended-lambda - -The enclosing function for the extended lambda must be named and its address can be taken. If the enclosing function is a class member, then the following conditions must be satisfied: - - All classes enclosing the member function must have a name. - The member function must not have private or protected access within its parent class. - All enclosing classes must not have private or protected access within their respective parent classes. - +namespace FastFFT { -If the enclosing function is an instantiation of a function template or a member function template, and/or the function is a member of a class template, the template(s) must satisfy the following constraints: +// TODO: this may be expanded, for now it is to be used in the case where we have +// packed the real values of a c2r into the first half of the complex array. +// The output type pointer needs to be cast to the correct type AND posibly converted +template +inline void static_no_match( ) { static_assert(flag, "no match"); } - The template must have at most one variadic parameter, and it must be listed last in the template parameter list. - The template parameters must be named. - The template instantiation argument types cannot involve types that are either local to a function (except for closure types for extended lambdas), or are private or protected class members. -#define IS_EXT_LAMBDA( type ) __nv_is_extended_device_lambda_closure_type( type ) +template +inline void static_no_doubles( ) { static_assert(flag, "no doubles are allowed"); } +template +inline void static_no_half_support_yet( ) { static_assert(flag, "no __half support yet"); } -*/ -namespace FastFFT { - -// For debugging - -inline void PrintVectorType(int3 input) { - std::cerr << "(x,y,z) " << input.x << " " << input.y << " " << input.z << std::endl; -} - -inline void PrintVectorType(int4 input) { - std::cerr << "(x,y,z,w) " << input.x << " " << input.y << " " << input.z << " " << input.w << std::endl; -} - -inline void PrintVectorType(dim3 input) { - std::cerr << "(x,y,z) " << input.x << " " << input.y << " " << input.z << std::endl; -} - -inline void PrintVectorType(short3 input) { - std::cerr << "(x,y,z) " << input.x << " " << input.y << " " << input.z << std::endl; -} - -inline void PrintVectorType(short4 input) { - std::cerr << "(x,y,z,w) " << input.x << " " << input.y << " " << input.z << " " << input.w << std::endl; -} - -typedef struct __align__(32) _DeviceProps { - int device_id; - int device_arch; - int max_shared_memory_per_block; - int max_shared_memory_per_SM; - int max_registers_per_block; - int max_persisting_L2_cache_size; -} - -DeviceProps; - -typedef struct __align__(8) _FFT_Size { - // Following Sorensen & Burrus 1993 for clarity - short N; // N : 1d FFT size - short L; // L : number of non-zero output/input points - short P; // P >= L && N % P == 0 : The size of the sub-FFT used to compute the full transform. Currently also must be a power of 2. - short Q; // Q = N/P : The number of sub-FFTs used to compute the full transform -} - -FFT_Size; - -typedef struct __align__(8) _Offsets { - unsigned short shared_input; - unsigned short shared_output; - unsigned short physical_x_input; - unsigned short physical_x_output; -} - -Offsets; - -typedef struct __align__(64) _LaunchParams { - int Q; - float twiddle_in; - dim3 gridDims; - dim3 threadsPerBlock; - Offsets mem_offsets; -} - -LaunchParams; - -template +// Currently the buffer types match the input type which also determines the output type. +// The compute and otherImage type are independent. +template struct DevicePointers { // Use this to catch unsupported input/ compute types and throw exception. - int* position_space = nullptr; - int* position_space_buffer = nullptr; - int* momentum_space = nullptr; - int* momentum_space_buffer = nullptr; - int* image_to_search = nullptr; + std::nullptr_t external_input; + std::nullptr_t external_output; + std::nullptr_t buffer_1; + std::nullptr_t buffer_2; }; -// Input real, compute single-precision template <> -struct DevicePointers { - float* position_space; - float* position_space_buffer; - float2* momentum_space; - float2* momentum_space_buffer; - float2* image_to_search; +struct DevicePointers { + float* external_input{ }; + float* external_output{ }; + float2* buffer_1{ }; + float2* buffer_2{ }; }; -// Input real, compute half-precision FP16 template <> -struct DevicePointers<__half*, __half*> { - __half* position_space; - __half* position_space_buffer; - __half2* momentum_space; - __half2* momentum_space_buffer; - __half2* image_to_search; +struct DevicePointers { + float* external_input{ }; + float* external_output{ }; + float2* buffer_1{ }; + float2* buffer_2{ }; }; -// Input complex, compute single-precision template <> -struct DevicePointers { - float2* position_space; - float2* position_space_buffer; - float2* momentum_space; - float2* momentum_space_buffer; - float2* image_to_search; +struct DevicePointers { + __half* external_input{ }; + __half* external_output{ }; + float2* buffer_1{ }; + float2* buffer_2{ }; }; -// Input complex, compute half-precision FP16 template <> -struct DevicePointers<__half2*, __half*> { - __half2* position_space; - __half2* position_space_buffer; - __half2* momentum_space; - __half2* momentum_space_buffer; - __half2* image_to_search; +struct DevicePointers { + __half* external_input{ }; + __half* external_output{ }; + float2* buffer_1{ }; + float2* buffer_2{ }; }; -template +/** + * @brief Construct a new Fourier Transformer< Compute Type, Input Type, OtherImageType , Rank>:: Fourier Transformer object + * + * + * @tparam ComputeBaseType - float. Support for ieee half precision is not yet implemented. + * @tparam InputType - __half or float for real valued input, __half2 or float2 for complex valued input images. + * @tparam OtherImageType - __half or float. Actual type depends on position/momentum space representation. + * @tparam Rank - only 2,3 supported. Support for 3d is partial + */ +template class FourierTransformer { - private: - public: - // Using the enum directly from python is not something I've figured out yet. Just make simple methods. - inline void SetOriginTypeNatural(bool set_input_type = true) { - if ( set_input_type ) - input_origin_type = OriginType::natural; - else - output_origin_type = OriginType::natural; - } - - inline void SetOriginTypeCentered(bool set_input_type = true) { - if ( set_input_type ) - input_origin_type = OriginType::centered; - else - output_origin_type = OriginType::centered; - } - - inline void SetOriginTypeQuadrantSwapped(bool set_input_type = true) { - if ( set_input_type ) - input_origin_type = OriginType::quadrant_swapped; - else - output_origin_type = OriginType::quadrant_swapped; - } - - short padding_jump_val; - int input_memory_allocated; - int fwd_output_memory_allocated; - int inv_output_memory_allocated; - int compute_memory_allocated; - int memory_size_to_copy; - /////////////////////////////////////////////// // Initialization functions /////////////////////////////////////////////// FourierTransformer( ); - // FourierTransformer(const FourierTransformer &); // Copy constructor - virtual ~FourierTransformer( ); + ~FourierTransformer( ); + + // For now, we do not want to allow Copy or Move + FourierTransformer(const FourierTransformer&) = delete; + FourierTransformer& operator=(const FourierTransformer&) = delete; + FourierTransformer(FourierTransformer&&) = delete; + FourierTransformer& operator=(FourierTransformer&&) = delete; // This is pretty similar to an FFT plan, I should probably make it align with CufftPlan void SetForwardFFTPlan(size_t input_logical_x_dimension, size_t input_logical_y_dimension, size_t input_logical_z_dimension, @@ -226,83 +107,117 @@ class FourierTransformer { size_t output_logical_x_dimension, size_t output_logical_y_dimension, size_t output_logical_z_dimension, bool is_padded_output = true); - // For the time being, the caller is responsible for having the memory allocated for any of these input/output pointers. - void SetInputPointer(InputType* input_pointer, bool is_input_on_device); // When passing in a pointer from python (cupy or pytorch) it is a long, and needs to be cast to input type. // For now, we are assuming memory ops are all handled in the python code. - void SetInputPointer(long input_pointer); - void SetCallerPinnedInputPointer(InputType* input_pointer); + void SetInputPointerFromPython(long input_pointer); + + // template + // void SetDataPointers(InputDataPtr_t input_ptr, OutputDataPtr_t output_ptr, ImageToSearchPtr_t image_to_search_ptr); /////////////////////////////////////////////// // Public actions: // ALL public actions should call ::CheckDimensions() to ensure the meta data are properly intialized. // this ensures the prior three methods have been called and are valid. /////////////////////////////////////////////// - inline void Wait( ) { cudaStreamSynchronize(cudaStreamPerThread); }; + inline void Wait( ) { + cudaStreamSynchronize(cudaStreamPerThread); + }; - void CopyHostToDevceAndSynchronize(InputType* input_pointer, int n_elements_to_copy = 0); - void CopyHostToDevice(InputType* input_pointer, int n_elements_to_copy = 0); - // If int n_elements_to_copy = 0 the appropriate size will be determined by the state of the transform completed (none, fwd, inv.) - // For partial increase/decrease transforms, needed for testing, this will be invalid, so specify the int n_elements_to_copy. - // When the size changes, we need a new host pointer - void CopyDeviceToHostAndSynchronize(OutputType* output_pointer, bool free_gpu_memory = true, int n_elements_to_copy = 0); - void CopyDeviceToHost(OutputType* output_pointer, bool free_gpu_memory = true, int n_elements_to_copy = 0); - - // Ideally, in addition to position/momentum space (buffer) ponters, there would also be a input pointer, which may point - // to a gpu address that is from an external process or to the FastFFT buffer space. This way, when calling CopyHostToDevice, - // that input is set to the FastFFT buffer space, data is copied and the first Fwd kernels are called as they are currently. - // This would also allow the input pointer to point to a different address than the FastFFT buffer only accessed on initial kernel - // calls and read only. In turn we could skip the device to device transfer we are doing in the following method. - void CopyDeviceToDeviceFromNonOwningAddress(InputType* input_pointer, int n_elements_to_copy = 0); - - // Here we may be copying input data type from another GPU buffer, OR output data type to another GPU buffer. - // Check in these methods that the types match - template - void CopyDeviceToDeviceAndSynchronize(TransferDataType* input_pointer, bool free_gpu_memory = true, int n_elements_to_copy = 0); - template - void CopyDeviceToDevice(TransferDataType* input_pointer, bool free_gpu_memory = true, int n_elements_to_copy = 0); - // FFT calls + auto inline GetDeviceBufferPointer( ) { + return d_ptr.buffer_1; + } - // void FwdFFT(bool swap_real_space_quadrants = false, bool transpose_output = true); - // void InvFFT(bool transpose_output = true); - // void CrossCorrelate(float2* image_to_search, bool swap_real_space_quadrants); - // void CrossCorrelate(__half2* image_to_search, bool swap_real_space_quadrants); + void CopyHostToDeviceAndSynchronize(InputType* input_pointer, int n_elements_to_copy = 0); + void CopyHostToDevice(InputType* input_pointer, int n_elements_to_copy = 0); - // could float2* be replaced with decltype(DevicePointers.momentum_space) - template - void Generic_Fwd_Image_Inv(float2* data, PreOpType pre_op = nullptr, IntraOpType intra_op = nullptr, PostOpType post_op = nullptr); + void CopyDeviceToHostAndSynchronize(InputType* input_pointer, int n_elements_to_copy = 0); - template - void Generic_Fwd(PreOpType pre_op = nullptr, IntraOpType intra_op = nullptr); + // Using the enum directly from python is not something I've figured out yet. Just make simple methods. + // FIXME: these are not currently used, and perhaps are not needed. + inline void SetOriginTypeNatural(bool set_input_type = true) { + if ( set_input_type ) + input_origin_type = OriginType::natural; + else + output_origin_type = OriginType::natural; + } - template - void Generic_Inv(IntraOpType intra_op = nullptr, PostOpType post_op = nullptr); + inline void SetOriginTypeCentered(bool set_input_type = true) { + if ( set_input_type ) + input_origin_type = OriginType::centered; + else + output_origin_type = OriginType::centered; + } - // Alias for FwdFFT, is there any overhead? - template - void FwdFFT(PreOpType pre_op = nullptr, IntraOpType intra_op = nullptr) { Generic_Fwd(pre_op, intra_op); } + inline void SetOriginTypeQuadrantSwapped(bool set_input_type = true) { + if ( set_input_type ) + input_origin_type = OriginType::quadrant_swapped; + else + output_origin_type = OriginType::quadrant_swapped; + } - template - void InvFFT(IntraOpType intra_op = nullptr, PostOpType post_op = nullptr) { Generic_Inv(intra_op, post_op); } + // FFT calls - void ClipIntoTopLeft( ); - void ClipIntoReal(int wanted_coordinate_of_box_center_x, int wanted_coordinate_of_box_center_y, int wanted_coordinate_of_box_center_z); + // TODO: when picking up tomorrow, remove default values for input pointers and move EnableIf to the declarations for the generic functions and instantiate these from fastFFT.cus + // Following this + // Alias for FwdFFT, is there any overhead? + template + void FwdFFT(InputType* input_ptr, + InputType* output_ptr = nullptr, + PreOpType pre_op = nullptr, + IntraOpType intra_op = nullptr); + + template + void InvFFT(InputType* input_ptr, + InputType* output_ptr = nullptr, + IntraOpType intra_op = nullptr, + PostOpType post_op = nullptr); + + template + void FwdImageInvFFT(InputType* input_ptr, + OtherImageType* image_to_search, + InputType* output_ptr = nullptr, + PreOpType pre_op = nullptr, + IntraOpType intra_op = nullptr, + PostOpType post_op = nullptr); + + void ClipIntoTopLeft(InputType* input_ptr); + void ClipIntoReal(InputType*, int wanted_coordinate_of_box_center_x, int wanted_coordinate_of_box_center_y, int wanted_coordinate_of_box_center_z); // For all real valued inputs, assumed for any InputType that is not float2 or __half2 - int inline ReturnInputMemorySize( ) { return input_memory_allocated; } + int inline ReturnInputMemorySize( ) { + return input_memory_wanted_; + } - int inline ReturnFwdOutputMemorySize( ) { return fwd_output_memory_allocated; } + int inline ReturnFwdOutputMemorySize( ) { + return fwd_output_memory_wanted_; + } - int inline ReturnInvOutputMemorySize( ) { return inv_output_memory_allocated; } + int inline ReturnInvOutputMemorySize( ) { + return inv_output_memory_wanted_; + } - short4 inline ReturnFwdInputDimensions( ) { return fwd_dims_in; } + short4 inline ReturnFwdInputDimensions( ) { + return fwd_dims_in; + } - short4 inline ReturnFwdOutputDimensions( ) { return fwd_dims_out; } + short4 inline ReturnFwdOutputDimensions( ) { + return fwd_dims_out; + } - short4 inline ReturnInvInputDimensions( ) { return inv_dims_in; } + short4 inline ReturnInvInputDimensions( ) { + return inv_dims_in; + } - short4 inline ReturnInvOutputDimensions( ) { return inv_dims_out; } + short4 inline ReturnInvOutputDimensions( ) { + return inv_dims_out; + } template void SetToConstant(T* input_pointer, int N_values, const T& wanted_value) { @@ -333,8 +248,17 @@ class FourierTransformer { } } - // Input is real or complex inferred from InputType - DevicePointers d_ptr; + enum buffer_location : int { fastfft_external_input, + fastfft_external_output, + fastfft_internal_buffer_1, + fastfft_internal_buffer_2 }; + + const std::string_view buffer_name[4] = {"fastfft_external_input", + "fastfft_external_output", + "fastfft_internal_buffer_1", + "fastfft_internal_buffer_2"}; + + buffer_location current_buffer; void PrintState( ) { std::cerr << "================================================================" << std::endl; @@ -350,16 +274,14 @@ class FourierTransformer { std::cerr << "State Variables:\n" << std::endl; - std::cerr << "is_in_memory_host_pointer " << is_in_memory_host_pointer << std::endl; - std::cerr << "is_in_memory_device_pointer " << is_in_memory_device_pointer << std::endl; - std::cerr << "is_in_buffer_memory " << is_in_buffer_memory << std::endl; + // std::cerr << "is_in_memory_device_pointer " << is_in_memory_device_pointer << std::endl; // FIXME: switched to is_pointer_in_device_memory(d_ptr.buffer_1) defined in FastFFT.cuh + std::cerr << "in buffer " << buffer_name[current_buffer] << std::endl; std::cerr << "is_fftw_padded_input " << is_fftw_padded_input << std::endl; std::cerr << "is_fftw_padded_output " << is_fftw_padded_output << std::endl; - std::cerr << "is_real_valued_input " << is_real_valued_input << std::endl; + std::cerr << "is_real_valued_input " << IsAllowedRealType << std::endl; std::cerr << "is_set_input_params " << is_set_input_params << std::endl; std::cerr << "is_set_output_params " << is_set_output_params << std::endl; std::cerr << "is_size_validated " << is_size_validated << std::endl; - std::cerr << "is_set_input_pointer " << is_set_input_pointer << std::endl; std::cerr << std::endl; std::cerr << "Size variables:\n" @@ -384,11 +306,11 @@ class FourierTransformer { std::cerr << "Misc:\n" << std::endl; - std::cerr << "compute_memory_allocated " << compute_memory_allocated << std::endl; - std::cerr << "memory size to copy " << memory_size_to_copy << std::endl; + std::cerr << "compute_memory_wanted_ " << compute_memory_wanted_ << std::endl; + std::cerr << "memory size to copy " << memory_size_to_copy_ << std::endl; std::cerr << "fwd_size_change_type " << SizeChangeName[fwd_size_change_type] << std::endl; std::cerr << "inv_size_change_type " << SizeChangeName[inv_size_change_type] << std::endl; - std::cerr << "transform stage complete " << TransformStageCompletedName[transform_stage_completed] << std::endl; + std::cerr << "transform stage complete " << transform_stage_completed << std::endl; std::cerr << "input_origin_type " << OriginType::name[input_origin_type] << std::endl; std::cerr << "output_origin_type " << OriginType::name[output_origin_type] << std::endl; @@ -401,39 +323,27 @@ class FourierTransformer { OriginType::Enum output_origin_type; // booleans to track state, could be bit fields but that seem opaque to me. - bool is_in_memory_host_pointer; // To track allocation of host side memory - bool is_in_memory_device_pointer; // To track allocation of device side memory. - bool is_in_buffer_memory; // To track whether the current result is in dev_ptr.position_space or dev_ptr.position_space_buffer (momemtum space/ momentum space buffer respectively.) bool is_fftw_padded_input; // Padding for in place r2c transforms bool is_fftw_padded_output; // Currently the output state will match the input state, otherwise it is an error. - bool is_real_valued_input; // This is determined by the input type. If it is a float2 or __half2, then it is assumed to be a complex valued input function. - bool is_set_input_params; // Yes, yes, "are" set. bool is_set_output_params; bool is_size_validated; // Defaults to false, set after both input/output dimensions are set and checked. - bool is_set_input_pointer; // May be on the host of the device. int transform_dimension; // 1,2,3d. FFT_Size transform_size; - int elements_per_thread_complex; // Set depending on the kernel and size of the transform. std::vector SizeChangeName{"increase", "decrease", "no_change"}; - std::vector TransformStageCompletedName{"", "", "", "", "", // padding of 5 - "", "", "", "", "", // padding of 5 - "none", "fwd", "inv"}; - std::vector DimensionCheckName{"CopyFromHost", "CopyToHost", "FwdTransform", "InvTransform"}; - bool is_from_python_call; - bool is_owner_of_memory; - SizeChangeType::Enum fwd_size_change_type; SizeChangeType::Enum inv_size_change_type; - TransformStageCompleted::Enum transform_stage_completed; + bool implicit_dimension_change; + + int transform_stage_completed; // dims_in may change during calculation, depending on padding, but is reset after each call. short4 dims_in; @@ -444,73 +354,175 @@ class FourierTransformer { short4 inv_dims_in; short4 inv_dims_out; - InputType* host_pointer; - InputType* pinnedPtr; - void Deallocate( ); - void UnPinHostMemory( ); void SetDefaults( ); void ValidateDimensions( ); void SetDimensions(DimensionCheckType::Enum check_op_type); - void SetDevicePointers(bool should_allocate_buffer_memory); + inline int ReturnPaddedMemorySize(short4& wanted_dims) { + // FIXME: Assumes a) SetInputDimensionsAndType has been called and is_fftw_padded is set before this call. (Currently RuntimeAssert to die if false) + int wanted_memory_n_elements = 0; + constexpr int scale_compute_base_type_to_full_type = 2; + // The odd sized block is probably not needed. + if constexpr ( IsAllowedRealType ) { + if ( wanted_dims.x % 2 == 0 ) { + padding_jump_val_ = 2; + wanted_memory_n_elements = wanted_dims.x / 2 + 1; + } + else { + padding_jump_val_ = 1; + wanted_memory_n_elements = (wanted_dims.x - 1) / 2 + 1; + } + + wanted_memory_n_elements *= wanted_dims.y * wanted_dims.z; // other dimensions + wanted_dims.w = (wanted_dims.x + padding_jump_val_) / 2; // number of complex elements in the X dimesnions after FFT. + } + else if constexpr ( IsAllowedComplexType ) { + wanted_memory_n_elements = wanted_dims.x * wanted_dims.y * wanted_dims.z; + wanted_dims.w = wanted_dims.x; // pitch is constant + } + else { + constexpr InputType a; + static_assert_type_name(a); + } + + // Here wanted_memory_n_elements contains enough memory for in-place real/complex transforms. + // We need to to scale it up as we use sizeof(compute_base_type) when allocating. + wanted_memory_n_elements *= scale_compute_base_type_to_full_type; + + // FIXME: For 3d tranforms we need either need an additional buffer space or we will have to do an extra device to + // device copy for simple forward/inverse transforms. For now, we'll do the extra copy to keep buffer assignments easier. + // if constepxr (Rank == 3) { + // wanted_memory_n_elements *= 2; + // } + + // The total compute_memory_wanted_ will depend on the largest image in the forward/inverse transform plans, + // so we need to keep track of the largest value. + compute_memory_wanted_ = std::max(compute_memory_wanted_, wanted_memory_n_elements); + return wanted_memory_n_elements; + } + + template + void FFT_C2C_WithPadding_ConjMul_C2C_t(float2* image_to_search, bool swap_real_space_quadrants); + template + void FFT_C2C_decomposed_ConjMul_C2C_t(float2* image_to_search, bool swap_real_space_quadrants); + + void PrintLaunchParameters(LaunchParams LP) { + std::cerr << "Launch parameters: " << std::endl; + std::cerr << " Threads per block: "; + PrintVectorType(LP.threadsPerBlock); + std::cerr << " Grid dimensions: "; + PrintVectorType(LP.gridDims); + std::cerr << " Q: " << LP.Q << std::endl; + std::cerr << " Twiddle in: " << LP.twiddle_in << std::endl; + std::cerr << " shared input: " << LP.mem_offsets.shared_input << std::endl; + std::cerr << " shared output (memlimit in r2c): " << LP.mem_offsets.shared_output << std::endl; + std::cerr << " physical_x_input: " << LP.mem_offsets.physical_x_input << std::endl; + std::cerr << " physical_x_output: " << LP.mem_offsets.physical_x_output << std::endl; + }; + + // TODO: start hiding things that should not be public + private: /* IMPORTANT: if you add a kernel, you need to modify 1) enum KernelType 2) KernelName: this is positionally dependent on KernelType - 3) If appropriate: - a) IsThreadType() - b) IsR2CType() - c) IsC2RType() - d) IsForwardType() - e) IsTransformAlongZ() + + The names should match the enum exactly and adhere to the following rules, which are used to query the string view + to obtain properties about the transform kernel. */ - enum KernelType { r2c_decomposed, // Thread based, full length. - r2c_decomposed_transposed, // Thread based, full length, transposed. - r2c_none_XY, - r2c_none_XZ, - r2c_decrease, - r2c_increase, + /* + MEANING of KERNEL TYPE NAMES: + + - r2c and c2r are for real valued input/output images + - r2c implies forward transform, c2r implies inverse transform + - all other transform enum/names must contain _fwd_ or _inv_ to indicate direction + + - any kernel with "_decomposed_" is a thread based routine (not currently supported) + + - if a kernel is part of a size change routine it is specified as none/increase/decrease + + - if 2 axes are specified, those dimensions are transposed. + - 1d - this is meaningless. Many XY routines are currently also used for 1d with a constexpr check on rank. + - 2d - should always be XY + - 3d - should always be XZ + + - if 3 axes are specified, those dimensions are permuted XYZ (only 3d) + - Having _XY or _XYZ is used to check a kernel name to see if it is 3d + + - Data are always transposed in XY in momentum space + - any c2c FWD method without an axes specifier must be a terminal stage of a forward transform + - any c2c INV method without an axes specifier must be a initial stage of an inverse transform + + */ + + // FIXME: in the execution blocks, we should have some check that the correct direction is implemented. + // Or better yet, have this templated and + + enum KernelType { r2c_decomposed, // 1D fwd + r2c_decomposed_transposed, // 2d fwd 1st stage + r2c_none_XY, // 1d fwd // 2d fwd 1st stage + r2c_none_XZ, // 3d fwd 1st stage + r2c_decrease_XY, + r2c_increase_XY, r2c_increase_XZ, - c2c_fwd_none, - c2c_fwd_none_Z, + c2c_fwd_none, // 1d complex valued input, or final stage of Fwd 2d or 3d + c2c_fwd_none_XYZ, c2c_fwd_decrease, c2c_fwd_increase, - c2c_fwd_increase_Z, + c2c_fwd_increase_XYZ, c2c_inv_none, c2c_inv_none_XZ, - c2c_inv_none_Z, + c2c_inv_none_XYZ, c2c_inv_decrease, c2c_inv_increase, - c2c_decomposed, + c2c_fwd_decomposed, + c2c_inv_decomposed, c2r_decomposed, c2r_decomposed_transposed, c2r_none, c2r_none_XY, - c2r_decrease, + c2r_decrease_XY, c2r_increase, xcorr_fwd_increase_inv_none, // (e.g. template matching) xcorr_fwd_decrease_inv_none, // (e.g. Fourier cropping) xcorr_fwd_none_inv_decrease, // (e.g. movie/particle translational search) xcorr_fwd_decrease_inv_decrease, // (e.g. bandlimit, xcorr, translational search) xcorr_decomposed, - generic_fwd_increase_op_inv_none }; + generic_fwd_increase_op_inv_none, + COUNT }; + static const int n_kernel_types = static_cast(KernelType::COUNT); // WARNING this is flimsy and prone to breaking, you must ensure the order matches the KernelType enum. - std::vector + std::array KernelName{"r2c_decomposed", "r2c_decomposed_transposed", - "r2c_none_XY", "r2c_none_XZ", - "r2c_decrease", "r2c_increase", "r2c_increase_XZ", - "c2c_fwd_none", "c2c_fwd_none_Z", "c2c_fwd_increase", "c2c_fwd_increase", "c2c_fwd_increase_Z", - "c2c_inv_none", "c2c_inv_none_XZ", "c2c_inv_none_Z", "c2c_inv_increase", "c2c_inv_increase", - "c2c_decomposed", + "r2c_none_XY", + "r2c_none_XZ", + "r2c_decrease_XY", + "r2c_increase_XY", + "r2c_increase_XZ", + "c2c_fwd_none", + "c2c_fwd_none_XYZ", + "c2c_fwd_decrease", + "c2c_fwd_increase", + "c2c_fwd_increase_XYZ", + "c2c_inv_none", + "c2c_inv_none_XZ", + "c2c_inv_none_XYZ", + "c2c_inv_decrease", + "c2c_inv_increase", + "c2c_fwd_decomposed", + "c2c_inv_decomposed", "c2r_decomposed", "c2r_decomposed_transposed", - "c2r_none", "c2r_none_XY", "c2r_decrease", "c2r_increase", + "c2r_none", + "c2r_none_XY", + "c2r_decrease_XY", + "c2r_increase", "xcorr_fwd_increase_inv_none", "xcorr_fwd_decrease_inv_none", "xcorr_fwd_none_inv_decrease", @@ -518,156 +530,107 @@ class FourierTransformer { "xcorr_decomposed", "generic_fwd_increase_op_inv_none"}; + // All in a column so it is obvious if a "==" is missing which will of course break the or co nditions and + // always evaluate true. inline bool IsThreadType(KernelType kernel_type) { - if ( kernel_type == r2c_decomposed || kernel_type == r2c_decomposed_transposed || - kernel_type == c2c_decomposed || - kernel_type == c2r_decomposed || kernel_type == c2r_decomposed_transposed || kernel_type == xcorr_decomposed ) { + if ( KernelName.at(kernel_type).find("decomposed") != KernelName.at(kernel_type).npos ) return true; - } - - else if ( kernel_type == r2c_none_XY || kernel_type == r2c_none_XZ || - kernel_type == r2c_decrease || kernel_type == r2c_increase || kernel_type == r2c_increase_XZ || - kernel_type == c2c_fwd_none || c2c_fwd_none_Z || - kernel_type == c2c_fwd_decrease || - kernel_type == c2c_fwd_increase || kernel_type == c2c_fwd_increase_Z || - kernel_type == c2c_inv_none || kernel_type == c2c_inv_none_XZ || kernel_type == c2c_inv_none_Z || - kernel_type == c2c_inv_decrease || kernel_type == c2c_inv_increase || - kernel_type == c2r_none || kernel_type == c2r_none_XY || kernel_type == c2r_decrease || kernel_type == c2r_increase || - kernel_type == xcorr_fwd_increase_inv_none || kernel_type == xcorr_fwd_decrease_inv_none || kernel_type == xcorr_fwd_none_inv_decrease || kernel_type == xcorr_fwd_decrease_inv_decrease || - kernel_type == generic_fwd_increase_op_inv_none ) { + else return false; - } - else { - std::cerr << "Function IsThreadType does not recognize the kernel type ( " << KernelName[kernel_type] << " )" << std::endl; - exit(-1); - } - }; + } inline bool IsR2CType(KernelType kernel_type) { - if ( kernel_type == r2c_decomposed || kernel_type == r2c_decomposed_transposed || - kernel_type == r2c_none_XY || kernel_type == r2c_none_XZ || - kernel_type == r2c_decrease || kernel_type == r2c_increase || kernel_type == r2c_increase_XZ ) { + if ( KernelName.at(kernel_type).find("r2c_") != KernelName.at(kernel_type).npos ) return true; - } else return false; } inline bool IsC2RType(KernelType kernel_type) { - if ( kernel_type == c2r_decomposed || kernel_type == c2r_decomposed_transposed || - kernel_type == c2r_none || kernel_type == c2r_none_XY || kernel_type == c2r_decrease || kernel_type == c2r_increase ) { + if ( KernelName.at(kernel_type).find("c2r_") != KernelName.at(kernel_type).npos ) return true; - } else return false; } - // This is used to set the sign of the twiddle factor for decomposed kernels, whether threaded, or part of a block fft. - // For mixed kernels (eg. xcorr_* the size type is defined by where the size change happens. + // Note: round trip transforms are forward types + // TODO: this is a bit confusing and should be cleaned up. inline bool IsForwardType(KernelType kernel_type) { - if ( kernel_type == r2c_decomposed || kernel_type == r2c_decomposed_transposed || - kernel_type == r2c_none_XY || kernel_type == r2c_none_XZ || - kernel_type == r2c_decrease || kernel_type == r2c_increase || kernel_type == r2c_increase_XZ || - kernel_type == c2c_fwd_none || kernel_type == c2c_fwd_none_Z || kernel_type == c2c_fwd_increase_Z || - kernel_type == c2c_fwd_decrease || - kernel_type == c2c_fwd_increase || - kernel_type == xcorr_fwd_decrease_inv_none || kernel_type == xcorr_fwd_increase_inv_none || - kernel_type == generic_fwd_increase_op_inv_none ) - - { + if ( IsR2CType(kernel_type) || KernelName.at(kernel_type).find("_fwd_") != KernelName.at(kernel_type).npos ) return true; - } else return false; } - inline bool IsTransormAlongZ(KernelType kernel_type) { - if ( kernel_type == c2c_fwd_none_Z || kernel_type == c2c_fwd_increase_Z || - kernel_type == c2c_inv_none_Z ) { + inline bool IsInverseType(KernelType kernel_type) { + if ( IsC2RType(kernel_type) || KernelName.at(kernel_type).find("_inv_") != KernelName.at(kernel_type).npos ) return true; - } else return false; } - inline bool IsRank3(KernelType kernel_type) { - if ( kernel_type == r2c_none_XZ || kernel_type == r2c_increase_XZ || - kernel_type == c2c_fwd_increase_Z || kernel_type == c2c_inv_none_XZ || - kernel_type == c2c_fwd_none_Z || kernel_type == c2c_inv_none_Z ) { + inline bool IsIncreaseSizeType(KernelType kernel_type) { + if ( KernelName.at(kernel_type).find("_increase_") != KernelName.at(kernel_type).npos ) return true; - } else return false; } - inline void AssertDivisibleAndFactorOf2(int full_size_transform, int number_non_zero_inputs_or_outputs) { - // FIXME: This function could be named more appropriately. - transform_size.N = full_size_transform; - transform_size.L = number_non_zero_inputs_or_outputs; - // FIXME: in principle, transform_size.L should equal number_non_zero_inputs_or_outputs and transform_size.P only needs to be >= and satisfy other requirements, e.g. power of two (currently.) - transform_size.P = number_non_zero_inputs_or_outputs; + inline bool IsDecreaseSizeType(KernelType kernel_type) { + if ( KernelName.at(kernel_type).find("_decrease_") != KernelName.at(kernel_type).npos ) + return true; + else + return false; + } - if ( transform_size.N % transform_size.P == 0 ) { - transform_size.Q = transform_size.N / transform_size.P; - } - else { - std::cerr << "Array size " << transform_size.N << " is not divisible by wanted output size " << transform_size.P << std::endl; - exit(1); - } + // Note: currently unused + inline bool IsRoundTripType(KernelType kernel_type) { + if ( KernelName.at(kernel_type).find("_fwd_") != KernelName.at(kernel_type).npos && + KernelName.at(kernel_type).find("_inv_") != KernelName.at(kernel_type).npos ) + return true; + else + return false; + } - if ( abs(fmod(log2(float(transform_size.P)), 1)) > 1e-6 ) { - std::cerr << "Wanted output size " << transform_size.P << " is not a power of 2." << std::endl; - exit(1); - } + inline bool IsTransormAlongZ(KernelType kernel_type) { + if ( KernelName.at(kernel_type).find("_XYZ") != KernelName.at(kernel_type).npos ) + return true; + else + return false; } - void GetTransformSize(KernelType kernel_type); - void GetTransformSize_thread(KernelType kernel_type, int thread_fft_size); - LaunchParams SetLaunchParameters(const int& ept, KernelType kernel_type, bool do_forward_transform = true); + inline bool IsRank3(KernelType kernel_type) { + if ( KernelName.at(kernel_type).find("_XZ") != KernelName.at(kernel_type).npos || + KernelName.at(kernel_type).find("_XYZ") != KernelName.at(kernel_type).npos ) + return true; + else + return false; + } - inline int ReturnPaddedMemorySize(short4& wanted_dims) { - // Assumes a) SetInputDimensionsAndType has been called and is_fftw_padded is set before this call. (Currently RuntimeAssert to die if false) FIXME - int wanted_memory = 0; + void + GetTransformSize(KernelType kernel_type); - if ( is_real_valued_input ) { - if ( wanted_dims.x % 2 == 0 ) { - padding_jump_val = 2; - wanted_memory = wanted_dims.x / 2 + 1; - } - else { - padding_jump_val = 1; - wanted_memory = (wanted_dims.x - 1) / 2 + 1; - } + void GetTransformSize_thread(KernelType kernel_type, int thread_fft_size); + LaunchParams SetLaunchParameters(KernelType kernel_type); - wanted_memory *= wanted_dims.y * wanted_dims.z; // other dimensions - wanted_memory *= 2; // room for complex - wanted_dims.w = (wanted_dims.x + padding_jump_val) / 2; // number of complex elements in the X dimesnions after FFT. - compute_memory_allocated = std::max(compute_memory_allocated, 2 * wanted_memory); // scaling by 2 making room for the buffer. - } - else { - wanted_memory = wanted_dims.x * wanted_dims.y * wanted_dims.z; - wanted_dims.w = wanted_dims.x; // pitch is constant - // We allocate using sizeof(ComputeType) which is either __half or float, so we need an extra factor of 2 - // Note: this must be considered when setting the address of the buffer memory based on the address of the regular memory. - compute_memory_allocated = std::max(compute_memory_allocated, 4 * wanted_memory); - } - return wanted_memory; + inline void SetEptForUseInLaunchParameters(const int elements_per_thread) { + elements_per_thread_complex = elements_per_thread; } - template - void FFT_C2C_WithPadding_ConjMul_C2C_t(float2* image_to_search, bool swap_real_space_quadrants); - template - void FFT_C2C_decomposed_ConjMul_C2C_t(float2* image_to_search, bool swap_real_space_quadrants); - // 1. // First call passed from a public transform function, selects block or thread and the transform precision. - template // bool is just used as a dummy type - void SetPrecisionAndExectutionMethod(KernelType kernel_type, bool do_forward_transform = true, PreOpType pre_op_functor = nullptr, IntraOpType intra_op_functor = nullptr, PostOpType post_op_functor = nullptr); + template + EnableIf> + SetPrecisionAndExectutionMethod(OtherImageType* other_image_ptr, + KernelType kernel_type, + PreOpType pre_op_functor = nullptr, + IntraOpType intra_op_functor = nullptr, + PostOpType post_op_functor = nullptr); // 2. // TODO: remove this now that the functors are working // Check to see if any intra kernel functions are wanted, and if so set the appropriate device pointers. - template - void SetIntraKernelFunctions(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); + template + void SetIntraKernelFunctions(OtherImageType* other_image_ptr, KernelType kernel_type, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); // 3. // Second call, sets size of the transform kernel, selects the appropriate GPU arch @@ -676,30 +639,95 @@ class FourierTransformer { // void SelectSizeAndType(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); // This allows us to iterate through a set of constexpr sizes passed as a template parameter pack. The value is in providing a means to have different size packs // for different fft configurations, eg. 2d vs 3d - template - void SelectSizeAndType(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); + template + void SelectSizeAndType(OtherImageType* other_image_ptr, KernelType kernel_type, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); - template - void SelectSizeAndType(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); + template + void SelectSizeAndType(OtherImageType* other_image_ptr, KernelType kernel_type, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); // 3. // Third call, sets the input and output dimensions and type - template - void SetAndLaunchKernel(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); + template + void SetAndLaunchKernel(OtherImageType* other_image_ptr, KernelType kernel_type, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor); + + short padding_jump_val_; + int input_memory_wanted_; + int fwd_output_memory_wanted_; + int inv_output_memory_wanted_; + int compute_memory_wanted_; + int memory_size_to_copy_; + + bool input_data_is_on_device; + bool output_data_is_on_device; + bool external_image_is_on_device; + void AllocateBufferMemory( ); + + template + EnableIf && IsAllowedInputType> + Generic_Fwd_Image_Inv(OtherImageType* image_to_search, + PreOpType pre_op, + IntraOpType intra_op, + PostOpType post_op); + + template + EnableIf> + Generic_Fwd(PreOpType pre_op, + IntraOpType intra_op); + + template + EnableIf> + Generic_Inv(IntraOpType intra_op, + PostOpType post_op); + + // FIXME: This function could be named more appropriately. + // FIXME: kernel_type is only needed for the current debug checks based on the blockDim.z bug + inline bool IsAPowerOfTwo(const int input_value) { + int tmp_val = 1; + while ( tmp_val < input_value ) + tmp_val = tmp_val << 1; + + if ( tmp_val > input_value ) + return false; + else + return true; + } - void PrintLaunchParameters(LaunchParams LP) { - std::cerr << "Launch parameters: " << std::endl; - std::cerr << " Threads per block: "; - PrintVectorType(LP.threadsPerBlock); - std::cerr << " Grid dimensions: "; - PrintVectorType(LP.gridDims); - std::cerr << " Q: " << LP.Q << std::endl; - std::cerr << " Twiddle in: " << LP.twiddle_in << std::endl; - std::cerr << " shared input: " << LP.mem_offsets.shared_input << std::endl; - std::cerr << " shared output (memlimit in r2c): " << LP.mem_offsets.shared_output << std::endl; - std::cerr << " physical_x_input: " << LP.mem_offsets.physical_x_input << std::endl; - std::cerr << " physical_x_output: " << LP.mem_offsets.physical_x_output << std::endl; - }; + inline void AssertDivisibleAndFactorOf2(KernelType kernel_type, int full_size_transform, const int number_non_zero_inputs_or_outputs) { + + // The size we would need to use with a general purpose library, eg. FFTW + // Note: this is not limited by the power of 2 restriction as we can compose this with sub ffts that are power of 2 + transform_size.N = full_size_transform; + // The input/output size we care about. non-zero comes from zero padding, but probably doesn't make sense + // for a size reduction algo e.g. TODO: rename + transform_size.L = number_non_zero_inputs_or_outputs; + + // Get the closest >= power of 2 + transform_size.P = 1; + while ( transform_size.P < number_non_zero_inputs_or_outputs ) + transform_size.P = transform_size.P << 1; + + MyFFTDebugAssertFalse(transform_size.P > transform_size.N, "transform_size.P > tranform_size.N"); + + // Our full transform size must have AT LEAST one factor of 2 + MyFFTDebugAssertTrue(transform_size.N % transform_size.P == 0, "transform_size.N % tranform_size.P != 0"); + transform_size.Q = transform_size.N / transform_size.P; + + // FIXME: there is a bug in cuda that crashes for thread block size > 64 in the Z dimension. + // Note: for size increase or rount trip transforms, we can use on chip explicit padding, so this bug + // does not apply. + // if ( IsDecreaseSizeType(kernel_type) ) + // MyFFTRunTimeAssertFalse(transform_size.Q > 64, "transform_size.Q > 64, see Nvidia bug report 4417253"); + } + + // Input is real or complex inferred from InputType + DevicePointers d_ptr; + // Check to make sure we haven't fouled up the explicit instantiation of DevicePointers + + int elements_per_thread_complex; // Set depending on the kernel and size of the transform. }; // class Fourier Transformer diff --git a/include/detail/checks_and_debug.h b/include/detail/checks_and_debug.h new file mode 100644 index 0000000..bd61262 --- /dev/null +++ b/include/detail/checks_and_debug.h @@ -0,0 +1,151 @@ +#ifndef __INCLUDE_DETAILS_CHECKS_AND_DEBUG_H__ +#define __INCLUDE_DETAILS_CHECKS_AND_DEBUG_H__ + +#include "types.h" +#include + +namespace FastFFT { +// hacky and non-conclusive way to trouble shoot mismatched types in function calls +// Intented to be place ind constexpr conditionals that should not be reached. +// Relies on exhaustive list of types at compile time, so there is also a runtime +// bool to catch any types that are not in the list. +template +__device__ __host__ inline void static_assert_type_name(T v) { + if constexpr ( std::is_pointer_v ) { + static_assert(! std::is_convertible_v, "int*"); + static_assert(! std::is_convertible_v, "int2*"); + static_assert(! std::is_convertible_v, "int3*"); + static_assert(! std::is_convertible_v, "int4*"); + static_assert(! std::is_convertible_v, "float*"); + static_assert(! std::is_convertible_v, "float2*"); + static_assert(! std::is_convertible_v, "float3*"); + static_assert(! std::is_convertible_v, "float4*"); + static_assert(! std::is_convertible_v, "double*"); + static_assert(! std::is_convertible_v, "__half*"); + static_assert(! std::is_convertible_v, "__half2*"); + static_assert(! std::is_convertible_v, "nullptr_t"); + } + else { + static_assert(! std::is_convertible_v, "int"); + static_assert(! std::is_convertible_v, "int2"); + static_assert(! std::is_convertible_v, "int3"); + static_assert(! std::is_convertible_v, "int4"); + static_assert(! std::is_convertible_v, "float"); + static_assert(! std::is_convertible_v, "float2"); + static_assert(! std::is_convertible_v, "float3"); + static_assert(! std::is_convertible_v, "float4"); + static_assert(! std::is_convertible_v, "double"); + static_assert(! std::is_convertible_v, "__half"); + static_assert(! std::is_convertible_v, "__half2"); + static_assert(! std::is_convertible_v, "nullptr_t"); + } +}; + + // clang-format off + +#if FFT_DEBUG_LEVEL < 1 + +#define MyFFTDebugPrintWithDetails(...) +#define MyFFTDebugAssertTrue(cond, msg, ...) +#define MyFFTDebugAssertFalse(cond, msg, ...) +#define MyFFTDebugAssertTestTrue(cond, msg, ...) +#define MyFFTDebugAssertTestFalse(cond, msg, ...) +#define DebugUnused + +#else +// Minimally define asserts that check state variables and setup. +#define MyFFTDebugAssertTrue(cond, msg, ...) { if ( (cond) != true ) { std::cerr << msg << std::endl << " Failed Assert at " << __FILE__ << ":" << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; std::abort(); } } +#define MyFFTDebugAssertFalse(cond, msg, ...) { if ( (cond) == true ) { std::cerr << msg << std::endl << " Failed Assert at " << __FILE__ << ":" << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; std::abort(); } } + +#endif + +#if FFT_DEBUG_LEVEL > 1 +// Turn on checkpoints in the testing functions. +#define MyFFTDebugAssertTestTrue(cond, msg, ...) { if ( (cond) != true ) { std::cerr << " Test " << msg << " FAILED!" << std::endl << " at " << __FILE__ << ":" << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl;std::abort(); } else { std::cerr << " Test " << msg << " passed!" << std::endl; }} +#define MyFFTDebugAssertTestFalse(cond, msg, ...) { if ( (cond) == true ) { std::cerr << " Test " << msg << " FAILED!" << std::endl << " at " << __FILE__ << ":" << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; std::abort(); } else { std::cerr << " Test " << msg << " passed!" << std::endl; } } +#define DebugUnused [[maybe_unused]] +#endif + +#if FFT_DEBUG_LEVEL == 2 +#define MyFFTDebugPrintWithDetails(...) +#endif + +#if FFT_DEBUG_LEVEL >= 3 +// More verbose debug info +#define MyFFTDebugPrint(...) { std::cerr << __VA_ARGS__ << std::endl; } +#define MyFFTDebugPrintWithDetails(...) { std::cerr << __VA_ARGS__ << " From: " << __FILE__ << ":" << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; } +#endif + +#if FFT_DEBUG_LEVEL == 4 +// Activates HEAVY error checking and sets an std::abort() in cudaErr9) macro below (if not defined already by external like cisTEM.) +#endif + +// Always in use +#define MyFFTPrint(...) { std::cerr << __VA_ARGS__ << std::endl; } +#define MyFFTPrintWithDetails(...) { std::cerr << __VA_ARGS__ << " From: " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; } +#define MyFFTRunTimeAssertTrue(cond, msg, ...) { if ( (cond) != true ) { std::cerr << msg << std::endl << " Failed Assert at " << __FILE__ << ":" << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl;std::abort(); } } +#define MyFFTRunTimeAssertFalse(cond, msg, ...) { if ( (cond) == true ) {std::cerr << msg << std::endl << " Failed Assert at " << __FILE__ << ":" << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl;std::abort(); } } + + + +// I use the same things in cisTEM, so check for them. FIXME, get rid of defines and also find a better sharing mechanism. +#ifndef cudaErr +// Note we are using std::cerr b/c the wxWidgets apps running in cisTEM are capturing std::cout +// If I leave cudaErr blank when HEAVYERRORCHECKING_FFT is not defined, I get some reports/warnings about unused or unreferenced variables. I suspect the performance hit is very small so just leave this on. +// The real cost is in the synchronization of in pre/postcheck. +#if FFT_DEBUG_LEVEL == 4 +#define cudaErr(error) { auto status = static_cast(error); if ( status != cudaSuccess ) { std::cerr << cudaGetErrorString(status) << " :-> "; MyFFTPrintWithDetails(""); std::abort(); } }; +#else +#define cudaErr(error) { auto status = static_cast(error); if ( status != cudaSuccess ) { std::cerr << cudaGetErrorString(status) << " :-> "; MyFFTPrintWithDetails(""); } }; + +#endif + +#endif + +#ifndef postcheck +#ifndef precheck +#ifndef HEAVYERRORCHECKING_FFT +#define postcheck +#define precheck +#else +#define postcheck { cudaErr(cudaPeekAtLastError( )); cudaError_t error = cudaStreamSynchronize(cudaStreamPerThread); cudaErr(error) } +#define precheck { cudaErr(cudaGetLastError( )) } +#endif +#endif +#endif + + + inline void checkCudaErr(cudaError_t err) { + if (err != cudaSuccess) { + std::cerr << cudaGetErrorString(err) << " :-> " << std::endl; + MyFFTPrintWithDetails(" "); + } + }; + +// clang-format on + +// For debugging + +inline void PrintVectorType(int3 input) { + std::cerr << "(x,y,z) " << input.x << " " << input.y << " " << input.z << std::endl; +} + +inline void PrintVectorType(int4 input) { + std::cerr << "(x,y,z,w) " << input.x << " " << input.y << " " << input.z << " " << input.w << std::endl; +} + +inline void PrintVectorType(dim3 input) { + std::cerr << "(x,y,z) " << input.x << " " << input.y << " " << input.z << std::endl; +} + +inline void PrintVectorType(short3 input) { + std::cerr << "(x,y,z) " << input.x << " " << input.y << " " << input.z << std::endl; +} + +inline void PrintVectorType(short4 input) { + std::cerr << "(x,y,z,w) " << input.x << " " << input.y << " " << input.z << " " << input.w << std::endl; +} + +} // namespace FastFFT + +#endif \ No newline at end of file diff --git a/include/detail/concepts.h b/include/detail/concepts.h new file mode 100644 index 0000000..02885fa --- /dev/null +++ b/include/detail/concepts.h @@ -0,0 +1,75 @@ +#ifndef __INCLUDE_DETAIL_CONCEPTS_H__ +#define __INCLUDE_DETAIL_CONCEPTS_H__ + +#include + +template +constexpr inline bool IS_IKF_t( ) { + if constexpr ( std::is_final_v ) { + return true; + } + else { + return false; + } +}; + +namespace FastFFT { + +namespace KernelFunction { + +// Define an enum for different functors +// Intra Kernel Function Type +enum IKF_t { NOOP, + SCALE, + CONJ_MUL, + CONJ_MUL_THEN_SCALE }; +} // namespace KernelFunction + +// To limit which kernels are instantiated, define a set of constants for the FFT method to be used at compile time. +constexpr int Generic_Fwd_FFT = 1; +constexpr int Generic_Inv_FFT = 2; +constexpr int Generic_Fwd_Image_Inv_FFT = 3; + +template +struct EnableIfT {}; + +template +struct EnableIfT { using Type = T; }; + +template +using EnableIf = typename EnableIfT::Type; + +template +constexpr bool HasIntraOpFunctor = IS_IKF_t( ); + +// 3d is always odd (3 for fwd/inv or 5 for round trip) +// 2d is odd if it is round trip (3) and even if fwd/inv (2 ) +template +constexpr bool IsAlgoRoundTrip = (FFT_ALGO_t == Generic_Fwd_Image_Inv_FFT); + +template +constexpr bool IfAppliesIntraOpFunctor_HasIntraOpFunctor = (FFT_ALGO_t != Generic_Fwd_Image_Inv_FFT || (FFT_ALGO_t == Generic_Fwd_Image_Inv_FFT && HasIntraOpFunctor)); + +template +constexpr bool IsComplexType = (std::is_same_v || std::is_same_v); + +template +constexpr bool IsPointerOrNullPtrType = (... && (std::is_same::value || std::is_pointer_v>)); + +template +constexpr bool IsAllowedRealType = (... && (std::is_same_v || std::is_same_v)); + +template +constexpr bool IsAllowedComplexBaseType = IsAllowedRealType; + +template +constexpr bool IsAllowedComplexType = (... && (std::is_same_v || std::is_same_v)); + +template +constexpr bool IsAllowedInputType = (... && (std::is_same_v || std::is_same_v || std::is_same_v || std::is_same_v)); + +template +constexpr bool CheckPointerTypesForMatch = (std::is_same_v && std::is_same_v); + +} // namespace FastFFT +#endif // __INCLUDE_DETAIL_CONCEPTS_H__ \ No newline at end of file diff --git a/include/detail/config.h b/include/detail/config.h new file mode 100644 index 0000000..2b2b4d9 --- /dev/null +++ b/include/detail/config.h @@ -0,0 +1,28 @@ +#ifndef __INCLUDE_DETAILS_CONFIG_H__ +#define __INCLUDE_DETAILS_CONFIG_H__ + +#include +#include +#include + +// Forward declaration so we can leave the inclusion of cuda_fp16.h to FastFFT.cu +struct __half; +struct __half2; + +#ifndef cisTEM_USING_FastFFT // ifdef being used in cisTEM that defines these +#if __cplusplus >= 202002L +#include +using namespace std::numbers; +#else +#if __cplusplus < 201703L +#message "C++ is " __cplusplus +#error "C++17 or later required" +#else +template +// inline constexpr _Tp pi_v = _Enable_if_floating<_Tp>(3.141592653589793238462643383279502884L); +inline constexpr _Tp pi_v = 3.141592653589793238462643383279502884L; +#endif // __cplusplus require > 17 +#endif // __cplusplus 20 support +#endif // enable FastFFT + +#endif \ No newline at end of file diff --git a/include/detail/constants.h b/include/detail/constants.h new file mode 100644 index 0000000..dd1a341 --- /dev/null +++ b/include/detail/constants.h @@ -0,0 +1,19 @@ +#ifndef __INCLUDE_DETAIL_CONSTANTS_H__ +#define __INCLUDE_DETAIL_CONSTANTS_H__ + +namespace FastFFT { +// TODO this probably needs to depend on the size of the xform, at least small vs large. +constexpr const int elements_per_thread_16 = 4; +constexpr const int elements_per_thread_32 = 8; +constexpr const int elements_per_thread_64 = 8; +constexpr const int elements_per_thread_128 = 8; +constexpr const int elements_per_thread_256 = 8; +constexpr const int elements_per_thread_512 = 8; +constexpr const int elements_per_thread_1024 = 8; +constexpr const int elements_per_thread_2048 = 8; +constexpr const int elements_per_thread_4096 = 8; +constexpr const int elements_per_thread_8192 = 16; + +} // namespace FastFFT + +#endif \ No newline at end of file diff --git a/include/detail/detail.cuh b/include/detail/detail.cuh new file mode 100644 index 0000000..4108a0e --- /dev/null +++ b/include/detail/detail.cuh @@ -0,0 +1,11 @@ +#ifndef __INCLUDE_DETAIL_DETAIL_CUH_ +#define __INCLUDE_DETAIL_DETAIL_CUH_ + +#include "constants.h" +#include "device_helpers.h" +#include "checks_and_debug.h" +#include "device_functions.h" +#include "memory_addressing.h" +#include "functors.h" + +#endif \ No newline at end of file diff --git a/include/detail/detail.h b/include/detail/detail.h new file mode 100644 index 0000000..465b018 --- /dev/null +++ b/include/detail/detail.h @@ -0,0 +1,11 @@ +#ifndef __INCLUDE_DETAIL_DETAIL_H__ +#define __INCLUDE_DETAIL_DETAIL_H__ + +#include "config.h" +#include "device_properties.h" +#include "fft_properties.h" +#include "types.h" +#include "concepts.h" +#include "checks_and_debug.h" + +#endif \ No newline at end of file diff --git a/include/detail/device_functions.h b/include/detail/device_functions.h new file mode 100644 index 0000000..d87401c --- /dev/null +++ b/include/detail/device_functions.h @@ -0,0 +1,31 @@ +#ifndef __INCLUDE_DETAIL_DEVICE_FUNCTIONS_H__ +#define __INCLUDE_DETAIL_DEVICE_FUNCTIONS_H__ + +#include "../cufftdx/include/cufftdx.hpp" + +namespace FastFFT { + +// Complex a * conj b multiplication +template +static __device__ __host__ inline auto ComplexConjMulAndScale(const ComplexType a, const ComplexType b, ScalarType s) -> decltype(b) { + ComplexType c; + c.x = s * (a.x * b.x + a.y * b.y); + c.y = s * (a.y * b.x - a.x * b.y); + return c; +} + +#define USEFASTSINCOS +// The __sincosf doesn't appear to be the problem with accuracy, likely just the extra additions, but it probably also is less flexible with other types. I don't see a half precision equivalent. +#ifdef USEFASTSINCOS +__device__ __forceinline__ void SINCOS(float arg, float* s, float* c) { + __sincosf(arg, s, c); +} +#else +__device__ __forceinline__ void SINCOS(float arg, float* s, float* c) { + sincos(arg, s, c); +} +#endif + +} // namespace FastFFT + +#endif // __INCLUDE_DETAIL_DEVICE_FUNCTIONS_H__ \ No newline at end of file diff --git a/include/detail/device_helpers.h b/include/detail/device_helpers.h new file mode 100644 index 0000000..eb2e644 --- /dev/null +++ b/include/detail/device_helpers.h @@ -0,0 +1,52 @@ +#ifndef __INCLUDE_DETAIL_DEVICE_HELPERS_H__ +#define __INCLUDE_DETAIL_DEVICE_HELPERS_H__ + +#include +#include "../cufftdx/include/cufftdx.hpp" + +#include "checks_and_debug.h" + +namespace FastFFT { + +// GetCudaDeviceArch from https://github.com/mnicely/cufft_examples/blob/master/Common/cuda_helper.h +void GetCudaDeviceProps(DeviceProps& dp); + +void CheckSharedMemory(int& memory_requested, DeviceProps& dp); +void CheckSharedMemory(unsigned int& memory_requested, DeviceProps& dp); + +template +inline bool is_pointer_in_memory_and_registered(T ptr) { + // FIXME: I don't think this is thread safe, add a mutex as in cistem::GpuImage + cudaPointerAttributes attr; + cudaErr(cudaPointerGetAttributes(&attr, ptr)); + + if ( attr.type == 1 && attr.devicePointer == attr.hostPointer ) { + return true; + } + else { + return false; + } +} + +template +inline bool is_pointer_in_device_memory(T ptr) { + // FIXME: I don't think this is thread safe, add a mutex as in cistem::GpuImage + cudaPointerAttributes attr; + cudaErr(cudaPointerGetAttributes(&attr, ptr)); + + if ( attr.type == 2 || attr.type == 3 ) { + return true; + } + else { + return false; + } +} + +__device__ __forceinline__ int +d_ReturnReal1DAddressFromPhysicalCoord(int3 coords, short4 img_dims) { + return ((((int)coords.z * (int)img_dims.y + coords.y) * (int)img_dims.w * 2) + (int)coords.x); +} + +} // namespace FastFFT + +#endif \ No newline at end of file diff --git a/include/detail/device_properties.h b/include/detail/device_properties.h new file mode 100644 index 0000000..98fa72c --- /dev/null +++ b/include/detail/device_properties.h @@ -0,0 +1,18 @@ +#ifndef __INCLUDE_DETAIL_DEVICE_PROPERTIES_H__ +#define __INCLUDE_DETAIL_DEVICE_PROPERTIES_H__ + +namespace FastFFT { +typedef struct __align__(32) _DeviceProps { + int device_id; + int device_arch; + int max_shared_memory_per_block; + int max_shared_memory_per_SM; + int max_registers_per_block; + int max_persisting_L2_cache_size; +} + +DeviceProps; + +} // namespace FastFFT + +#endif // __INCLUDE_DETAIL_DEVICE_PROPERTIES_H__ \ No newline at end of file diff --git a/include/detail/fft_properties.h b/include/detail/fft_properties.h new file mode 100644 index 0000000..279b9e1 --- /dev/null +++ b/include/detail/fft_properties.h @@ -0,0 +1,37 @@ +#ifndef __INCLUDE_DETAIL_FFT_PROPERTIES_H__ +#define __INCLUDE_DETAIL_FFT_PROPERTIES_H__ + +namespace FastFFT { + +typedef struct __align__(8) _FFT_Size { + // Following Sorensen & Burrus 1993 for clarity + short N; // N : 1d FFT size + short L; // L : number of non-zero output/input points + short P; // P >= L && N % P == 0 : The size of the sub-FFT used to compute the full transform. Currently also must be a power of 2. + short Q; // Q = N/P : The number of sub-FFTs used to compute the full transform +} + +FFT_Size; + +typedef struct __align__(8) _Offsets { + unsigned short shared_input; + unsigned short shared_output; + unsigned short physical_x_input; + unsigned short physical_x_output; +} + +Offsets; + +typedef struct __align__(64) _LaunchParams { + int Q; + float twiddle_in; + dim3 gridDims; + dim3 threadsPerBlock; + Offsets mem_offsets; +} + +LaunchParams; + +} // namespace FastFFT + +#endif \ No newline at end of file diff --git a/include/detail/functors.h b/include/detail/functors.h new file mode 100644 index 0000000..a319b27 --- /dev/null +++ b/include/detail/functors.h @@ -0,0 +1,85 @@ +#ifndef __INCLUDE_DETAILS_FUNCTORS_H__ +#define __INCLUDE_DETAILS_FUNCTORS_H__ + +#include "concepts.h" + +// TODO: doc and namespace +// FIXME: the is_final is a bit of a hack to make sure we can tell if the functor is a NOOP or not + +namespace FastFFT { + +namespace KernelFunction { + +// Maybe a better way to check , but using keyword final to statically check for non NONE types +// TODO: is marking the operatior()() inline sufficient or does the struct need to be marked inline as well? +template +struct my_functor {}; + +template +struct my_functor { + __device__ __forceinline__ + T + operator( )( ) { + printf("really specific NOOP\n"); + return 0; + } +}; + +// EnableIf> +//struct my_functor>> final { +template +struct my_functor>> final { + __device__ __forceinline__ void + operator( )(T& template_fft_x, T& template_fft_y, const T& target_fft_x, const T& target_fft_y) { + // Is there a better way than declaring this variable each time? + // This is target * conj(template) + T tmp = (template_fft_x * target_fft_x + template_fft_y * target_fft_y); + template_fft_y = (template_fft_x * target_fft_y - template_fft_y * target_fft_x); + template_fft_x = tmp; + } +}; + +template +struct my_functor>> final { + + // Pass in the scale factor on construction + my_functor(const T& scale_factor) : scale_factor(scale_factor) {} + + __device__ __forceinline__ void + operator( )(T& template_fft_x, T& template_fft_y, const T& target_fft_x, const T& target_fft_y) { + // Is there a better way than declaring this variable each time? + // This is target * conj(template) + T tmp = (template_fft_x * target_fft_x + template_fft_y * target_fft_y) * scale_factor; + template_fft_y = (template_fft_x * target_fft_y - template_fft_y * target_fft_x) * scale_factor; + template_fft_x = tmp; + } + + private: + const T scale_factor; +}; + +template +struct my_functor>> final { + + const T scale_factor; + + // Pass in the scale factor on construction + __device__ __forceinline__ my_functor(const T& scale_factor) : scale_factor(scale_factor) {} + + __device__ __forceinline__ float + operator( )(float input_value) { + return input_value *= scale_factor; + } + + __device__ __forceinline__ float2 + operator( )(float2 input_value) { + input_value.x *= scale_factor; + input_value.y *= scale_factor; + return input_value; + } +}; + +} // namespace KernelFunction +} // namespace FastFFT + +#endif \ No newline at end of file diff --git a/include/detail/memory_addressing.h b/include/detail/memory_addressing.h new file mode 100644 index 0000000..3b1c990 --- /dev/null +++ b/include/detail/memory_addressing.h @@ -0,0 +1,107 @@ +#ifndef __INCLUDE_DETAIL_MEMORY_ADDRESSING_H__ +#define __INCLUDE_DETAIL_MEMORY_ADDRESSING_H__ + +#include +#include "../cufftdx/include/detail/system_checks.hpp" + +namespace FastFFT { + +static constexpr const int XZ_STRIDE = 16; + +static constexpr const int bank_size = 32; +static constexpr const int bank_padded = bank_size + 1; +static constexpr const unsigned int ubank_size = 32; + +static constexpr const unsigned int ubank_padded = ubank_size + 1; + +__device__ __forceinline__ int GetSharedMemPaddedIndex(const int index) { + return (index % bank_size) + ((index / bank_size) * bank_padded); +} + +__device__ __forceinline__ int GetSharedMemPaddedIndex(const unsigned int index) { + return (index % ubank_size) + ((index / ubank_size) * ubank_padded); +} + +// Return the address of the 1D transform index 0 +static __device__ __forceinline__ unsigned int Return1DFFTAddress(const unsigned int pixel_pitch) { + return pixel_pitch * (blockIdx.y + blockIdx.z * gridDim.y); +} + +// Return the address of the 1D transform index 0. Right now testing for a stride of 2, but this could be modifiable if it works. +static __device__ __forceinline__ unsigned int Return1DFFTAddress_strided_Z(const unsigned int pixel_pitch) { + // In the current condition, threadIdx.y is either 0 || 1, and gridDim.z = size_z / 2 + // index into a 2D tile in the XZ plane, for output in the ZX transposed plane (for coalsced write.) + return pixel_pitch * (blockIdx.y + (XZ_STRIDE * blockIdx.z + threadIdx.y) * gridDim.y); +} + +// Return the address of the 1D transform index 0 +static __device__ __forceinline__ unsigned int ReturnZplane(const unsigned int NX, const unsigned int NY) { + return (blockIdx.z * NX * NY); +} + +// Return the address of the 1D transform index 0 +static __device__ __forceinline__ unsigned int Return1DFFTAddress_Z(const unsigned int NY) { + return blockIdx.y + (blockIdx.z * NY); +} + +// Return the address of the 1D transform index 0 +static __device__ __forceinline__ unsigned int Return1DFFTColumn_XYZ_transpose(const unsigned int NX) { + // NX should be size_of::value for this method. Should this be templated? + // presumably the XZ axis is alread transposed on the forward, used to index into this state. Indexs in (ZY)' plane for input, to be transposed and permuted to output.' + return NX * (XZ_STRIDE * (blockIdx.y + gridDim.y * blockIdx.z) + threadIdx.y); +} + +// Return the address of the 1D transform index 0 +static __device__ __forceinline__ unsigned int Return1DFFTAddress_XZ_transpose(const unsigned int X) { + return blockIdx.z + gridDim.z * (blockIdx.y + X * gridDim.y); +} + +// Return the address of the 1D transform index 0 +static __device__ __forceinline__ unsigned int Return1DFFTAddress_XZ_transpose_strided_Z(const unsigned int IDX) { + // return (XZ_STRIDE*blockIdx.z + (X % XZ_STRIDE)) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + (X / XZ_STRIDE) * gridDim.y ); + // (IDX % XZ_STRIDE) -> transposed x coordinate in tile + // ((blockIdx.z*XZ_STRIDE) -> tile offest in physical X (with above gives physical X out (transposed Z)) + // (XZ_STRIDE*gridDim.z) -> n elements in physical X (transposed Z) + // above * blockIdx.y -> offset in physical Y (transposed Y) + // (IDX / XZ_STRIDE) -> n elements physical Z (transposed X) + return ((IDX % XZ_STRIDE) + (blockIdx.z * XZ_STRIDE)) + (XZ_STRIDE * gridDim.z) * (blockIdx.y + (IDX / XZ_STRIDE) * gridDim.y); +} + +static __device__ __forceinline__ unsigned int Return1DFFTAddress_XZ_transpose_strided_Z(const unsigned int IDX, const unsigned int Q, const unsigned int sub_fft) { + // return (XZ_STRIDE*blockIdx.z + (X % XZ_STRIDE)) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + (X / XZ_STRIDE) * gridDim.y ); + // (IDX % XZ_STRIDE) -> transposed x coordinate in tile + // ((blockIdx.z*XZ_STRIDE) -> tile offest in physical X (with above gives physical X out (transposed Z)) + // (XZ_STRIDE*gridDim.z) -> n elements in physical X (transposed Z) + // above * blockIdx.y -> offset in physical Y (transposed Y) + // (IDX / XZ_STRIDE) -> n elements physical Z (transposed X) + return ((IDX % XZ_STRIDE) + (blockIdx.z * XZ_STRIDE)) + (XZ_STRIDE * gridDim.z) * (blockIdx.y + ((IDX / XZ_STRIDE) * Q + sub_fft) * gridDim.y); +} + +// Return the address of the 1D transform index 0 +static __device__ __forceinline__ unsigned int Return1DFFTAddress_YZ_transpose_strided_Z(const unsigned int IDX) { + // return (XZ_STRIDE*blockIdx.z + (X % XZ_STRIDE)) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + (X / XZ_STRIDE) * gridDim.y ); + return ((IDX % XZ_STRIDE) + (blockIdx.y * XZ_STRIDE)) + (gridDim.y * XZ_STRIDE) * (blockIdx.z + (IDX / XZ_STRIDE) * gridDim.z); +} + +// Return the address of the 1D transform index 0 +static __device__ __forceinline__ unsigned int Return1DFFTAddress_YZ_transpose_strided_Z(const unsigned int IDX, const unsigned int Q, const unsigned int sub_fft) { + // return (XZ_STRIDE*blockIdx.z + (X % XZ_STRIDE)) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + (X / XZ_STRIDE) * gridDim.y ); + return ((IDX % XZ_STRIDE) + (blockIdx.y * XZ_STRIDE)) + (gridDim.y * XZ_STRIDE) * (blockIdx.z + ((IDX / XZ_STRIDE) * Q + sub_fft) * gridDim.z); +} + +// Return the address of the 1D transform index 0 +static __device__ __forceinline__ unsigned int Return1DFFTColumn_XZ_to_XY( ) { + // return blockIdx.y + gridDim.y * ( blockIdx.z + gridDim.z * X); + return blockIdx.y + gridDim.y * blockIdx.z; +} + +static __device__ __forceinline__ unsigned int Return1DFFTAddress_YX_to_XY( ) { + return blockIdx.z + gridDim.z * blockIdx.y; +} + +static __device__ __forceinline__ unsigned int Return1DFFTAddress_YX( ) { + return Return1DFFTColumn_XZ_to_XY( ); +} +} // namespace FastFFT + +#endif \ No newline at end of file diff --git a/src/fastfft/types.cuh b/include/detail/types.h similarity index 75% rename from src/fastfft/types.cuh rename to include/detail/types.h index d261ca5..373ae85 100644 --- a/src/fastfft/types.cuh +++ b/include/detail/types.h @@ -1,5 +1,5 @@ -#ifndef _SRC_FASTFFT_TYPES_H_ -#define _SRC_FASTFFT_TYPES_H_ +#ifndef __INCLUDE_DETAIL_FASTFFT_TYPES_H__ +#define __INCLUDE_DETAIL_FASTFFT_TYPES_H__ #include #include @@ -41,16 +41,9 @@ constexpr std::array name = {"natural", "centered", "quadra } // namespace OriginType -namespace TransformStageCompleted { -enum Enum : uint8_t { none = 10, - fwd = 11, - inv = 12 }; // none must be greater than number of sizeChangeTypes, padding must match in TransformStageCompletedName vector -} // namespace TransformStageCompleted - namespace DimensionCheckType { enum Enum : uint8_t { CopyFromHost, CopyToHost, - CopyDeviceToDevice, FwdTransform, InvTransform }; @@ -58,4 +51,4 @@ enum Enum : uint8_t { CopyFromHost, } // namespace FastFFT -#endif /* _SRC_FASTFFT_TYPES_H_ */ \ No newline at end of file +#endif // __INCLUDE_DETAIL_FASTFFT_TYPES_H__ \ No newline at end of file diff --git a/include/ieee-754-half/ChangeLog.txt b/include/ieee-754-half/ChangeLog.txt new file mode 100644 index 0000000..37f3dbf --- /dev/null +++ b/include/ieee-754-half/ChangeLog.txt @@ -0,0 +1,213 @@ +Release Notes {#changelog} +============= + +2.2.0 release (2021-06-12): +--------------------------- + +- Added `rsqrt` function for inverse square root. +- Improved performance of `pow` function. +- Fixed bug that forgot to include `` for F16C intrinsics. + + +2.1.0 release (2019-08-05): +--------------------------- + +- Added detection of IEEE floating-point exceptions to operators and functions. +- Added configuration options for automatic exception handling. +- Added functions for explicitly managing floating-point exception flags. +- Improved accuracy of `pow` and `atan2` functions. + + +2.0.0 release (2019-07-23): +--------------------------- + +- Made internal implementation independent from built-in floating point + facilities for increased reliability and IEEE-conformance. +- Changed default rounding mode to rounding to nearest. +- Always round ties to even when rounding to nearest. +- Extended `constexpr` support to comparison and classification functions. +- Added support for F16C compiler intrinsics for conversions. +- Enabled C++11 feature detection for Intel compilers. + + +1.12.0 release (2017-03-06): +---------------------------- + +- Changed behaviour of `half_cast` to perform conversions to/from `double` + and `long double` directly according to specified rounding mode, without an + intermediate `float` conversion. +- Added `noexcept` specifiers to constructors. +- Fixed minor portability problem with `logb` and `ilogb`. +- Tested for *VC++ 2015*. + + +1.11.0 release (2013-11-16): +---------------------------- + +- Made tie-breaking behaviour in round to nearest configurable by + `HALF_ROUND_TIES_TO_EVEN` macro. +- Completed support for all C++11 mathematical functions even if single- + precision versions from `` are unsupported. +- Fixed inability to disable support for C++11 mathematical functions on + *VC++ 2013*. + + +1.10.0 release (2013-11-09): +---------------------------- + +- Made default rounding mode configurable by `HALF_ROUND_STYLE` macro. +- Added support for non-IEEE single-precision implementations. +- Added `HALF_ENABLE_CPP11_TYPE_TRAITS` preprocessor flag for checking + support for C++11 type traits and TMP features. +- Restricted `half_cast` to support built-in arithmetic types only. +- Changed behaviour of `half_cast` to respect rounding mode when casting + to/from integer types. + + +1.9.2 release (2013-11-01): +--------------------------- + +- Tested for *gcc 4.8*. +- Tested and fixed for *VC++ 2013*. +- Removed unnecessary warnings in *MSVC*. + + +1.9.1 release (2013-08-08): +--------------------------- + +- Fixed problems with older gcc and MSVC versions. +- Small fix to non-C++11 implementations of `remainder` and `remquo`. + + +1.9.0 release (2013-08-07): +--------------------------- + +- Changed behaviour of `nearbyint`, `rint`, `lrint` and `llrint` to use + rounding mode of half-precision implementation (which is + truncating/indeterminate) instead of single-precision rounding mode. +- Added support for more C++11 mathematical functions even if single- + precision versions from `` are unsupported, in particular + `remainder`, `remquo` and `cbrt`. +- Minor implementation changes. + + +1.8.1 release (2013-01-22): +--------------------------- + +- Fixed bug resulting in multiple definitions of the `nanh` function due to + a missing `inline` specification. + + +1.8.0 release (2013-01-19): +--------------------------- + +- Added support for more C++11 mathematical functions even if single- + precision versions from `` are unsupported, in particular + exponential and logarithm functions, hyperbolic area functions and the + hypotenuse function. +- Made `fma` function use default implementation if single-precision version + from `` is not faster and thus `FP_FAST_FMAH` to be defined always. +- Fixed overload resolution issues when invoking certain mathematical + functions by unqualified calls. + + +1.7.0 release (2012-10-26): +--------------------------- + +- Added support for C++11 `noexcept` specifiers. +- Changed C++11 `long long` to be supported on *VC++ 2003* and up. + + +1.6.1 release (2012-09-13): +--------------------------- + +- Made `fma` and `fdim` functions available even if corresponding + single-precision functions are not. + + +1.6.0 release (2012-09-12): +--------------------------- + +- Added `HALF_ENABLE_CPP11_LONG_LONG` to control support for `long long` + integers and corresponding mathematical functions. +- Fixed C++98 compatibility on non-VC compilers. + + +1.5.1 release (2012-08-17): +--------------------------- + +- Recorrected `std::numeric_limits::round_style` to always return + `std::round_indeterminate`, due to overflow-handling deviating from + correct round-toward-zero behaviour. + + +1.5.0 release (2012-08-16): +--------------------------- + +- Added `half_cast` for explicitly casting between half and any type + convertible to/from `float` and allowing the explicit specification of + the rounding mode to use. + + +1.4.0 release (2012-08-12): +--------------------------- + +- Added support for C++11 generalized constant expressions (`constexpr`). + + +1.3.1 release (2012-08-11): +--------------------------- + +- Fixed requirement for `std::signbit` and `std::isnan` (even if C++11 + `` functions disabled) on non-VC compilers. + + +1.3.0 release (2012-08-10): +--------------------------- + +- Made requirement for `` and `static_assert` optional and thus + made the library C++98-compatible. +- Made support for C++11 features user-overridable through explicit + definition of corresponding preprocessor symbols to either 0 or 1. +- Renamed `HALF_ENABLE_HASH` to `HALF_ENABLE_CPP11_HASH` in correspondence + with other C++11 preprocessor symbols. + + +1.2.0 release (2012-08-07): +--------------------------- + +- Added proper preprocessor definitions for `HUGE_VALH` and `FP_FAST_FMAH` + in correspondence with their single-precision counterparts from ``. +- Fixed internal preprocessor macros to be properly undefined after use. + + +1.1.2 release (2012-08-07): +--------------------------- + +- Revised `std::numeric_limits::round_style` to return + `std::round_toward_zero` if the `float` version also does and + `std::round_indeterminate` otherwise. +- Fixed `std::numeric_limits::round_error` to reflect worst-case round + toward zero behaviour. + + +1.1.1 release (2012-08-06): +--------------------------- + +- Fixed `std::numeric_limits::min` to return smallest positive normal + number, instead of subnormal number. +- Fixed `std::numeric_limits::round_style` to return + `std::round_indeterminate` due to mixture of separately rounded + single-precision arithmetics with truncating single-to-half conversions. + + +1.1.0 release (2012-08-06): +--------------------------- + +- Added half-precision literals. + + +1.0.0 release (2012-08-05): +--------------------------- + +- First release. diff --git a/include/ieee-754-half/README.txt b/include/ieee-754-half/README.txt new file mode 100644 index 0000000..3dd0d1c --- /dev/null +++ b/include/ieee-754-half/README.txt @@ -0,0 +1,317 @@ +HALF-PRECISION FLOATING-POINT LIBRARY (Version 2.2.0) +----------------------------------------------------- + +This is a C++ header-only library to provide an IEEE 754 conformant 16-bit +half-precision floating-point type along with corresponding arithmetic +operators, type conversions and common mathematical functions. It aims for both +efficiency and ease of use, trying to accurately mimic the behaviour of the +built-in floating-point types at the best performance possible. + + +INSTALLATION AND REQUIREMENTS +----------------------------- + +Conveniently, the library consists of just a single header file containing all +the functionality, which can be directly included by your projects, without the +neccessity to build anything or link to anything. + +Whereas this library is fully C++98-compatible, it can profit from certain +C++11 features. Support for those features is checked automatically at compile +(or rather preprocessing) time, but can be explicitly enabled or disabled by +predefining the corresponding preprocessor symbols to either 1 or 0 yourself +before including half.hpp. This is useful when the automatic detection fails +(for more exotic implementations) or when a feature should be explicitly +disabled: + + - 'long long' integer type for mathematical functions returning 'long long' + results (enabled for VC++ 2003 and icc 11.1 and newer, gcc and clang, + overridable with 'HALF_ENABLE_CPP11_LONG_LONG'). + + - Static assertions for extended compile-time checks (enabled for VC++ 2010, + gcc 4.3, clang 2.9, icc 11.1 and newer, overridable with + 'HALF_ENABLE_CPP11_STATIC_ASSERT'). + + - Generalized constant expressions (enabled for VC++ 2015, gcc 4.6, clang 3.1, + icc 14.0 and newer, overridable with 'HALF_ENABLE_CPP11_CONSTEXPR'). + + - noexcept exception specifications (enabled for VC++ 2015, gcc 4.6, + clang 3.0, icc 14.0 and newer, overridable with 'HALF_ENABLE_CPP11_NOEXCEPT'). + + - User-defined literals for half-precision literals to work (enabled for + VC++ 2015, gcc 4.7, clang 3.1, icc 15.0 and newer, overridable with + 'HALF_ENABLE_CPP11_USER_LITERALS'). + + - Thread-local storage for per-thread floating-point exception flags (enabled + for VC++ 2015, gcc 4.8, clang 3.3, icc 15.0 and newer, overridable with + 'HALF_ENABLE_CPP11_THREAD_LOCAL'). + + - Type traits and template meta-programming features from + (enabled for VC++ 2010, libstdc++ 4.3, libc++ and newer, overridable with + 'HALF_ENABLE_CPP11_TYPE_TRAITS'). + + - Special integer types from (enabled for VC++ 2010, libstdc++ 4.3, + libc++ and newer, overridable with 'HALF_ENABLE_CPP11_CSTDINT'). + + - Certain C++11 single-precision mathematical functions from for + floating-point classification during conversions from higher precision types + (enabled for VC++ 2013, libstdc++ 4.3, libc++ and newer, overridable with + 'HALF_ENABLE_CPP11_CMATH'). + + - Floating-point environment control from for possible exception + propagation to the built-in floating-point platform (enabled for VC++ 2013, + libstdc++ 4.3, libc++ and newer, overridable with 'HALF_ENABLE_CPP11_CFENV'). + + - Hash functor 'std::hash' from (enabled for VC++ 2010, + libstdc++ 4.3, libc++ and newer, overridable with 'HALF_ENABLE_CPP11_HASH'). + +The library has been tested successfully with Visual C++ 2005-2015, gcc 4-8 +and clang 3-8 on 32- and 64-bit x86 systems. Please contact me if you have any +problems, suggestions or even just success testing it on other platforms. + + +DOCUMENTATION +------------- + +What follows are some general words about the usage of the library and its +implementation. For a complete documentation of its interface consult the +corresponding website http://half.sourceforge.net. You may also generate the +complete developer documentation from the library's only include file's doxygen +comments, but this is more relevant to developers rather than mere users. + +BASIC USAGE + +To make use of the library just include its only header file half.hpp, which +defines all half-precision functionality inside the 'half_float' namespace. The +actual 16-bit half-precision data type is represented by the 'half' type, which +uses the standard IEEE representation with 1 sign bit, 5 exponent bits and 11 +mantissa bits (including the hidden bit) and supports all types of special +values, like subnormal values, infinity and NaNs. This type behaves like the +built-in floating-point types as much as possible, supporting the usual +arithmetic, comparison and streaming operators, which makes its use pretty +straight-forward: + + using half_float::half; + half a(3.4), b(5); + half c = a * b; + c += 3; + if(c > a) + std::cout << c << std::endl; + +Additionally the 'half_float' namespace also defines half-precision versions +for all mathematical functions of the C++ standard library, which can be used +directly through ADL: + + half a(-3.14159); + half s = sin(abs(a)); + long l = lround(s); + +You may also specify explicit half-precision literals, since the library +provides a user-defined literal inside the 'half_float::literal' namespace, +which you just need to import (assuming support for C++11 user-defined literals): + + using namespace half_float::literal; + half x = 1.0_h; + +Furthermore the library provides proper specializations for +'std::numeric_limits', defining various implementation properties, and +'std::hash' for hashing half-precision numbers (assuming support for C++11 +'std::hash'). Similar to the corresponding preprocessor symbols from +the library also defines the 'HUGE_VALH' constant and maybe the 'FP_FAST_FMAH' +symbol. + +CONVERSIONS AND ROUNDING + +The half is explicitly constructible/convertible from a single-precision float +argument. Thus it is also explicitly constructible/convertible from any type +implicitly convertible to float, but constructing it from types like double or +int will involve the usual warnings arising when implicitly converting those to +float because of the lost precision. On the one hand those warnings are +intentional, because converting those types to half neccessarily also reduces +precision. But on the other hand they are raised for explicit conversions from +those types, when the user knows what he is doing. So if those warnings keep +bugging you, then you won't get around first explicitly converting to float +before converting to half, or use the 'half_cast' described below. In addition +you can also directly assign float values to halfs. + +In contrast to the float-to-half conversion, which reduces precision, the +conversion from half to float (and thus to any other type implicitly +convertible from float) is implicit, because all values represetable with +half-precision are also representable with single-precision. This way the +half-to-float conversion behaves similar to the builtin float-to-double +conversion and all arithmetic expressions involving both half-precision and +single-precision arguments will be of single-precision type. This way you can +also directly use the mathematical functions of the C++ standard library, +though in this case you will invoke the single-precision versions which will +also return single-precision values, which is (even if maybe performing the +exact same computation, see below) not as conceptually clean when working in a +half-precision environment. + +The default rounding mode for conversions between half and more precise types +as well as for rounding results of arithmetic operations and mathematical +functions rounds to the nearest representable value. But by predefining the +'HALF_ROUND_STYLE' preprocessor symbol this default can be overridden with one +of the other standard rounding modes using their respective constants or the +equivalent values of 'std::float_round_style' (it can even be synchronized with +the built-in single-precision implementation by defining it to +'std::numeric_limits::round_style'): + + - 'std::round_indeterminate' (-1) for the fastest rounding. + + - 'std::round_toward_zero' (0) for rounding toward zero. + + - 'std::round_to_nearest' (1) for rounding to the nearest value (default). + + - 'std::round_toward_infinity' (2) for rounding toward positive infinity. + + - 'std::round_toward_neg_infinity' (3) for rounding toward negative infinity. + +In addition to changing the overall default rounding mode one can also use the +'half_cast'. This converts between half and any built-in arithmetic type using +a configurable rounding mode (or the default rounding mode if none is +specified). In addition to a configurable rounding mode, 'half_cast' has +another big difference to a mere 'static_cast': Any conversions are performed +directly using the given rounding mode, without any intermediate conversion +to/from 'float'. This is especially relevant for conversions to integer types, +which don't necessarily truncate anymore. But also for conversions from +'double' or 'long double' this may produce more precise results than a +pre-conversion to 'float' using the single-precision implementation's current +rounding mode would. + + half a = half_cast(4.2); + half b = half_cast::round_style>(4.2f); + assert( half_cast( 0.7_h ) == 1 ); + assert( half_cast( 4097 ) == 4096.0_h ); + assert( half_cast( 4097 ) == 4100.0_h ); + assert( half_cast( std::numeric_limits::min() ) > 0.0_h ); + +ACCURACY AND PERFORMANCE + +From version 2.0 onward the library is implemented without employing the +underlying floating-point implementation of the system (except for conversions, +of course), providing an entirely self-contained half-precision implementation +with results independent from the system's existing single- or double-precision +implementation and its rounding behaviour. + +As to accuracy, many of the operators and functions provided by this library +are exact to rounding for all rounding modes, i.e. the error to the exact +result is at most 0.5 ULP (unit in the last place) for rounding to nearest and +less than 1 ULP for all other rounding modes. This holds for all the operations +required by the IEEE 754 standard and many more. Specifically the following +functions might exhibit a deviation from the correctly rounded exact result by +1 ULP for a select few input values: 'expm1', 'log1p', 'pow', 'atan2', 'erf', +'erfc', 'lgamma', 'tgamma' (for more details see the documentation of the +individual functions). All other functions and operators are always exact to +rounding or independent of the rounding mode altogether. + +The increased IEEE-conformance and cleanliness of this implementation comes +with a certain performance cost compared to doing computations and mathematical +functions in hardware-accelerated single-precision. On average and depending on +the platform, the arithemtic operators are about 75% as fast and the +mathematical functions about 33-50% as fast as performing the corresponding +operations in single-precision and converting between the inputs and outputs. +However, directly computing with half-precision values is a rather rare +use-case and usually using actual 'float' values for all computations and +temproraries and using 'half's only for storage is the recommended way. But +nevertheless the goal of this library was to provide a complete and +conceptually clean IEEE-confromant half-precision implementation and in the few +cases when you do need to compute directly in half-precision you do so for a +reason and want accurate results. + +If necessary, this internal implementation can be overridden by predefining the +'HALF_ARITHMETIC_TYPE' preprocessor symbol to one of the built-in +floating-point types ('float', 'double' or 'long double'), which will cause the +library to use this type for computing arithmetic operations and mathematical +functions (if available). However, due to using the platform's floating-point +implementation (and its rounding behaviour) internally, this might cause +results to deviate from the specified half-precision rounding mode. It will of +course also inhibit the automatic exception detection described below. + +The conversion operations between half-precision and single-precision types can +also make use of the F16C extension for x86 processors by using the +corresponding compiler intrinsics from . Support for this is +checked at compile-time by looking for the '__F16C__' macro which at least gcc +and clang define based on the target platform. It can also be enabled manually +by predefining the 'HALF_ENABLE_F16C_INTRINSICS' preprocessor symbol to 1, or 0 +for explicitly disabling it. However, this will directly use the corresponding +intrinsics for conversion without checking if they are available at runtime +(possibly crashing if they are not), so make sure they are supported on the +target platform before enabling this. + +EXCEPTION HANDLING + +The half-precision implementation supports all 5 required floating-point +exceptions from the IEEE standard to indicate erroneous inputs or inexact +results during operations. These are represented by exception flags which +actually use the same values as the corresponding 'FE_...' flags defined in +C++11's header if supported, specifically: + + - 'FE_INVALID' for invalid inputs to an operation. + - 'FE_DIVBYZERO' for finite inputs producing infinite results. + - 'FE_OVERFLOW' if a result is too large to represent finitely. + - 'FE_UNDERFLOW' for a subnormal or zero result after rounding. + - 'FE_INEXACT' if a result needed rounding to be representable. + - 'FE_ALL_EXCEPT' as a convenient OR of all possible exception flags. + +The internal exception flag state will start with all flags cleared and is +maintained per thread if C++11 thread-local storage is supported, otherwise it +will be maintained globally and will theoretically NOT be thread-safe (while +practically being as thread-safe as a simple integer variable can be). These +flags can be managed explicitly using the library's error handling functions, +which again try to mimic the built-in functions for handling floating-point +exceptions from . You can clear them with 'feclearexcept' (which is the +only way a flag can be cleared), test them with 'fetestexcept', explicitly +raise errors with 'feraiseexcept' and save and restore their state using +'fegetexceptflag' and 'fesetexceptflag'. You can also throw corresponding C++ +exceptions based on the current flag state using 'fethrowexcept'. + +However, any automatic exception detection and handling during half-precision +operations and functions is DISABLED by default, since it comes with a minor +performance overhead due to runtime checks, and reacting to IEEE floating-point +exceptions is rarely ever needed in application code. But the library fully +supports IEEE-conformant detection of floating-point exceptions and various +ways for handling them, which can be enabled by pre-defining the corresponding +preprocessor symbols to 1. They can be enabled individually or all at once and +they will be processed in the order they are listed here: + + - 'HALF_ERRHANDLING_FLAGS' sets the internal exception flags described above + whenever the corresponding exception occurs. + - 'HALF_ERRHANDLING_ERRNO' sets the value of 'errno' from similar to + the behaviour of the built-in floating-point types when 'MATH_ERRNO' is used. + - 'HALF_ERRHANDLING_FENV' will propagate exceptions to the built-in + floating-point implementation using 'std::feraiseexcept' if support for + C++11 floating-point control is enabled. However, this does not synchronize + exceptions: neither will clearing propagate nor will it work in reverse. + - 'HALF_ERRHANDLING_THROW_...' can be defined to a string literal which will + be used as description message for a C++ exception that is thrown whenever + a 'FE_...' exception occurs, similar to the behaviour of 'fethrowexcept'. + +If any of the above error handling is activated, non-quiet operations on +half-precision values will also raise a 'FE_INVALID' exception whenever +they encounter a signaling NaN value, in addition to transforming the value +into a quiet NaN. If error handling is disabled, signaling NaNs will be +treated like quiet NaNs (while still getting explicitly quieted if propagated +to the result). There can also be additional treatment of overflow and +underflow errors after they have been processed as above, which is ENABLED by +default (but of course only takes effect if any other exception handling is +activated) unless overridden by pre-defining the corresponding preprocessor +symbol to 0: + + - 'HALF_ERRHANDLING_OVERFLOW_TO_INEXACT' will cause overflow errors to also + raise a 'FE_INEXACT' exception. + - 'HALF_ERRHANDLING_UNDERFLOW_TO_INEXACT' will cause underflow errors to also + raise a 'FE_INEXACT' exception. This will also slightly change the + behaviour of the underflow exception, which will ONLY be raised if the + result is actually inexact due to underflow. If this is disabled, underflow + exceptions will be raised for ANY (possibly exact) subnormal result. + + +CREDITS AND CONTACT +------------------- + +This library is developed by CHRISTIAN RAU and released under the MIT License +(see LICENSE.txt). If you have any questions or problems with it, feel free to +contact me at rauy@users.sourceforge.net. + +Additional credit goes to JEROEN VAN DER ZIJP for his paper on "Fast Half Float +Conversions", whose algorithms have been used in the library for converting +between half-precision and single-precision values. diff --git a/include/ieee-754-half/half.hpp b/include/ieee-754-half/half.hpp new file mode 100644 index 0000000..f4d8614 --- /dev/null +++ b/include/ieee-754-half/half.hpp @@ -0,0 +1,4601 @@ +// half - IEEE 754-based half-precision floating-point library. +// +// Copyright (c) 2012-2021 Christian Rau +// +// Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation +// files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, +// modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the +// Software is furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE +// WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +// COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, +// ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +// Version 2.2.0 + +/// \file +/// Main header file for half-precision functionality. + +#ifndef HALF_HALF_HPP +#define HALF_HALF_HPP + +#define HALF_GCC_VERSION (__GNUC__*100+__GNUC_MINOR__) + +#if defined(__INTEL_COMPILER) + #define HALF_ICC_VERSION __INTEL_COMPILER +#elif defined(__ICC) + #define HALF_ICC_VERSION __ICC +#elif defined(__ICL) + #define HALF_ICC_VERSION __ICL +#else + #define HALF_ICC_VERSION 0 +#endif + +// check C++11 language features +#if defined(__clang__) // clang + #if __has_feature(cxx_static_assert) && !defined(HALF_ENABLE_CPP11_STATIC_ASSERT) + #define HALF_ENABLE_CPP11_STATIC_ASSERT 1 + #endif + #if __has_feature(cxx_constexpr) && !defined(HALF_ENABLE_CPP11_CONSTEXPR) + #define HALF_ENABLE_CPP11_CONSTEXPR 1 + #endif + #if __has_feature(cxx_noexcept) && !defined(HALF_ENABLE_CPP11_NOEXCEPT) + #define HALF_ENABLE_CPP11_NOEXCEPT 1 + #endif + #if __has_feature(cxx_user_literals) && !defined(HALF_ENABLE_CPP11_USER_LITERALS) + #define HALF_ENABLE_CPP11_USER_LITERALS 1 + #endif + #if __has_feature(cxx_thread_local) && !defined(HALF_ENABLE_CPP11_THREAD_LOCAL) + #define HALF_ENABLE_CPP11_THREAD_LOCAL 1 + #endif + #if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L) && !defined(HALF_ENABLE_CPP11_LONG_LONG) + #define HALF_ENABLE_CPP11_LONG_LONG 1 + #endif +#elif HALF_ICC_VERSION && defined(__INTEL_CXX11_MODE__) // Intel C++ + #if HALF_ICC_VERSION >= 1500 && !defined(HALF_ENABLE_CPP11_THREAD_LOCAL) + #define HALF_ENABLE_CPP11_THREAD_LOCAL 1 + #endif + #if HALF_ICC_VERSION >= 1500 && !defined(HALF_ENABLE_CPP11_USER_LITERALS) + #define HALF_ENABLE_CPP11_USER_LITERALS 1 + #endif + #if HALF_ICC_VERSION >= 1400 && !defined(HALF_ENABLE_CPP11_CONSTEXPR) + #define HALF_ENABLE_CPP11_CONSTEXPR 1 + #endif + #if HALF_ICC_VERSION >= 1400 && !defined(HALF_ENABLE_CPP11_NOEXCEPT) + #define HALF_ENABLE_CPP11_NOEXCEPT 1 + #endif + #if HALF_ICC_VERSION >= 1110 && !defined(HALF_ENABLE_CPP11_STATIC_ASSERT) + #define HALF_ENABLE_CPP11_STATIC_ASSERT 1 + #endif + #if HALF_ICC_VERSION >= 1110 && !defined(HALF_ENABLE_CPP11_LONG_LONG) + #define HALF_ENABLE_CPP11_LONG_LONG 1 + #endif +#elif defined(__GNUC__) // gcc + #if defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L + #if HALF_GCC_VERSION >= 408 && !defined(HALF_ENABLE_CPP11_THREAD_LOCAL) + #define HALF_ENABLE_CPP11_THREAD_LOCAL 1 + #endif + #if HALF_GCC_VERSION >= 407 && !defined(HALF_ENABLE_CPP11_USER_LITERALS) + #define HALF_ENABLE_CPP11_USER_LITERALS 1 + #endif + #if HALF_GCC_VERSION >= 406 && !defined(HALF_ENABLE_CPP11_CONSTEXPR) + #define HALF_ENABLE_CPP11_CONSTEXPR 1 + #endif + #if HALF_GCC_VERSION >= 406 && !defined(HALF_ENABLE_CPP11_NOEXCEPT) + #define HALF_ENABLE_CPP11_NOEXCEPT 1 + #endif + #if HALF_GCC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_STATIC_ASSERT) + #define HALF_ENABLE_CPP11_STATIC_ASSERT 1 + #endif + #if !defined(HALF_ENABLE_CPP11_LONG_LONG) + #define HALF_ENABLE_CPP11_LONG_LONG 1 + #endif + #endif + #define HALF_TWOS_COMPLEMENT_INT 1 +#elif defined(_MSC_VER) // Visual C++ + #if _MSC_VER >= 1900 && !defined(HALF_ENABLE_CPP11_THREAD_LOCAL) + #define HALF_ENABLE_CPP11_THREAD_LOCAL 1 + #endif + #if _MSC_VER >= 1900 && !defined(HALF_ENABLE_CPP11_USER_LITERALS) + #define HALF_ENABLE_CPP11_USER_LITERALS 1 + #endif + #if _MSC_VER >= 1900 && !defined(HALF_ENABLE_CPP11_CONSTEXPR) + #define HALF_ENABLE_CPP11_CONSTEXPR 1 + #endif + #if _MSC_VER >= 1900 && !defined(HALF_ENABLE_CPP11_NOEXCEPT) + #define HALF_ENABLE_CPP11_NOEXCEPT 1 + #endif + #if _MSC_VER >= 1600 && !defined(HALF_ENABLE_CPP11_STATIC_ASSERT) + #define HALF_ENABLE_CPP11_STATIC_ASSERT 1 + #endif + #if _MSC_VER >= 1310 && !defined(HALF_ENABLE_CPP11_LONG_LONG) + #define HALF_ENABLE_CPP11_LONG_LONG 1 + #endif + #define HALF_TWOS_COMPLEMENT_INT 1 + #define HALF_POP_WARNINGS 1 + #pragma warning(push) + #pragma warning(disable : 4099 4127 4146) //struct vs class, constant in if, negative unsigned +#endif + +// check C++11 library features +#include +#if defined(_LIBCPP_VERSION) // libc++ + #if defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103 + #ifndef HALF_ENABLE_CPP11_TYPE_TRAITS + #define HALF_ENABLE_CPP11_TYPE_TRAITS 1 + #endif + #ifndef HALF_ENABLE_CPP11_CSTDINT + #define HALF_ENABLE_CPP11_CSTDINT 1 + #endif + #ifndef HALF_ENABLE_CPP11_CMATH + #define HALF_ENABLE_CPP11_CMATH 1 + #endif + #ifndef HALF_ENABLE_CPP11_HASH + #define HALF_ENABLE_CPP11_HASH 1 + #endif + #ifndef HALF_ENABLE_CPP11_CFENV + #define HALF_ENABLE_CPP11_CFENV 1 + #endif + #endif +#elif defined(__GLIBCXX__) // libstdc++ + #if defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103 + #ifdef __clang__ + #if __GLIBCXX__ >= 20080606 && !defined(HALF_ENABLE_CPP11_TYPE_TRAITS) + #define HALF_ENABLE_CPP11_TYPE_TRAITS 1 + #endif + #if __GLIBCXX__ >= 20080606 && !defined(HALF_ENABLE_CPP11_CSTDINT) + #define HALF_ENABLE_CPP11_CSTDINT 1 + #endif + #if __GLIBCXX__ >= 20080606 && !defined(HALF_ENABLE_CPP11_CMATH) + #define HALF_ENABLE_CPP11_CMATH 1 + #endif + #if __GLIBCXX__ >= 20080606 && !defined(HALF_ENABLE_CPP11_HASH) + #define HALF_ENABLE_CPP11_HASH 1 + #endif + #if __GLIBCXX__ >= 20080606 && !defined(HALF_ENABLE_CPP11_CFENV) + #define HALF_ENABLE_CPP11_CFENV 1 + #endif + #else + #if HALF_GCC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_TYPE_TRAITS) + #define HALF_ENABLE_CPP11_TYPE_TRAITS 1 + #endif + #if HALF_GCC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_CSTDINT) + #define HALF_ENABLE_CPP11_CSTDINT 1 + #endif + #if HALF_GCC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_CMATH) + #define HALF_ENABLE_CPP11_CMATH 1 + #endif + #if HALF_GCC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_HASH) + #define HALF_ENABLE_CPP11_HASH 1 + #endif + #if HALF_GCC_VERSION >= 403 && !defined(HALF_ENABLE_CPP11_CFENV) + #define HALF_ENABLE_CPP11_CFENV 1 + #endif + #endif + #endif +#elif defined(_CPPLIB_VER) // Dinkumware/Visual C++ + #if _CPPLIB_VER >= 520 && !defined(HALF_ENABLE_CPP11_TYPE_TRAITS) + #define HALF_ENABLE_CPP11_TYPE_TRAITS 1 + #endif + #if _CPPLIB_VER >= 520 && !defined(HALF_ENABLE_CPP11_CSTDINT) + #define HALF_ENABLE_CPP11_CSTDINT 1 + #endif + #if _CPPLIB_VER >= 520 && !defined(HALF_ENABLE_CPP11_HASH) + #define HALF_ENABLE_CPP11_HASH 1 + #endif + #if _CPPLIB_VER >= 610 && !defined(HALF_ENABLE_CPP11_CMATH) + #define HALF_ENABLE_CPP11_CMATH 1 + #endif + #if _CPPLIB_VER >= 610 && !defined(HALF_ENABLE_CPP11_CFENV) + #define HALF_ENABLE_CPP11_CFENV 1 + #endif +#endif +#undef HALF_GCC_VERSION +#undef HALF_ICC_VERSION + +// any error throwing C++ exceptions? +#if defined(HALF_ERRHANDLING_THROW_INVALID) || defined(HALF_ERRHANDLING_THROW_DIVBYZERO) || defined(HALF_ERRHANDLING_THROW_OVERFLOW) || defined(HALF_ERRHANDLING_THROW_UNDERFLOW) || defined(HALF_ERRHANDLING_THROW_INEXACT) +#define HALF_ERRHANDLING_THROWS 1 +#endif + +// any error handling enabled? +#define HALF_ERRHANDLING (HALF_ERRHANDLING_FLAGS||HALF_ERRHANDLING_ERRNO||HALF_ERRHANDLING_FENV||HALF_ERRHANDLING_THROWS) + +#if HALF_ERRHANDLING + #define HALF_UNUSED_NOERR(name) name +#else + #define HALF_UNUSED_NOERR(name) +#endif + +// support constexpr +#if HALF_ENABLE_CPP11_CONSTEXPR + #define HALF_CONSTEXPR constexpr + #define HALF_CONSTEXPR_CONST constexpr + #if HALF_ERRHANDLING + #define HALF_CONSTEXPR_NOERR + #else + #define HALF_CONSTEXPR_NOERR constexpr + #endif +#else + #define HALF_CONSTEXPR + #define HALF_CONSTEXPR_CONST const + #define HALF_CONSTEXPR_NOERR +#endif + +// support noexcept +#if HALF_ENABLE_CPP11_NOEXCEPT + #define HALF_NOEXCEPT noexcept + #define HALF_NOTHROW noexcept +#else + #define HALF_NOEXCEPT + #define HALF_NOTHROW throw() +#endif + +// support thread storage +#if HALF_ENABLE_CPP11_THREAD_LOCAL + #define HALF_THREAD_LOCAL thread_local +#else + #define HALF_THREAD_LOCAL static +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#if HALF_ENABLE_CPP11_TYPE_TRAITS + #include +#endif +#if HALF_ENABLE_CPP11_CSTDINT + #include +#endif +#if HALF_ERRHANDLING_ERRNO + #include +#endif +#if HALF_ENABLE_CPP11_CFENV + #include +#endif +#if HALF_ENABLE_CPP11_HASH + #include +#endif + + +#ifndef HALF_ENABLE_F16C_INTRINSICS + /// Enable F16C intruction set intrinsics. + /// Defining this to 1 enables the use of [F16C compiler intrinsics](https://en.wikipedia.org/wiki/F16C) for converting between + /// half-precision and single-precision values which may result in improved performance. This will not perform additional checks + /// for support of the F16C instruction set, so an appropriate target platform is required when enabling this feature. + /// + /// Unless predefined it will be enabled automatically when the `__F16C__` symbol is defined, which some compilers do on supporting platforms. + #define HALF_ENABLE_F16C_INTRINSICS __F16C__ +#endif +#if HALF_ENABLE_F16C_INTRINSICS + #include +#endif + +#ifdef HALF_DOXYGEN_ONLY +/// Type for internal floating-point computations. +/// This can be predefined to a built-in floating-point type (`float`, `double` or `long double`) to override the internal +/// half-precision implementation to use this type for computing arithmetic operations and mathematical function (if available). +/// This can result in improved performance for arithmetic operators and mathematical functions but might cause results to +/// deviate from the specified half-precision rounding mode and inhibits proper detection of half-precision exceptions. +#define HALF_ARITHMETIC_TYPE (undefined) + +/// Enable internal exception flags. +/// Defining this to 1 causes operations on half-precision values to raise internal floating-point exception flags according to +/// the IEEE 754 standard. These can then be cleared and checked with clearexcept(), testexcept(). +#define HALF_ERRHANDLING_FLAGS 0 + +/// Enable exception propagation to `errno`. +/// Defining this to 1 causes operations on half-precision values to propagate floating-point exceptions to +/// [errno](https://en.cppreference.com/w/cpp/error/errno) from ``. Specifically this will propagate domain errors as +/// [EDOM](https://en.cppreference.com/w/cpp/error/errno_macros) and pole, overflow and underflow errors as +/// [ERANGE](https://en.cppreference.com/w/cpp/error/errno_macros). Inexact errors won't be propagated. +#define HALF_ERRHANDLING_ERRNO 0 + +/// Enable exception propagation to built-in floating-point platform. +/// Defining this to 1 causes operations on half-precision values to propagate floating-point exceptions to the built-in +/// single- and double-precision implementation's exception flags using the +/// [C++11 floating-point environment control](https://en.cppreference.com/w/cpp/numeric/fenv) from ``. However, this +/// does not work in reverse and single- or double-precision exceptions will not raise the corresponding half-precision +/// exception flags, nor will explicitly clearing flags clear the corresponding built-in flags. +#define HALF_ERRHANDLING_FENV 0 + +/// Throw C++ exception on domain errors. +/// Defining this to a string literal causes operations on half-precision values to throw a +/// [std::domain_error](https://en.cppreference.com/w/cpp/error/domain_error) with the specified message on domain errors. +#define HALF_ERRHANDLING_THROW_INVALID (undefined) + +/// Throw C++ exception on pole errors. +/// Defining this to a string literal causes operations on half-precision values to throw a +/// [std::domain_error](https://en.cppreference.com/w/cpp/error/domain_error) with the specified message on pole errors. +#define HALF_ERRHANDLING_THROW_DIVBYZERO (undefined) + +/// Throw C++ exception on overflow errors. +/// Defining this to a string literal causes operations on half-precision values to throw a +/// [std::overflow_error](https://en.cppreference.com/w/cpp/error/overflow_error) with the specified message on overflows. +#define HALF_ERRHANDLING_THROW_OVERFLOW (undefined) + +/// Throw C++ exception on underflow errors. +/// Defining this to a string literal causes operations on half-precision values to throw a +/// [std::underflow_error](https://en.cppreference.com/w/cpp/error/underflow_error) with the specified message on underflows. +#define HALF_ERRHANDLING_THROW_UNDERFLOW (undefined) + +/// Throw C++ exception on rounding errors. +/// Defining this to 1 causes operations on half-precision values to throw a +/// [std::range_error](https://en.cppreference.com/w/cpp/error/range_error) with the specified message on general rounding errors. +#define HALF_ERRHANDLING_THROW_INEXACT (undefined) +#endif + +#ifndef HALF_ERRHANDLING_OVERFLOW_TO_INEXACT +/// Raise INEXACT exception on overflow. +/// Defining this to 1 (default) causes overflow errors to automatically raise inexact exceptions in addition. +/// These will be raised after any possible handling of the underflow exception. +#define HALF_ERRHANDLING_OVERFLOW_TO_INEXACT 1 +#endif + +#ifndef HALF_ERRHANDLING_UNDERFLOW_TO_INEXACT +/// Raise INEXACT exception on underflow. +/// Defining this to 1 (default) causes underflow errors to automatically raise inexact exceptions in addition. +/// These will be raised after any possible handling of the underflow exception. +/// +/// **Note:** This will actually cause underflow (and the accompanying inexact) exceptions to be raised *only* when the result +/// is inexact, while if disabled bare underflow errors will be raised for *any* (possibly exact) subnormal result. +#define HALF_ERRHANDLING_UNDERFLOW_TO_INEXACT 1 +#endif + +/// Default rounding mode. +/// This specifies the rounding mode used for all conversions between [half](\ref half_float::half)s and more precise types +/// (unless using half_cast() and specifying the rounding mode directly) as well as in arithmetic operations and mathematical +/// functions. It can be redefined (before including half.hpp) to one of the standard rounding modes using their respective +/// constants or the equivalent values of +/// [std::float_round_style](https://en.cppreference.com/w/cpp/types/numeric_limits/float_round_style): +/// +/// `std::float_round_style` | value | rounding +/// ---------------------------------|-------|------------------------- +/// `std::round_indeterminate` | -1 | fastest +/// `std::round_toward_zero` | 0 | toward zero +/// `std::round_to_nearest` | 1 | to nearest (default) +/// `std::round_toward_infinity` | 2 | toward positive infinity +/// `std::round_toward_neg_infinity` | 3 | toward negative infinity +/// +/// By default this is set to `1` (`std::round_to_nearest`), which rounds results to the nearest representable value. It can even +/// be set to [std::numeric_limits::round_style](https://en.cppreference.com/w/cpp/types/numeric_limits/round_style) to synchronize +/// the rounding mode with that of the built-in single-precision implementation (which is likely `std::round_to_nearest`, though). +#ifndef HALF_ROUND_STYLE + #define HALF_ROUND_STYLE 1 // = std::round_to_nearest +#endif + +/// Value signaling overflow. +/// In correspondence with `HUGE_VAL[F|L]` from `` this symbol expands to a positive value signaling the overflow of an +/// operation, in particular it just evaluates to positive infinity. +/// +/// **See also:** Documentation for [HUGE_VAL](https://en.cppreference.com/w/cpp/numeric/math/HUGE_VAL) +#define HUGE_VALH std::numeric_limits::infinity() + +/// Fast half-precision fma function. +/// This symbol is defined if the fma() function generally executes as fast as, or faster than, a separate +/// half-precision multiplication followed by an addition, which is always the case. +/// +/// **See also:** Documentation for [FP_FAST_FMA](https://en.cppreference.com/w/cpp/numeric/math/fma) +#define FP_FAST_FMAH 1 + +/// Half rounding mode. +/// In correspondence with `FLT_ROUNDS` from `` this symbol expands to the rounding mode used for +/// half-precision operations. It is an alias for [HALF_ROUND_STYLE](\ref HALF_ROUND_STYLE). +/// +/// **See also:** Documentation for [FLT_ROUNDS](https://en.cppreference.com/w/cpp/types/climits/FLT_ROUNDS) +#define HLF_ROUNDS HALF_ROUND_STYLE + +#ifndef FP_ILOGB0 + #define FP_ILOGB0 INT_MIN +#endif +#ifndef FP_ILOGBNAN + #define FP_ILOGBNAN INT_MAX +#endif +#ifndef FP_SUBNORMAL + #define FP_SUBNORMAL 0 +#endif +#ifndef FP_ZERO + #define FP_ZERO 1 +#endif +#ifndef FP_NAN + #define FP_NAN 2 +#endif +#ifndef FP_INFINITE + #define FP_INFINITE 3 +#endif +#ifndef FP_NORMAL + #define FP_NORMAL 4 +#endif + +#if !HALF_ENABLE_CPP11_CFENV && !defined(FE_ALL_EXCEPT) + #define FE_INVALID 0x10 + #define FE_DIVBYZERO 0x08 + #define FE_OVERFLOW 0x04 + #define FE_UNDERFLOW 0x02 + #define FE_INEXACT 0x01 + #define FE_ALL_EXCEPT (FE_INVALID|FE_DIVBYZERO|FE_OVERFLOW|FE_UNDERFLOW|FE_INEXACT) +#endif + + +/// Main namespace for half-precision functionality. +/// This namespace contains all the functionality provided by the library. +namespace half_float +{ + class half; + +#if HALF_ENABLE_CPP11_USER_LITERALS + /// Library-defined half-precision literals. + /// Import this namespace to enable half-precision floating-point literals: + /// ~~~~{.cpp} + /// using namespace half_float::literal; + /// half_float::half = 4.2_h; + /// ~~~~ + namespace literal + { + half operator "" _h(long double); + } +#endif + + /// \internal + /// \brief Implementation details. + namespace detail + { + #if HALF_ENABLE_CPP11_TYPE_TRAITS + /// Conditional type. + template struct conditional : std::conditional {}; + + /// Helper for tag dispatching. + template struct bool_type : std::integral_constant {}; + using std::true_type; + using std::false_type; + + /// Type traits for floating-point types. + template struct is_float : std::is_floating_point {}; + #else + /// Conditional type. + template struct conditional { typedef T type; }; + template struct conditional { typedef F type; }; + + /// Helper for tag dispatching. + template struct bool_type {}; + typedef bool_type true_type; + typedef bool_type false_type; + + /// Type traits for floating-point types. + template struct is_float : false_type {}; + template struct is_float : is_float {}; + template struct is_float : is_float {}; + template struct is_float : is_float {}; + template<> struct is_float : true_type {}; + template<> struct is_float : true_type {}; + template<> struct is_float : true_type {}; + #endif + + /// Type traits for floating-point bits. + template struct bits { typedef unsigned char type; }; + template struct bits : bits {}; + template struct bits : bits {}; + template struct bits : bits {}; + + #if HALF_ENABLE_CPP11_CSTDINT + /// Unsigned integer of (at least) 16 bits width. + typedef std::uint_least16_t uint16; + + /// Fastest unsigned integer of (at least) 32 bits width. + typedef std::uint_fast32_t uint32; + + /// Fastest signed integer of (at least) 32 bits width. + typedef std::int_fast32_t int32; + + /// Unsigned integer of (at least) 32 bits width. + template<> struct bits { typedef std::uint_least32_t type; }; + + /// Unsigned integer of (at least) 64 bits width. + template<> struct bits { typedef std::uint_least64_t type; }; + #else + /// Unsigned integer of (at least) 16 bits width. + typedef unsigned short uint16; + + /// Fastest unsigned integer of (at least) 32 bits width. + typedef unsigned long uint32; + + /// Fastest unsigned integer of (at least) 32 bits width. + typedef long int32; + + /// Unsigned integer of (at least) 32 bits width. + template<> struct bits : conditional::digits>=32,unsigned int,unsigned long> {}; + + #if HALF_ENABLE_CPP11_LONG_LONG + /// Unsigned integer of (at least) 64 bits width. + template<> struct bits : conditional::digits>=64,unsigned long,unsigned long long> {}; + #else + /// Unsigned integer of (at least) 64 bits width. + template<> struct bits { typedef unsigned long type; }; + #endif + #endif + + #ifdef HALF_ARITHMETIC_TYPE + /// Type to use for arithmetic computations and mathematic functions internally. + typedef HALF_ARITHMETIC_TYPE internal_t; + #endif + + /// Tag type for binary construction. + struct binary_t {}; + + /// Tag for binary construction. + HALF_CONSTEXPR_CONST binary_t binary = binary_t(); + + /// \name Implementation defined classification and arithmetic + /// \{ + + /// Check for infinity. + /// \tparam T argument type (builtin floating-point type) + /// \param arg value to query + /// \retval true if infinity + /// \retval false else + template bool builtin_isinf(T arg) + { + #if HALF_ENABLE_CPP11_CMATH + return std::isinf(arg); + #elif defined(_MSC_VER) + return !::_finite(static_cast(arg)) && !::_isnan(static_cast(arg)); + #else + return arg == std::numeric_limits::infinity() || arg == -std::numeric_limits::infinity(); + #endif + } + + /// Check for NaN. + /// \tparam T argument type (builtin floating-point type) + /// \param arg value to query + /// \retval true if not a number + /// \retval false else + template bool builtin_isnan(T arg) + { + #if HALF_ENABLE_CPP11_CMATH + return std::isnan(arg); + #elif defined(_MSC_VER) + return ::_isnan(static_cast(arg)) != 0; + #else + return arg != arg; + #endif + } + + /// Check sign. + /// \tparam T argument type (builtin floating-point type) + /// \param arg value to query + /// \retval true if signbit set + /// \retval false else + template bool builtin_signbit(T arg) + { + #if HALF_ENABLE_CPP11_CMATH + return std::signbit(arg); + #else + return arg < T() || (arg == T() && T(1)/arg < T()); + #endif + } + + /// Platform-independent sign mask. + /// \param arg integer value in two's complement + /// \retval -1 if \a arg negative + /// \retval 0 if \a arg positive + inline uint32 sign_mask(uint32 arg) + { + static const int N = std::numeric_limits::digits - 1; + #if HALF_TWOS_COMPLEMENT_INT + return static_cast(arg) >> N; + #else + return -((arg>>N)&1); + #endif + } + + /// Platform-independent arithmetic right shift. + /// \param arg integer value in two's complement + /// \param i shift amount (at most 31) + /// \return \a arg right shifted for \a i bits with possible sign extension + inline uint32 arithmetic_shift(uint32 arg, int i) + { + #if HALF_TWOS_COMPLEMENT_INT + return static_cast(arg) >> i; + #else + return static_cast(arg)/(static_cast(1)<>(std::numeric_limits::digits-1))&1); + #endif + } + + /// \} + /// \name Error handling + /// \{ + + /// Internal exception flags. + /// \return reference to global exception flags + inline int& errflags() { HALF_THREAD_LOCAL int flags = 0; return flags; } + + /// Raise floating-point exception. + /// \param flags exceptions to raise + /// \param cond condition to raise exceptions for + inline void raise(int HALF_UNUSED_NOERR(flags), bool HALF_UNUSED_NOERR(cond) = true) + { + #if HALF_ERRHANDLING + if(!cond) + return; + #if HALF_ERRHANDLING_FLAGS + errflags() |= flags; + #endif + #if HALF_ERRHANDLING_ERRNO + if(flags & FE_INVALID) + errno = EDOM; + else if(flags & (FE_DIVBYZERO|FE_OVERFLOW|FE_UNDERFLOW)) + errno = ERANGE; + #endif + #if HALF_ERRHANDLING_FENV && HALF_ENABLE_CPP11_CFENV + std::feraiseexcept(flags); + #endif + #ifdef HALF_ERRHANDLING_THROW_INVALID + if(flags & FE_INVALID) + throw std::domain_error(HALF_ERRHANDLING_THROW_INVALID); + #endif + #ifdef HALF_ERRHANDLING_THROW_DIVBYZERO + if(flags & FE_DIVBYZERO) + throw std::domain_error(HALF_ERRHANDLING_THROW_DIVBYZERO); + #endif + #ifdef HALF_ERRHANDLING_THROW_OVERFLOW + if(flags & FE_OVERFLOW) + throw std::overflow_error(HALF_ERRHANDLING_THROW_OVERFLOW); + #endif + #ifdef HALF_ERRHANDLING_THROW_UNDERFLOW + if(flags & FE_UNDERFLOW) + throw std::underflow_error(HALF_ERRHANDLING_THROW_UNDERFLOW); + #endif + #ifdef HALF_ERRHANDLING_THROW_INEXACT + if(flags & FE_INEXACT) + throw std::range_error(HALF_ERRHANDLING_THROW_INEXACT); + #endif + #if HALF_ERRHANDLING_UNDERFLOW_TO_INEXACT + if((flags & FE_UNDERFLOW) && !(flags & FE_INEXACT)) + raise(FE_INEXACT); + #endif + #if HALF_ERRHANDLING_OVERFLOW_TO_INEXACT + if((flags & FE_OVERFLOW) && !(flags & FE_INEXACT)) + raise(FE_INEXACT); + #endif + #endif + } + + /// Check and signal for any NaN. + /// \param x first half-precision value to check + /// \param y second half-precision value to check + /// \retval true if either \a x or \a y is NaN + /// \retval false else + /// \exception FE_INVALID if \a x or \a y is NaN + inline HALF_CONSTEXPR_NOERR bool compsignal(unsigned int x, unsigned int y) + { + #if HALF_ERRHANDLING + raise(FE_INVALID, (x&0x7FFF)>0x7C00 || (y&0x7FFF)>0x7C00); + #endif + return (x&0x7FFF) > 0x7C00 || (y&0x7FFF) > 0x7C00; + } + + /// Signal and silence signaling NaN. + /// \param nan half-precision NaN value + /// \return quiet NaN + /// \exception FE_INVALID if \a nan is signaling NaN + inline HALF_CONSTEXPR_NOERR unsigned int signal(unsigned int nan) + { + #if HALF_ERRHANDLING + raise(FE_INVALID, !(nan&0x200)); + #endif + return nan | 0x200; + } + + /// Signal and silence signaling NaNs. + /// \param x first half-precision value to check + /// \param y second half-precision value to check + /// \return quiet NaN + /// \exception FE_INVALID if \a x or \a y is signaling NaN + inline HALF_CONSTEXPR_NOERR unsigned int signal(unsigned int x, unsigned int y) + { + #if HALF_ERRHANDLING + raise(FE_INVALID, ((x&0x7FFF)>0x7C00 && !(x&0x200)) || ((y&0x7FFF)>0x7C00 && !(y&0x200))); + #endif + return ((x&0x7FFF)>0x7C00) ? (x|0x200) : (y|0x200); + } + + /// Signal and silence signaling NaNs. + /// \param x first half-precision value to check + /// \param y second half-precision value to check + /// \param z third half-precision value to check + /// \return quiet NaN + /// \exception FE_INVALID if \a x, \a y or \a z is signaling NaN + inline HALF_CONSTEXPR_NOERR unsigned int signal(unsigned int x, unsigned int y, unsigned int z) + { + #if HALF_ERRHANDLING + raise(FE_INVALID, ((x&0x7FFF)>0x7C00 && !(x&0x200)) || ((y&0x7FFF)>0x7C00 && !(y&0x200)) || ((z&0x7FFF)>0x7C00 && !(z&0x200))); + #endif + return ((x&0x7FFF)>0x7C00) ? (x|0x200) : ((y&0x7FFF)>0x7C00) ? (y|0x200) : (z|0x200); + } + + /// Select value or signaling NaN. + /// \param x preferred half-precision value + /// \param y ignored half-precision value except for signaling NaN + /// \return \a y if signaling NaN, \a x otherwise + /// \exception FE_INVALID if \a y is signaling NaN + inline HALF_CONSTEXPR_NOERR unsigned int select(unsigned int x, unsigned int HALF_UNUSED_NOERR(y)) + { + #if HALF_ERRHANDLING + return (((y&0x7FFF)>0x7C00) && !(y&0x200)) ? signal(y) : x; + #else + return x; + #endif + } + + /// Raise domain error and return NaN. + /// return quiet NaN + /// \exception FE_INVALID + inline HALF_CONSTEXPR_NOERR unsigned int invalid() + { + #if HALF_ERRHANDLING + raise(FE_INVALID); + #endif + return 0x7FFF; + } + + /// Raise pole error and return infinity. + /// \param sign half-precision value with sign bit only + /// \return half-precision infinity with sign of \a sign + /// \exception FE_DIVBYZERO + inline HALF_CONSTEXPR_NOERR unsigned int pole(unsigned int sign = 0) + { + #if HALF_ERRHANDLING + raise(FE_DIVBYZERO); + #endif + return sign | 0x7C00; + } + + /// Check value for underflow. + /// \param arg non-zero half-precision value to check + /// \return \a arg + /// \exception FE_UNDERFLOW if arg is subnormal + inline HALF_CONSTEXPR_NOERR unsigned int check_underflow(unsigned int arg) + { + #if HALF_ERRHANDLING && !HALF_ERRHANDLING_UNDERFLOW_TO_INEXACT + raise(FE_UNDERFLOW, !(arg&0x7C00)); + #endif + return arg; + } + + /// \} + /// \name Conversion and rounding + /// \{ + + /// Half-precision overflow. + /// \tparam R rounding mode to use + /// \param sign half-precision value with sign bit only + /// \return rounded overflowing half-precision value + /// \exception FE_OVERFLOW + template HALF_CONSTEXPR_NOERR unsigned int overflow(unsigned int sign = 0) + { + #if HALF_ERRHANDLING + raise(FE_OVERFLOW); + #endif + return (R==std::round_toward_infinity) ? (sign+0x7C00-(sign>>15)) : + (R==std::round_toward_neg_infinity) ? (sign+0x7BFF+(sign>>15)) : + (R==std::round_toward_zero) ? (sign|0x7BFF) : + (sign|0x7C00); + } + + /// Half-precision underflow. + /// \tparam R rounding mode to use + /// \param sign half-precision value with sign bit only + /// \return rounded underflowing half-precision value + /// \exception FE_UNDERFLOW + template HALF_CONSTEXPR_NOERR unsigned int underflow(unsigned int sign = 0) + { + #if HALF_ERRHANDLING + raise(FE_UNDERFLOW); + #endif + return (R==std::round_toward_infinity) ? (sign+1-(sign>>15)) : + (R==std::round_toward_neg_infinity) ? (sign+(sign>>15)) : + sign; + } + + /// Round half-precision number. + /// \tparam R rounding mode to use + /// \tparam I `true` to always raise INEXACT exception, `false` to raise only for rounded results + /// \param value finite half-precision number to round + /// \param g guard bit (most significant discarded bit) + /// \param s sticky bit (or of all but the most significant discarded bits) + /// \return rounded half-precision value + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if value had to be rounded or \a I is `true` + template HALF_CONSTEXPR_NOERR unsigned int rounded(unsigned int value, int g, int s) + { + #if HALF_ERRHANDLING + value += (R==std::round_to_nearest) ? (g&(s|value)) : + (R==std::round_toward_infinity) ? (~(value>>15)&(g|s)) : + (R==std::round_toward_neg_infinity) ? ((value>>15)&(g|s)) : 0; + if((value&0x7C00) == 0x7C00) + raise(FE_OVERFLOW); + else if(value & 0x7C00) + raise(FE_INEXACT, I || (g|s)!=0); + else + raise(FE_UNDERFLOW, !(HALF_ERRHANDLING_UNDERFLOW_TO_INEXACT) || I || (g|s)!=0); + return value; + #else + return (R==std::round_to_nearest) ? (value+(g&(s|value))) : + (R==std::round_toward_infinity) ? (value+(~(value>>15)&(g|s))) : + (R==std::round_toward_neg_infinity) ? (value+((value>>15)&(g|s))) : + value; + #endif + } + + /// Round half-precision number to nearest integer value. + /// \tparam R rounding mode to use + /// \tparam E `true` for round to even, `false` for round away from zero + /// \tparam I `true` to raise INEXACT exception (if inexact), `false` to never raise it + /// \param value half-precision value to round + /// \return half-precision bits for nearest integral value + /// \exception FE_INVALID for signaling NaN + /// \exception FE_INEXACT if value had to be rounded and \a I is `true` + template unsigned int integral(unsigned int value) + { + unsigned int abs = value & 0x7FFF; + if(abs < 0x3C00) + { + raise(FE_INEXACT, I); + return ((R==std::round_to_nearest) ? (0x3C00&-static_cast(abs>=(0x3800+E))) : + (R==std::round_toward_infinity) ? (0x3C00&-(~(value>>15)&(abs!=0))) : + (R==std::round_toward_neg_infinity) ? (0x3C00&-static_cast(value>0x8000)) : + 0) | (value&0x8000); + } + if(abs >= 0x6400) + return (abs>0x7C00) ? signal(value) : value; + unsigned int exp = 25 - (abs>>10), mask = (1<>exp)&E)) : + (R==std::round_toward_infinity) ? (mask&((value>>15)-1)) : + (R==std::round_toward_neg_infinity) ? (mask&-(value>>15)) : + 0) + value) & ~mask; + } + + /// Convert fixed point to half-precision floating-point. + /// \tparam R rounding mode to use + /// \tparam F number of fractional bits in [11,31] + /// \tparam S `true` for signed, `false` for unsigned + /// \tparam N `true` for additional normalization step, `false` if already normalized to 1.F + /// \tparam I `true` to always raise INEXACT exception, `false` to raise only for rounded results + /// \param m mantissa in Q1.F fixed point format + /// \param exp biased exponent - 1 + /// \param sign half-precision value with sign bit only + /// \param s sticky bit (or of all but the most significant already discarded bits) + /// \return value converted to half-precision + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if value had to be rounded or \a I is `true` + template unsigned int fixed2half(uint32 m, int exp = 14, unsigned int sign = 0, int s = 0) + { + if(S) + { + uint32 msign = sign_mask(m); + m = (m^msign) - msign; + sign = msign & 0x8000; + } + if(N) + for(; m<(static_cast(1)<(sign+(m>>(F-10-exp)), (m>>(F-11-exp))&1, s|((m&((static_cast(1)<<(F-11-exp))-1))!=0)); + return rounded(sign+(exp<<10)+(m>>(F-10)), (m>>(F-11))&1, s|((m&((static_cast(1)<<(F-11))-1))!=0)); + } + + /// Convert IEEE single-precision to half-precision. + /// Credit for this goes to [Jeroen van der Zijp](ftp://ftp.fox-toolkit.org/pub/fasthalffloatconversion.pdf). + /// \tparam R rounding mode to use + /// \param value single-precision value to convert + /// \return rounded half-precision value + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if value had to be rounded + template unsigned int float2half_impl(float value, true_type) + { + #if HALF_ENABLE_F16C_INTRINSICS + return _mm_cvtsi128_si32(_mm_cvtps_ph(_mm_set_ss(value), + (R==std::round_to_nearest) ? _MM_FROUND_TO_NEAREST_INT : + (R==std::round_toward_zero) ? _MM_FROUND_TO_ZERO : + (R==std::round_toward_infinity) ? _MM_FROUND_TO_POS_INF : + (R==std::round_toward_neg_infinity) ? _MM_FROUND_TO_NEG_INF : + _MM_FROUND_CUR_DIRECTION)); + #else + bits::type fbits; + std::memcpy(&fbits, &value, sizeof(float)); + #if 1 + unsigned int sign = (fbits>>16) & 0x8000; + fbits &= 0x7FFFFFFF; + if(fbits >= 0x7F800000) + return sign | 0x7C00 | ((fbits>0x7F800000) ? (0x200|((fbits>>13)&0x3FF)) : 0); + if(fbits >= 0x47800000) + return overflow(sign); + if(fbits >= 0x38800000) + return rounded(sign|(((fbits>>23)-112)<<10)|((fbits>>13)&0x3FF), (fbits>>12)&1, (fbits&0xFFF)!=0); + if(fbits >= 0x33000000) + { + int i = 125 - (fbits>>23); + fbits = (fbits&0x7FFFFF) | 0x800000; + return rounded(sign|(fbits>>(i+1)), (fbits>>i)&1, (fbits&((static_cast(1)<(sign); + return sign; + #else + static const uint16 base_table[512] = { + 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, + 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, + 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, + 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, + 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, + 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, + 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0000, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, + 0x0200, 0x0400, 0x0800, 0x0C00, 0x1000, 0x1400, 0x1800, 0x1C00, 0x2000, 0x2400, 0x2800, 0x2C00, 0x3000, 0x3400, 0x3800, 0x3C00, + 0x4000, 0x4400, 0x4800, 0x4C00, 0x5000, 0x5400, 0x5800, 0x5C00, 0x6000, 0x6400, 0x6800, 0x6C00, 0x7000, 0x7400, 0x7800, 0x7BFF, + 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, + 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, + 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, + 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, + 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, + 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, + 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7BFF, 0x7C00, + 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, + 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, + 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, + 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, + 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, + 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, + 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8001, 0x8002, 0x8004, 0x8008, 0x8010, 0x8020, 0x8040, 0x8080, 0x8100, + 0x8200, 0x8400, 0x8800, 0x8C00, 0x9000, 0x9400, 0x9800, 0x9C00, 0xA000, 0xA400, 0xA800, 0xAC00, 0xB000, 0xB400, 0xB800, 0xBC00, + 0xC000, 0xC400, 0xC800, 0xCC00, 0xD000, 0xD400, 0xD800, 0xDC00, 0xE000, 0xE400, 0xE800, 0xEC00, 0xF000, 0xF400, 0xF800, 0xFBFF, + 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, + 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, + 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, + 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, + 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, + 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, + 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFBFF, 0xFC00 }; + static const unsigned char shift_table[256] = { + 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, + 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, + 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, + 25, 25, 25, 25, 25, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, + 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, + 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, + 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, + 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 13 }; + int sexp = fbits >> 23, exp = sexp & 0xFF, i = shift_table[exp]; + fbits &= 0x7FFFFF; + uint32 m = (fbits|((exp!=0)<<23)) & -static_cast(exp!=0xFF); + return rounded(base_table[sexp]+(fbits>>i), (m>>(i-1))&1, (((static_cast(1)<<(i-1))-1)&m)!=0); + #endif + #endif + } + + /// Convert IEEE double-precision to half-precision. + /// \tparam R rounding mode to use + /// \param value double-precision value to convert + /// \return rounded half-precision value + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if value had to be rounded + template unsigned int float2half_impl(double value, true_type) + { + #if HALF_ENABLE_F16C_INTRINSICS + if(R == std::round_indeterminate) + return _mm_cvtsi128_si32(_mm_cvtps_ph(_mm_cvtpd_ps(_mm_set_sd(value)), _MM_FROUND_CUR_DIRECTION)); + #endif + bits::type dbits; + std::memcpy(&dbits, &value, sizeof(double)); + uint32 hi = dbits >> 32, lo = dbits & 0xFFFFFFFF; + unsigned int sign = (hi>>16) & 0x8000; + hi &= 0x7FFFFFFF; + if(hi >= 0x7FF00000) + return sign | 0x7C00 | ((dbits&0xFFFFFFFFFFFFF) ? (0x200|((hi>>10)&0x3FF)) : 0); + if(hi >= 0x40F00000) + return overflow(sign); + if(hi >= 0x3F100000) + return rounded(sign|(((hi>>20)-1008)<<10)|((hi>>10)&0x3FF), (hi>>9)&1, ((hi&0x1FF)|lo)!=0); + if(hi >= 0x3E600000) + { + int i = 1018 - (hi>>20); + hi = (hi&0xFFFFF) | 0x100000; + return rounded(sign|(hi>>(i+1)), (hi>>i)&1, ((hi&((static_cast(1)<(sign); + return sign; + } + + /// Convert non-IEEE floating-point to half-precision. + /// \tparam R rounding mode to use + /// \tparam T source type (builtin floating-point type) + /// \param value floating-point value to convert + /// \return rounded half-precision value + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if value had to be rounded + template unsigned int float2half_impl(T value, ...) + { + unsigned int hbits = static_cast(builtin_signbit(value)) << 15; + if(value == T()) + return hbits; + if(builtin_isnan(value)) + return hbits | 0x7FFF; + if(builtin_isinf(value)) + return hbits | 0x7C00; + int exp; + std::frexp(value, &exp); + if(exp > 16) + return overflow(hbits); + if(exp < -13) + value = std::ldexp(value, 25); + else + { + value = std::ldexp(value, 12-exp); + hbits |= ((exp+13)<<10); + } + T ival, frac = std::modf(value, &ival); + int m = std::abs(static_cast(ival)); + return rounded(hbits+(m>>1), m&1, frac!=T()); + } + + /// Convert floating-point to half-precision. + /// \tparam R rounding mode to use + /// \tparam T source type (builtin floating-point type) + /// \param value floating-point value to convert + /// \return rounded half-precision value + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if value had to be rounded + template unsigned int float2half(T value) + { + return float2half_impl(value, bool_type::is_iec559&&sizeof(typename bits::type)==sizeof(T)>()); + } + + /// Convert integer to half-precision floating-point. + /// \tparam R rounding mode to use + /// \tparam T type to convert (builtin integer type) + /// \param value integral value to convert + /// \return rounded half-precision value + /// \exception FE_OVERFLOW on overflows + /// \exception FE_INEXACT if value had to be rounded + template unsigned int int2half(T value) + { + unsigned int bits = static_cast(value<0) << 15; + if(!value) + return bits; + if(bits) + value = -value; + if(value > 0xFFFF) + return overflow(bits); + unsigned int m = static_cast(value), exp = 24; + for(; m<0x400; m<<=1,--exp) ; + for(; m>0x7FF; m>>=1,++exp) ; + bits |= (exp<<10) + m; + return (exp>24) ? rounded(bits, (value>>(exp-25))&1, (((1<<(exp-25))-1)&value)!=0) : bits; + } + + /// Convert half-precision to IEEE single-precision. + /// Credit for this goes to [Jeroen van der Zijp](ftp://ftp.fox-toolkit.org/pub/fasthalffloatconversion.pdf). + /// \param value half-precision value to convert + /// \return single-precision value + inline float half2float_impl(unsigned int value, float, true_type) + { + #if HALF_ENABLE_F16C_INTRINSICS + return _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(value))); + #else + #if 0 + bits::type fbits = static_cast::type>(value&0x8000) << 16; + int abs = value & 0x7FFF; + if(abs) + { + fbits |= 0x38000000 << static_cast(abs>=0x7C00); + for(; abs<0x400; abs<<=1,fbits-=0x800000) ; + fbits += static_cast::type>(abs) << 13; + } + #else + static const bits::type mantissa_table[2048] = { + 0x00000000, 0x33800000, 0x34000000, 0x34400000, 0x34800000, 0x34A00000, 0x34C00000, 0x34E00000, 0x35000000, 0x35100000, 0x35200000, 0x35300000, 0x35400000, 0x35500000, 0x35600000, 0x35700000, + 0x35800000, 0x35880000, 0x35900000, 0x35980000, 0x35A00000, 0x35A80000, 0x35B00000, 0x35B80000, 0x35C00000, 0x35C80000, 0x35D00000, 0x35D80000, 0x35E00000, 0x35E80000, 0x35F00000, 0x35F80000, + 0x36000000, 0x36040000, 0x36080000, 0x360C0000, 0x36100000, 0x36140000, 0x36180000, 0x361C0000, 0x36200000, 0x36240000, 0x36280000, 0x362C0000, 0x36300000, 0x36340000, 0x36380000, 0x363C0000, + 0x36400000, 0x36440000, 0x36480000, 0x364C0000, 0x36500000, 0x36540000, 0x36580000, 0x365C0000, 0x36600000, 0x36640000, 0x36680000, 0x366C0000, 0x36700000, 0x36740000, 0x36780000, 0x367C0000, + 0x36800000, 0x36820000, 0x36840000, 0x36860000, 0x36880000, 0x368A0000, 0x368C0000, 0x368E0000, 0x36900000, 0x36920000, 0x36940000, 0x36960000, 0x36980000, 0x369A0000, 0x369C0000, 0x369E0000, + 0x36A00000, 0x36A20000, 0x36A40000, 0x36A60000, 0x36A80000, 0x36AA0000, 0x36AC0000, 0x36AE0000, 0x36B00000, 0x36B20000, 0x36B40000, 0x36B60000, 0x36B80000, 0x36BA0000, 0x36BC0000, 0x36BE0000, + 0x36C00000, 0x36C20000, 0x36C40000, 0x36C60000, 0x36C80000, 0x36CA0000, 0x36CC0000, 0x36CE0000, 0x36D00000, 0x36D20000, 0x36D40000, 0x36D60000, 0x36D80000, 0x36DA0000, 0x36DC0000, 0x36DE0000, + 0x36E00000, 0x36E20000, 0x36E40000, 0x36E60000, 0x36E80000, 0x36EA0000, 0x36EC0000, 0x36EE0000, 0x36F00000, 0x36F20000, 0x36F40000, 0x36F60000, 0x36F80000, 0x36FA0000, 0x36FC0000, 0x36FE0000, + 0x37000000, 0x37010000, 0x37020000, 0x37030000, 0x37040000, 0x37050000, 0x37060000, 0x37070000, 0x37080000, 0x37090000, 0x370A0000, 0x370B0000, 0x370C0000, 0x370D0000, 0x370E0000, 0x370F0000, + 0x37100000, 0x37110000, 0x37120000, 0x37130000, 0x37140000, 0x37150000, 0x37160000, 0x37170000, 0x37180000, 0x37190000, 0x371A0000, 0x371B0000, 0x371C0000, 0x371D0000, 0x371E0000, 0x371F0000, + 0x37200000, 0x37210000, 0x37220000, 0x37230000, 0x37240000, 0x37250000, 0x37260000, 0x37270000, 0x37280000, 0x37290000, 0x372A0000, 0x372B0000, 0x372C0000, 0x372D0000, 0x372E0000, 0x372F0000, + 0x37300000, 0x37310000, 0x37320000, 0x37330000, 0x37340000, 0x37350000, 0x37360000, 0x37370000, 0x37380000, 0x37390000, 0x373A0000, 0x373B0000, 0x373C0000, 0x373D0000, 0x373E0000, 0x373F0000, + 0x37400000, 0x37410000, 0x37420000, 0x37430000, 0x37440000, 0x37450000, 0x37460000, 0x37470000, 0x37480000, 0x37490000, 0x374A0000, 0x374B0000, 0x374C0000, 0x374D0000, 0x374E0000, 0x374F0000, + 0x37500000, 0x37510000, 0x37520000, 0x37530000, 0x37540000, 0x37550000, 0x37560000, 0x37570000, 0x37580000, 0x37590000, 0x375A0000, 0x375B0000, 0x375C0000, 0x375D0000, 0x375E0000, 0x375F0000, + 0x37600000, 0x37610000, 0x37620000, 0x37630000, 0x37640000, 0x37650000, 0x37660000, 0x37670000, 0x37680000, 0x37690000, 0x376A0000, 0x376B0000, 0x376C0000, 0x376D0000, 0x376E0000, 0x376F0000, + 0x37700000, 0x37710000, 0x37720000, 0x37730000, 0x37740000, 0x37750000, 0x37760000, 0x37770000, 0x37780000, 0x37790000, 0x377A0000, 0x377B0000, 0x377C0000, 0x377D0000, 0x377E0000, 0x377F0000, + 0x37800000, 0x37808000, 0x37810000, 0x37818000, 0x37820000, 0x37828000, 0x37830000, 0x37838000, 0x37840000, 0x37848000, 0x37850000, 0x37858000, 0x37860000, 0x37868000, 0x37870000, 0x37878000, + 0x37880000, 0x37888000, 0x37890000, 0x37898000, 0x378A0000, 0x378A8000, 0x378B0000, 0x378B8000, 0x378C0000, 0x378C8000, 0x378D0000, 0x378D8000, 0x378E0000, 0x378E8000, 0x378F0000, 0x378F8000, + 0x37900000, 0x37908000, 0x37910000, 0x37918000, 0x37920000, 0x37928000, 0x37930000, 0x37938000, 0x37940000, 0x37948000, 0x37950000, 0x37958000, 0x37960000, 0x37968000, 0x37970000, 0x37978000, + 0x37980000, 0x37988000, 0x37990000, 0x37998000, 0x379A0000, 0x379A8000, 0x379B0000, 0x379B8000, 0x379C0000, 0x379C8000, 0x379D0000, 0x379D8000, 0x379E0000, 0x379E8000, 0x379F0000, 0x379F8000, + 0x37A00000, 0x37A08000, 0x37A10000, 0x37A18000, 0x37A20000, 0x37A28000, 0x37A30000, 0x37A38000, 0x37A40000, 0x37A48000, 0x37A50000, 0x37A58000, 0x37A60000, 0x37A68000, 0x37A70000, 0x37A78000, + 0x37A80000, 0x37A88000, 0x37A90000, 0x37A98000, 0x37AA0000, 0x37AA8000, 0x37AB0000, 0x37AB8000, 0x37AC0000, 0x37AC8000, 0x37AD0000, 0x37AD8000, 0x37AE0000, 0x37AE8000, 0x37AF0000, 0x37AF8000, + 0x37B00000, 0x37B08000, 0x37B10000, 0x37B18000, 0x37B20000, 0x37B28000, 0x37B30000, 0x37B38000, 0x37B40000, 0x37B48000, 0x37B50000, 0x37B58000, 0x37B60000, 0x37B68000, 0x37B70000, 0x37B78000, + 0x37B80000, 0x37B88000, 0x37B90000, 0x37B98000, 0x37BA0000, 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0x3833E000, + 0x38340000, 0x38342000, 0x38344000, 0x38346000, 0x38348000, 0x3834A000, 0x3834C000, 0x3834E000, 0x38350000, 0x38352000, 0x38354000, 0x38356000, 0x38358000, 0x3835A000, 0x3835C000, 0x3835E000, + 0x38360000, 0x38362000, 0x38364000, 0x38366000, 0x38368000, 0x3836A000, 0x3836C000, 0x3836E000, 0x38370000, 0x38372000, 0x38374000, 0x38376000, 0x38378000, 0x3837A000, 0x3837C000, 0x3837E000, + 0x38380000, 0x38382000, 0x38384000, 0x38386000, 0x38388000, 0x3838A000, 0x3838C000, 0x3838E000, 0x38390000, 0x38392000, 0x38394000, 0x38396000, 0x38398000, 0x3839A000, 0x3839C000, 0x3839E000, + 0x383A0000, 0x383A2000, 0x383A4000, 0x383A6000, 0x383A8000, 0x383AA000, 0x383AC000, 0x383AE000, 0x383B0000, 0x383B2000, 0x383B4000, 0x383B6000, 0x383B8000, 0x383BA000, 0x383BC000, 0x383BE000, + 0x383C0000, 0x383C2000, 0x383C4000, 0x383C6000, 0x383C8000, 0x383CA000, 0x383CC000, 0x383CE000, 0x383D0000, 0x383D2000, 0x383D4000, 0x383D6000, 0x383D8000, 0x383DA000, 0x383DC000, 0x383DE000, + 0x383E0000, 0x383E2000, 0x383E4000, 0x383E6000, 0x383E8000, 0x383EA000, 0x383EC000, 0x383EE000, 0x383F0000, 0x383F2000, 0x383F4000, 0x383F6000, 0x383F8000, 0x383FA000, 0x383FC000, 0x383FE000, + 0x38400000, 0x38402000, 0x38404000, 0x38406000, 0x38408000, 0x3840A000, 0x3840C000, 0x3840E000, 0x38410000, 0x38412000, 0x38414000, 0x38416000, 0x38418000, 0x3841A000, 0x3841C000, 0x3841E000, + 0x38420000, 0x38422000, 0x38424000, 0x38426000, 0x38428000, 0x3842A000, 0x3842C000, 0x3842E000, 0x38430000, 0x38432000, 0x38434000, 0x38436000, 0x38438000, 0x3843A000, 0x3843C000, 0x3843E000, + 0x38440000, 0x38442000, 0x38444000, 0x38446000, 0x38448000, 0x3844A000, 0x3844C000, 0x3844E000, 0x38450000, 0x38452000, 0x38454000, 0x38456000, 0x38458000, 0x3845A000, 0x3845C000, 0x3845E000, + 0x38460000, 0x38462000, 0x38464000, 0x38466000, 0x38468000, 0x3846A000, 0x3846C000, 0x3846E000, 0x38470000, 0x38472000, 0x38474000, 0x38476000, 0x38478000, 0x3847A000, 0x3847C000, 0x3847E000, + 0x38480000, 0x38482000, 0x38484000, 0x38486000, 0x38488000, 0x3848A000, 0x3848C000, 0x3848E000, 0x38490000, 0x38492000, 0x38494000, 0x38496000, 0x38498000, 0x3849A000, 0x3849C000, 0x3849E000, + 0x384A0000, 0x384A2000, 0x384A4000, 0x384A6000, 0x384A8000, 0x384AA000, 0x384AC000, 0x384AE000, 0x384B0000, 0x384B2000, 0x384B4000, 0x384B6000, 0x384B8000, 0x384BA000, 0x384BC000, 0x384BE000, + 0x384C0000, 0x384C2000, 0x384C4000, 0x384C6000, 0x384C8000, 0x384CA000, 0x384CC000, 0x384CE000, 0x384D0000, 0x384D2000, 0x384D4000, 0x384D6000, 0x384D8000, 0x384DA000, 0x384DC000, 0x384DE000, + 0x384E0000, 0x384E2000, 0x384E4000, 0x384E6000, 0x384E8000, 0x384EA000, 0x384EC000, 0x384EE000, 0x384F0000, 0x384F2000, 0x384F4000, 0x384F6000, 0x384F8000, 0x384FA000, 0x384FC000, 0x384FE000, + 0x38500000, 0x38502000, 0x38504000, 0x38506000, 0x38508000, 0x3850A000, 0x3850C000, 0x3850E000, 0x38510000, 0x38512000, 0x38514000, 0x38516000, 0x38518000, 0x3851A000, 0x3851C000, 0x3851E000, + 0x38520000, 0x38522000, 0x38524000, 0x38526000, 0x38528000, 0x3852A000, 0x3852C000, 0x3852E000, 0x38530000, 0x38532000, 0x38534000, 0x38536000, 0x38538000, 0x3853A000, 0x3853C000, 0x3853E000, + 0x38540000, 0x38542000, 0x38544000, 0x38546000, 0x38548000, 0x3854A000, 0x3854C000, 0x3854E000, 0x38550000, 0x38552000, 0x38554000, 0x38556000, 0x38558000, 0x3855A000, 0x3855C000, 0x3855E000, + 0x38560000, 0x38562000, 0x38564000, 0x38566000, 0x38568000, 0x3856A000, 0x3856C000, 0x3856E000, 0x38570000, 0x38572000, 0x38574000, 0x38576000, 0x38578000, 0x3857A000, 0x3857C000, 0x3857E000, + 0x38580000, 0x38582000, 0x38584000, 0x38586000, 0x38588000, 0x3858A000, 0x3858C000, 0x3858E000, 0x38590000, 0x38592000, 0x38594000, 0x38596000, 0x38598000, 0x3859A000, 0x3859C000, 0x3859E000, + 0x385A0000, 0x385A2000, 0x385A4000, 0x385A6000, 0x385A8000, 0x385AA000, 0x385AC000, 0x385AE000, 0x385B0000, 0x385B2000, 0x385B4000, 0x385B6000, 0x385B8000, 0x385BA000, 0x385BC000, 0x385BE000, + 0x385C0000, 0x385C2000, 0x385C4000, 0x385C6000, 0x385C8000, 0x385CA000, 0x385CC000, 0x385CE000, 0x385D0000, 0x385D2000, 0x385D4000, 0x385D6000, 0x385D8000, 0x385DA000, 0x385DC000, 0x385DE000, + 0x385E0000, 0x385E2000, 0x385E4000, 0x385E6000, 0x385E8000, 0x385EA000, 0x385EC000, 0x385EE000, 0x385F0000, 0x385F2000, 0x385F4000, 0x385F6000, 0x385F8000, 0x385FA000, 0x385FC000, 0x385FE000, + 0x38600000, 0x38602000, 0x38604000, 0x38606000, 0x38608000, 0x3860A000, 0x3860C000, 0x3860E000, 0x38610000, 0x38612000, 0x38614000, 0x38616000, 0x38618000, 0x3861A000, 0x3861C000, 0x3861E000, + 0x38620000, 0x38622000, 0x38624000, 0x38626000, 0x38628000, 0x3862A000, 0x3862C000, 0x3862E000, 0x38630000, 0x38632000, 0x38634000, 0x38636000, 0x38638000, 0x3863A000, 0x3863C000, 0x3863E000, + 0x38640000, 0x38642000, 0x38644000, 0x38646000, 0x38648000, 0x3864A000, 0x3864C000, 0x3864E000, 0x38650000, 0x38652000, 0x38654000, 0x38656000, 0x38658000, 0x3865A000, 0x3865C000, 0x3865E000, + 0x38660000, 0x38662000, 0x38664000, 0x38666000, 0x38668000, 0x3866A000, 0x3866C000, 0x3866E000, 0x38670000, 0x38672000, 0x38674000, 0x38676000, 0x38678000, 0x3867A000, 0x3867C000, 0x3867E000, + 0x38680000, 0x38682000, 0x38684000, 0x38686000, 0x38688000, 0x3868A000, 0x3868C000, 0x3868E000, 0x38690000, 0x38692000, 0x38694000, 0x38696000, 0x38698000, 0x3869A000, 0x3869C000, 0x3869E000, + 0x386A0000, 0x386A2000, 0x386A4000, 0x386A6000, 0x386A8000, 0x386AA000, 0x386AC000, 0x386AE000, 0x386B0000, 0x386B2000, 0x386B4000, 0x386B6000, 0x386B8000, 0x386BA000, 0x386BC000, 0x386BE000, + 0x386C0000, 0x386C2000, 0x386C4000, 0x386C6000, 0x386C8000, 0x386CA000, 0x386CC000, 0x386CE000, 0x386D0000, 0x386D2000, 0x386D4000, 0x386D6000, 0x386D8000, 0x386DA000, 0x386DC000, 0x386DE000, + 0x386E0000, 0x386E2000, 0x386E4000, 0x386E6000, 0x386E8000, 0x386EA000, 0x386EC000, 0x386EE000, 0x386F0000, 0x386F2000, 0x386F4000, 0x386F6000, 0x386F8000, 0x386FA000, 0x386FC000, 0x386FE000, + 0x38700000, 0x38702000, 0x38704000, 0x38706000, 0x38708000, 0x3870A000, 0x3870C000, 0x3870E000, 0x38710000, 0x38712000, 0x38714000, 0x38716000, 0x38718000, 0x3871A000, 0x3871C000, 0x3871E000, + 0x38720000, 0x38722000, 0x38724000, 0x38726000, 0x38728000, 0x3872A000, 0x3872C000, 0x3872E000, 0x38730000, 0x38732000, 0x38734000, 0x38736000, 0x38738000, 0x3873A000, 0x3873C000, 0x3873E000, + 0x38740000, 0x38742000, 0x38744000, 0x38746000, 0x38748000, 0x3874A000, 0x3874C000, 0x3874E000, 0x38750000, 0x38752000, 0x38754000, 0x38756000, 0x38758000, 0x3875A000, 0x3875C000, 0x3875E000, + 0x38760000, 0x38762000, 0x38764000, 0x38766000, 0x38768000, 0x3876A000, 0x3876C000, 0x3876E000, 0x38770000, 0x38772000, 0x38774000, 0x38776000, 0x38778000, 0x3877A000, 0x3877C000, 0x3877E000, + 0x38780000, 0x38782000, 0x38784000, 0x38786000, 0x38788000, 0x3878A000, 0x3878C000, 0x3878E000, 0x38790000, 0x38792000, 0x38794000, 0x38796000, 0x38798000, 0x3879A000, 0x3879C000, 0x3879E000, + 0x387A0000, 0x387A2000, 0x387A4000, 0x387A6000, 0x387A8000, 0x387AA000, 0x387AC000, 0x387AE000, 0x387B0000, 0x387B2000, 0x387B4000, 0x387B6000, 0x387B8000, 0x387BA000, 0x387BC000, 0x387BE000, + 0x387C0000, 0x387C2000, 0x387C4000, 0x387C6000, 0x387C8000, 0x387CA000, 0x387CC000, 0x387CE000, 0x387D0000, 0x387D2000, 0x387D4000, 0x387D6000, 0x387D8000, 0x387DA000, 0x387DC000, 0x387DE000, + 0x387E0000, 0x387E2000, 0x387E4000, 0x387E6000, 0x387E8000, 0x387EA000, 0x387EC000, 0x387EE000, 0x387F0000, 0x387F2000, 0x387F4000, 0x387F6000, 0x387F8000, 0x387FA000, 0x387FC000, 0x387FE000 }; + static const bits::type exponent_table[64] = { + 0x00000000, 0x00800000, 0x01000000, 0x01800000, 0x02000000, 0x02800000, 0x03000000, 0x03800000, 0x04000000, 0x04800000, 0x05000000, 0x05800000, 0x06000000, 0x06800000, 0x07000000, 0x07800000, + 0x08000000, 0x08800000, 0x09000000, 0x09800000, 0x0A000000, 0x0A800000, 0x0B000000, 0x0B800000, 0x0C000000, 0x0C800000, 0x0D000000, 0x0D800000, 0x0E000000, 0x0E800000, 0x0F000000, 0x47800000, + 0x80000000, 0x80800000, 0x81000000, 0x81800000, 0x82000000, 0x82800000, 0x83000000, 0x83800000, 0x84000000, 0x84800000, 0x85000000, 0x85800000, 0x86000000, 0x86800000, 0x87000000, 0x87800000, + 0x88000000, 0x88800000, 0x89000000, 0x89800000, 0x8A000000, 0x8A800000, 0x8B000000, 0x8B800000, 0x8C000000, 0x8C800000, 0x8D000000, 0x8D800000, 0x8E000000, 0x8E800000, 0x8F000000, 0xC7800000 }; + static const unsigned short offset_table[64] = { + 0, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, + 0, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024 }; + bits::type fbits = mantissa_table[offset_table[value>>10]+(value&0x3FF)] + exponent_table[value>>10]; + #endif + float out; + std::memcpy(&out, &fbits, sizeof(float)); + return out; + #endif + } + + /// Convert half-precision to IEEE double-precision. + /// \param value half-precision value to convert + /// \return double-precision value + inline double half2float_impl(unsigned int value, double, true_type) + { + #if HALF_ENABLE_F16C_INTRINSICS + return _mm_cvtsd_f64(_mm_cvtps_pd(_mm_cvtph_ps(_mm_cvtsi32_si128(value)))); + #else + uint32 hi = static_cast(value&0x8000) << 16; + unsigned int abs = value & 0x7FFF; + if(abs) + { + hi |= 0x3F000000 << static_cast(abs>=0x7C00); + for(; abs<0x400; abs<<=1,hi-=0x100000) ; + hi += static_cast(abs) << 10; + } + bits::type dbits = static_cast::type>(hi) << 32; + double out; + std::memcpy(&out, &dbits, sizeof(double)); + return out; + #endif + } + + /// Convert half-precision to non-IEEE floating-point. + /// \tparam T type to convert to (builtin integer type) + /// \param value half-precision value to convert + /// \return floating-point value + template T half2float_impl(unsigned int value, T, ...) + { + T out; + unsigned int abs = value & 0x7FFF; + if(abs > 0x7C00) + out = (std::numeric_limits::has_signaling_NaN && !(abs&0x200)) ? std::numeric_limits::signaling_NaN() : + std::numeric_limits::has_quiet_NaN ? std::numeric_limits::quiet_NaN() : T(); + else if(abs == 0x7C00) + out = std::numeric_limits::has_infinity ? std::numeric_limits::infinity() : std::numeric_limits::max(); + else if(abs > 0x3FF) + out = std::ldexp(static_cast((abs&0x3FF)|0x400), (abs>>10)-25); + else + out = std::ldexp(static_cast(abs), -24); + return (value&0x8000) ? -out : out; + } + + /// Convert half-precision to floating-point. + /// \tparam T type to convert to (builtin integer type) + /// \param value half-precision value to convert + /// \return floating-point value + template T half2float(unsigned int value) + { + return half2float_impl(value, T(), bool_type::is_iec559&&sizeof(typename bits::type)==sizeof(T)>()); + } + + /// Convert half-precision floating-point to integer. + /// \tparam R rounding mode to use + /// \tparam E `true` for round to even, `false` for round away from zero + /// \tparam I `true` to raise INEXACT exception (if inexact), `false` to never raise it + /// \tparam T type to convert to (buitlin integer type with at least 16 bits precision, excluding any implicit sign bits) + /// \param value half-precision value to convert + /// \return rounded integer value + /// \exception FE_INVALID if value is not representable in type \a T + /// \exception FE_INEXACT if value had to be rounded and \a I is `true` + template T half2int(unsigned int value) + { + unsigned int abs = value & 0x7FFF; + if(abs >= 0x7C00) + { + raise(FE_INVALID); + return (value&0x8000) ? std::numeric_limits::min() : std::numeric_limits::max(); + } + if(abs < 0x3800) + { + raise(FE_INEXACT, I); + return (R==std::round_toward_infinity) ? T(~(value>>15)&(abs!=0)) : + (R==std::round_toward_neg_infinity) ? -T(value>0x8000) : + T(); + } + int exp = 25 - (abs>>10); + unsigned int m = (value&0x3FF) | 0x400; + int32 i = static_cast((exp<=0) ? (m<<-exp) : ((m+( + (R==std::round_to_nearest) ? ((1<<(exp-1))-(~(m>>exp)&E)) : + (R==std::round_toward_infinity) ? (((1<>15)-1)) : + (R==std::round_toward_neg_infinity) ? (((1<>15)) : 0))>>exp)); + if((!std::numeric_limits::is_signed && (value&0x8000)) || (std::numeric_limits::digits<16 && + ((value&0x8000) ? (-i::min()) : (i>std::numeric_limits::max())))) + raise(FE_INVALID); + else if(I && exp > 0 && (m&((1<((value&0x8000) ? -i : i); + } + + /// \} + /// \name Mathematics + /// \{ + + /// upper part of 64-bit multiplication. + /// \tparam R rounding mode to use + /// \param x first factor + /// \param y second factor + /// \return upper 32 bit of \a x * \a y + template uint32 mulhi(uint32 x, uint32 y) + { + uint32 xy = (x>>16) * (y&0xFFFF), yx = (x&0xFFFF) * (y>>16), c = (xy&0xFFFF) + (yx&0xFFFF) + (((x&0xFFFF)*(y&0xFFFF))>>16); + return (x>>16)*(y>>16) + (xy>>16) + (yx>>16) + (c>>16) + + ((R==std::round_to_nearest) ? ((c>>15)&1) : (R==std::round_toward_infinity) ? ((c&0xFFFF)!=0) : 0); + } + + /// 64-bit multiplication. + /// \param x first factor + /// \param y second factor + /// \return upper 32 bit of \a x * \a y rounded to nearest + inline uint32 multiply64(uint32 x, uint32 y) + { + #if HALF_ENABLE_CPP11_LONG_LONG + return static_cast((static_cast(x)*static_cast(y)+0x80000000)>>32); + #else + return mulhi(x, y); + #endif + } + + /// 64-bit division. + /// \param x upper 32 bit of dividend + /// \param y divisor + /// \param s variable to store sticky bit for rounding + /// \return (\a x << 32) / \a y + inline uint32 divide64(uint32 x, uint32 y, int &s) + { + #if HALF_ENABLE_CPP11_LONG_LONG + unsigned long long xx = static_cast(x) << 32; + return s = (xx%y!=0), static_cast(xx/y); + #else + y >>= 1; + uint32 rem = x, div = 0; + for(unsigned int i=0; i<32; ++i) + { + div <<= 1; + if(rem >= y) + { + rem -= y; + div |= 1; + } + rem <<= 1; + } + return s = rem > 1, div; + #endif + } + + /// Half precision positive modulus. + /// \tparam Q `true` to compute full quotient, `false` else + /// \tparam R `true` to compute signed remainder, `false` for positive remainder + /// \param x first operand as positive finite half-precision value + /// \param y second operand as positive finite half-precision value + /// \param quo adress to store quotient at, `nullptr` if \a Q `false` + /// \return modulus of \a x / \a y + template unsigned int mod(unsigned int x, unsigned int y, int *quo = NULL) + { + unsigned int q = 0; + if(x > y) + { + int absx = x, absy = y, expx = 0, expy = 0; + for(; absx<0x400; absx<<=1,--expx) ; + for(; absy<0x400; absy<<=1,--expy) ; + expx += absx >> 10; + expy += absy >> 10; + int mx = (absx&0x3FF) | 0x400, my = (absy&0x3FF) | 0x400; + for(int d=expx-expy; d; --d) + { + if(!Q && mx == my) + return 0; + if(mx >= my) + { + mx -= my; + q += Q; + } + mx <<= 1; + q <<= static_cast(Q); + } + if(!Q && mx == my) + return 0; + if(mx >= my) + { + mx -= my; + ++q; + } + if(Q) + { + q &= (1<<(std::numeric_limits::digits-1)) - 1; + if(!mx) + return *quo = q, 0; + } + for(; mx<0x400; mx<<=1,--expy) ; + x = (expy>0) ? ((expy<<10)|(mx&0x3FF)) : (mx>>(1-expy)); + } + if(R) + { + unsigned int a, b; + if(y < 0x800) + { + a = (x<0x400) ? (x<<1) : (x+0x400); + b = y; + } + else + { + a = x; + b = y - 0x400; + } + if(a > b || (a == b && (q&1))) + { + int exp = (y>>10) + (y<=0x3FF), d = exp - (x>>10) - (x<=0x3FF); + int m = (((y&0x3FF)|((y>0x3FF)<<10))<<1) - (((x&0x3FF)|((x>0x3FF)<<10))<<(1-d)); + for(; m<0x800 && exp>1; m<<=1,--exp) ; + x = 0x8000 + ((exp-1)<<10) + (m>>1); + q += Q; + } + } + if(Q) + *quo = q; + return x; + } + + /// Fixed point square root. + /// \tparam F number of fractional bits + /// \param r radicand in Q1.F fixed point format + /// \param exp exponent + /// \return square root as Q1.F/2 + template uint32 sqrt(uint32 &r, int &exp) + { + int i = exp & 1; + r <<= i; + exp = (exp-i) / 2; + uint32 m = 0; + for(uint32 bit=static_cast(1)<>=2) + { + if(r < m+bit) + m >>= 1; + else + { + r -= m + bit; + m = (m>>1) + bit; + } + } + return m; + } + + /// Fixed point binary exponential. + /// This uses the BKM algorithm in E-mode. + /// \param m exponent in [0,1) as Q0.31 + /// \param n number of iterations (at most 32) + /// \return 2 ^ \a m as Q1.31 + inline uint32 exp2(uint32 m, unsigned int n = 32) + { + static const uint32 logs[] = { + 0x80000000, 0x4AE00D1D, 0x2934F098, 0x15C01A3A, 0x0B31FB7D, 0x05AEB4DD, 0x02DCF2D1, 0x016FE50B, + 0x00B84E23, 0x005C3E10, 0x002E24CA, 0x001713D6, 0x000B8A47, 0x0005C53B, 0x0002E2A3, 0x00017153, + 0x0000B8AA, 0x00005C55, 0x00002E2B, 0x00001715, 0x00000B8B, 0x000005C5, 0x000002E3, 0x00000171, + 0x000000B9, 0x0000005C, 0x0000002E, 0x00000017, 0x0000000C, 0x00000006, 0x00000003, 0x00000001 }; + if(!m) + return 0x80000000; + uint32 mx = 0x80000000, my = 0; + for(unsigned int i=1; i> i; + } + } + return mx; + } + + /// Fixed point binary logarithm. + /// This uses the BKM algorithm in L-mode. + /// \param m mantissa in [1,2) as Q1.30 + /// \param n number of iterations (at most 32) + /// \return log2(\a m) as Q0.31 + inline uint32 log2(uint32 m, unsigned int n = 32) + { + static const uint32 logs[] = { + 0x80000000, 0x4AE00D1D, 0x2934F098, 0x15C01A3A, 0x0B31FB7D, 0x05AEB4DD, 0x02DCF2D1, 0x016FE50B, + 0x00B84E23, 0x005C3E10, 0x002E24CA, 0x001713D6, 0x000B8A47, 0x0005C53B, 0x0002E2A3, 0x00017153, + 0x0000B8AA, 0x00005C55, 0x00002E2B, 0x00001715, 0x00000B8B, 0x000005C5, 0x000002E3, 0x00000171, + 0x000000B9, 0x0000005C, 0x0000002E, 0x00000017, 0x0000000C, 0x00000006, 0x00000003, 0x00000001 }; + if(m == 0x40000000) + return 0; + uint32 mx = 0x40000000, my = 0; + for(unsigned int i=1; i>i); + if(mz <= m) + { + mx = mz; + my += logs[i]; + } + } + return my; + } + + /// Fixed point sine and cosine. + /// This uses the CORDIC algorithm in rotation mode. + /// \param mz angle in [-pi/2,pi/2] as Q1.30 + /// \param n number of iterations (at most 31) + /// \return sine and cosine of \a mz as Q1.30 + inline std::pair sincos(uint32 mz, unsigned int n = 31) + { + static const uint32 angles[] = { + 0x3243F6A9, 0x1DAC6705, 0x0FADBAFD, 0x07F56EA7, 0x03FEAB77, 0x01FFD55C, 0x00FFFAAB, 0x007FFF55, + 0x003FFFEB, 0x001FFFFD, 0x00100000, 0x00080000, 0x00040000, 0x00020000, 0x00010000, 0x00008000, + 0x00004000, 0x00002000, 0x00001000, 0x00000800, 0x00000400, 0x00000200, 0x00000100, 0x00000080, + 0x00000040, 0x00000020, 0x00000010, 0x00000008, 0x00000004, 0x00000002, 0x00000001 }; + uint32 mx = 0x26DD3B6A, my = 0; + for(unsigned int i=0; i0x3FF)<<10); + int exp = (abs>>10) + (abs<=0x3FF) - 15; + if(abs < 0x3A48) + return k = 0, m << (exp+20); + #if HALF_ENABLE_CPP11_LONG_LONG + unsigned long long y = m * 0xA2F9836E4E442, mask = (1ULL<<(62-exp)) - 1, yi = (y+(mask>>1)) & ~mask, f = y - yi; + uint32 sign = -static_cast(f>>63); + k = static_cast(yi>>(62-exp)); + return (multiply64(static_cast((sign ? -f : f)>>(31-exp)), 0xC90FDAA2)^sign) - sign; + #else + uint32 yh = m*0xA2F98 + mulhi(m, 0x36E4E442), yl = (m*0x36E4E442) & 0xFFFFFFFF; + uint32 mask = (static_cast(1)<<(30-exp)) - 1, yi = (yh+(mask>>1)) & ~mask, sign = -static_cast(yi>yh); + k = static_cast(yi>>(30-exp)); + uint32 fh = (yh^sign) + (yi^~sign) - ~sign, fl = (yl^sign) - sign; + return (multiply64((exp>-1) ? (((fh<<(1+exp))&0xFFFFFFFF)|((fl&0xFFFFFFFF)>>(31-exp))) : fh, 0xC90FDAA2)^sign) - sign; + #endif + } + + /// Get arguments for atan2 function. + /// \param abs half-precision floating-point value + /// \return \a abs and sqrt(1 - \a abs^2) as Q0.30 + inline std::pair atan2_args(unsigned int abs) + { + int exp = -15; + for(; abs<0x400; abs<<=1,--exp) ; + exp += abs >> 10; + uint32 my = ((abs&0x3FF)|0x400) << 5, r = my * my; + int rexp = 2 * exp; + r = 0x40000000 - ((rexp>-31) ? ((r>>-rexp)|((r&((static_cast(1)<<-rexp)-1))!=0)) : 1); + for(rexp=0; r<0x40000000; r<<=1,--rexp) ; + uint32 mx = sqrt<30>(r, rexp); + int d = exp - rexp; + if(d < 0) + return std::make_pair((d<-14) ? ((my>>(-d-14))+((my>>(-d-15))&1)) : (my<<(14+d)), (mx<<14)+(r<<13)/mx); + if(d > 0) + return std::make_pair(my<<14, (d>14) ? ((mx>>(d-14))+((mx>>(d-15))&1)) : ((d==14) ? mx : ((mx<<(14-d))+(r<<(13-d))/mx))); + return std::make_pair(my<<13, (mx<<13)+(r<<12)/mx); + } + + /// Get exponentials for hyperbolic computation + /// \param abs half-precision floating-point value + /// \param exp variable to take unbiased exponent of larger result + /// \param n number of BKM iterations (at most 32) + /// \return exp(abs) and exp(-\a abs) as Q1.31 with same exponent + inline std::pair hyperbolic_args(unsigned int abs, int &exp, unsigned int n = 32) + { + uint32 mx = detail::multiply64(static_cast((abs&0x3FF)+((abs>0x3FF)<<10))<<21, 0xB8AA3B29), my; + int e = (abs>>10) + (abs<=0x3FF); + if(e < 14) + { + exp = 0; + mx >>= 14 - e; + } + else + { + exp = mx >> (45-e); + mx = (mx<<(e-14)) & 0x7FFFFFFF; + } + mx = exp2(mx, n); + int d = exp << 1, s; + if(mx > 0x80000000) + { + my = divide64(0x80000000, mx, s); + my |= s; + ++d; + } + else + my = mx; + return std::make_pair(mx, (d<31) ? ((my>>d)|((my&((static_cast(1)< unsigned int exp2_post(uint32 m, int exp, bool esign, unsigned int sign = 0, unsigned int n = 32) + { + if(esign) + { + exp = -exp - (m!=0); + if(exp < -25) + return underflow(sign); + else if(exp == -25) + return rounded(sign, 1, m!=0); + } + else if(exp > 15) + return overflow(sign); + if(!m) + return sign | (((exp+=15)>0) ? (exp<<10) : check_underflow(0x200>>-exp)); + m = exp2(m, n); + int s = 0; + if(esign) + m = divide64(0x80000000, m, s); + return fixed2half(m, exp+14, sign, s); + } + + /// Postprocessing for binary logarithm. + /// \tparam R rounding mode to use + /// \tparam L logarithm for base transformation as Q1.31 + /// \param m fractional part of logarithm as Q0.31 + /// \param ilog signed integer part of logarithm + /// \param exp biased exponent of result + /// \param sign sign bit of result + /// \return value base-transformed and converted to half-precision + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if no other exception occurred + template unsigned int log2_post(uint32 m, int ilog, int exp, unsigned int sign = 0) + { + uint32 msign = sign_mask(ilog); + m = (((static_cast(ilog)<<27)+(m>>4))^msign) - msign; + if(!m) + return 0; + for(; m<0x80000000; m<<=1,--exp) ; + int i = m >= L, s; + exp += i; + m >>= 1 + i; + sign ^= msign & 0x8000; + if(exp < -11) + return underflow(sign); + m = divide64(m, L, s); + return fixed2half(m, exp, sign, 1); + } + + /// Hypotenuse square root and postprocessing. + /// \tparam R rounding mode to use + /// \param r mantissa as Q2.30 + /// \param exp biased exponent + /// \return square root converted to half-precision + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if value had to be rounded + template unsigned int hypot_post(uint32 r, int exp) + { + int i = r >> 31; + if((exp+=i) > 46) + return overflow(); + if(exp < -34) + return underflow(); + r = (r>>i) | (r&i); + uint32 m = sqrt<30>(r, exp+=15); + return fixed2half(m, exp-1, 0, r!=0); + } + + /// Division and postprocessing for tangents. + /// \tparam R rounding mode to use + /// \param my dividend as Q1.31 + /// \param mx divisor as Q1.31 + /// \param exp biased exponent of result + /// \param sign sign bit of result + /// \return quotient converted to half-precision + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if no other exception occurred + template unsigned int tangent_post(uint32 my, uint32 mx, int exp, unsigned int sign = 0) + { + int i = my >= mx, s; + exp += i; + if(exp > 29) + return overflow(sign); + if(exp < -11) + return underflow(sign); + uint32 m = divide64(my>>(i+1), mx, s); + return fixed2half(m, exp, sign, s); + } + + /// Area function and postprocessing. + /// This computes the value directly in Q2.30 using the representation `asinh|acosh(x) = log(x+sqrt(x^2+|-1))`. + /// \tparam R rounding mode to use + /// \tparam S `true` for asinh, `false` for acosh + /// \param arg half-precision argument + /// \return asinh|acosh(\a arg) converted to half-precision + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if no other exception occurred + template unsigned int area(unsigned int arg) + { + int abs = arg & 0x7FFF, expx = (abs>>10) + (abs<=0x3FF) - 15, expy = -15, ilog, i; + uint32 mx = static_cast((abs&0x3FF)|((abs>0x3FF)<<10)) << 20, my, r; + for(; abs<0x400; abs<<=1,--expy) ; + expy += abs >> 10; + r = ((abs&0x3FF)|0x400) << 5; + r *= r; + i = r >> 31; + expy = 2*expy + i; + r >>= i; + if(S) + { + if(expy < 0) + { + r = 0x40000000 + ((expy>-30) ? ((r>>-expy)|((r&((static_cast(1)<<-expy)-1))!=0)) : 1); + expy = 0; + } + else + { + r += 0x40000000 >> expy; + i = r >> 31; + r = (r>>i) | (r&i); + expy += i; + } + } + else + { + r -= 0x40000000 >> expy; + for(; r<0x40000000; r<<=1,--expy) ; + } + my = sqrt<30>(r, expy); + my = (my<<15) + (r<<14)/my; + if(S) + { + mx >>= expy - expx; + ilog = expy; + } + else + { + my >>= expx - expy; + ilog = expx; + } + my += mx; + i = my >> 31; + static const int G = S && (R==std::round_to_nearest); + return log2_post(log2(my>>i, 26+S+G)+(G<<3), ilog+i, 17, arg&(static_cast(S)<<15)); + } + + /// Class for 1.31 unsigned floating-point computation + struct f31 + { + /// Constructor. + /// \param mant mantissa as 1.31 + /// \param e exponent + HALF_CONSTEXPR f31(uint32 mant, int e) : m(mant), exp(e) {} + + /// Constructor. + /// \param abs unsigned half-precision value + f31(unsigned int abs) : exp(-15) + { + for(; abs<0x400; abs<<=1,--exp) ; + m = static_cast((abs&0x3FF)|0x400) << 21; + exp += (abs>>10); + } + + /// Addition operator. + /// \param a first operand + /// \param b second operand + /// \return \a a + \a b + friend f31 operator+(f31 a, f31 b) + { + if(b.exp > a.exp) + std::swap(a, b); + int d = a.exp - b.exp; + uint32 m = a.m + ((d<32) ? (b.m>>d) : 0); + int i = (m&0xFFFFFFFF) < a.m; + return f31(((m+i)>>i)|0x80000000, a.exp+i); + } + + /// Subtraction operator. + /// \param a first operand + /// \param b second operand + /// \return \a a - \a b + friend f31 operator-(f31 a, f31 b) + { + int d = a.exp - b.exp, exp = a.exp; + uint32 m = a.m - ((d<32) ? (b.m>>d) : 0); + if(!m) + return f31(0, -32); + for(; m<0x80000000; m<<=1,--exp) ; + return f31(m, exp); + } + + /// Multiplication operator. + /// \param a first operand + /// \param b second operand + /// \return \a a * \a b + friend f31 operator*(f31 a, f31 b) + { + uint32 m = multiply64(a.m, b.m); + int i = m >> 31; + return f31(m<<(1-i), a.exp + b.exp + i); + } + + /// Division operator. + /// \param a first operand + /// \param b second operand + /// \return \a a / \a b + friend f31 operator/(f31 a, f31 b) + { + int i = a.m >= b.m, s; + uint32 m = divide64((a.m+i)>>i, b.m, s); + return f31(m, a.exp - b.exp + i - 1); + } + + uint32 m; ///< mantissa as 1.31. + int exp; ///< exponent. + }; + + /// Error function and postprocessing. + /// This computes the value directly in Q1.31 using the approximations given + /// [here](https://en.wikipedia.org/wiki/Error_function#Approximation_with_elementary_functions). + /// \tparam R rounding mode to use + /// \tparam C `true` for comlementary error function, `false` else + /// \param arg half-precision function argument + /// \return approximated value of error function in half-precision + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if no other exception occurred + template unsigned int erf(unsigned int arg) + { + unsigned int abs = arg & 0x7FFF, sign = arg & 0x8000; + f31 x(abs), x2 = x * x * f31(0xB8AA3B29, 0), t = f31(0x80000000, 0) / (f31(0x80000000, 0)+f31(0xA7BA054A, -2)*x), t2 = t * t; + f31 e = ((f31(0x87DC2213, 0)*t2+f31(0xB5F0E2AE, 0))*t2+f31(0x82790637, -2)-(f31(0xBA00E2B8, 0)*t2+f31(0x91A98E62, -2))*t) * t / + ((x2.exp<0) ? f31(exp2((x2.exp>-32) ? (x2.m>>-x2.exp) : 0, 30), 0) : f31(exp2((x2.m<>(31-x2.exp))); + return (!C || sign) ? fixed2half(0x80000000-(e.m>>(C-e.exp)), 14+C, sign&(C-1U)) : + (e.exp<-25) ? underflow() : fixed2half(e.m>>1, e.exp+14, 0, e.m&1); + } + + /// Gamma function and postprocessing. + /// This approximates the value of either the gamma function or its logarithm directly in Q1.31. + /// \tparam R rounding mode to use + /// \tparam L `true` for lograithm of gamma function, `false` for gamma function + /// \param arg half-precision floating-point value + /// \return lgamma/tgamma(\a arg) in half-precision + /// \exception FE_OVERFLOW on overflows + /// \exception FE_UNDERFLOW on underflows + /// \exception FE_INEXACT if \a arg is not a positive integer + template unsigned int gamma(unsigned int arg) + { +/* static const double p[] ={ 2.50662827563479526904, 225.525584619175212544, -268.295973841304927459, 80.9030806934622512966, -5.00757863970517583837, 0.0114684895434781459556 }; + double t = arg + 4.65, s = p[0]; + for(unsigned int i=0; i<5; ++i) + s += p[i+1] / (arg+i); + return std::log(s) + (arg-0.5)*std::log(t) - t; +*/ static const f31 pi(0xC90FDAA2, 1), lbe(0xB8AA3B29, 0); + unsigned int abs = arg & 0x7FFF, sign = arg & 0x8000; + bool bsign = sign != 0; + f31 z(abs), x = sign ? (z+f31(0x80000000, 0)) : z, t = x + f31(0x94CCCCCD, 2), s = + f31(0xA06C9901, 1) + f31(0xBBE654E2, -7)/(x+f31(0x80000000, 2)) + f31(0xA1CE6098, 6)/(x+f31(0x80000000, 1)) + + f31(0xE1868CB7, 7)/x - f31(0x8625E279, 8)/(x+f31(0x80000000, 0)) - f31(0xA03E158F, 2)/(x+f31(0xC0000000, 1)); + int i = (s.exp>=2) + (s.exp>=4) + (s.exp>=8) + (s.exp>=16); + s = f31((static_cast(s.exp)<<(31-i))+(log2(s.m>>1, 28)>>i), i) / lbe; + if(x.exp != -1 || x.m != 0x80000000) + { + i = (t.exp>=2) + (t.exp>=4) + (t.exp>=8); + f31 l = f31((static_cast(t.exp)<<(31-i))+(log2(t.m>>1, 30)>>i), i) / lbe; + s = (x.exp<-1) ? (s-(f31(0x80000000, -1)-x)*l) : (s+(x-f31(0x80000000, -1))*l); + } + s = x.exp ? (s-t) : (t-s); + if(bsign) + { + if(z.exp >= 0) + { + sign &= (L|((z.m>>(31-z.exp))&1)) - 1; + for(z=f31((z.m<<(1+z.exp))&0xFFFFFFFF, -1); z.m<0x80000000; z.m<<=1,--z.exp) ; + } + if(z.exp == -1) + z = f31(0x80000000, 0) - z; + if(z.exp < -1) + { + z = z * pi; + z.m = sincos(z.m>>(1-z.exp), 30).first; + for(z.exp=1; z.m<0x80000000; z.m<<=1,--z.exp) ; + } + else + z = f31(0x80000000, 0); + } + if(L) + { + if(bsign) + { + f31 l(0x92868247, 0); + if(z.exp < 0) + { + uint32 m = log2((z.m+1)>>1, 27); + z = f31(-((static_cast(z.exp)<<26)+(m>>5)), 5); + for(; z.m<0x80000000; z.m<<=1,--z.exp) ; + l = l + z / lbe; + } + sign = static_cast(x.exp&&(l.exp(x.exp==0) << 15; + if(s.exp < -24) + return underflow(sign); + if(s.exp > 15) + return overflow(sign); + } + } + else + { + s = s * lbe; + uint32 m; + if(s.exp < 0) + { + m = s.m >> -s.exp; + s.exp = 0; + } + else + { + m = (s.m<>(31-s.exp)); + } + s.m = exp2(m, 27); + if(!x.exp) + s = f31(0x80000000, 0) / s; + if(bsign) + { + if(z.exp < 0) + s = s * z; + s = pi / s; + if(s.exp < -24) + return underflow(sign); + } + else if(z.exp > 0 && !(z.m&((1<<(31-z.exp))-1))) + return ((s.exp+14)<<10) + (s.m>>21); + if(s.exp > 15) + return overflow(sign); + } + return fixed2half(s.m, s.exp+14, sign); + } + /// \} + + template struct half_caster; + } + + /// Half-precision floating-point type. + /// This class implements an IEEE-conformant half-precision floating-point type with the usual arithmetic + /// operators and conversions. It is implicitly convertible to single-precision floating-point, which makes artihmetic + /// expressions and functions with mixed-type operands to be of the most precise operand type. + /// + /// According to the C++98/03 definition, the half type is not a POD type. But according to C++11's less strict and + /// extended definitions it is both a standard layout type and a trivially copyable type (even if not a POD type), which + /// means it can be standard-conformantly copied using raw binary copies. But in this context some more words about the + /// actual size of the type. Although the half is representing an IEEE 16-bit type, it does not neccessarily have to be of + /// exactly 16-bits size. But on any reasonable implementation the actual binary representation of this type will most + /// probably not ivolve any additional "magic" or padding beyond the simple binary representation of the underlying 16-bit + /// IEEE number, even if not strictly guaranteed by the standard. But even then it only has an actual size of 16 bits if + /// your C++ implementation supports an unsigned integer type of exactly 16 bits width. But this should be the case on + /// nearly any reasonable platform. + /// + /// So if your C++ implementation is not totally exotic or imposes special alignment requirements, it is a reasonable + /// assumption that the data of a half is just comprised of the 2 bytes of the underlying IEEE representation. + class half + { + public: + /// \name Construction and assignment + /// \{ + + /// Default constructor. + /// This initializes the half to 0. Although this does not match the builtin types' default-initialization semantics + /// and may be less efficient than no initialization, it is needed to provide proper value-initialization semantics. + HALF_CONSTEXPR half() HALF_NOEXCEPT : data_() {} + + /// Conversion constructor. + /// \param rhs float to convert + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + explicit half(float rhs) : data_(static_cast(detail::float2half(rhs))) {} + + /// Conversion to single-precision. + /// \return single precision value representing expression value + operator float() const { return detail::half2float(data_); } + + /// Assignment operator. + /// \param rhs single-precision value to copy from + /// \return reference to this half + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + half& operator=(float rhs) { data_ = static_cast(detail::float2half(rhs)); return *this; } + + /// \} + /// \name Arithmetic updates + /// \{ + + /// Arithmetic assignment. + /// \tparam T type of concrete half expression + /// \param rhs half expression to add + /// \return reference to this half + /// \exception FE_... according to operator+(half,half) + half& operator+=(half rhs) { return *this = *this + rhs; } + + /// Arithmetic assignment. + /// \tparam T type of concrete half expression + /// \param rhs half expression to subtract + /// \return reference to this half + /// \exception FE_... according to operator-(half,half) + half& operator-=(half rhs) { return *this = *this - rhs; } + + /// Arithmetic assignment. + /// \tparam T type of concrete half expression + /// \param rhs half expression to multiply with + /// \return reference to this half + /// \exception FE_... according to operator*(half,half) + half& operator*=(half rhs) { return *this = *this * rhs; } + + /// Arithmetic assignment. + /// \tparam T type of concrete half expression + /// \param rhs half expression to divide by + /// \return reference to this half + /// \exception FE_... according to operator/(half,half) + half& operator/=(half rhs) { return *this = *this / rhs; } + + /// Arithmetic assignment. + /// \param rhs single-precision value to add + /// \return reference to this half + /// \exception FE_... according to operator=() + half& operator+=(float rhs) { return *this = *this + rhs; } + + /// Arithmetic assignment. + /// \param rhs single-precision value to subtract + /// \return reference to this half + /// \exception FE_... according to operator=() + half& operator-=(float rhs) { return *this = *this - rhs; } + + /// Arithmetic assignment. + /// \param rhs single-precision value to multiply with + /// \return reference to this half + /// \exception FE_... according to operator=() + half& operator*=(float rhs) { return *this = *this * rhs; } + + /// Arithmetic assignment. + /// \param rhs single-precision value to divide by + /// \return reference to this half + /// \exception FE_... according to operator=() + half& operator/=(float rhs) { return *this = *this / rhs; } + + /// \} + /// \name Increment and decrement + /// \{ + + /// Prefix increment. + /// \return incremented half value + /// \exception FE_... according to operator+(half,half) + half& operator++() { return *this = *this + half(detail::binary, 0x3C00); } + + /// Prefix decrement. + /// \return decremented half value + /// \exception FE_... according to operator-(half,half) + half& operator--() { return *this = *this + half(detail::binary, 0xBC00); } + + /// Postfix increment. + /// \return non-incremented half value + /// \exception FE_... according to operator+(half,half) + half operator++(int) { half out(*this); ++*this; return out; } + + /// Postfix decrement. + /// \return non-decremented half value + /// \exception FE_... according to operator-(half,half) + half operator--(int) { half out(*this); --*this; return out; } + /// \} + + private: + /// Rounding mode to use + static const std::float_round_style round_style = (std::float_round_style)(HALF_ROUND_STYLE); + + /// Constructor. + /// \param bits binary representation to set half to + HALF_CONSTEXPR half(detail::binary_t, unsigned int bits) HALF_NOEXCEPT : data_(static_cast(bits)) {} + + /// Internal binary representation + detail::uint16 data_; + + #ifndef HALF_DOXYGEN_ONLY + friend HALF_CONSTEXPR_NOERR bool operator==(half, half); + friend HALF_CONSTEXPR_NOERR bool operator!=(half, half); + friend HALF_CONSTEXPR_NOERR bool operator<(half, half); + friend HALF_CONSTEXPR_NOERR bool operator>(half, half); + friend HALF_CONSTEXPR_NOERR bool operator<=(half, half); + friend HALF_CONSTEXPR_NOERR bool operator>=(half, half); + friend HALF_CONSTEXPR half operator-(half); + friend half operator+(half, half); + friend half operator-(half, half); + friend half operator*(half, half); + friend half operator/(half, half); + template friend std::basic_ostream& operator<<(std::basic_ostream&, half); + template friend std::basic_istream& operator>>(std::basic_istream&, half&); + friend HALF_CONSTEXPR half fabs(half); + friend half fmod(half, half); + friend half remainder(half, half); + friend half remquo(half, half, int*); + friend half fma(half, half, half); + friend HALF_CONSTEXPR_NOERR half fmax(half, half); + friend HALF_CONSTEXPR_NOERR half fmin(half, half); + friend half fdim(half, half); + friend half nanh(const char*); + friend half exp(half); + friend half exp2(half); + friend half expm1(half); + friend half log(half); + friend half log10(half); + friend half log2(half); + friend half log1p(half); + friend half sqrt(half); + friend half rsqrt(half); + friend half cbrt(half); + friend half hypot(half, half); + friend half hypot(half, half, half); + friend half pow(half, half); + friend void sincos(half, half*, half*); + friend half sin(half); + friend half cos(half); + friend half tan(half); + friend half asin(half); + friend half acos(half); + friend half atan(half); + friend half atan2(half, half); + friend half sinh(half); + friend half cosh(half); + friend half tanh(half); + friend half asinh(half); + friend half acosh(half); + friend half atanh(half); + friend half erf(half); + friend half erfc(half); + friend half lgamma(half); + friend half tgamma(half); + friend half ceil(half); + friend half floor(half); + friend half trunc(half); + friend half round(half); + friend long lround(half); + friend half rint(half); + friend long lrint(half); + friend half nearbyint(half); + #ifdef HALF_ENABLE_CPP11_LONG_LONG + friend long long llround(half); + friend long long llrint(half); + #endif + friend half frexp(half, int*); + friend half scalbln(half, long); + friend half modf(half, half*); + friend int ilogb(half); + friend half logb(half); + friend half nextafter(half, half); + friend half nexttoward(half, long double); + friend HALF_CONSTEXPR half copysign(half, half); + friend HALF_CONSTEXPR int fpclassify(half); + friend HALF_CONSTEXPR bool isfinite(half); + friend HALF_CONSTEXPR bool isinf(half); + friend HALF_CONSTEXPR bool isnan(half); + friend HALF_CONSTEXPR bool isnormal(half); + friend HALF_CONSTEXPR bool signbit(half); + friend HALF_CONSTEXPR bool isgreater(half, half); + friend HALF_CONSTEXPR bool isgreaterequal(half, half); + friend HALF_CONSTEXPR bool isless(half, half); + friend HALF_CONSTEXPR bool islessequal(half, half); + friend HALF_CONSTEXPR bool islessgreater(half, half); + template friend struct detail::half_caster; + friend class std::numeric_limits; + #if HALF_ENABLE_CPP11_HASH + friend struct std::hash; + #endif + #if HALF_ENABLE_CPP11_USER_LITERALS + friend half literal::operator "" _h(long double); + #endif + #endif + }; + +#if HALF_ENABLE_CPP11_USER_LITERALS + namespace literal + { + /// Half literal. + /// While this returns a properly rounded half-precision value, half literals can unfortunately not be constant + /// expressions due to rather involved conversions. So don't expect this to be a literal literal without involving + /// conversion operations at runtime. It is a convenience feature, not a performance optimization. + /// \param value literal value + /// \return half with of given value (possibly rounded) + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half operator "" _h(long double value) { return half(detail::binary, detail::float2half(value)); } + } +#endif + + namespace detail + { + /// Helper class for half casts. + /// This class template has to be specialized for all valid cast arguments to define an appropriate static + /// `cast` member function and a corresponding `type` member denoting its return type. + /// \tparam T destination type + /// \tparam U source type + /// \tparam R rounding mode to use + template struct half_caster {}; + template struct half_caster + { + #if HALF_ENABLE_CPP11_STATIC_ASSERT && HALF_ENABLE_CPP11_TYPE_TRAITS + static_assert(std::is_arithmetic::value, "half_cast from non-arithmetic type unsupported"); + #endif + + static half cast(U arg) { return cast_impl(arg, is_float()); }; + + private: + static half cast_impl(U arg, true_type) { return half(binary, float2half(arg)); } + static half cast_impl(U arg, false_type) { return half(binary, int2half(arg)); } + }; + template struct half_caster + { + #if HALF_ENABLE_CPP11_STATIC_ASSERT && HALF_ENABLE_CPP11_TYPE_TRAITS + static_assert(std::is_arithmetic::value, "half_cast to non-arithmetic type unsupported"); + #endif + + static T cast(half arg) { return cast_impl(arg, is_float()); } + + private: + static T cast_impl(half arg, true_type) { return half2float(arg.data_); } + static T cast_impl(half arg, false_type) { return half2int(arg.data_); } + }; + template struct half_caster + { + static half cast(half arg) { return arg; } + }; + } +} + +/// Extensions to the C++ standard library. +namespace std +{ + /// Numeric limits for half-precision floats. + /// **See also:** Documentation for [std::numeric_limits](https://en.cppreference.com/w/cpp/types/numeric_limits) + template<> class numeric_limits + { + public: + /// Is template specialization. + static HALF_CONSTEXPR_CONST bool is_specialized = true; + + /// Supports signed values. + static HALF_CONSTEXPR_CONST bool is_signed = true; + + /// Is not an integer type. + static HALF_CONSTEXPR_CONST bool is_integer = false; + + /// Is not exact. + static HALF_CONSTEXPR_CONST bool is_exact = false; + + /// Doesn't provide modulo arithmetic. + static HALF_CONSTEXPR_CONST bool is_modulo = false; + + /// Has a finite set of values. + static HALF_CONSTEXPR_CONST bool is_bounded = true; + + /// IEEE conformant. + static HALF_CONSTEXPR_CONST bool is_iec559 = true; + + /// Supports infinity. + static HALF_CONSTEXPR_CONST bool has_infinity = true; + + /// Supports quiet NaNs. + static HALF_CONSTEXPR_CONST bool has_quiet_NaN = true; + + /// Supports signaling NaNs. + static HALF_CONSTEXPR_CONST bool has_signaling_NaN = true; + + /// Supports subnormal values. + static HALF_CONSTEXPR_CONST float_denorm_style has_denorm = denorm_present; + + /// Supports no denormalization detection. + static HALF_CONSTEXPR_CONST bool has_denorm_loss = false; + + #if HALF_ERRHANDLING_THROWS + static HALF_CONSTEXPR_CONST bool traps = true; + #else + /// Traps only if [HALF_ERRHANDLING_THROW_...](\ref HALF_ERRHANDLING_THROW_INVALID) is acitvated. + static HALF_CONSTEXPR_CONST bool traps = false; + #endif + + /// Does not support no pre-rounding underflow detection. + static HALF_CONSTEXPR_CONST bool tinyness_before = false; + + /// Rounding mode. + static HALF_CONSTEXPR_CONST float_round_style round_style = half_float::half::round_style; + + /// Significant digits. + static HALF_CONSTEXPR_CONST int digits = 11; + + /// Significant decimal digits. + static HALF_CONSTEXPR_CONST int digits10 = 3; + + /// Required decimal digits to represent all possible values. + static HALF_CONSTEXPR_CONST int max_digits10 = 5; + + /// Number base. + static HALF_CONSTEXPR_CONST int radix = 2; + + /// One more than smallest exponent. + static HALF_CONSTEXPR_CONST int min_exponent = -13; + + /// Smallest normalized representable power of 10. + static HALF_CONSTEXPR_CONST int min_exponent10 = -4; + + /// One more than largest exponent + static HALF_CONSTEXPR_CONST int max_exponent = 16; + + /// Largest finitely representable power of 10. + static HALF_CONSTEXPR_CONST int max_exponent10 = 4; + + /// Smallest positive normal value. + static HALF_CONSTEXPR half_float::half min() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x0400); } + + /// Smallest finite value. + static HALF_CONSTEXPR half_float::half lowest() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0xFBFF); } + + /// Largest finite value. + static HALF_CONSTEXPR half_float::half max() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x7BFF); } + + /// Difference between 1 and next representable value. + static HALF_CONSTEXPR half_float::half epsilon() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x1400); } + + /// Maximum rounding error in ULP (units in the last place). + static HALF_CONSTEXPR half_float::half round_error() HALF_NOTHROW + { return half_float::half(half_float::detail::binary, (round_style==std::round_to_nearest) ? 0x3800 : 0x3C00); } + + /// Positive infinity. + static HALF_CONSTEXPR half_float::half infinity() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x7C00); } + + /// Quiet NaN. + static HALF_CONSTEXPR half_float::half quiet_NaN() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x7FFF); } + + /// Signaling NaN. + static HALF_CONSTEXPR half_float::half signaling_NaN() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x7DFF); } + + /// Smallest positive subnormal value. + static HALF_CONSTEXPR half_float::half denorm_min() HALF_NOTHROW { return half_float::half(half_float::detail::binary, 0x0001); } + }; + +#if HALF_ENABLE_CPP11_HASH + /// Hash function for half-precision floats. + /// This is only defined if C++11 `std::hash` is supported and enabled. + /// + /// **See also:** Documentation for [std::hash](https://en.cppreference.com/w/cpp/utility/hash) + template<> struct hash + { + /// Type of function argument. + typedef half_float::half argument_type; + + /// Function return type. + typedef size_t result_type; + + /// Compute hash function. + /// \param arg half to hash + /// \return hash value + result_type operator()(argument_type arg) const { return hash()(arg.data_&-static_cast(arg.data_!=0x8000)); } + }; +#endif +} + +namespace half_float +{ + /// \anchor compop + /// \name Comparison operators + /// \{ + + /// Comparison for equality. + /// \param x first operand + /// \param y second operand + /// \retval true if operands equal + /// \retval false else + /// \exception FE_INVALID if \a x or \a y is NaN + inline HALF_CONSTEXPR_NOERR bool operator==(half x, half y) + { + return !detail::compsignal(x.data_, y.data_) && (x.data_==y.data_ || !((x.data_|y.data_)&0x7FFF)); + } + + /// Comparison for inequality. + /// \param x first operand + /// \param y second operand + /// \retval true if operands not equal + /// \retval false else + /// \exception FE_INVALID if \a x or \a y is NaN + inline HALF_CONSTEXPR_NOERR bool operator!=(half x, half y) + { + return detail::compsignal(x.data_, y.data_) || (x.data_!=y.data_ && ((x.data_|y.data_)&0x7FFF)); + } + + /// Comparison for less than. + /// \param x first operand + /// \param y second operand + /// \retval true if \a x less than \a y + /// \retval false else + /// \exception FE_INVALID if \a x or \a y is NaN + inline HALF_CONSTEXPR_NOERR bool operator<(half x, half y) + { + return !detail::compsignal(x.data_, y.data_) && + ((x.data_^(0x8000|(0x8000-(x.data_>>15))))+(x.data_>>15)) < ((y.data_^(0x8000|(0x8000-(y.data_>>15))))+(y.data_>>15)); + } + + /// Comparison for greater than. + /// \param x first operand + /// \param y second operand + /// \retval true if \a x greater than \a y + /// \retval false else + /// \exception FE_INVALID if \a x or \a y is NaN + inline HALF_CONSTEXPR_NOERR bool operator>(half x, half y) + { + return !detail::compsignal(x.data_, y.data_) && + ((x.data_^(0x8000|(0x8000-(x.data_>>15))))+(x.data_>>15)) > ((y.data_^(0x8000|(0x8000-(y.data_>>15))))+(y.data_>>15)); + } + + /// Comparison for less equal. + /// \param x first operand + /// \param y second operand + /// \retval true if \a x less equal \a y + /// \retval false else + /// \exception FE_INVALID if \a x or \a y is NaN + inline HALF_CONSTEXPR_NOERR bool operator<=(half x, half y) + { + return !detail::compsignal(x.data_, y.data_) && + ((x.data_^(0x8000|(0x8000-(x.data_>>15))))+(x.data_>>15)) <= ((y.data_^(0x8000|(0x8000-(y.data_>>15))))+(y.data_>>15)); + } + + /// Comparison for greater equal. + /// \param x first operand + /// \param y second operand + /// \retval true if \a x greater equal \a y + /// \retval false else + /// \exception FE_INVALID if \a x or \a y is NaN + inline HALF_CONSTEXPR_NOERR bool operator>=(half x, half y) + { + return !detail::compsignal(x.data_, y.data_) && + ((x.data_^(0x8000|(0x8000-(x.data_>>15))))+(x.data_>>15)) >= ((y.data_^(0x8000|(0x8000-(y.data_>>15))))+(y.data_>>15)); + } + + /// \} + /// \anchor arithmetics + /// \name Arithmetic operators + /// \{ + + /// Identity. + /// \param arg operand + /// \return unchanged operand + inline HALF_CONSTEXPR half operator+(half arg) { return arg; } + + /// Negation. + /// \param arg operand + /// \return negated operand + inline HALF_CONSTEXPR half operator-(half arg) { return half(detail::binary, arg.data_^0x8000); } + + /// Addition. + /// This operation is exact to rounding for all rounding modes. + /// \param x left operand + /// \param y right operand + /// \return sum of half expressions + /// \exception FE_INVALID if \a x and \a y are infinities with different signs or signaling NaNs + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half operator+(half x, half y) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(detail::half2float(x.data_)+detail::half2float(y.data_))); + #else + int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF; + bool sub = ((x.data_^y.data_)&0x8000) != 0; + if(absx >= 0x7C00 || absy >= 0x7C00) + return half(detail::binary, (absx>0x7C00 || absy>0x7C00) ? detail::signal(x.data_, y.data_) : (absy!=0x7C00) ? x.data_ : + (sub && absx==0x7C00) ? detail::invalid() : y.data_); + if(!absx) + return absy ? y : half(detail::binary, (half::round_style==std::round_toward_neg_infinity) ? (x.data_|y.data_) : (x.data_&y.data_)); + if(!absy) + return x; + unsigned int sign = ((sub && absy>absx) ? y.data_ : x.data_) & 0x8000; + if(absy > absx) + std::swap(absx, absy); + int exp = (absx>>10) + (absx<=0x3FF), d = exp - (absy>>10) - (absy<=0x3FF), mx = ((absx&0x3FF)|((absx>0x3FF)<<10)) << 3, my; + if(d < 13) + { + my = ((absy&0x3FF)|((absy>0x3FF)<<10)) << 3; + my = (my>>d) | ((my&((1<(half::round_style==std::round_toward_neg_infinity)<<15); + for(; mx<0x2000 && exp>1; mx<<=1,--exp) ; + } + else + { + mx += my; + int i = mx >> 14; + if((exp+=i) > 30) + return half(detail::binary, detail::overflow(sign)); + mx = (mx>>i) | (mx&i); + } + return half(detail::binary, detail::rounded(sign+((exp-1)<<10)+(mx>>3), (mx>>2)&1, (mx&0x3)!=0)); + #endif + } + + /// Subtraction. + /// This operation is exact to rounding for all rounding modes. + /// \param x left operand + /// \param y right operand + /// \return difference of half expressions + /// \exception FE_INVALID if \a x and \a y are infinities with equal signs or signaling NaNs + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half operator-(half x, half y) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(detail::half2float(x.data_)-detail::half2float(y.data_))); + #else + return x + -y; + #endif + } + + /// Multiplication. + /// This operation is exact to rounding for all rounding modes. + /// \param x left operand + /// \param y right operand + /// \return product of half expressions + /// \exception FE_INVALID if multiplying 0 with infinity or if \a x or \a y is signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half operator*(half x, half y) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(detail::half2float(x.data_)*detail::half2float(y.data_))); + #else + int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, exp = -16; + unsigned int sign = (x.data_^y.data_) & 0x8000; + if(absx >= 0x7C00 || absy >= 0x7C00) + return half(detail::binary, (absx>0x7C00 || absy>0x7C00) ? detail::signal(x.data_, y.data_) : + ((absx==0x7C00 && !absy)||(absy==0x7C00 && !absx)) ? detail::invalid() : (sign|0x7C00)); + if(!absx || !absy) + return half(detail::binary, sign); + for(; absx<0x400; absx<<=1,--exp) ; + for(; absy<0x400; absy<<=1,--exp) ; + detail::uint32 m = static_cast((absx&0x3FF)|0x400) * static_cast((absy&0x3FF)|0x400); + int i = m >> 21, s = m & i; + exp += (absx>>10) + (absy>>10) + i; + if(exp > 29) + return half(detail::binary, detail::overflow(sign)); + else if(exp < -11) + return half(detail::binary, detail::underflow(sign)); + return half(detail::binary, detail::fixed2half(m>>i, exp, sign, s)); + #endif + } + + /// Division. + /// This operation is exact to rounding for all rounding modes. + /// \param x left operand + /// \param y right operand + /// \return quotient of half expressions + /// \exception FE_INVALID if dividing 0s or infinities with each other or if \a x or \a y is signaling NaN + /// \exception FE_DIVBYZERO if dividing finite value by 0 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half operator/(half x, half y) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(detail::half2float(x.data_)/detail::half2float(y.data_))); + #else + int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, exp = 14; + unsigned int sign = (x.data_^y.data_) & 0x8000; + if(absx >= 0x7C00 || absy >= 0x7C00) + return half(detail::binary, (absx>0x7C00 || absy>0x7C00) ? detail::signal(x.data_, y.data_) : + (absx==absy) ? detail::invalid() : (sign|((absx==0x7C00) ? 0x7C00 : 0))); + if(!absx) + return half(detail::binary, absy ? sign : detail::invalid()); + if(!absy) + return half(detail::binary, detail::pole(sign)); + for(; absx<0x400; absx<<=1,--exp) ; + for(; absy<0x400; absy<<=1,++exp) ; + detail::uint32 mx = (absx&0x3FF) | 0x400, my = (absy&0x3FF) | 0x400; + int i = mx < my; + exp += (absx>>10) - (absy>>10) - i; + if(exp > 29) + return half(detail::binary, detail::overflow(sign)); + else if(exp < -11) + return half(detail::binary, detail::underflow(sign)); + mx <<= 12 + i; + my <<= 1; + return half(detail::binary, detail::fixed2half(mx/my, exp, sign, mx%my!=0)); + #endif + } + + /// \} + /// \anchor streaming + /// \name Input and output + /// \{ + + /// Output operator. + /// This uses the built-in functionality for streaming out floating-point numbers. + /// \param out output stream to write into + /// \param arg half expression to write + /// \return reference to output stream + template std::basic_ostream& operator<<(std::basic_ostream &out, half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return out << detail::half2float(arg.data_); + #else + return out << detail::half2float(arg.data_); + #endif + } + + /// Input operator. + /// This uses the built-in functionality for streaming in floating-point numbers, specifically double precision floating + /// point numbers (unless overridden with [HALF_ARITHMETIC_TYPE](\ref HALF_ARITHMETIC_TYPE)). So the input string is first + /// rounded to double precision using the underlying platform's current floating-point rounding mode before being rounded + /// to half-precision using the library's half-precision rounding mode. + /// \param in input stream to read from + /// \param arg half to read into + /// \return reference to input stream + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + template std::basic_istream& operator>>(std::basic_istream &in, half &arg) + { + #ifdef HALF_ARITHMETIC_TYPE + detail::internal_t f; + #else + double f; + #endif + if(in >> f) + arg.data_ = detail::float2half(f); + return in; + } + + /// \} + /// \anchor basic + /// \name Basic mathematical operations + /// \{ + + /// Absolute value. + /// **See also:** Documentation for [std::fabs](https://en.cppreference.com/w/cpp/numeric/math/fabs). + /// \param arg operand + /// \return absolute value of \a arg + inline HALF_CONSTEXPR half fabs(half arg) { return half(detail::binary, arg.data_&0x7FFF); } + + /// Absolute value. + /// **See also:** Documentation for [std::abs](https://en.cppreference.com/w/cpp/numeric/math/fabs). + /// \param arg operand + /// \return absolute value of \a arg + inline HALF_CONSTEXPR half abs(half arg) { return fabs(arg); } + + /// Remainder of division. + /// **See also:** Documentation for [std::fmod](https://en.cppreference.com/w/cpp/numeric/math/fmod). + /// \param x first operand + /// \param y second operand + /// \return remainder of floating-point division. + /// \exception FE_INVALID if \a x is infinite or \a y is 0 or if \a x or \a y is signaling NaN + inline half fmod(half x, half y) + { + unsigned int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, sign = x.data_ & 0x8000; + if(absx >= 0x7C00 || absy >= 0x7C00) + return half(detail::binary, (absx>0x7C00 || absy>0x7C00) ? detail::signal(x.data_, y.data_) : + (absx==0x7C00) ? detail::invalid() : x.data_); + if(!absy) + return half(detail::binary, detail::invalid()); + if(!absx) + return x; + if(absx == absy) + return half(detail::binary, sign); + return half(detail::binary, sign|detail::mod(absx, absy)); + } + + /// Remainder of division. + /// **See also:** Documentation for [std::remainder](https://en.cppreference.com/w/cpp/numeric/math/remainder). + /// \param x first operand + /// \param y second operand + /// \return remainder of floating-point division. + /// \exception FE_INVALID if \a x is infinite or \a y is 0 or if \a x or \a y is signaling NaN + inline half remainder(half x, half y) + { + unsigned int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, sign = x.data_ & 0x8000; + if(absx >= 0x7C00 || absy >= 0x7C00) + return half(detail::binary, (absx>0x7C00 || absy>0x7C00) ? detail::signal(x.data_, y.data_) : + (absx==0x7C00) ? detail::invalid() : x.data_); + if(!absy) + return half(detail::binary, detail::invalid()); + if(absx == absy) + return half(detail::binary, sign); + return half(detail::binary, sign^detail::mod(absx, absy)); + } + + /// Remainder of division. + /// **See also:** Documentation for [std::remquo](https://en.cppreference.com/w/cpp/numeric/math/remquo). + /// \param x first operand + /// \param y second operand + /// \param quo address to store some bits of quotient at + /// \return remainder of floating-point division. + /// \exception FE_INVALID if \a x is infinite or \a y is 0 or if \a x or \a y is signaling NaN + inline half remquo(half x, half y, int *quo) + { + unsigned int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, value = x.data_ & 0x8000; + if(absx >= 0x7C00 || absy >= 0x7C00) + return half(detail::binary, (absx>0x7C00 || absy>0x7C00) ? detail::signal(x.data_, y.data_) : + (absx==0x7C00) ? detail::invalid() : (*quo = 0, x.data_)); + if(!absy) + return half(detail::binary, detail::invalid()); + bool qsign = ((value^y.data_)&0x8000) != 0; + int q = 1; + if(absx != absy) + value ^= detail::mod(absx, absy, &q); + return *quo = qsign ? -q : q, half(detail::binary, value); + } + + /// Fused multiply add. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::fma](https://en.cppreference.com/w/cpp/numeric/math/fma). + /// \param x first operand + /// \param y second operand + /// \param z third operand + /// \return ( \a x * \a y ) + \a z rounded as one operation. + /// \exception FE_INVALID according to operator*() and operator+() unless any argument is a quiet NaN and no argument is a signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding the final addition + inline half fma(half x, half y, half z) + { + #ifdef HALF_ARITHMETIC_TYPE + detail::internal_t fx = detail::half2float(x.data_), fy = detail::half2float(y.data_), fz = detail::half2float(z.data_); + #if HALF_ENABLE_CPP11_CMATH && FP_FAST_FMA + return half(detail::binary, detail::float2half(std::fma(fx, fy, fz))); + #else + return half(detail::binary, detail::float2half(fx*fy+fz)); + #endif + #else + int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, absz = z.data_ & 0x7FFF, exp = -15; + unsigned int sign = (x.data_^y.data_) & 0x8000; + bool sub = ((sign^z.data_)&0x8000) != 0; + if(absx >= 0x7C00 || absy >= 0x7C00 || absz >= 0x7C00) + return (absx>0x7C00 || absy>0x7C00 || absz>0x7C00) ? half(detail::binary, detail::signal(x.data_, y.data_, z.data_)) : + (absx==0x7C00) ? half(detail::binary, (!absy || (sub && absz==0x7C00)) ? detail::invalid() : (sign|0x7C00)) : + (absy==0x7C00) ? half(detail::binary, (!absx || (sub && absz==0x7C00)) ? detail::invalid() : (sign|0x7C00)) : z; + if(!absx || !absy) + return absz ? z : half(detail::binary, (half::round_style==std::round_toward_neg_infinity) ? (z.data_|sign) : (z.data_&sign)); + for(; absx<0x400; absx<<=1,--exp) ; + for(; absy<0x400; absy<<=1,--exp) ; + detail::uint32 m = static_cast((absx&0x3FF)|0x400) * static_cast((absy&0x3FF)|0x400); + int i = m >> 21; + exp += (absx>>10) + (absy>>10) + i; + m <<= 3 - i; + if(absz) + { + int expz = 0; + for(; absz<0x400; absz<<=1,--expz) ; + expz += absz >> 10; + detail::uint32 mz = static_cast((absz&0x3FF)|0x400) << 13; + if(expz > exp || (expz == exp && mz > m)) + { + std::swap(m, mz); + std::swap(exp, expz); + if(sub) + sign = z.data_ & 0x8000; + } + int d = exp - expz; + mz = (d<23) ? ((mz>>d)|((mz&((static_cast(1)<(half::round_style==std::round_toward_neg_infinity)<<15); + for(; m<0x800000; m<<=1,--exp) ; + } + else + { + m += mz; + i = m >> 24; + m = (m>>i) | (m&i); + exp += i; + } + } + if(exp > 30) + return half(detail::binary, detail::overflow(sign)); + else if(exp < -10) + return half(detail::binary, detail::underflow(sign)); + return half(detail::binary, detail::fixed2half(m, exp-1, sign)); + #endif + } + + /// Maximum of half expressions. + /// **See also:** Documentation for [std::fmax](https://en.cppreference.com/w/cpp/numeric/math/fmax). + /// \param x first operand + /// \param y second operand + /// \return maximum of operands, ignoring quiet NaNs + /// \exception FE_INVALID if \a x or \a y is signaling NaN + inline HALF_CONSTEXPR_NOERR half fmax(half x, half y) + { + return half(detail::binary, (!isnan(y) && (isnan(x) || (x.data_^(0x8000|(0x8000-(x.data_>>15)))) < + (y.data_^(0x8000|(0x8000-(y.data_>>15)))))) ? detail::select(y.data_, x.data_) : detail::select(x.data_, y.data_)); + } + + /// Minimum of half expressions. + /// **See also:** Documentation for [std::fmin](https://en.cppreference.com/w/cpp/numeric/math/fmin). + /// \param x first operand + /// \param y second operand + /// \return minimum of operands, ignoring quiet NaNs + /// \exception FE_INVALID if \a x or \a y is signaling NaN + inline HALF_CONSTEXPR_NOERR half fmin(half x, half y) + { + return half(detail::binary, (!isnan(y) && (isnan(x) || (x.data_^(0x8000|(0x8000-(x.data_>>15)))) > + (y.data_^(0x8000|(0x8000-(y.data_>>15)))))) ? detail::select(y.data_, x.data_) : detail::select(x.data_, y.data_)); + } + + /// Positive difference. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::fdim](https://en.cppreference.com/w/cpp/numeric/math/fdim). + /// \param x first operand + /// \param y second operand + /// \return \a x - \a y or 0 if difference negative + /// \exception FE_... according to operator-(half,half) + inline half fdim(half x, half y) + { + if(isnan(x) || isnan(y)) + return half(detail::binary, detail::signal(x.data_, y.data_)); + return (x.data_^(0x8000|(0x8000-(x.data_>>15)))) <= (y.data_^(0x8000|(0x8000-(y.data_>>15)))) ? half(detail::binary, 0) : (x-y); + } + + /// Get NaN value. + /// **See also:** Documentation for [std::nan](https://en.cppreference.com/w/cpp/numeric/math/nan). + /// \param arg string code + /// \return quiet NaN + inline half nanh(const char *arg) + { + unsigned int value = 0x7FFF; + while(*arg) + value ^= static_cast(*arg++) & 0xFF; + return half(detail::binary, value); + } + + /// \} + /// \anchor exponential + /// \name Exponential functions + /// \{ + + /// Exponential function. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::exp](https://en.cppreference.com/w/cpp/numeric/math/exp). + /// \param arg function argument + /// \return e raised to \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half exp(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::exp(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, e = (abs>>10) + (abs<=0x3FF), exp; + if(!abs) + return half(detail::binary, 0x3C00); + if(abs >= 0x7C00) + return half(detail::binary, (abs==0x7C00) ? (0x7C00&((arg.data_>>15)-1U)) : detail::signal(arg.data_)); + if(abs >= 0x4C80) + return half(detail::binary, (arg.data_&0x8000) ? detail::underflow() : detail::overflow()); + detail::uint32 m = detail::multiply64(static_cast((abs&0x3FF)+((abs>0x3FF)<<10))<<21, 0xB8AA3B29); + if(e < 14) + { + exp = 0; + m >>= 14 - e; + } + else + { + exp = m >> (45-e); + m = (m<<(e-14)) & 0x7FFFFFFF; + } + return half(detail::binary, detail::exp2_post(m, exp, (arg.data_&0x8000)!=0, 0, 26)); + #endif + } + + /// Binary exponential. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::exp2](https://en.cppreference.com/w/cpp/numeric/math/exp2). + /// \param arg function argument + /// \return 2 raised to \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half exp2(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::exp2(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, e = (abs>>10) + (abs<=0x3FF), exp = (abs&0x3FF) + ((abs>0x3FF)<<10); + if(!abs) + return half(detail::binary, 0x3C00); + if(abs >= 0x7C00) + return half(detail::binary, (abs==0x7C00) ? (0x7C00&((arg.data_>>15)-1U)) : detail::signal(arg.data_)); + if(abs >= 0x4E40) + return half(detail::binary, (arg.data_&0x8000) ? detail::underflow() : detail::overflow()); + return half(detail::binary, detail::exp2_post( + (static_cast(exp)<<(6+e))&0x7FFFFFFF, exp>>(25-e), (arg.data_&0x8000)!=0, 0, 28)); + #endif + } + + /// Exponential minus one. + /// This function may be 1 ULP off the correctly rounded exact result in <0.05% of inputs for `std::round_to_nearest` + /// and in <1% of inputs for any other rounding mode. + /// + /// **See also:** Documentation for [std::expm1](https://en.cppreference.com/w/cpp/numeric/math/expm1). + /// \param arg function argument + /// \return e raised to \a arg and subtracted by 1 + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half expm1(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::expm1(detail::half2float(arg.data_)))); + #else + unsigned int abs = arg.data_ & 0x7FFF, sign = arg.data_ & 0x8000, e = (abs>>10) + (abs<=0x3FF), exp; + if(!abs) + return arg; + if(abs >= 0x7C00) + return half(detail::binary, (abs==0x7C00) ? (0x7C00+(sign>>1)) : detail::signal(arg.data_)); + if(abs >= 0x4A00) + return half(detail::binary, (arg.data_&0x8000) ? detail::rounded(0xBBFF, 1, 1) : detail::overflow()); + detail::uint32 m = detail::multiply64(static_cast((abs&0x3FF)+((abs>0x3FF)<<10))<<21, 0xB8AA3B29); + if(e < 14) + { + exp = 0; + m >>= 14 - e; + } + else + { + exp = m >> (45-e); + m = (m<<(e-14)) & 0x7FFFFFFF; + } + m = detail::exp2(m); + if(sign) + { + int s = 0; + if(m > 0x80000000) + { + ++exp; + m = detail::divide64(0x80000000, m, s); + } + m = 0x80000000 - ((m>>exp)|((m&((static_cast(1)<>exp) : 1; + for(exp+=14; m<0x80000000 && exp; m<<=1,--exp) ; + if(exp > 29) + return half(detail::binary, detail::overflow()); + return half(detail::binary, detail::rounded(sign+(exp<<10)+(m>>21), (m>>20)&1, (m&0xFFFFF)!=0)); + #endif + } + + /// Natural logarithm. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::log](https://en.cppreference.com/w/cpp/numeric/math/log). + /// \param arg function argument + /// \return logarithm of \a arg to base e + /// \exception FE_INVALID for signaling NaN or negative argument + /// \exception FE_DIVBYZERO for 0 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half log(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::log(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp = -15; + if(!abs) + return half(detail::binary, detail::pole(0x8000)); + if(arg.data_ & 0x8000) + return half(detail::binary, (arg.data_<=0xFC00) ? detail::invalid() : detail::signal(arg.data_)); + if(abs >= 0x7C00) + return (abs==0x7C00) ? arg : half(detail::binary, detail::signal(arg.data_)); + for(; abs<0x400; abs<<=1,--exp) ; + exp += abs >> 10; + return half(detail::binary, detail::log2_post( + detail::log2(static_cast((abs&0x3FF)|0x400)<<20, 27)+8, exp, 17)); + #endif + } + + /// Common logarithm. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::log10](https://en.cppreference.com/w/cpp/numeric/math/log10). + /// \param arg function argument + /// \return logarithm of \a arg to base 10 + /// \exception FE_INVALID for signaling NaN or negative argument + /// \exception FE_DIVBYZERO for 0 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half log10(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::log10(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp = -15; + if(!abs) + return half(detail::binary, detail::pole(0x8000)); + if(arg.data_ & 0x8000) + return half(detail::binary, (arg.data_<=0xFC00) ? detail::invalid() : detail::signal(arg.data_)); + if(abs >= 0x7C00) + return (abs==0x7C00) ? arg : half(detail::binary, detail::signal(arg.data_)); + switch(abs) + { + case 0x4900: return half(detail::binary, 0x3C00); + case 0x5640: return half(detail::binary, 0x4000); + case 0x63D0: return half(detail::binary, 0x4200); + case 0x70E2: return half(detail::binary, 0x4400); + } + for(; abs<0x400; abs<<=1,--exp) ; + exp += abs >> 10; + return half(detail::binary, detail::log2_post( + detail::log2(static_cast((abs&0x3FF)|0x400)<<20, 27)+8, exp, 16)); + #endif + } + + /// Binary logarithm. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::log2](https://en.cppreference.com/w/cpp/numeric/math/log2). + /// \param arg function argument + /// \return logarithm of \a arg to base 2 + /// \exception FE_INVALID for signaling NaN or negative argument + /// \exception FE_DIVBYZERO for 0 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half log2(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::log2(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp = -15, s = 0; + if(!abs) + return half(detail::binary, detail::pole(0x8000)); + if(arg.data_ & 0x8000) + return half(detail::binary, (arg.data_<=0xFC00) ? detail::invalid() : detail::signal(arg.data_)); + if(abs >= 0x7C00) + return (abs==0x7C00) ? arg : half(detail::binary, detail::signal(arg.data_)); + if(abs == 0x3C00) + return half(detail::binary, 0); + for(; abs<0x400; abs<<=1,--exp) ; + exp += (abs>>10); + if(!(abs&0x3FF)) + { + unsigned int value = static_cast(exp<0) << 15, m = std::abs(exp) << 6; + for(exp=18; m<0x400; m<<=1,--exp) ; + return half(detail::binary, value+(exp<<10)+m); + } + detail::uint32 ilog = exp, sign = detail::sign_mask(ilog), m = + (((ilog<<27)+(detail::log2(static_cast((abs&0x3FF)|0x400)<<20, 28)>>4))^sign) - sign; + if(!m) + return half(detail::binary, 0); + for(exp=14; m<0x8000000 && exp; m<<=1,--exp) ; + for(; m>0xFFFFFFF; m>>=1,++exp) + s |= m & 1; + return half(detail::binary, detail::fixed2half(m, exp, sign&0x8000, s)); + #endif + } + + /// Natural logarithm plus one. + /// This function may be 1 ULP off the correctly rounded exact result in <0.05% of inputs for `std::round_to_nearest` + /// and in ~1% of inputs for any other rounding mode. + /// + /// **See also:** Documentation for [std::log1p](https://en.cppreference.com/w/cpp/numeric/math/log1p). + /// \param arg function argument + /// \return logarithm of \a arg plus 1 to base e + /// \exception FE_INVALID for signaling NaN or argument <-1 + /// \exception FE_DIVBYZERO for -1 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half log1p(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::log1p(detail::half2float(arg.data_)))); + #else + if(arg.data_ >= 0xBC00) + return half(detail::binary, (arg.data_==0xBC00) ? detail::pole(0x8000) : (arg.data_<=0xFC00) ? detail::invalid() : detail::signal(arg.data_)); + int abs = arg.data_ & 0x7FFF, exp = -15; + if(!abs || abs >= 0x7C00) + return (abs>0x7C00) ? half(detail::binary, detail::signal(arg.data_)) : arg; + for(; abs<0x400; abs<<=1,--exp) ; + exp += abs >> 10; + detail::uint32 m = static_cast((abs&0x3FF)|0x400) << 20; + if(arg.data_ & 0x8000) + { + m = 0x40000000 - (m>>-exp); + for(exp=0; m<0x40000000; m<<=1,--exp) ; + } + else + { + if(exp < 0) + { + m = 0x40000000 + (m>>-exp); + exp = 0; + } + else + { + m += 0x40000000 >> exp; + int i = m >> 31; + m >>= i; + exp += i; + } + } + return half(detail::binary, detail::log2_post(detail::log2(m), exp, 17)); + #endif + } + + /// \} + /// \anchor power + /// \name Power functions + /// \{ + + /// Square root. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::sqrt](https://en.cppreference.com/w/cpp/numeric/math/sqrt). + /// \param arg function argument + /// \return square root of \a arg + /// \exception FE_INVALID for signaling NaN and negative arguments + /// \exception FE_INEXACT according to rounding + inline half sqrt(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::sqrt(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp = 15; + if(!abs || arg.data_ >= 0x7C00) + return half(detail::binary, (abs>0x7C00) ? detail::signal(arg.data_) : (arg.data_>0x8000) ? detail::invalid() : arg.data_); + for(; abs<0x400; abs<<=1,--exp) ; + detail::uint32 r = static_cast((abs&0x3FF)|0x400) << 10, m = detail::sqrt<20>(r, exp+=abs>>10); + return half(detail::binary, detail::rounded((exp<<10)+(m&0x3FF), r>m, r!=0)); + #endif + } + + /// Inverse square root. + /// This function is exact to rounding for all rounding modes and thus generally more accurate than directly computing + /// 1 / sqrt(\a arg) in half-precision, in addition to also being faster. + /// \param arg function argument + /// \return reciprocal of square root of \a arg + /// \exception FE_INVALID for signaling NaN and negative arguments + /// \exception FE_INEXACT according to rounding + inline half rsqrt(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(detail::internal_t(1)/std::sqrt(detail::half2float(arg.data_)))); + #else + unsigned int abs = arg.data_ & 0x7FFF, bias = 0x4000; + if(!abs || arg.data_ >= 0x7C00) + return half(detail::binary, (abs>0x7C00) ? detail::signal(arg.data_) : (arg.data_>0x8000) ? + detail::invalid() : !abs ? detail::pole(arg.data_&0x8000) : 0); + for(; abs<0x400; abs<<=1,bias-=0x400) ; + unsigned int frac = (abs+=bias) & 0x7FF; + if(frac == 0x400) + return half(detail::binary, 0x7A00-(abs>>1)); + if((half::round_style == std::round_to_nearest && (frac == 0x3FE || frac == 0x76C)) || + (half::round_style != std::round_to_nearest && (frac == 0x15A || frac == 0x3FC || frac == 0x401 || frac == 0x402 || frac == 0x67B))) + return pow(arg, half(detail::binary, 0xB800)); + detail::uint32 f = 0x17376 - abs, mx = (abs&0x3FF) | 0x400, my = ((f>>1)&0x3FF) | 0x400, mz = my * my; + int expy = (f>>11) - 31, expx = 32 - (abs>>10), i = mz >> 21; + for(mz=0x60000000-(((mz>>i)*mx)>>(expx-2*expy-i)); mz<0x40000000; mz<<=1,--expy) ; + i = (my*=mz>>10) >> 31; + expy += i; + my = (my>>(20+i)) + 1; + i = (mz=my*my) >> 21; + for(mz=0x60000000-(((mz>>i)*mx)>>(expx-2*expy-i)); mz<0x40000000; mz<<=1,--expy) ; + i = (my*=(mz>>10)+1) >> 31; + return half(detail::binary, detail::fixed2half(my>>i, expy+i+14)); + #endif + } + + /// Cubic root. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::cbrt](https://en.cppreference.com/w/cpp/numeric/math/cbrt). + /// \param arg function argument + /// \return cubic root of \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_INEXACT according to rounding + inline half cbrt(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::cbrt(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp = -15; + if(!abs || abs == 0x3C00 || abs >= 0x7C00) + return (abs>0x7C00) ? half(detail::binary, detail::signal(arg.data_)) : arg; + for(; abs<0x400; abs<<=1, --exp); + detail::uint32 ilog = exp + (abs>>10), sign = detail::sign_mask(ilog), f, m = + (((ilog<<27)+(detail::log2(static_cast((abs&0x3FF)|0x400)<<20, 24)>>4))^sign) - sign; + for(exp=2; m<0x80000000; m<<=1,--exp) ; + m = detail::multiply64(m, 0xAAAAAAAB); + int i = m >> 31, s; + exp += i; + m <<= 1 - i; + if(exp < 0) + { + f = m >> -exp; + exp = 0; + } + else + { + f = (m<> (31-exp); + } + m = detail::exp2(f, (half::round_style==std::round_to_nearest) ? 29 : 26); + if(sign) + { + if(m > 0x80000000) + { + m = detail::divide64(0x80000000, m, s); + ++exp; + } + exp = -exp; + } + return half(detail::binary, (half::round_style==std::round_to_nearest) ? + detail::fixed2half(m, exp+14, arg.data_&0x8000) : + detail::fixed2half((m+0x80)>>8, exp+14, arg.data_&0x8000)); + #endif + } + + /// Hypotenuse function. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::hypot](https://en.cppreference.com/w/cpp/numeric/math/hypot). + /// \param x first argument + /// \param y second argument + /// \return square root of sum of squares without internal over- or underflows + /// \exception FE_INVALID if \a x or \a y is signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding of the final square root + inline half hypot(half x, half y) + { + #ifdef HALF_ARITHMETIC_TYPE + detail::internal_t fx = detail::half2float(x.data_), fy = detail::half2float(y.data_); + #if HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::hypot(fx, fy))); + #else + return half(detail::binary, detail::float2half(std::sqrt(fx*fx+fy*fy))); + #endif + #else + int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, expx = 0, expy = 0; + if(absx >= 0x7C00 || absy >= 0x7C00) + return half(detail::binary, (absx==0x7C00) ? detail::select(0x7C00, y.data_) : + (absy==0x7C00) ? detail::select(0x7C00, x.data_) : detail::signal(x.data_, y.data_)); + if(!absx) + return half(detail::binary, absy ? detail::check_underflow(absy) : 0); + if(!absy) + return half(detail::binary, detail::check_underflow(absx)); + if(absy > absx) + std::swap(absx, absy); + for(; absx<0x400; absx<<=1,--expx) ; + for(; absy<0x400; absy<<=1,--expy) ; + detail::uint32 mx = (absx&0x3FF) | 0x400, my = (absy&0x3FF) | 0x400; + mx *= mx; + my *= my; + int ix = mx >> 21, iy = my >> 21; + expx = 2*(expx+(absx>>10)) - 15 + ix; + expy = 2*(expy+(absy>>10)) - 15 + iy; + mx <<= 10 - ix; + my <<= 10 - iy; + int d = expx - expy; + my = (d<30) ? ((my>>d)|((my&((static_cast(1)<(mx+my, expx)); + #endif + } + + /// Hypotenuse function. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::hypot](https://en.cppreference.com/w/cpp/numeric/math/hypot). + /// \param x first argument + /// \param y second argument + /// \param z third argument + /// \return square root of sum of squares without internal over- or underflows + /// \exception FE_INVALID if \a x, \a y or \a z is signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding of the final square root + inline half hypot(half x, half y, half z) + { + #ifdef HALF_ARITHMETIC_TYPE + detail::internal_t fx = detail::half2float(x.data_), fy = detail::half2float(y.data_), fz = detail::half2float(z.data_); + return half(detail::binary, detail::float2half(std::sqrt(fx*fx+fy*fy+fz*fz))); + #else + int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, absz = z.data_ & 0x7FFF, expx = 0, expy = 0, expz = 0; + if(!absx) + return hypot(y, z); + if(!absy) + return hypot(x, z); + if(!absz) + return hypot(x, y); + if(absx >= 0x7C00 || absy >= 0x7C00 || absz >= 0x7C00) + return half(detail::binary, (absx==0x7C00) ? detail::select(0x7C00, detail::select(y.data_, z.data_)) : + (absy==0x7C00) ? detail::select(0x7C00, detail::select(x.data_, z.data_)) : + (absz==0x7C00) ? detail::select(0x7C00, detail::select(x.data_, y.data_)) : + detail::signal(x.data_, y.data_, z.data_)); + if(absz > absy) + std::swap(absy, absz); + if(absy > absx) + std::swap(absx, absy); + if(absz > absy) + std::swap(absy, absz); + for(; absx<0x400; absx<<=1,--expx) ; + for(; absy<0x400; absy<<=1,--expy) ; + for(; absz<0x400; absz<<=1,--expz) ; + detail::uint32 mx = (absx&0x3FF) | 0x400, my = (absy&0x3FF) | 0x400, mz = (absz&0x3FF) | 0x400; + mx *= mx; + my *= my; + mz *= mz; + int ix = mx >> 21, iy = my >> 21, iz = mz >> 21; + expx = 2*(expx+(absx>>10)) - 15 + ix; + expy = 2*(expy+(absy>>10)) - 15 + iy; + expz = 2*(expz+(absz>>10)) - 15 + iz; + mx <<= 10 - ix; + my <<= 10 - iy; + mz <<= 10 - iz; + int d = expy - expz; + mz = (d<30) ? ((mz>>d)|((mz&((static_cast(1)<>1) | (my&1); + if(++expy > expx) + { + std::swap(mx, my); + std::swap(expx, expy); + } + } + d = expx - expy; + my = (d<30) ? ((my>>d)|((my&((static_cast(1)<(mx+my, expx)); + #endif + } + + /// Power function. + /// This function may be 1 ULP off the correctly rounded exact result for any rounding mode in ~0.00025% of inputs. + /// + /// **See also:** Documentation for [std::pow](https://en.cppreference.com/w/cpp/numeric/math/pow). + /// \param x base + /// \param y exponent + /// \return \a x raised to \a y + /// \exception FE_INVALID if \a x or \a y is signaling NaN or if \a x is finite an negative and \a y is finite and not integral + /// \exception FE_DIVBYZERO if \a x is 0 and \a y is negative + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half pow(half x, half y) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::pow(detail::half2float(x.data_), detail::half2float(y.data_)))); + #else + int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, exp = -15; + if(!absy || x.data_ == 0x3C00) + return half(detail::binary, detail::select(0x3C00, (x.data_==0x3C00) ? y.data_ : x.data_)); + bool is_int = absy >= 0x6400 || (absy>=0x3C00 && !(absy&((1<<(25-(absy>>10)))-1))); + unsigned int sign = x.data_ & (static_cast((absy<0x6800)&&is_int&&((absy>>(25-(absy>>10)))&1))<<15); + if(absx >= 0x7C00 || absy >= 0x7C00) + return half(detail::binary, (absx>0x7C00 || absy>0x7C00) ? detail::signal(x.data_, y.data_) : + (absy==0x7C00) ? ((absx==0x3C00) ? 0x3C00 : (!absx && y.data_==0xFC00) ? detail::pole() : + (0x7C00&-((y.data_>>15)^(absx>0x3C00)))) : (sign|(0x7C00&((y.data_>>15)-1U)))); + if(!absx) + return half(detail::binary, (y.data_&0x8000) ? detail::pole(sign) : sign); + if((x.data_&0x8000) && !is_int) + return half(detail::binary, detail::invalid()); + if(x.data_ == 0xBC00) + return half(detail::binary, sign|0x3C00); + switch(y.data_) + { + case 0x3800: return sqrt(x); + case 0x3C00: return half(detail::binary, detail::check_underflow(x.data_)); + case 0x4000: return x * x; + case 0xBC00: return half(detail::binary, 0x3C00) / x; + } + for(; absx<0x400; absx<<=1,--exp) ; + detail::uint32 ilog = exp + (absx>>10), msign = detail::sign_mask(ilog), f, m = + (((ilog<<27)+((detail::log2(static_cast((absx&0x3FF)|0x400)<<20)+8)>>4))^msign) - msign; + for(exp=-11; m<0x80000000; m<<=1,--exp) ; + for(; absy<0x400; absy<<=1,--exp) ; + m = detail::multiply64(m, static_cast((absy&0x3FF)|0x400)<<21); + int i = m >> 31; + exp += (absy>>10) + i; + m <<= 1 - i; + if(exp < 0) + { + f = m >> -exp; + exp = 0; + } + else + { + f = (m<> (31-exp); + } + return half(detail::binary, detail::exp2_post(f, exp, ((msign&1)^(y.data_>>15))!=0, sign)); + #endif + } + + /// \} + /// \anchor trigonometric + /// \name Trigonometric functions + /// \{ + + /// Compute sine and cosine simultaneously. + /// This returns the same results as sin() and cos() but is faster than calling each function individually. + /// + /// This function is exact to rounding for all rounding modes. + /// \param arg function argument + /// \param sin variable to take sine of \a arg + /// \param cos variable to take cosine of \a arg + /// \exception FE_INVALID for signaling NaN or infinity + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline void sincos(half arg, half *sin, half *cos) + { + #ifdef HALF_ARITHMETIC_TYPE + detail::internal_t f = detail::half2float(arg.data_); + *sin = half(detail::binary, detail::float2half(std::sin(f))); + *cos = half(detail::binary, detail::float2half(std::cos(f))); + #else + int abs = arg.data_ & 0x7FFF, sign = arg.data_ >> 15, k; + if(abs >= 0x7C00) + *sin = *cos = half(detail::binary, (abs==0x7C00) ? detail::invalid() : detail::signal(arg.data_)); + else if(!abs) + { + *sin = arg; + *cos = half(detail::binary, 0x3C00); + } + else if(abs < 0x2500) + { + *sin = half(detail::binary, detail::rounded(arg.data_-1, 1, 1)); + *cos = half(detail::binary, detail::rounded(0x3BFF, 1, 1)); + } + else + { + if(half::round_style != std::round_to_nearest) + { + switch(abs) + { + case 0x48B7: + *sin = half(detail::binary, detail::rounded((~arg.data_&0x8000)|0x1D07, 1, 1)); + *cos = half(detail::binary, detail::rounded(0xBBFF, 1, 1)); + return; + case 0x598C: + *sin = half(detail::binary, detail::rounded((arg.data_&0x8000)|0x3BFF, 1, 1)); + *cos = half(detail::binary, detail::rounded(0x80FC, 1, 1)); + return; + case 0x6A64: + *sin = half(detail::binary, detail::rounded((~arg.data_&0x8000)|0x3BFE, 1, 1)); + *cos = half(detail::binary, detail::rounded(0x27FF, 1, 1)); + return; + case 0x6D8C: + *sin = half(detail::binary, detail::rounded((arg.data_&0x8000)|0x0FE6, 1, 1)); + *cos = half(detail::binary, detail::rounded(0x3BFF, 1, 1)); + return; + } + } + std::pair sc = detail::sincos(detail::angle_arg(abs, k), 28); + switch(k & 3) + { + case 1: sc = std::make_pair(sc.second, -sc.first); break; + case 2: sc = std::make_pair(-sc.first, -sc.second); break; + case 3: sc = std::make_pair(-sc.second, sc.first); break; + } + *sin = half(detail::binary, detail::fixed2half((sc.first^-static_cast(sign))+sign)); + *cos = half(detail::binary, detail::fixed2half(sc.second)); + } + #endif + } + + /// Sine function. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::sin](https://en.cppreference.com/w/cpp/numeric/math/sin). + /// \param arg function argument + /// \return sine value of \a arg + /// \exception FE_INVALID for signaling NaN or infinity + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half sin(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::sin(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, k; + if(!abs) + return arg; + if(abs >= 0x7C00) + return half(detail::binary, (abs==0x7C00) ? detail::invalid() : detail::signal(arg.data_)); + if(abs < 0x2900) + return half(detail::binary, detail::rounded(arg.data_-1, 1, 1)); + if(half::round_style != std::round_to_nearest) + switch(abs) + { + case 0x48B7: return half(detail::binary, detail::rounded((~arg.data_&0x8000)|0x1D07, 1, 1)); + case 0x6A64: return half(detail::binary, detail::rounded((~arg.data_&0x8000)|0x3BFE, 1, 1)); + case 0x6D8C: return half(detail::binary, detail::rounded((arg.data_&0x8000)|0x0FE6, 1, 1)); + } + std::pair sc = detail::sincos(detail::angle_arg(abs, k), 28); + detail::uint32 sign = -static_cast(((k>>1)&1)^(arg.data_>>15)); + return half(detail::binary, detail::fixed2half((((k&1) ? sc.second : sc.first)^sign) - sign)); + #endif + } + + /// Cosine function. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::cos](https://en.cppreference.com/w/cpp/numeric/math/cos). + /// \param arg function argument + /// \return cosine value of \a arg + /// \exception FE_INVALID for signaling NaN or infinity + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half cos(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::cos(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, k; + if(!abs) + return half(detail::binary, 0x3C00); + if(abs >= 0x7C00) + return half(detail::binary, (abs==0x7C00) ? detail::invalid() : detail::signal(arg.data_)); + if(abs < 0x2500) + return half(detail::binary, detail::rounded(0x3BFF, 1, 1)); + if(half::round_style != std::round_to_nearest && abs == 0x598C) + return half(detail::binary, detail::rounded(0x80FC, 1, 1)); + std::pair sc = detail::sincos(detail::angle_arg(abs, k), 28); + detail::uint32 sign = -static_cast(((k>>1)^k)&1); + return half(detail::binary, detail::fixed2half((((k&1) ? sc.first : sc.second)^sign) - sign)); + #endif + } + + /// Tangent function. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::tan](https://en.cppreference.com/w/cpp/numeric/math/tan). + /// \param arg function argument + /// \return tangent value of \a arg + /// \exception FE_INVALID for signaling NaN or infinity + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half tan(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::tan(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp = 13, k; + if(!abs) + return arg; + if(abs >= 0x7C00) + return half(detail::binary, (abs==0x7C00) ? detail::invalid() : detail::signal(arg.data_)); + if(abs < 0x2700) + return half(detail::binary, detail::rounded(arg.data_, 0, 1)); + if(half::round_style != std::round_to_nearest) + switch(abs) + { + case 0x658C: return half(detail::binary, detail::rounded((arg.data_&0x8000)|0x07E6, 1, 1)); + case 0x7330: return half(detail::binary, detail::rounded((~arg.data_&0x8000)|0x4B62, 1, 1)); + } + std::pair sc = detail::sincos(detail::angle_arg(abs, k), 30); + if(k & 1) + sc = std::make_pair(-sc.second, sc.first); + detail::uint32 signy = detail::sign_mask(sc.first), signx = detail::sign_mask(sc.second); + detail::uint32 my = (sc.first^signy) - signy, mx = (sc.second^signx) - signx; + for(; my<0x80000000; my<<=1,--exp) ; + for(; mx<0x80000000; mx<<=1,++exp) ; + return half(detail::binary, detail::tangent_post(my, mx, exp, (signy^signx^arg.data_)&0x8000)); + #endif + } + + /// Arc sine. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::asin](https://en.cppreference.com/w/cpp/numeric/math/asin). + /// \param arg function argument + /// \return arc sine value of \a arg + /// \exception FE_INVALID for signaling NaN or if abs(\a arg) > 1 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half asin(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::asin(detail::half2float(arg.data_)))); + #else + unsigned int abs = arg.data_ & 0x7FFF, sign = arg.data_ & 0x8000; + if(!abs) + return arg; + if(abs >= 0x3C00) + return half(detail::binary, (abs>0x7C00) ? detail::signal(arg.data_) : (abs>0x3C00) ? detail::invalid() : + detail::rounded(sign|0x3E48, 0, 1)); + if(abs < 0x2900) + return half(detail::binary, detail::rounded(arg.data_, 0, 1)); + if(half::round_style != std::round_to_nearest && (abs == 0x2B44 || abs == 0x2DC3)) + return half(detail::binary, detail::rounded(arg.data_+1, 1, 1)); + std::pair sc = detail::atan2_args(abs); + detail::uint32 m = detail::atan2(sc.first, sc.second, (half::round_style==std::round_to_nearest) ? 27 : 26); + return half(detail::binary, detail::fixed2half(m, 14, sign)); + #endif + } + + /// Arc cosine function. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::acos](https://en.cppreference.com/w/cpp/numeric/math/acos). + /// \param arg function argument + /// \return arc cosine value of \a arg + /// \exception FE_INVALID for signaling NaN or if abs(\a arg) > 1 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half acos(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::acos(detail::half2float(arg.data_)))); + #else + unsigned int abs = arg.data_ & 0x7FFF, sign = arg.data_ >> 15; + if(!abs) + return half(detail::binary, detail::rounded(0x3E48, 0, 1)); + if(abs >= 0x3C00) + return half(detail::binary, (abs>0x7C00) ? detail::signal(arg.data_) : (abs>0x3C00) ? detail::invalid() : + sign ? detail::rounded(0x4248, 0, 1) : 0); + std::pair cs = detail::atan2_args(abs); + detail::uint32 m = detail::atan2(cs.second, cs.first, 28); + return half(detail::binary, detail::fixed2half(sign ? (0xC90FDAA2-m) : m, 15, 0, sign)); + #endif + } + + /// Arc tangent function. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::atan](https://en.cppreference.com/w/cpp/numeric/math/atan). + /// \param arg function argument + /// \return arc tangent value of \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half atan(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::atan(detail::half2float(arg.data_)))); + #else + unsigned int abs = arg.data_ & 0x7FFF, sign = arg.data_ & 0x8000; + if(!abs) + return arg; + if(abs >= 0x7C00) + return half(detail::binary, (abs==0x7C00) ? detail::rounded(sign|0x3E48, 0, 1) : detail::signal(arg.data_)); + if(abs <= 0x2700) + return half(detail::binary, detail::rounded(arg.data_-1, 1, 1)); + int exp = (abs>>10) + (abs<=0x3FF); + detail::uint32 my = (abs&0x3FF) | ((abs>0x3FF)<<10); + detail::uint32 m = (exp>15) ? detail::atan2(my<<19, 0x20000000>>(exp-15), (half::round_style==std::round_to_nearest) ? 26 : 24) : + detail::atan2(my<<(exp+4), 0x20000000, (half::round_style==std::round_to_nearest) ? 30 : 28); + return half(detail::binary, detail::fixed2half(m, 14, sign)); + #endif + } + + /// Arc tangent function. + /// This function may be 1 ULP off the correctly rounded exact result in ~0.005% of inputs for `std::round_to_nearest`, + /// in ~0.1% of inputs for `std::round_toward_zero` and in ~0.02% of inputs for any other rounding mode. + /// + /// **See also:** Documentation for [std::atan2](https://en.cppreference.com/w/cpp/numeric/math/atan2). + /// \param y numerator + /// \param x denominator + /// \return arc tangent value + /// \exception FE_INVALID if \a x or \a y is signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half atan2(half y, half x) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::atan2(detail::half2float(y.data_), detail::half2float(x.data_)))); + #else + unsigned int absx = x.data_ & 0x7FFF, absy = y.data_ & 0x7FFF, signx = x.data_ >> 15, signy = y.data_ & 0x8000; + if(absx >= 0x7C00 || absy >= 0x7C00) + { + if(absx > 0x7C00 || absy > 0x7C00) + return half(detail::binary, detail::signal(x.data_, y.data_)); + if(absy == 0x7C00) + return half(detail::binary, (absx<0x7C00) ? detail::rounded(signy|0x3E48, 0, 1) : + signx ? detail::rounded(signy|0x40B6, 0, 1) : + detail::rounded(signy|0x3A48, 0, 1)); + return (x.data_==0x7C00) ? half(detail::binary, signy) : half(detail::binary, detail::rounded(signy|0x4248, 0, 1)); + } + if(!absy) + return signx ? half(detail::binary, detail::rounded(signy|0x4248, 0, 1)) : y; + if(!absx) + return half(detail::binary, detail::rounded(signy|0x3E48, 0, 1)); + int d = (absy>>10) + (absy<=0x3FF) - (absx>>10) - (absx<=0x3FF); + if(d > (signx ? 18 : 12)) + return half(detail::binary, detail::rounded(signy|0x3E48, 0, 1)); + if(signx && d < -11) + return half(detail::binary, detail::rounded(signy|0x4248, 0, 1)); + if(!signx && d < ((half::round_style==std::round_toward_zero) ? -15 : -9)) + { + for(; absy<0x400; absy<<=1,--d) ; + detail::uint32 mx = ((absx<<1)&0x7FF) | 0x800, my = ((absy<<1)&0x7FF) | 0x800; + int i = my < mx; + d -= i; + if(d < -25) + return half(detail::binary, detail::underflow(signy)); + my <<= 11 + i; + return half(detail::binary, detail::fixed2half(my/mx, d+14, signy, my%mx!=0)); + } + detail::uint32 m = detail::atan2( ((absy&0x3FF)|((absy>0x3FF)<<10))<<(19+((d<0) ? d : (d>0) ? 0 : -1)), + ((absx&0x3FF)|((absx>0x3FF)<<10))<<(19-((d>0) ? d : (d<0) ? 0 : 1))); + return half(detail::binary, detail::fixed2half(signx ? (0xC90FDAA2-m) : m, 15, signy, signx)); + #endif + } + + /// \} + /// \anchor hyperbolic + /// \name Hyperbolic functions + /// \{ + + /// Hyperbolic sine. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::sinh](https://en.cppreference.com/w/cpp/numeric/math/sinh). + /// \param arg function argument + /// \return hyperbolic sine value of \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half sinh(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::sinh(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp; + if(!abs || abs >= 0x7C00) + return (abs>0x7C00) ? half(detail::binary, detail::signal(arg.data_)) : arg; + if(abs <= 0x2900) + return half(detail::binary, detail::rounded(arg.data_, 0, 1)); + std::pair mm = detail::hyperbolic_args(abs, exp, (half::round_style==std::round_to_nearest) ? 29 : 27); + detail::uint32 m = mm.first - mm.second; + for(exp+=13; m<0x80000000 && exp; m<<=1,--exp) ; + unsigned int sign = arg.data_ & 0x8000; + if(exp > 29) + return half(detail::binary, detail::overflow(sign)); + return half(detail::binary, detail::fixed2half(m, exp, sign)); + #endif + } + + /// Hyperbolic cosine. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::cosh](https://en.cppreference.com/w/cpp/numeric/math/cosh). + /// \param arg function argument + /// \return hyperbolic cosine value of \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half cosh(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::cosh(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp; + if(!abs) + return half(detail::binary, 0x3C00); + if(abs >= 0x7C00) + return half(detail::binary, (abs>0x7C00) ? detail::signal(arg.data_) : 0x7C00); + std::pair mm = detail::hyperbolic_args(abs, exp, (half::round_style==std::round_to_nearest) ? 23 : 26); + detail::uint32 m = mm.first + mm.second, i = (~m&0xFFFFFFFF) >> 31; + m = (m>>i) | (m&i) | 0x80000000; + if((exp+=13+i) > 29) + return half(detail::binary, detail::overflow()); + return half(detail::binary, detail::fixed2half(m, exp)); + #endif + } + + /// Hyperbolic tangent. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::tanh](https://en.cppreference.com/w/cpp/numeric/math/tanh). + /// \param arg function argument + /// \return hyperbolic tangent value of \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half tanh(half arg) + { + #ifdef HALF_ARITHMETIC_TYPE + return half(detail::binary, detail::float2half(std::tanh(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp; + if(!abs) + return arg; + if(abs >= 0x7C00) + return half(detail::binary, (abs>0x7C00) ? detail::signal(arg.data_) : (arg.data_-0x4000)); + if(abs >= 0x4500) + return half(detail::binary, detail::rounded((arg.data_&0x8000)|0x3BFF, 1, 1)); + if(abs < 0x2700) + return half(detail::binary, detail::rounded(arg.data_-1, 1, 1)); + if(half::round_style != std::round_to_nearest && abs == 0x2D3F) + return half(detail::binary, detail::rounded(arg.data_-3, 0, 1)); + std::pair mm = detail::hyperbolic_args(abs, exp, 27); + detail::uint32 my = mm.first - mm.second - (half::round_style!=std::round_to_nearest), mx = mm.first + mm.second, i = (~mx&0xFFFFFFFF) >> 31; + for(exp=13; my<0x80000000; my<<=1,--exp) ; + mx = (mx>>i) | 0x80000000; + return half(detail::binary, detail::tangent_post(my, mx, exp-i, arg.data_&0x8000)); + #endif + } + + /// Hyperbolic area sine. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::asinh](https://en.cppreference.com/w/cpp/numeric/math/asinh). + /// \param arg function argument + /// \return area sine value of \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half asinh(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::asinh(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF; + if(!abs || abs >= 0x7C00) + return (abs>0x7C00) ? half(detail::binary, detail::signal(arg.data_)) : arg; + if(abs <= 0x2900) + return half(detail::binary, detail::rounded(arg.data_-1, 1, 1)); + if(half::round_style != std::round_to_nearest) + switch(abs) + { + case 0x32D4: return half(detail::binary, detail::rounded(arg.data_-13, 1, 1)); + case 0x3B5B: return half(detail::binary, detail::rounded(arg.data_-197, 1, 1)); + } + return half(detail::binary, detail::area(arg.data_)); + #endif + } + + /// Hyperbolic area cosine. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::acosh](https://en.cppreference.com/w/cpp/numeric/math/acosh). + /// \param arg function argument + /// \return area cosine value of \a arg + /// \exception FE_INVALID for signaling NaN or arguments <1 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half acosh(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::acosh(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF; + if((arg.data_&0x8000) || abs < 0x3C00) + return half(detail::binary, (abs<=0x7C00) ? detail::invalid() : detail::signal(arg.data_)); + if(abs == 0x3C00) + return half(detail::binary, 0); + if(arg.data_ >= 0x7C00) + return (abs>0x7C00) ? half(detail::binary, detail::signal(arg.data_)) : arg; + return half(detail::binary, detail::area(arg.data_)); + #endif + } + + /// Hyperbolic area tangent. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::atanh](https://en.cppreference.com/w/cpp/numeric/math/atanh). + /// \param arg function argument + /// \return area tangent value of \a arg + /// \exception FE_INVALID for signaling NaN or if abs(\a arg) > 1 + /// \exception FE_DIVBYZERO for +/-1 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half atanh(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::atanh(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF, exp = 0; + if(!abs) + return arg; + if(abs >= 0x3C00) + return half(detail::binary, (abs==0x3C00) ? detail::pole(arg.data_&0x8000) : (abs<=0x7C00) ? detail::invalid() : detail::signal(arg.data_)); + if(abs < 0x2700) + return half(detail::binary, detail::rounded(arg.data_, 0, 1)); + detail::uint32 m = static_cast((abs&0x3FF)|((abs>0x3FF)<<10)) << ((abs>>10)+(abs<=0x3FF)+6), my = 0x80000000 + m, mx = 0x80000000 - m; + for(; mx<0x80000000; mx<<=1,++exp) ; + int i = my >= mx, s; + return half(detail::binary, detail::log2_post(detail::log2( + (detail::divide64(my>>i, mx, s)+1)>>1, 27)+0x10, exp+i-1, 16, arg.data_&0x8000)); + #endif + } + + /// \} + /// \anchor special + /// \name Error and gamma functions + /// \{ + + /// Error function. + /// This function may be 1 ULP off the correctly rounded exact result for any rounding mode in <0.5% of inputs. + /// + /// **See also:** Documentation for [std::erf](https://en.cppreference.com/w/cpp/numeric/math/erf). + /// \param arg function argument + /// \return error function value of \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half erf(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::erf(detail::half2float(arg.data_)))); + #else + unsigned int abs = arg.data_ & 0x7FFF; + if(!abs || abs >= 0x7C00) + return (abs>=0x7C00) ? half(detail::binary, (abs==0x7C00) ? (arg.data_-0x4000) : detail::signal(arg.data_)) : arg; + if(abs >= 0x4200) + return half(detail::binary, detail::rounded((arg.data_&0x8000)|0x3BFF, 1, 1)); + return half(detail::binary, detail::erf(arg.data_)); + #endif + } + + /// Complementary error function. + /// This function may be 1 ULP off the correctly rounded exact result for any rounding mode in <0.5% of inputs. + /// + /// **See also:** Documentation for [std::erfc](https://en.cppreference.com/w/cpp/numeric/math/erfc). + /// \param arg function argument + /// \return 1 minus error function value of \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half erfc(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::erfc(detail::half2float(arg.data_)))); + #else + unsigned int abs = arg.data_ & 0x7FFF, sign = arg.data_ & 0x8000; + if(abs >= 0x7C00) + return (abs>=0x7C00) ? half(detail::binary, (abs==0x7C00) ? (sign>>1) : detail::signal(arg.data_)) : arg; + if(!abs) + return half(detail::binary, 0x3C00); + if(abs >= 0x4400) + return half(detail::binary, detail::rounded((sign>>1)-(sign>>15), sign>>15, 1)); + return half(detail::binary, detail::erf(arg.data_)); + #endif + } + + /// Natural logarithm of gamma function. + /// This function may be 1 ULP off the correctly rounded exact result for any rounding mode in ~0.025% of inputs. + /// + /// **See also:** Documentation for [std::lgamma](https://en.cppreference.com/w/cpp/numeric/math/lgamma). + /// \param arg function argument + /// \return natural logarith of gamma function for \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_DIVBYZERO for 0 or negative integer arguments + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half lgamma(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::lgamma(detail::half2float(arg.data_)))); + #else + int abs = arg.data_ & 0x7FFF; + if(abs >= 0x7C00) + return half(detail::binary, (abs==0x7C00) ? 0x7C00 : detail::signal(arg.data_)); + if(!abs || arg.data_ >= 0xE400 || (arg.data_ >= 0xBC00 && !(abs&((1<<(25-(abs>>10)))-1)))) + return half(detail::binary, detail::pole()); + if(arg.data_ == 0x3C00 || arg.data_ == 0x4000) + return half(detail::binary, 0); + return half(detail::binary, detail::gamma(arg.data_)); + #endif + } + + /// Gamma function. + /// This function may be 1 ULP off the correctly rounded exact result for any rounding mode in <0.25% of inputs. + /// + /// **See also:** Documentation for [std::tgamma](https://en.cppreference.com/w/cpp/numeric/math/tgamma). + /// \param arg function argument + /// \return gamma function value of \a arg + /// \exception FE_INVALID for signaling NaN, negative infinity or negative integer arguments + /// \exception FE_DIVBYZERO for 0 + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half tgamma(half arg) + { + #if defined(HALF_ARITHMETIC_TYPE) && HALF_ENABLE_CPP11_CMATH + return half(detail::binary, detail::float2half(std::tgamma(detail::half2float(arg.data_)))); + #else + unsigned int abs = arg.data_ & 0x7FFF; + if(!abs) + return half(detail::binary, detail::pole(arg.data_)); + if(abs >= 0x7C00) + return (arg.data_==0x7C00) ? arg : half(detail::binary, detail::signal(arg.data_)); + if(arg.data_ >= 0xE400 || (arg.data_ >= 0xBC00 && !(abs&((1<<(25-(abs>>10)))-1)))) + return half(detail::binary, detail::invalid()); + if(arg.data_ >= 0xCA80) + return half(detail::binary, detail::underflow((1-((abs>>(25-(abs>>10)))&1))<<15)); + if(arg.data_ <= 0x100 || (arg.data_ >= 0x4900 && arg.data_ < 0x8000)) + return half(detail::binary, detail::overflow()); + if(arg.data_ == 0x3C00) + return arg; + return half(detail::binary, detail::gamma(arg.data_)); + #endif + } + + /// \} + /// \anchor rounding + /// \name Rounding + /// \{ + + /// Nearest integer not less than half value. + /// **See also:** Documentation for [std::ceil](https://en.cppreference.com/w/cpp/numeric/math/ceil). + /// \param arg half to round + /// \return nearest integer not less than \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_INEXACT if value had to be rounded + inline half ceil(half arg) { return half(detail::binary, detail::integral(arg.data_)); } + + /// Nearest integer not greater than half value. + /// **See also:** Documentation for [std::floor](https://en.cppreference.com/w/cpp/numeric/math/floor). + /// \param arg half to round + /// \return nearest integer not greater than \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_INEXACT if value had to be rounded + inline half floor(half arg) { return half(detail::binary, detail::integral(arg.data_)); } + + /// Nearest integer not greater in magnitude than half value. + /// **See also:** Documentation for [std::trunc](https://en.cppreference.com/w/cpp/numeric/math/trunc). + /// \param arg half to round + /// \return nearest integer not greater in magnitude than \a arg + /// \exception FE_INVALID for signaling NaN + /// \exception FE_INEXACT if value had to be rounded + inline half trunc(half arg) { return half(detail::binary, detail::integral(arg.data_)); } + + /// Nearest integer. + /// **See also:** Documentation for [std::round](https://en.cppreference.com/w/cpp/numeric/math/round). + /// \param arg half to round + /// \return nearest integer, rounded away from zero in half-way cases + /// \exception FE_INVALID for signaling NaN + /// \exception FE_INEXACT if value had to be rounded + inline half round(half arg) { return half(detail::binary, detail::integral(arg.data_)); } + + /// Nearest integer. + /// **See also:** Documentation for [std::lround](https://en.cppreference.com/w/cpp/numeric/math/round). + /// \param arg half to round + /// \return nearest integer, rounded away from zero in half-way cases + /// \exception FE_INVALID if value is not representable as `long` + inline long lround(half arg) { return detail::half2int(arg.data_); } + + /// Nearest integer using half's internal rounding mode. + /// **See also:** Documentation for [std::rint](https://en.cppreference.com/w/cpp/numeric/math/rint). + /// \param arg half expression to round + /// \return nearest integer using default rounding mode + /// \exception FE_INVALID for signaling NaN + /// \exception FE_INEXACT if value had to be rounded + inline half rint(half arg) { return half(detail::binary, detail::integral(arg.data_)); } + + /// Nearest integer using half's internal rounding mode. + /// **See also:** Documentation for [std::lrint](https://en.cppreference.com/w/cpp/numeric/math/rint). + /// \param arg half expression to round + /// \return nearest integer using default rounding mode + /// \exception FE_INVALID if value is not representable as `long` + /// \exception FE_INEXACT if value had to be rounded + inline long lrint(half arg) { return detail::half2int(arg.data_); } + + /// Nearest integer using half's internal rounding mode. + /// **See also:** Documentation for [std::nearbyint](https://en.cppreference.com/w/cpp/numeric/math/nearbyint). + /// \param arg half expression to round + /// \return nearest integer using default rounding mode + /// \exception FE_INVALID for signaling NaN + inline half nearbyint(half arg) { return half(detail::binary, detail::integral(arg.data_)); } +#if HALF_ENABLE_CPP11_LONG_LONG + /// Nearest integer. + /// **See also:** Documentation for [std::llround](https://en.cppreference.com/w/cpp/numeric/math/round). + /// \param arg half to round + /// \return nearest integer, rounded away from zero in half-way cases + /// \exception FE_INVALID if value is not representable as `long long` + inline long long llround(half arg) { return detail::half2int(arg.data_); } + + /// Nearest integer using half's internal rounding mode. + /// **See also:** Documentation for [std::llrint](https://en.cppreference.com/w/cpp/numeric/math/rint). + /// \param arg half expression to round + /// \return nearest integer using default rounding mode + /// \exception FE_INVALID if value is not representable as `long long` + /// \exception FE_INEXACT if value had to be rounded + inline long long llrint(half arg) { return detail::half2int(arg.data_); } +#endif + + /// \} + /// \anchor float + /// \name Floating point manipulation + /// \{ + + /// Decompress floating-point number. + /// **See also:** Documentation for [std::frexp](https://en.cppreference.com/w/cpp/numeric/math/frexp). + /// \param arg number to decompress + /// \param exp address to store exponent at + /// \return significant in range [0.5, 1) + /// \exception FE_INVALID for signaling NaN + inline half frexp(half arg, int *exp) + { + *exp = 0; + unsigned int abs = arg.data_ & 0x7FFF; + if(abs >= 0x7C00 || !abs) + return (abs>0x7C00) ? half(detail::binary, detail::signal(arg.data_)) : arg; + for(; abs<0x400; abs<<=1,--*exp) ; + *exp += (abs>>10) - 14; + return half(detail::binary, (arg.data_&0x8000)|0x3800|(abs&0x3FF)); + } + + /// Multiply by power of two. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::scalbln](https://en.cppreference.com/w/cpp/numeric/math/scalbn). + /// \param arg number to modify + /// \param exp power of two to multiply with + /// \return \a arg multplied by 2 raised to \a exp + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half scalbln(half arg, long exp) + { + unsigned int abs = arg.data_ & 0x7FFF, sign = arg.data_ & 0x8000; + if(abs >= 0x7C00 || !abs) + return (abs>0x7C00) ? half(detail::binary, detail::signal(arg.data_)) : arg; + for(; abs<0x400; abs<<=1,--exp) ; + exp += abs >> 10; + if(exp > 30) + return half(detail::binary, detail::overflow(sign)); + else if(exp < -10) + return half(detail::binary, detail::underflow(sign)); + else if(exp > 0) + return half(detail::binary, sign|(exp<<10)|(abs&0x3FF)); + unsigned int m = (abs&0x3FF) | 0x400; + return half(detail::binary, detail::rounded(sign|(m>>(1-exp)), (m>>-exp)&1, (m&((1<<-exp)-1))!=0)); + } + + /// Multiply by power of two. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::scalbn](https://en.cppreference.com/w/cpp/numeric/math/scalbn). + /// \param arg number to modify + /// \param exp power of two to multiply with + /// \return \a arg multplied by 2 raised to \a exp + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half scalbn(half arg, int exp) { return scalbln(arg, exp); } + + /// Multiply by power of two. + /// This function is exact to rounding for all rounding modes. + /// + /// **See also:** Documentation for [std::ldexp](https://en.cppreference.com/w/cpp/numeric/math/ldexp). + /// \param arg number to modify + /// \param exp power of two to multiply with + /// \return \a arg multplied by 2 raised to \a exp + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + inline half ldexp(half arg, int exp) { return scalbln(arg, exp); } + + /// Extract integer and fractional parts. + /// **See also:** Documentation for [std::modf](https://en.cppreference.com/w/cpp/numeric/math/modf). + /// \param arg number to decompress + /// \param iptr address to store integer part at + /// \return fractional part + /// \exception FE_INVALID for signaling NaN + inline half modf(half arg, half *iptr) + { + unsigned int abs = arg.data_ & 0x7FFF; + if(abs > 0x7C00) + { + arg = half(detail::binary, detail::signal(arg.data_)); + return *iptr = arg, arg; + } + if(abs >= 0x6400) + return *iptr = arg, half(detail::binary, arg.data_&0x8000); + if(abs < 0x3C00) + return iptr->data_ = arg.data_ & 0x8000, arg; + unsigned int exp = abs >> 10, mask = (1<<(25-exp)) - 1, m = arg.data_ & mask; + iptr->data_ = arg.data_ & ~mask; + if(!m) + return half(detail::binary, arg.data_&0x8000); + for(; m<0x400; m<<=1,--exp) ; + return half(detail::binary, (arg.data_&0x8000)|(exp<<10)|(m&0x3FF)); + } + + /// Extract exponent. + /// **See also:** Documentation for [std::ilogb](https://en.cppreference.com/w/cpp/numeric/math/ilogb). + /// \param arg number to query + /// \return floating-point exponent + /// \retval FP_ILOGB0 for zero + /// \retval FP_ILOGBNAN for NaN + /// \retval INT_MAX for infinity + /// \exception FE_INVALID for 0 or infinite values + inline int ilogb(half arg) + { + int abs = arg.data_ & 0x7FFF, exp; + if(!abs || abs >= 0x7C00) + { + detail::raise(FE_INVALID); + return !abs ? FP_ILOGB0 : (abs==0x7C00) ? INT_MAX : FP_ILOGBNAN; + } + for(exp=(abs>>10)-15; abs<0x200; abs<<=1,--exp) ; + return exp; + } + + /// Extract exponent. + /// **See also:** Documentation for [std::logb](https://en.cppreference.com/w/cpp/numeric/math/logb). + /// \param arg number to query + /// \return floating-point exponent + /// \exception FE_INVALID for signaling NaN + /// \exception FE_DIVBYZERO for 0 + inline half logb(half arg) + { + int abs = arg.data_ & 0x7FFF, exp; + if(!abs) + return half(detail::binary, detail::pole(0x8000)); + if(abs >= 0x7C00) + return half(detail::binary, (abs==0x7C00) ? 0x7C00 : detail::signal(arg.data_)); + for(exp=(abs>>10)-15; abs<0x200; abs<<=1,--exp) ; + unsigned int value = static_cast(exp<0) << 15; + if(exp) + { + unsigned int m = std::abs(exp) << 6; + for(exp=18; m<0x400; m<<=1,--exp) ; + value |= (exp<<10) + m; + } + return half(detail::binary, value); + } + + /// Next representable value. + /// **See also:** Documentation for [std::nextafter](https://en.cppreference.com/w/cpp/numeric/math/nextafter). + /// \param from value to compute next representable value for + /// \param to direction towards which to compute next value + /// \return next representable value after \a from in direction towards \a to + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW for infinite result from finite argument + /// \exception FE_UNDERFLOW for subnormal result + inline half nextafter(half from, half to) + { + int fabs = from.data_ & 0x7FFF, tabs = to.data_ & 0x7FFF; + if(fabs > 0x7C00 || tabs > 0x7C00) + return half(detail::binary, detail::signal(from.data_, to.data_)); + if(from.data_ == to.data_ || !(fabs|tabs)) + return to; + if(!fabs) + { + detail::raise(FE_UNDERFLOW, !HALF_ERRHANDLING_UNDERFLOW_TO_INEXACT); + return half(detail::binary, (to.data_&0x8000)+1); + } + unsigned int out = from.data_ + (((from.data_>>15)^static_cast( + (from.data_^(0x8000|(0x8000-(from.data_>>15))))<(to.data_^(0x8000|(0x8000-(to.data_>>15))))))<<1) - 1; + detail::raise(FE_OVERFLOW, fabs<0x7C00 && (out&0x7C00)==0x7C00); + detail::raise(FE_UNDERFLOW, !HALF_ERRHANDLING_UNDERFLOW_TO_INEXACT && (out&0x7C00)<0x400); + return half(detail::binary, out); + } + + /// Next representable value. + /// **See also:** Documentation for [std::nexttoward](https://en.cppreference.com/w/cpp/numeric/math/nexttoward). + /// \param from value to compute next representable value for + /// \param to direction towards which to compute next value + /// \return next representable value after \a from in direction towards \a to + /// \exception FE_INVALID for signaling NaN + /// \exception FE_OVERFLOW for infinite result from finite argument + /// \exception FE_UNDERFLOW for subnormal result + inline half nexttoward(half from, long double to) + { + int fabs = from.data_ & 0x7FFF; + if(fabs > 0x7C00) + return half(detail::binary, detail::signal(from.data_)); + long double lfrom = static_cast(from); + if(detail::builtin_isnan(to) || lfrom == to) + return half(static_cast(to)); + if(!fabs) + { + detail::raise(FE_UNDERFLOW, !HALF_ERRHANDLING_UNDERFLOW_TO_INEXACT); + return half(detail::binary, (static_cast(detail::builtin_signbit(to))<<15)+1); + } + unsigned int out = from.data_ + (((from.data_>>15)^static_cast(lfrom 0x7C00; } + + /// Check if normal number. + /// **See also:** Documentation for [std::isnormal](https://en.cppreference.com/w/cpp/numeric/math/isnormal). + /// \param arg number to check + /// \retval true if normal number + /// \retval false if either subnormal, zero, infinity or NaN + inline HALF_CONSTEXPR bool isnormal(half arg) { return ((arg.data_&0x7C00)!=0) & ((arg.data_&0x7C00)!=0x7C00); } + + /// Check sign. + /// **See also:** Documentation for [std::signbit](https://en.cppreference.com/w/cpp/numeric/math/signbit). + /// \param arg number to check + /// \retval true for negative number + /// \retval false for positive number + inline HALF_CONSTEXPR bool signbit(half arg) { return (arg.data_&0x8000) != 0; } + + /// \} + /// \anchor compfunc + /// \name Comparison + /// \{ + + /// Quiet comparison for greater than. + /// **See also:** Documentation for [std::isgreater](https://en.cppreference.com/w/cpp/numeric/math/isgreater). + /// \param x first operand + /// \param y second operand + /// \retval true if \a x greater than \a y + /// \retval false else + inline HALF_CONSTEXPR bool isgreater(half x, half y) + { + return ((x.data_^(0x8000|(0x8000-(x.data_>>15))))+(x.data_>>15)) > ((y.data_^(0x8000|(0x8000-(y.data_>>15))))+(y.data_>>15)) && !isnan(x) && !isnan(y); + } + + /// Quiet comparison for greater equal. + /// **See also:** Documentation for [std::isgreaterequal](https://en.cppreference.com/w/cpp/numeric/math/isgreaterequal). + /// \param x first operand + /// \param y second operand + /// \retval true if \a x greater equal \a y + /// \retval false else + inline HALF_CONSTEXPR bool isgreaterequal(half x, half y) + { + return ((x.data_^(0x8000|(0x8000-(x.data_>>15))))+(x.data_>>15)) >= ((y.data_^(0x8000|(0x8000-(y.data_>>15))))+(y.data_>>15)) && !isnan(x) && !isnan(y); + } + + /// Quiet comparison for less than. + /// **See also:** Documentation for [std::isless](https://en.cppreference.com/w/cpp/numeric/math/isless). + /// \param x first operand + /// \param y second operand + /// \retval true if \a x less than \a y + /// \retval false else + inline HALF_CONSTEXPR bool isless(half x, half y) + { + return ((x.data_^(0x8000|(0x8000-(x.data_>>15))))+(x.data_>>15)) < ((y.data_^(0x8000|(0x8000-(y.data_>>15))))+(y.data_>>15)) && !isnan(x) && !isnan(y); + } + + /// Quiet comparison for less equal. + /// **See also:** Documentation for [std::islessequal](https://en.cppreference.com/w/cpp/numeric/math/islessequal). + /// \param x first operand + /// \param y second operand + /// \retval true if \a x less equal \a y + /// \retval false else + inline HALF_CONSTEXPR bool islessequal(half x, half y) + { + return ((x.data_^(0x8000|(0x8000-(x.data_>>15))))+(x.data_>>15)) <= ((y.data_^(0x8000|(0x8000-(y.data_>>15))))+(y.data_>>15)) && !isnan(x) && !isnan(y); + } + + /// Quiet comarison for less or greater. + /// **See also:** Documentation for [std::islessgreater](https://en.cppreference.com/w/cpp/numeric/math/islessgreater). + /// \param x first operand + /// \param y second operand + /// \retval true if either less or greater + /// \retval false else + inline HALF_CONSTEXPR bool islessgreater(half x, half y) + { + return x.data_!=y.data_ && ((x.data_|y.data_)&0x7FFF) && !isnan(x) && !isnan(y); + } + + /// Quiet check if unordered. + /// **See also:** Documentation for [std::isunordered](https://en.cppreference.com/w/cpp/numeric/math/isunordered). + /// \param x first operand + /// \param y second operand + /// \retval true if unordered (one or two NaN operands) + /// \retval false else + inline HALF_CONSTEXPR bool isunordered(half x, half y) { return isnan(x) || isnan(y); } + + /// \} + /// \anchor casting + /// \name Casting + /// \{ + + /// Cast to or from half-precision floating-point number. + /// This casts between [half](\ref half_float::half) and any built-in arithmetic type. The values are converted + /// directly using the default rounding mode, without any roundtrip over `float` that a `static_cast` would otherwise do. + /// + /// Using this cast with neither of the two types being a [half](\ref half_float::half) or with any of the two types + /// not being a built-in arithmetic type (apart from [half](\ref half_float::half), of course) results in a compiler + /// error and casting between [half](\ref half_float::half)s returns the argument unmodified. + /// \tparam T destination type (half or built-in arithmetic type) + /// \tparam U source type (half or built-in arithmetic type) + /// \param arg value to cast + /// \return \a arg converted to destination type + /// \exception FE_INVALID if \a T is integer type and result is not representable as \a T + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + template T half_cast(U arg) { return detail::half_caster::cast(arg); } + + /// Cast to or from half-precision floating-point number. + /// This casts between [half](\ref half_float::half) and any built-in arithmetic type. The values are converted + /// directly using the specified rounding mode, without any roundtrip over `float` that a `static_cast` would otherwise do. + /// + /// Using this cast with neither of the two types being a [half](\ref half_float::half) or with any of the two types + /// not being a built-in arithmetic type (apart from [half](\ref half_float::half), of course) results in a compiler + /// error and casting between [half](\ref half_float::half)s returns the argument unmodified. + /// \tparam T destination type (half or built-in arithmetic type) + /// \tparam R rounding mode to use. + /// \tparam U source type (half or built-in arithmetic type) + /// \param arg value to cast + /// \return \a arg converted to destination type + /// \exception FE_INVALID if \a T is integer type and result is not representable as \a T + /// \exception FE_OVERFLOW, ...UNDERFLOW, ...INEXACT according to rounding + template T half_cast(U arg) { return detail::half_caster::cast(arg); } + /// \} + + /// \} + /// \anchor errors + /// \name Error handling + /// \{ + + /// Clear exception flags. + /// This function works even if [automatic exception flag handling](\ref HALF_ERRHANDLING_FLAGS) is disabled, + /// but in that case manual flag management is the only way to raise flags. + /// + /// **See also:** Documentation for [std::feclearexcept](https://en.cppreference.com/w/cpp/numeric/fenv/feclearexcept). + /// \param excepts OR of exceptions to clear + /// \retval 0 all selected flags cleared successfully + inline int feclearexcept(int excepts) { detail::errflags() &= ~excepts; return 0; } + + /// Test exception flags. + /// This function works even if [automatic exception flag handling](\ref HALF_ERRHANDLING_FLAGS) is disabled, + /// but in that case manual flag management is the only way to raise flags. + /// + /// **See also:** Documentation for [std::fetestexcept](https://en.cppreference.com/w/cpp/numeric/fenv/fetestexcept). + /// \param excepts OR of exceptions to test + /// \return OR of selected exceptions if raised + inline int fetestexcept(int excepts) { return detail::errflags() & excepts; } + + /// Raise exception flags. + /// This raises the specified floating point exceptions and also invokes any additional automatic exception handling as + /// configured with the [HALF_ERRHANDLIG_...](\ref HALF_ERRHANDLING_ERRNO) preprocessor symbols. + /// This function works even if [automatic exception flag handling](\ref HALF_ERRHANDLING_FLAGS) is disabled, + /// but in that case manual flag management is the only way to raise flags. + /// + /// **See also:** Documentation for [std::feraiseexcept](https://en.cppreference.com/w/cpp/numeric/fenv/feraiseexcept). + /// \param excepts OR of exceptions to raise + /// \retval 0 all selected exceptions raised successfully + inline int feraiseexcept(int excepts) { detail::errflags() |= excepts; detail::raise(excepts); return 0; } + + /// Save exception flags. + /// This function works even if [automatic exception flag handling](\ref HALF_ERRHANDLING_FLAGS) is disabled, + /// but in that case manual flag management is the only way to raise flags. + /// + /// **See also:** Documentation for [std::fegetexceptflag](https://en.cppreference.com/w/cpp/numeric/fenv/feexceptflag). + /// \param flagp adress to store flag state at + /// \param excepts OR of flags to save + /// \retval 0 for success + inline int fegetexceptflag(int *flagp, int excepts) { *flagp = detail::errflags() & excepts; return 0; } + + /// Restore exception flags. + /// This only copies the specified exception state (including unset flags) without incurring any additional exception handling. + /// This function works even if [automatic exception flag handling](\ref HALF_ERRHANDLING_FLAGS) is disabled, + /// but in that case manual flag management is the only way to raise flags. + /// + /// **See also:** Documentation for [std::fesetexceptflag](https://en.cppreference.com/w/cpp/numeric/fenv/feexceptflag). + /// \param flagp adress to take flag state from + /// \param excepts OR of flags to restore + /// \retval 0 for success + inline int fesetexceptflag(const int *flagp, int excepts) { detail::errflags() = (detail::errflags()|(*flagp&excepts)) & (*flagp|~excepts); return 0; } + + /// Throw C++ exceptions based on set exception flags. + /// This function manually throws a corresponding C++ exception if one of the specified flags is set, + /// no matter if automatic throwing (via [HALF_ERRHANDLING_THROW_...](\ref HALF_ERRHANDLING_THROW_INVALID)) is enabled or not. + /// This function works even if [automatic exception flag handling](\ref HALF_ERRHANDLING_FLAGS) is disabled, + /// but in that case manual flag management is the only way to raise flags. + /// \param excepts OR of exceptions to test + /// \param msg error message to use for exception description + /// \throw std::domain_error if `FE_INVALID` or `FE_DIVBYZERO` is selected and set + /// \throw std::overflow_error if `FE_OVERFLOW` is selected and set + /// \throw std::underflow_error if `FE_UNDERFLOW` is selected and set + /// \throw std::range_error if `FE_INEXACT` is selected and set + inline void fethrowexcept(int excepts, const char *msg = "") + { + excepts &= detail::errflags(); + if(excepts & (FE_INVALID|FE_DIVBYZERO)) + throw std::domain_error(msg); + if(excepts & FE_OVERFLOW) + throw std::overflow_error(msg); + if(excepts & FE_UNDERFLOW) + throw std::underflow_error(msg); + if(excepts & FE_INEXACT) + throw std::range_error(msg); + } + /// \} +} + + +#undef HALF_UNUSED_NOERR +#undef HALF_CONSTEXPR +#undef HALF_CONSTEXPR_CONST +#undef HALF_CONSTEXPR_NOERR +#undef HALF_NOEXCEPT +#undef HALF_NOTHROW +#undef HALF_THREAD_LOCAL +#undef HALF_TWOS_COMPLEMENT_INT +#ifdef HALF_POP_WARNINGS + #pragma warning(pop) + #undef HALF_POP_WARNINGS +#endif + +#endif diff --git a/src/fastfft/FastFFT.cu b/src/fastfft/FastFFT.cu index f3dbd78..3c75758 100644 --- a/src/fastfft/FastFFT.cu +++ b/src/fastfft/FastFFT.cu @@ -7,25 +7,35 @@ #include "../../include/FastFFT.cuh" +#ifndef FFT_DEBUG_STAGE +#error "FFT_DEBUG_STAGE must be defined" +#endif + +#ifndef FFT_DEBUG_LEVEL +#error "FFT_DEBUG_LEVEL must be defined" +#endif + namespace FastFFT { -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, + void block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE(const ExternalImage_t* __restrict__ image_to_search, const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, int apparent_Q, typename FFT::workspace_type workspace_fwd, typename invFFT::workspace_type workspace_inv, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor) { // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - // __shared__ complex_type shared_mem[invFFT::shared_memory_size/sizeof(complex_type)]; // Storage for the input data that is re-used each blcok - extern __shared__ complex_type shared_mem[]; // Storage for the input data that is re-used each blcok + // __shared__ complex_compute_t shared_mem[invFFT::shared_memory_size/sizeof(complex_compute_t)]; // Storage for the input data that is re-used each blcok + extern __shared__ complex_compute_t shared_mem[]; // Storage for the input data that is re-used each blcok - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; // For simplicity, we explicitly zeropad the input data to the size of the FFT. - // It may be worth trying to use threadIdx.z as in the DECREASE methods. + // It may be worth trying to use threadIdx.y as in the DECREASE methods. // Until then, this + io::load(&input_values[Return1DFFTAddress(size_of::value / apparent_Q)], thread_data, size_of::value / apparent_Q, pre_op_functor); // In the first FFT the modifying twiddle factor is 1 so the data are reeal @@ -45,73 +55,84 @@ __launch_bounds__(FFT::max_threads_per_block) __global__ #endif } -template -FourierTransformer::FourierTransformer( ) { +template +FourierTransformer::FourierTransformer( ) { SetDefaults( ); GetCudaDeviceProps(device_properties); + // FIXME: assert on OtherImageType being a complex type + static_assert(std::is_same_v, "Compute base type must be float"); + static_assert(Rank == 2 || Rank == 3, "Only 2D and 3D FFTs are supported"); + static_assert(std::is_same_v || std::is_same_v || std::is_same_v || std::is_same_v, + "Input base type must be either __half or float"); + // exit(0); // This assumption precludes the use of a packed _half2 that is really RRII layout for two arrays of __half. - // TODO could is_real_valued_input be constexpr? - if constexpr ( std::is_same::value || std::is_same::value ) { - is_real_valued_input = false; - } - else { - is_real_valued_input = true; - } + static_assert(IsAllowedRealType || IsAllowedComplexType, "Input type must be either float or __half"); + + // Make sure an explicit specializtion for the device pointers is available + static_assert(! std::is_same_v, "Device pointer type not specialized"); } -template -FourierTransformer::~FourierTransformer( ) { +template +FourierTransformer::~FourierTransformer( ) { Deallocate( ); SetDefaults( ); } -template -void FourierTransformer::SetDefaults( ) { +template +void FourierTransformer::SetDefaults( ) { // booleans to track state, could be bit fields but that seem opaque to me. - is_in_memory_host_pointer = false; // To track allocation of host side memory - is_in_memory_device_pointer = false; // To track allocation of device side memory. - is_in_buffer_memory = false; // To track whether the current result is in dev_ptr.position_space or dev_ptr.position_space_buffer (momemtum space/ momentum space buffer respectively.) - transform_stage_completed = TransformStageCompleted::none; + current_buffer = fastfft_external_input; + transform_stage_completed = 0; + + implicit_dimension_change = false; is_fftw_padded_input = false; // Padding for in place r2c transforms is_fftw_padded_output = false; // Currently the output state will match the input state, otherwise it is an error. - is_real_valued_input = true; // This is determined by the input type. If it is a float2 or __half2, then it is assumed to be a complex valued input function. - is_set_input_params = false; // Yes, yes, "are" set. is_set_output_params = false; is_size_validated = false; // Defaults to false, set after both input/output dimensions are set and checked. - is_set_input_pointer = false; // May be on the host of the device. - is_from_python_call = false; - is_owner_of_memory = false; + input_data_is_on_device = false; + output_data_is_on_device = false; + external_image_is_on_device = false; - compute_memory_allocated = 0; + compute_memory_wanted_ = 0; } -template -void FourierTransformer::Deallocate( ) { - // TODO: confirm this is NOT called when memory is allocated by external process. - if ( is_in_memory_device_pointer && is_owner_of_memory ) { - precheck; - cudaErr(cudaFreeAsync(d_ptr.position_space, cudaStreamPerThread)); - postcheck; - is_in_memory_device_pointer = false; - } +template +void FourierTransformer::Deallocate( ) { - if ( is_from_python_call ) { + if ( is_pointer_in_device_memory(d_ptr.buffer_1) ) { precheck; - cudaErr(cudaFreeAsync(d_ptr.position_space_buffer, cudaStreamPerThread)); + cudaErr(cudaFreeAsync(d_ptr.buffer_1, cudaStreamPerThread)); postcheck; } } -template -void FourierTransformer::SetForwardFFTPlan(size_t input_logical_x_dimension, size_t input_logical_y_dimension, size_t input_logical_z_dimension, - size_t output_logical_x_dimension, size_t output_logical_y_dimension, size_t output_logical_z_dimension, - bool is_padded_input) { +/** + * @brief Create a forward FFT plan. + * Buffer memory is allocated on the latter of creating forward/inverse plans. + * Data may be copied to this buffer and used directly + * + * @tparam ComputeBaseType + * @tparam InputType + * @tparam OtherImageType + * @tparam Rank + * @param input_logical_x_dimension + * @param input_logical_y_dimension + * @param input_logical_z_dimension + * @param output_logical_x_dimension + * @param output_logical_y_dimension + * @param output_logical_z_dimension + * @param is_padded_input + */ +template +void FourierTransformer::SetForwardFFTPlan(size_t input_logical_x_dimension, size_t input_logical_y_dimension, size_t input_logical_z_dimension, + size_t output_logical_x_dimension, size_t output_logical_y_dimension, size_t output_logical_z_dimension, + bool is_padded_input) { MyFFTDebugAssertTrue(input_logical_x_dimension > 0, "Input logical x dimension must be > 0"); MyFFTDebugAssertTrue(input_logical_y_dimension > 0, "Input logical y dimension must be > 0"); MyFFTDebugAssertTrue(input_logical_z_dimension > 0, "Input logical z dimension must be > 0"); @@ -126,21 +147,41 @@ void FourierTransformer::SetForwardFFT MyFFTRunTimeAssertTrue(is_fftw_padded_input, "Support for input arrays that are not FFTW padded needs to be implemented."); // FIXME // ReturnPaddedMemorySize also sets FFTW padding etc. - input_memory_allocated = ReturnPaddedMemorySize(fwd_dims_in); - fwd_output_memory_allocated = ReturnPaddedMemorySize(fwd_dims_out); // sets .w and also increases compute_memory_allocated if needed. + input_memory_wanted_ = ReturnPaddedMemorySize(fwd_dims_in); + // sets .w and also increases compute_memory_wanted_ if needed. + fwd_output_memory_wanted_ = ReturnPaddedMemorySize(fwd_dims_out); // The compute memory allocated is the max of all possible sizes. this->input_origin_type = OriginType::natural; is_set_input_params = true; + + if ( is_set_output_params ) + AllocateBufferMemory( ); // TODO: } -template -void FourierTransformer::SetInverseFFTPlan(size_t input_logical_x_dimension, size_t input_logical_y_dimension, size_t input_logical_z_dimension, - size_t output_logical_x_dimension, size_t output_logical_y_dimension, size_t output_logical_z_dimension, - bool is_padded_output) { - MyFFTDebugAssertTrue(is_set_input_params, "Please set the input paramters first.") - MyFFTDebugAssertTrue(output_logical_x_dimension > 0, "output logical x dimension must be > 0"); +/** + * @brief Create an inverse FFT plan. + * Buffer memory is allocated on the latter of creating forward/inverse plans. + * Data may be copied to this buffer and used directly + * + * @tparam ComputeBaseType + * @tparam InputType + * @tparam OtherImageType + * @tparam Rank + * @param input_logical_x_dimension + * @param input_logical_y_dimension + * @param input_logical_z_dimension + * @param output_logical_x_dimension + * @param output_logical_y_dimension + * @param output_logical_z_dimension + * @param is_padded_output + */ +template +void FourierTransformer::SetInverseFFTPlan(size_t input_logical_x_dimension, size_t input_logical_y_dimension, size_t input_logical_z_dimension, + size_t output_logical_x_dimension, size_t output_logical_y_dimension, size_t output_logical_z_dimension, + bool is_padded_output) { + MyFFTDebugAssertTrue(output_logical_x_dimension > 0, "output logical x dimension must be > 0"); MyFFTDebugAssertTrue(output_logical_y_dimension > 0, "output logical y dimension must be > 0"); MyFFTDebugAssertTrue(output_logical_z_dimension > 0, "output logical z dimension must be > 0"); MyFFTDebugAssertTrue(is_fftw_padded_input == is_padded_output, "If the input data are FFTW padded, so must the output."); @@ -148,366 +189,287 @@ void FourierTransformer::SetInverseFFT inv_dims_in = make_short4(input_logical_x_dimension, input_logical_y_dimension, input_logical_z_dimension, 0); inv_dims_out = make_short4(output_logical_x_dimension, output_logical_y_dimension, output_logical_z_dimension, 0); - ReturnPaddedMemorySize(inv_dims_in); // sets .w and also increases compute_memory_allocated if needed. - inv_output_memory_allocated = ReturnPaddedMemorySize(inv_dims_out); + ReturnPaddedMemorySize(inv_dims_in); // sets .w and also increases compute_memory_wanted_ if needed. + inv_output_memory_wanted_ = ReturnPaddedMemorySize(inv_dims_out); // The compute memory allocated is the max of all possible sizes. this->output_origin_type = OriginType::natural; is_set_output_params = true; + if ( is_set_input_params ) + AllocateBufferMemory( ); // TODO: } -template -void FourierTransformer::SetInputPointer(InputType* input_pointer, bool is_input_on_device) { - MyFFTDebugAssertFalse(input_memory_allocated == 0, "There is no input memory allocated."); - MyFFTDebugAssertTrue(is_set_output_params, "Output parameters not set"); - MyFFTRunTimeAssertFalse(is_set_input_pointer, "The input pointer has already been set!"); - - is_from_python_call = false; - - if ( is_input_on_device ) { - is_set_input_pointer = false; - - // TODO: This could be named more clearly to reflect that it is not the owner of the INPUT GPU memory - is_owner_of_memory = false; - } - else { - host_pointer = input_pointer; - // arguably this could be set when actually doing the allocation, but I think this also makes sense as there may be multiople allocation - // routines, but here we already know the calling process has not done it for us. - is_owner_of_memory = true; - is_set_input_pointer = true; - is_in_memory_host_pointer = true; - } -} - -template -void FourierTransformer::SetCallerPinnedInputPointer(InputType* input_pointer) { - MyFFTDebugAssertFalse(input_memory_allocated == 0, "There is no input memory allocated."); - MyFFTDebugAssertTrue(is_set_output_params, "Output parameters not set"); - MyFFTRunTimeAssertFalse(is_set_input_pointer, "The input pointer has already been set!"); - - host_pointer = input_pointer; - // arguably this could be set when actually doing the allocation, but I think this also makes sense as there may be multiople allocation - // routines, but here we already know the calling process has not done it for us. - is_owner_of_memory = false; - // Check to see if the host memory is pinned. +/** + * @brief Private method to allocate memory for the internal FastFFT buffer. + * + * @tparam ComputeBaseType + * @tparam InputType + * @tparam OtherImageType + * @tparam Rank + */ +template +void FourierTransformer::AllocateBufferMemory( ) { + MyFFTDebugAssertTrue(is_set_input_params && is_set_output_params, "Input and output parameters must be set before allocating buffer memory"); + + MyFFTDebugAssertTrue(compute_memory_wanted_ > 0, "Compute memory already allocated"); + + // Allocate enough for the out of place buffer as well. + constexpr size_t compute_memory_scalar = 2; + // To get the address of the second buffer we want half of the number of ComputeType, not ComputeBaseType elements + constexpr size_t buffer_address_scalar = 2; + precheck; + cudaErr(cudaMallocAsync(&d_ptr.buffer_1, compute_memory_scalar * compute_memory_wanted_ * sizeof(ComputeBaseType), cudaStreamPerThread)); + postcheck; - is_in_memory_host_pointer = true; - is_set_input_pointer = true; + // cudaMallocAsync returns the pointer immediately, even though the allocation has not yet completed, so we + // should be fine to go on and point our secondary buffer to the correct location. + d_ptr.buffer_2 = &d_ptr.buffer_1[compute_memory_wanted_ / buffer_address_scalar]; } -template -void FourierTransformer::SetInputPointer(long input_pointer) { - - MyFFTDebugAssertTrue(is_set_input_params, "Input parameters not set"); - - // The assumption for now is that access from python wrappers have taken care of device/host xfer - // and the passed pointer is in device memory. - // TODO: I should probably have a state variable to track is_python_call - d_ptr.position_space = reinterpret_cast(input_pointer); - is_set_input_pointer = true; - is_in_memory_device_pointer = true; - is_owner_of_memory = false; - - is_from_python_call = true; - - // These are normally set on CopyHostToDevice - SetDevicePointers(is_from_python_call); -} +template +void FourierTransformer::SetInputPointerFromPython(long input_pointer) { -template -void FourierTransformer::SetDevicePointers(bool should_allocate_buffer_memory) { + MyFFTRunTimeAssertFalse(true, "This needs to be re-implemented."); + // MyFFTDebugAssertTrue(is_set_input_params, "Input parameters not set"); - size_t buffer_address; - if ( is_real_valued_input ) - buffer_address = compute_memory_allocated / 2; - else - buffer_address = compute_memory_allocated / 4; - if ( should_allocate_buffer_memory ) { + // // The assumption for now is that access from python wrappers have taken care of device/host xfer + // // and the passed pointer is in device memory. + // // TODO: I should probably have a state variable to track is_python_call + // d_ptr.position_space = reinterpret_cast(input_pointer); - // TODO: confirm this is correct for complex valued input - precheck; - cudaErr(cudaMalloc(&d_ptr.position_space_buffer, buffer_address * sizeof(ComputeType))); - postcheck; - if constexpr ( std::is_same::value ) { - d_ptr.momentum_space = (__half2*)d_ptr.position_space; - d_ptr.momentum_space_buffer = (__half2*)d_ptr.position_space_buffer; - } - else { - d_ptr.momentum_space = (float2*)d_ptr.position_space; - d_ptr.momentum_space_buffer = (float2*)d_ptr.position_space_buffer; - } - } - else { - SetDimensions(DimensionCheckType::CopyFromHost); - if constexpr ( std::is_same::value ) { - d_ptr.momentum_space = (__half2*)d_ptr.position_space; - d_ptr.position_space_buffer = &d_ptr.position_space[buffer_address]; - d_ptr.momentum_space_buffer = (__half2*)d_ptr.position_space_buffer; - } - else { - d_ptr.momentum_space = (float2*)d_ptr.position_space; - d_ptr.position_space_buffer = &d_ptr.position_space[buffer_address]; // compute - d_ptr.momentum_space_buffer = (float2*)d_ptr.position_space_buffer; - } - } + // // These are normally set on CopyHostToDevice + // SetDevicePointers( ); } -template -void FourierTransformer::CopyHostToDevceAndSynchronize(InputType* input_pointer, int n_elements_to_copy) { +// FIXME: see header file for comments +template +void FourierTransformer::CopyHostToDeviceAndSynchronize(InputType* input_pointer, int n_elements_to_copy) { CopyHostToDevice(input_pointer, n_elements_to_copy); cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); } -template -void FourierTransformer::CopyHostToDevice(InputType* input_pointer, int n_elements_to_copy) { - +// FIXME: see header file for comments +template +void FourierTransformer::CopyHostToDevice(InputType* input_pointer, int n_elements_to_copy) { + MyFFTDebugAssertFalse(input_data_is_on_device, "External input pointer is on device, cannot copy from host"); + MyFFTRunTimeAssertTrue(false, "This method is being removed."); SetDimensions(DimensionCheckType::CopyFromHost); - MyFFTDebugAssertTrue(pointer_is_in_memory_and_registered(input_pointer), "Host memory not in memory and/or pinned"); - // FIXME switch to stream ordered malloc - if ( ! is_in_memory_device_pointer ) { - // Allocate enough for the out of place buffer as well. - // MyFFTPrintWithDetails("Allocating device memory for input pointer"); - precheck; - cudaErr(cudaMalloc(&d_ptr.position_space, compute_memory_allocated * sizeof(ComputeType))); - postcheck; - - SetDevicePointers(is_from_python_call); - - is_in_memory_device_pointer = true; - } precheck; - cudaErr(cudaMemcpyAsync(d_ptr.position_space, input_pointer, memory_size_to_copy * sizeof(InputType), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(d_ptr.buffer_1, input_pointer, memory_size_to_copy_ * sizeof(InputType), cudaMemcpyHostToDevice, cudaStreamPerThread)); postcheck; - - // TODO: Not sure if this is the cleanest way to do this. Other instances tagged SET_TRANFORMANDBUFFER - transform_stage_completed = TransformStageCompleted::none; - is_in_buffer_memory = false; -} - -template -void FourierTransformer::CopyDeviceToHostAndSynchronize(OutputType* output_pointer, bool free_gpu_memory, int n_elements_to_copy) { - CopyDeviceToHost(output_pointer, free_gpu_memory, n_elements_to_copy); - cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); -} - -template -void FourierTransformer::CopyDeviceToHost(OutputType* output_pointer, bool free_gpu_memory, int n_elements_to_copy) { - MyFFTDebugAssertTrue(pointer_is_in_memory_and_registered(output_pointer), "Host memory not in memory and/or pinned"); - - SetDimensions(DimensionCheckType::CopyToHost); - if ( n_elements_to_copy != 0 ) - memory_size_to_copy = n_elements_to_copy; - MyFFTDebugAssertTrue(is_in_memory_device_pointer, "GPU memory not allocated"); - if ( is_in_buffer_memory ) { - std::cerr << "Clopying from buffer memory" << std::endl; - precheck; - cudaErr(cudaMemcpyAsync(output_pointer, d_ptr.position_space_buffer, memory_size_to_copy * sizeof(OutputType), cudaMemcpyDeviceToHost, cudaStreamPerThread)); - postcheck; - } - else { - precheck; - cudaErr(cudaMemcpyAsync(output_pointer, d_ptr.position_space, memory_size_to_copy * sizeof(OutputType), cudaMemcpyDeviceToHost, cudaStreamPerThread)); - postcheck; - } - - if ( free_gpu_memory ) { - Deallocate( ); - } } -template -void FourierTransformer::CopyDeviceToDeviceFromNonOwningAddress(InputType* input_pointer, int n_elements_to_copy) { - SetDimensions(DimensionCheckType::CopyFromHost); - // FIXME switch to stream ordered malloc - if ( ! is_in_memory_device_pointer ) { - // Allocate enough for the out of place buffer as well. - // MyFFTPrintWithDetails("Allocating device memory for input pointer"); - precheck; - cudaErr(cudaMalloc(&d_ptr.position_space, compute_memory_allocated * sizeof(ComputeType))); - postcheck; - - SetDevicePointers(is_from_python_call); - - is_in_memory_device_pointer = true; - } - precheck; - cudaErr(cudaMemcpyAsync(d_ptr.position_space, input_pointer, memory_size_to_copy * sizeof(InputType), cudaMemcpyDeviceToDevice, cudaStreamPerThread)); - postcheck; - // TODO: Not sure if this is the cleanest way to do this. Other instances tagged SET_TRANFORMANDBUFFER - transform_stage_completed = TransformStageCompleted::none; - is_in_buffer_memory = false; +template +template +void FourierTransformer::FwdFFT(InputType* input_ptr, + InputType* output_ptr, + PreOpType pre_op, + IntraOpType intra_op) { + + transform_stage_completed = 0; + current_buffer = fastfft_external_input; + // Keep track of the device side pointer used when called + d_ptr.external_input = input_ptr; + d_ptr.external_output = output_ptr; + Generic_Fwd(pre_op, intra_op); } -template -template -void FourierTransformer::CopyDeviceToDeviceAndSynchronize(TransferDataType* output_pointer, bool free_gpu_memory, int n_elements_to_copy) { - CopyDeviceToDevice(output_pointer, free_gpu_memory, n_elements_to_copy); - cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); +template +template +void FourierTransformer::InvFFT(InputType* input_ptr, + InputType* output_ptr, + IntraOpType intra_op, + PostOpType post_op) { + transform_stage_completed = 4; + current_buffer = fastfft_external_input; + // Keep track of the device side pointer used when called + d_ptr.external_input = input_ptr; + d_ptr.external_output = output_ptr; + + Generic_Inv(intra_op, post_op); } -template -template -void FourierTransformer::CopyDeviceToDevice(TransferDataType* output_pointer, bool free_gpu_memory, int n_elements_to_copy) { - - // TODO: can the test for pinned pointers be used to directly assert if GPU memory is allocated rather than using a bool? - SetDimensions(DimensionCheckType::CopyDeviceToDevice); - if ( n_elements_to_copy != 0 ) - memory_size_to_copy = n_elements_to_copy; - MyFFTDebugAssertTrue(is_in_memory_device_pointer || ! is_owner_of_memory, "GPU memory not allocated"); - - if ( is_in_buffer_memory ) { - precheck; - cudaErr(cudaMemcpyAsync(output_pointer, d_ptr.position_space_buffer, memory_size_to_copy * sizeof(OutputType), cudaMemcpyDeviceToHost, cudaStreamPerThread)); - postcheck; - } - else { - precheck; - cudaErr(cudaMemcpyAsync(output_pointer, d_ptr.position_space, memory_size_to_copy * sizeof(OutputType), cudaMemcpyDeviceToHost, cudaStreamPerThread)); - postcheck; - } - - if ( free_gpu_memory ) { - Deallocate( ); - } +template + +template +void FourierTransformer::FwdImageInvFFT(InputType* input_ptr, + OtherImageType* image_to_search, + InputType* output_ptr, + PreOpType pre_op, + IntraOpType intra_op, + PostOpType post_op) { + transform_stage_completed = 0; + current_buffer = fastfft_external_input; + // Keep track of the device side pointer used when called + d_ptr.external_input = input_ptr; + d_ptr.external_output = output_ptr; + + Generic_Fwd_Image_Inv(image_to_search, pre_op, intra_op, post_op); } -template -template -void FourierTransformer::Generic_Fwd(PreOpType pre_op_functor, IntraOpType intra_op_functor) { +template +template +EnableIf> +FourierTransformer::Generic_Fwd(PreOpType pre_op_functor, + IntraOpType intra_op_functor) { SetDimensions(DimensionCheckType::FwdTransform); + // TODO: extend me + MyFFTRunTimeAssertFalse(implicit_dimension_change, "Implicit dimension change not yet supported for FwdFFT"); + // All placeholders - constexpr bool use_thread_method = false; - const bool do_forward_transform = true; + constexpr bool use_thread_method = false; // const bool swap_real_space_quadrants = false; // const bool transpose_output = true; - // SetPrecisionAndExectutionMethod(KernelType kernel_type, bool do_forward_transform, bool use_thread_method) - switch ( transform_dimension ) { - case 1: { - // FIXME there is some redundancy in specifying _decomposed and use_thread_method - // Note: the only time the non-transposed method should be used is for 1d data. - if constexpr ( use_thread_method ) { - if ( is_real_valued_input ) - SetPrecisionAndExectutionMethod(r2c_decomposed, do_forward_transform, pre_op_functor, intra_op_functor); //FFT_R2C_decomposed(transpose_output); - else - SetPrecisionAndExectutionMethod(c2c_decomposed, do_forward_transform, pre_op_functor, intra_op_functor); - transform_stage_completed = TransformStageCompleted::fwd; + // SetPrecisionAndExectutionMethod( ) { + SetPrecisionAndExectutionMethod(nullptr, r2c_decomposed, pre_op_functor, intra_op_functor); //FFT_R2C_decomposed(transpose_output); + transform_stage_completed = 1; } else { - if ( is_real_valued_input ) { - switch ( fwd_size_change_type ) { - case SizeChangeType::no_change: { - SetPrecisionAndExectutionMethod(r2c_none_XY, do_forward_transform, pre_op_functor, intra_op_functor); - break; - } - case SizeChangeType::decrease: { - SetPrecisionAndExectutionMethod(r2c_decrease, do_forward_transform, pre_op_functor, intra_op_functor); - break; - } - case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(r2c_increase, do_forward_transform, pre_op_functor, intra_op_functor); - break; - } - default: { - MyFFTDebugAssertTrue(false, "Invalid size change type"); - } + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_decomposed, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + } + } + else { + if constexpr ( IsAllowedRealType ) { + switch ( fwd_size_change_type ) { + case SizeChangeType::no_change: { + SetPrecisionAndExectutionMethod(nullptr, r2c_none_XY, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + break; } - } - else { - switch ( fwd_size_change_type ) { - case SizeChangeType::no_change: { - SetPrecisionAndExectutionMethod(c2c_fwd_none, do_forward_transform, pre_op_functor, intra_op_functor); - break; - } - case SizeChangeType::decrease: { - SetPrecisionAndExectutionMethod(c2c_fwd_decrease, do_forward_transform, pre_op_functor, intra_op_functor); - break; - } - case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(c2c_fwd_increase, do_forward_transform, pre_op_functor, intra_op_functor); - break; - } - default: { - MyFFTDebugAssertTrue(false, "Invalid size change type"); - } + case SizeChangeType::decrease: { + SetPrecisionAndExectutionMethod(nullptr, r2c_decrease_XY, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + break; + } + case SizeChangeType::increase: { + SetPrecisionAndExectutionMethod(nullptr, r2c_increase_XY, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + break; + } + default: { + MyFFTDebugAssertTrue(false, "Invalid size change type"); } } - transform_stage_completed = TransformStageCompleted::fwd; } - break; - } - case 2: { - switch ( fwd_size_change_type ) { - case SizeChangeType::no_change: { - // FIXME there is some redundancy in specifying _decomposed and use_thread_method - // Note: the only time the non-transposed method should be used is for 1d data. - if ( use_thread_method ) { - SetPrecisionAndExectutionMethod(r2c_decomposed_transposed, do_forward_transform, pre_op_functor, intra_op_functor); - transform_stage_completed = TransformStageCompleted::fwd; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2c_decomposed, do_forward_transform, pre_op_functor, intra_op_functor); + else { + switch ( fwd_size_change_type ) { + case SizeChangeType::no_change: { + MyFFTDebugAssertTrue(false, "Complex input images are not yet supported"); // FIXME: + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_none, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + break; } - else { - SetPrecisionAndExectutionMethod(r2c_none_XY, do_forward_transform, pre_op_functor, intra_op_functor); - transform_stage_completed = TransformStageCompleted::fwd; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2c_fwd_none, do_forward_transform, pre_op_functor, intra_op_functor); + case SizeChangeType::decrease: { + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_decrease, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + break; + } + case SizeChangeType::increase: { + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_increase, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + break; + } + default: { + MyFFTDebugAssertTrue(false, "Invalid size change type"); } - break; - } - case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(r2c_increase, do_forward_transform, pre_op_functor, intra_op_functor); - transform_stage_completed = TransformStageCompleted::fwd; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2c_fwd_increase, do_forward_transform, pre_op_functor, intra_op_functor); - break; - } - case SizeChangeType::decrease: { - SetPrecisionAndExectutionMethod(r2c_decrease, do_forward_transform, pre_op_functor, intra_op_functor); - transform_stage_completed = TransformStageCompleted::fwd; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2c_fwd_decrease, do_forward_transform, pre_op_functor, intra_op_functor); - break; } } - break; // case 2 } - case 3: { - switch ( fwd_size_change_type ) { - case SizeChangeType::no_change: { - SetPrecisionAndExectutionMethod(r2c_none_XZ, do_forward_transform, pre_op_functor, intra_op_functor); - transform_stage_completed = TransformStageCompleted::fwd; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2c_fwd_none_Z, do_forward_transform, pre_op_functor, intra_op_functor); - SetPrecisionAndExectutionMethod(c2c_fwd_none, do_forward_transform, pre_op_functor, intra_op_functor); - break; - } - case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(r2c_increase_XZ, do_forward_transform, pre_op_functor, intra_op_functor); - transform_stage_completed = TransformStageCompleted::fwd; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2c_fwd_increase_Z, do_forward_transform, pre_op_functor, intra_op_functor); - SetPrecisionAndExectutionMethod(c2c_fwd_increase, do_forward_transform, pre_op_functor, intra_op_functor); - // SetPrecisionAndExectutionMethod(c2c_fwd_increase_Z); - break; + } + else if constexpr ( Rank == 2 ) { + switch ( fwd_size_change_type ) { + case SizeChangeType::no_change: { + // FIXME there is some redundancy in specifying _decomposed and use_thread_method + // Note: the only time the non-transposed method should be used is for 1d data. + if ( use_thread_method ) { + SetPrecisionAndExectutionMethod(nullptr, r2c_decomposed_transposed, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_decomposed, pre_op_functor, intra_op_functor); + transform_stage_completed = 3; } - case SizeChangeType::decrease: { - // Not yet supported - MyFFTRunTimeAssertTrue(false, "3D FFT fwd no change not yet supported"); - break; + else { + SetPrecisionAndExectutionMethod(nullptr, r2c_none_XY, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_none, pre_op_functor, intra_op_functor); + transform_stage_completed = 3; } + break; + } + case SizeChangeType::increase: { + SetPrecisionAndExectutionMethod(nullptr, r2c_increase_XY, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_increase, pre_op_functor, intra_op_functor); + transform_stage_completed = 3; + break; + } + case SizeChangeType::decrease: { + SetPrecisionAndExectutionMethod(nullptr, r2c_decrease_XY, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_decrease, pre_op_functor, intra_op_functor); + transform_stage_completed = 3; + break; + } + } + } + else if constexpr ( Rank == 3 ) { + switch ( fwd_size_change_type ) { + case SizeChangeType::no_change: { + SetPrecisionAndExectutionMethod(nullptr, r2c_none_XZ, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_none_XYZ, pre_op_functor, intra_op_functor); + transform_stage_completed = 2; + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_none, pre_op_functor, intra_op_functor); + transform_stage_completed = 3; + break; + } + case SizeChangeType::increase: { + SetPrecisionAndExectutionMethod(nullptr, r2c_increase_XZ, pre_op_functor, intra_op_functor); + transform_stage_completed = 1; + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_increase_XYZ, pre_op_functor, intra_op_functor); + transform_stage_completed = 2; + SetPrecisionAndExectutionMethod(nullptr, c2c_fwd_increase, pre_op_functor, intra_op_functor); + transform_stage_completed = 3; + break; + } + case SizeChangeType::decrease: { + // Not yet supported + MyFFTRunTimeAssertTrue(false, "3D FFT fwd decrease not yet supported"); + break; } } } + else { + MyFFTDebugAssertTrue(false, "Invalid rank"); + } } -template -template -void FourierTransformer::Generic_Inv(IntraOpType intra_op, PostOpType post_op) { +template +template +EnableIf> +FourierTransformer::Generic_Inv(IntraOpType intra_op, + PostOpType post_op) { SetDimensions(DimensionCheckType::InvTransform); + MyFFTRunTimeAssertFalse(implicit_dimension_change, "Implicit dimension change not yet supported for InvFFT"); // All placeholders - constexpr bool use_thread_method = false; - const bool do_forward_transform = false; + constexpr bool use_thread_method = false; // const bool swap_real_space_quadrants = false; // const bool transpose_output = true; @@ -516,25 +478,31 @@ void FourierTransformer::Generic_Inv(I // FIXME there is some redundancy in specifying _decomposed and use_thread_method // Note: the only time the non-transposed method should be used is for 1d data. if constexpr ( use_thread_method ) { - if ( is_real_valued_input ) - SetPrecisionAndExectutionMethod(c2r_decomposed, do_forward_transform); //FFT_R2C_decomposed(transpose_output); - else - SetPrecisionAndExectutionMethod(c2c_decomposed, do_forward_transform); - transform_stage_completed = TransformStageCompleted::inv; + if constexpr ( IsAllowedRealType ) { + SetPrecisionAndExectutionMethod(nullptr, c2r_decomposed, intra_op, post_op); //FFT_R2C_decomposed(transpose_output); + transform_stage_completed = 5; + } + else { + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_decomposed, intra_op, post_op); + transform_stage_completed = 5; + } } else { - if ( is_real_valued_input ) { + if constexpr ( IsAllowedRealType ) { switch ( inv_size_change_type ) { case SizeChangeType::no_change: { - SetPrecisionAndExectutionMethod(c2r_none_XY); + SetPrecisionAndExectutionMethod(nullptr, c2r_none_XY, intra_op, post_op); + transform_stage_completed = 5; break; } case SizeChangeType::decrease: { - SetPrecisionAndExectutionMethod(c2r_decrease); + SetPrecisionAndExectutionMethod(nullptr, c2r_decrease_XY, intra_op, post_op); + transform_stage_completed = 5; break; } case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(c2r_increase); + SetPrecisionAndExectutionMethod(nullptr, c2r_increase, intra_op, post_op); + transform_stage_completed = 5; break; } default: { @@ -545,15 +513,18 @@ void FourierTransformer::Generic_Inv(I else { switch ( inv_size_change_type ) { case SizeChangeType::no_change: { - SetPrecisionAndExectutionMethod(c2c_inv_none); + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_none, intra_op, post_op); + transform_stage_completed = 5; break; } case SizeChangeType::decrease: { - SetPrecisionAndExectutionMethod(c2c_inv_decrease); + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_decrease, intra_op, post_op); + transform_stage_completed = 5; break; } case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(c2c_inv_increase); + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_increase, intra_op, post_op); + transform_stage_completed = 5; break; } default: { @@ -561,7 +532,6 @@ void FourierTransformer::Generic_Inv(I } } } - transform_stage_completed = TransformStageCompleted::inv; } break; } @@ -571,27 +541,31 @@ void FourierTransformer::Generic_Inv(I // FIXME there is some redundancy in specifying _decomposed and use_thread_method // Note: the only time the non-transposed method should be used is for 1d data. if ( use_thread_method ) { - SetPrecisionAndExectutionMethod(c2c_decomposed, do_forward_transform); - transform_stage_completed = TransformStageCompleted::inv; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2r_decomposed_transposed, do_forward_transform); + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_decomposed, intra_op, post_op); + transform_stage_completed = 5; + SetPrecisionAndExectutionMethod(nullptr, c2r_decomposed_transposed, intra_op, post_op); + transform_stage_completed = 7; } else { - SetPrecisionAndExectutionMethod(c2c_inv_none); - transform_stage_completed = TransformStageCompleted::inv; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2r_none_XY); + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_none, intra_op, post_op); + transform_stage_completed = 5; + SetPrecisionAndExectutionMethod(nullptr, c2r_none_XY, intra_op, post_op); + transform_stage_completed = 7; } break; } case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(c2c_inv_increase); - transform_stage_completed = TransformStageCompleted::inv; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2r_increase); + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_increase, intra_op, post_op); + transform_stage_completed = 5; + SetPrecisionAndExectutionMethod(nullptr, c2r_increase, intra_op, post_op); + transform_stage_completed = 7; break; } case SizeChangeType::decrease: { - SetPrecisionAndExectutionMethod(c2c_inv_decrease); - transform_stage_completed = TransformStageCompleted::inv; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2r_decrease); + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_decrease, intra_op, post_op); + transform_stage_completed = 5; + SetPrecisionAndExectutionMethod(nullptr, c2r_decrease_XY, intra_op, post_op); + transform_stage_completed = 7; break; } default: { @@ -604,16 +578,18 @@ void FourierTransformer::Generic_Inv(I case 3: { switch ( inv_size_change_type ) { case SizeChangeType::no_change: { - SetPrecisionAndExectutionMethod(c2c_inv_none_XZ); - transform_stage_completed = TransformStageCompleted::inv; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2c_inv_none_Z); - SetPrecisionAndExectutionMethod(c2r_none); + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_none_XZ, intra_op, post_op); + transform_stage_completed = 5; + SetPrecisionAndExectutionMethod(nullptr, c2c_inv_none_XYZ, intra_op, post_op); + transform_stage_completed = 6; + SetPrecisionAndExectutionMethod(nullptr, c2r_none, intra_op, post_op); + transform_stage_completed = 7; break; } case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(r2c_increase); - transform_stage_completed = TransformStageCompleted::fwd; // technically not complete, needed for copy on validation of partial fft. - // SetPrecisionAndExectutionMethod(c2c_fwd_increase_Z); + MyFFTRunTimeAssertFalse(true, "3D FFT inv increase not yet supported"); + SetPrecisionAndExectutionMethod(nullptr, r2c_increase_XY, intra_op, post_op); + // SetPrecisionAndExectutionMethod( nullptr, c2c_fwd_increase_XYZ); break; } case SizeChangeType::decrease: { @@ -630,191 +606,17 @@ void FourierTransformer::Generic_Inv(I } } -// template -// void FourierTransformer::CrossCorrelate(float2* image_to_search, bool swap_real_space_quadrants) { - -// // Set the member pointer to the passed pointer -// d_ptr.image_to_search = image_to_search; - -// switch ( transform_dimension ) { - -// case 1: { - -// MyFFTRunTimeAssertTrue(false, "1D FFT Cross correlation not yet supported"); -// break; -// } -// case 2: { - -// switch ( fwd_size_change_type ) { -// case no_change: { - -// SetDimensions(FwdTransform); -// SetPrecisionAndExectutionMethod(r2c_none_XY, true); -// switch ( inv_size_change_type ) { -// case no_change: { -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation no change/ nochange not yet supported"); -// break; -// } -// case increase: { -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation no change/increase not yet supported"); -// break; -// } -// case decrease: { - -// SetPrecisionAndExectutionMethod(xcorr_fwd_none_inv_decrease, true); -// SetPrecisionAndExectutionMethod(c2r_decrease, false); -// break; -// } -// default: { -// MyFFTDebugAssertTrue(false, "Invalid size change type"); -// break; -// } -// } // switch on inv size change type -// break; -// } // case fwd no change -// case increase: { - -// SetDimensions(FwdTransform); -// SetPrecisionAndExectutionMethod(r2c_increase, true); -// switch ( inv_size_change_type ) { -// case no_change: { - -// SetPrecisionAndExectutionMethod(xcorr_fwd_increase_inv_none, true); -// SetPrecisionAndExectutionMethod(c2r_none_XY, false); -// transform_stage_completed = TransformStageCompleted::inv; -// break; -// } -// case increase: { -// // I don't see where increase increase makes any sense -// // FIXME add a check on this in the validation step. -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation with fwd and inv size increase is not supported"); -// break; -// } -// case decrease: { -// // with FwdTransform set, call c2c -// // Set InvTransform -// // Call new kernel that handles the conj mul inv c2c trimmed, and inv c2r in one go. -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation with fwd increase and inv size decrease is a work in progress"); - -// break; -// } -// default: { -// MyFFTRunTimeAssertTrue(false, "Invalid size change type"); -// } -// } // switch on inv size change type - -// // FFT_R2C_WithPadding(); -// // FFT_C2C_INCREASE_ConjMul_C2C(image_to_search, swap_real_space_quadrants); -// // FFT_C2R_Transposed(); -// break; -// } -// case decrease: { - -// SetDimensions(FwdTransform); -// SetPrecisionAndExectutionMethod(r2c_decrease, true); -// switch ( inv_size_change_type ) { -// case no_change: { -// SetPrecisionAndExectutionMethod(xcorr_fwd_increase_inv_none, true); -// SetPrecisionAndExectutionMethod(c2r_none_XY, false); // TODO the output could be smaller -// transform_stage_completed = TransformStageCompleted::inv; -// break; -// } -// case increase: { - -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation with fwd and inv size increase is not supported"); -// break; -// } -// case decrease: { - -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation with fwd decrease and inv size decrease is a work in progress"); -// break; -// } -// default: { -// MyFFTRunTimeAssertTrue(false, "Invalid inv size change type"); -// } break; -// } -// break; -// } // case decrease -// default: { -// MyFFTRunTimeAssertTrue(false, "Invalid fwd size change type"); -// } -// } // switch on fwd size change type - -// break; // case 2 -// } -// case 3: { -// // Not yet supported -// MyFFTRunTimeAssertTrue(false, "3D FFT not yet supported"); -// break; -// } -// } -// } - -// template -// void FourierTransformer::CrossCorrelate(__half2* image_to_search, bool swap_real_space_quadrants) { - -// // Set the member pointer to the passed pointer -// d_ptr.image_to_search = image_to_search; -// switch ( transform_dimension ) { -// case 1: { -// MyFFTRunTimeAssertTrue(false, "1D FFT Cross correlation not yet supported"); -// break; -// } -// case 2: { -// switch ( fwd_size_change_type ) { -// case no_change: { -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation without size change not yet supported"); -// break; -// } -// case increase: { -// SetDimensions(FwdTransform); -// SetPrecisionAndExectutionMethod(r2c_increase, true); - -// switch ( inv_size_change_type ) { -// case no_change: { -// SetPrecisionAndExectutionMethod(xcorr_fwd_increase_inv_none, true); -// SetPrecisionAndExectutionMethod(c2r_none_XY, false); // TODO the output could be smaller -// transform_stage_completed = TransformStageCompleted::inv; - -// break; -// } -// case increase: { -// // I don't see where increase increase makes any sense -// // FIXME add a check on this in the validation step. -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation with fwd and inv size increase is not supported"); -// break; -// } -// case decrease: { -// // with FwdTransform set, call c2c -// // Set InvTransform -// // Call new kernel that handles the conj mul inv c2c trimmed, and inv c2r in one go. -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation with fwd increase and inv size decrease is a work in progress"); - -// break; -// } -// } // inv size change type -// } // case fwd_size_change = increase -// case decrease: { -// MyFFTRunTimeAssertTrue(false, "2D FFT Cross correlation without size decrease not yet supported"); -// break; -// } -// } // fwd size change type -// break; // case 2 -// } -// case 3: { -// // Not yet supported -// MyFFTRunTimeAssertTrue(false, "3D FFT not yet supported"); -// break; -// } -// } -// } - -template -template -void FourierTransformer::Generic_Fwd_Image_Inv(float2* image_to_search, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor) { +template +template +EnableIf && IsAllowedInputType> +FourierTransformer::Generic_Fwd_Image_Inv(OtherImageType* image_to_search_ptr, + PreOpType pre_op_functor, + IntraOpType intra_op_functor, + PostOpType post_op_functor) { // Set the member pointer to the passed pointer - d_ptr.image_to_search = image_to_search; SetDimensions(DimensionCheckType::FwdTransform); switch ( transform_dimension ) { @@ -823,10 +625,10 @@ void FourierTransformer::Generic_Fwd_I break; } case 2: { - ; switch ( fwd_size_change_type ) { case SizeChangeType::no_change: { - SetPrecisionAndExectutionMethod(r2c_none_XY, true); + SetPrecisionAndExectutionMethod(image_to_search_ptr, r2c_none_XY, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 1; switch ( inv_size_change_type ) { case SizeChangeType::no_change: { MyFFTRunTimeAssertTrue(false, "2D FFT generic lambda no change/nochange not yet supported"); @@ -837,8 +639,10 @@ void FourierTransformer::Generic_Fwd_I break; } case SizeChangeType::decrease: { - SetPrecisionAndExectutionMethod(xcorr_fwd_none_inv_decrease, true); - SetPrecisionAndExectutionMethod(c2r_decrease, false); + SetPrecisionAndExectutionMethod(image_to_search_ptr, xcorr_fwd_none_inv_decrease, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 5; + SetPrecisionAndExectutionMethod(image_to_search_ptr, c2r_decrease_XY, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 7; break; } default: { @@ -849,12 +653,14 @@ void FourierTransformer::Generic_Fwd_I break; } // case fwd no change case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(r2c_increase, true); + SetPrecisionAndExectutionMethod(image_to_search_ptr, r2c_increase_XY, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 1; switch ( inv_size_change_type ) { case SizeChangeType::no_change: { - SetPrecisionAndExectutionMethod(generic_fwd_increase_op_inv_none, true, pre_op_functor, intra_op_functor, post_op_functor); - SetPrecisionAndExectutionMethod(c2r_none_XY, false); - transform_stage_completed = TransformStageCompleted::inv; + SetPrecisionAndExectutionMethod(image_to_search_ptr, generic_fwd_increase_op_inv_none, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 5; + SetPrecisionAndExectutionMethod(image_to_search_ptr, c2r_none_XY, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 7; break; } @@ -880,12 +686,14 @@ void FourierTransformer::Generic_Fwd_I } case SizeChangeType::decrease: { - SetPrecisionAndExectutionMethod(r2c_decrease, true); + SetPrecisionAndExectutionMethod(image_to_search_ptr, r2c_decrease_XY, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 1; switch ( inv_size_change_type ) { case SizeChangeType::no_change: { - SetPrecisionAndExectutionMethod(xcorr_fwd_increase_inv_none, true); - SetPrecisionAndExectutionMethod(c2r_none_XY, false); // TODO the output could be smaller - transform_stage_completed = TransformStageCompleted::inv; + SetPrecisionAndExectutionMethod(image_to_search_ptr, generic_fwd_increase_op_inv_none, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 5; + SetPrecisionAndExectutionMethod(image_to_search_ptr, c2r_none_XY, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 7; break; } case SizeChangeType::increase: { @@ -931,17 +739,20 @@ void FourierTransformer::Generic_Fwd_I break; } case SizeChangeType::increase: { - SetPrecisionAndExectutionMethod(r2c_increase_XZ); - transform_stage_completed = TransformStageCompleted::fwd; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2c_fwd_increase_Z); + SetPrecisionAndExectutionMethod(image_to_search_ptr, r2c_increase_XZ, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 1; + SetPrecisionAndExectutionMethod(image_to_search_ptr, c2c_fwd_increase_XYZ, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 2; switch ( inv_size_change_type ) { case SizeChangeType::no_change: { // TODO: will need a kernel for generic_fwd_increase_op_inv_none_XZ - SetPrecisionAndExectutionMethod(generic_fwd_increase_op_inv_none); - // SetPrecisionAndExectutionMethod(c2c_inv_none_XZ); - transform_stage_completed = TransformStageCompleted::inv; // technically not complete, needed for copy on validation of partial fft. - SetPrecisionAndExectutionMethod(c2c_inv_none_Z); - SetPrecisionAndExectutionMethod(c2r_none); + SetPrecisionAndExectutionMethod(image_to_search_ptr, generic_fwd_increase_op_inv_none, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 5; + // SetPrecisionAndExectutionMethod( image_to_search_ptr, c2c_inv_none_XZ); + SetPrecisionAndExectutionMethod(image_to_search_ptr, c2c_inv_none_XYZ, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 6; + SetPrecisionAndExectutionMethod(image_to_search_ptr, c2r_none, pre_op_functor, intra_op_functor, post_op_functor); + transform_stage_completed = 7; break; } case SizeChangeType::increase: { @@ -989,8 +800,8 @@ void FourierTransformer::Generic_Fwd_I //////////////////////////////////////////////////// /// END PUBLIC METHODS //////////////////////////////////////////////////// -template -void FourierTransformer::ValidateDimensions( ) { +template +void FourierTransformer::ValidateDimensions( ) { // TODO - runtime asserts would be better as these are breaking errors that are under user control. // check to see if there is any measurable penalty for this. @@ -1062,17 +873,64 @@ void FourierTransformer::ValidateDimen else { transform_dimension = 3; constexpr unsigned int max_3d_size = 512; - MyFFTRunTimeAssertFalse(fwd_dims_in.z > max_3d_size || fwd_dims_out.z > max_3d_size || inv_dims_in.z > max_3d_size || inv_dims_out.z > max_3d_size || - fwd_dims_in.y > max_3d_size || fwd_dims_out.y > max_3d_size || inv_dims_in.y > max_3d_size || inv_dims_out.y > max_3d_size || - fwd_dims_in.x > max_3d_size || fwd_dims_out.x > max_3d_size || inv_dims_in.x > max_3d_size || inv_dims_out.x > max_3d_size, + MyFFTRunTimeAssertFalse(fwd_dims_in.z > max_3d_size || + fwd_dims_out.z > max_3d_size || + inv_dims_in.z > max_3d_size || + inv_dims_out.z > max_3d_size || + fwd_dims_in.y > max_3d_size || + fwd_dims_out.y > max_3d_size || + inv_dims_in.y > max_3d_size || + inv_dims_out.y > max_3d_size || + fwd_dims_in.x > max_3d_size || + fwd_dims_out.x > max_3d_size || + inv_dims_in.x > max_3d_size || + inv_dims_out.x > max_3d_size, "Error in validating the dimension: Currently all dimensions must be <= 512 for 3d transforms."); } + // Allow some non-power of 2 sizes if there is a size change type. + // For now, just forward increase. + // Note: initially we'll only allow this logic for round trip transforms (that don't write out fwd_dims_out or read inv_dims_in) where + // the caller may be "surprised" by the size change. + if ( ! IsAPowerOfTwo(fwd_dims_in.x) ) { + MyFFTRunTimeAssertTrue(fwd_size_change_type == SizeChangeType::increase, "Input x dimension must be a power of 2"); + implicit_dimension_change = true; + } + if ( ! IsAPowerOfTwo(fwd_dims_in.y) ) { + MyFFTRunTimeAssertTrue(fwd_size_change_type == SizeChangeType::increase, "Input y dimension must be a power of 2"); + implicit_dimension_change = true; + } + if ( ! IsAPowerOfTwo(fwd_dims_in.z) ) { + MyFFTRunTimeAssertTrue(fwd_size_change_type == SizeChangeType::increase, "Input z dimension must be a power of 2"); + implicit_dimension_change = true; + } + + MyFFTRunTimeAssertTrue(IsAPowerOfTwo(fwd_dims_out.x), "Output x dimension must be a power of 2"); + MyFFTRunTimeAssertTrue(IsAPowerOfTwo(fwd_dims_out.y), "Output y dimension must be a power of 2"); + MyFFTRunTimeAssertTrue(IsAPowerOfTwo(fwd_dims_out.z), "Output z dimension must be a power of 2"); + + MyFFTRunTimeAssertTrue(IsAPowerOfTwo(inv_dims_in.x), "Input x dimension must be a power of 2"); + MyFFTRunTimeAssertTrue(IsAPowerOfTwo(inv_dims_in.y), "Input y dimension must be a power of 2"); + MyFFTRunTimeAssertTrue(IsAPowerOfTwo(inv_dims_in.z), "Input z dimension must be a power of 2"); + + if ( ! IsAPowerOfTwo(inv_dims_out.x) ) { + MyFFTRunTimeAssertTrue(inv_size_change_type == SizeChangeType::decrease, "Output x dimension must be a power of 2"); + implicit_dimension_change = true; + } + if ( ! IsAPowerOfTwo(inv_dims_out.y) ) { + MyFFTRunTimeAssertTrue(inv_size_change_type == SizeChangeType::decrease, "Output y dimension must be a power of 2"); + implicit_dimension_change = true; + } + if ( ! IsAPowerOfTwo(inv_dims_out.z) ) { + MyFFTRunTimeAssertTrue(inv_size_change_type == SizeChangeType::decrease, "Output z dimension must be a power of 2"); + implicit_dimension_change = true; + } + is_size_validated = true; } -template -void FourierTransformer::SetDimensions(DimensionCheckType::Enum check_op_type) { +template +void FourierTransformer::SetDimensions(DimensionCheckType::Enum check_op_type) { // This should be run inside any public method call to ensure things ar properly setup. if ( ! is_size_validated ) { ValidateDimensions( ); @@ -1082,58 +940,23 @@ void FourierTransformer::SetDimensions case DimensionCheckType::CopyFromHost: { // MyFFTDebugAssertTrue(transform_stage_completed == none, "When copying from host, the transform stage should be none, something has gone wrong."); // FIXME: is this the right thing to do? Maybe this should be explicitly "reset" when the input image is "refereshed." - transform_stage_completed = TransformStageCompleted::none; - memory_size_to_copy = input_memory_allocated; + memory_size_to_copy_ = input_memory_wanted_; break; } case DimensionCheckType::CopyToHost: { - // FIXME currently there is no check that the right amount of memory is allocated on the host side array. - switch ( transform_stage_completed ) { - case SizeChangeType::no_change: { - memory_size_to_copy = input_memory_allocated; - break; - } - case TransformStageCompleted::fwd: { - memory_size_to_copy = fwd_output_memory_allocated; - break; - } - case TransformStageCompleted::inv: { - memory_size_to_copy = inv_output_memory_allocated; - break; - } - } // switch transform_stage_completed - break; - } // case CopToHost - - case DimensionCheckType::CopyDeviceToDevice: { - // FIXME currently there is no check that the right amount of memory is allocated on the host side array. - switch ( transform_stage_completed ) { - case SizeChangeType::no_change: { - memory_size_to_copy = input_memory_allocated; - break; - } - case TransformStageCompleted::fwd: { - memory_size_to_copy = fwd_output_memory_allocated; - break; - } - case TransformStageCompleted::inv: { - memory_size_to_copy = inv_output_memory_allocated; - break; - } - } // switch transform_stage_completed - break; - } // case CopyDeviceToDevice - - case DimensionCheckType::FwdTransform: { - MyFFTDebugAssertTrue(transform_stage_completed == TransformStageCompleted::none || transform_stage_completed == TransformStageCompleted::inv, "When doing a forward transform, the transform stage completed should be none, something has gone wrong."); - break; - } + if ( transform_stage_completed == 0 ) { + memory_size_to_copy_ = input_memory_wanted_; + } + else if ( transform_stage_completed < 5 ) { + memory_size_to_copy_ = fwd_output_memory_wanted_; + } + else { + memory_size_to_copy_ = inv_output_memory_wanted_; + } + } // switch transform_stage_completed + break; - case DimensionCheckType::InvTransform: { - MyFFTDebugAssertTrue(transform_stage_completed == TransformStageCompleted::fwd, "When doing an inverse transform, the transform stage completed should be fwd, something has gone wrong."); - break; - } } // end switch on operation type } @@ -1143,16 +966,58 @@ void FourierTransformer::SetDimensions // R2C_decomposed -template -__global__ void thread_fft_kernel_R2C_decomposed(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { +template +void FourierTransformer::CopyDeviceToHostAndSynchronize(InputType* input_pointer, int n_elements_to_copy) { + SetDimensions(DimensionCheckType::CopyToHost); + int n_to_actually_copy = (n_elements_to_copy > 0) ? n_elements_to_copy : memory_size_to_copy_; + + MyFFTDebugAssertTrue(n_to_actually_copy > 0, "Error in CopyDeviceToHostAndSynchronize: n_elements_to_copy must be > 0"); + MyFFTDebugAssertTrue(is_pointer_in_memory_and_registered(input_pointer), "Error in CopyDeviceToHostAndSynchronize: input_pointer must be in memory and registered"); + + switch ( current_buffer ) { + case fastfft_external_input: { + MyFFTDebugAssertTrue(is_pointer_in_device_memory(d_ptr.external_input), "Error in CopyDeviceToHostAndSynchronize: input_pointer must be in device memory"); + cudaErr(cudaMemcpyAsync(input_pointer, d_ptr.external_input, n_to_actually_copy * sizeof(InputType), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + break; + } + case fastfft_external_output: { + MyFFTDebugAssertTrue(is_pointer_in_device_memory(d_ptr.external_input), "Error in CopyDeviceToHostAndSynchronize: input_pointer must be in device memory"); + cudaErr(cudaMemcpyAsync(input_pointer, d_ptr.external_output, n_to_actually_copy * sizeof(InputType), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + break; + } + // If we are in the internal buffers, our data is ComputeBaseType + case fastfft_internal_buffer_1: { + MyFFTDebugAssertTrue(is_pointer_in_device_memory(d_ptr.buffer_1), "Error in CopyDeviceToHostAndSynchronize: input_pointer must be in device memory"); + if ( sizeof(ComputeBaseType) != sizeof(InputType) ) + std::cerr << "\n\tWarning: CopyDeviceToHostAndSynchronize: sizeof(ComputeBaseType) != sizeof(InputType) - this may be a problem\n\n"; + cudaErr(cudaMemcpyAsync(input_pointer, d_ptr.buffer_1, n_to_actually_copy * sizeof(ComputeBaseType), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + break; + } + case fastfft_internal_buffer_2: { + MyFFTDebugAssertTrue(is_pointer_in_device_memory(d_ptr.buffer_2), "Error in CopyDeviceToHostAndSynchronize: input_pointer must be in device memory"); + if ( sizeof(ComputeBaseType) != sizeof(InputType) ) + std::cerr << "\n\tWarning: CopyDeviceToHostAndSynchronize: sizeof(ComputeBaseType) != sizeof(InputType) - this may be a problem\n\n"; + cudaErr(cudaMemcpyAsync(input_pointer, d_ptr.buffer_2, n_to_actually_copy * sizeof(ComputeBaseType), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + break; + } + default: { + MyFFTDebugAssertTrue(false, "Error in CopyDeviceToHostAndSynchronize: current_buffer must be one of fastfft_external_input, fastfft_internal_buffer_1, fastfft_internal_buffer_2"); + } + } + + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); +}; + +template +__global__ void thread_fft_kernel_R2C_decomposed(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; // Memory used by FFT - for Thread() type, FFT::storage_size == FFT::elements_per_thread == size_of::value - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io_thread::load_r2c(&input_values[Return1DFFTAddress(mem_offsets.physical_x_input)], thread_data, Q); @@ -1163,21 +1028,18 @@ __global__ void thread_fft_kernel_R2C_decomposed(const ScalarType* __restrict__ io_thread::remap_decomposed_segments(thread_data, shared_mem, twiddle_in, Q, mem_offsets.physical_x_output); io_thread::store_r2c(shared_mem, &output_values[Return1DFFTAddress(mem_offsets.physical_x_output)], Q, mem_offsets.physical_x_output); +} -} // end of thread_fft_kernel_R2C - -// R2C_decomposed_transposed - -template -__global__ void thread_fft_kernel_R2C_decomposed_transposed(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { +template +__global__ void thread_fft_kernel_R2C_decomposed_transposed(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; // Memory used by FFT - for Thread() type, FFT::storage_size == FFT::elements_per_thread == size_of::value - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io_thread::load_r2c(&input_values[Return1DFFTAddress(mem_offsets.physical_x_input)], thread_data, Q); @@ -1188,23 +1050,20 @@ __global__ void thread_fft_kernel_R2C_decomposed_transposed(const ScalarType* __ io_thread::remap_decomposed_segments(thread_data, shared_mem, twiddle_in, Q, mem_offsets.physical_x_output); io_thread::store_r2c_transposed_xy(shared_mem, &output_values[ReturnZplane(blockDim.y, mem_offsets.physical_x_output)], Q, gridDim.y, mem_offsets.physical_x_output); +} -} // end of thread_fft_kernel_R2C_transposed - -// R2C - -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_R2C_NONE_XY(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { + void block_fft_kernel_R2C_NONE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; // Memory used by FFT - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; // No need to __syncthreads as each thread only accesses its own shared mem anyway // multiply Q*fwd_dims_out.w because x maps to y in the output transposed FFT @@ -1215,28 +1074,27 @@ __launch_bounds__(FFT::max_threads_per_block) __global__ FFT( ).execute(thread_data, shared_mem, workspace); io::store_r2c_transposed_xy(thread_data, &output_values[ReturnZplane(gridDim.y, mem_offsets.physical_x_output)], gridDim.y); - -} // end of block_fft_kernel_R2C_NONE_XY +} // 2 ffts/block via threadIdx.x, notice launch bounds. Creates partial coalescing. -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_R2C_NONE_XZ(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { + void block_fft_kernel_R2C_NONE_XZ(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; // Memory used by FFT - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io::load_r2c(&input_values[Return1DFFTAddress_strided_Z(mem_offsets.physical_x_input)], thread_data); - constexpr const unsigned int n_compute_elements = FFT::shared_memory_size / sizeof(complex_type); - FFT( ).execute(thread_data, &shared_mem[threadIdx.z * n_compute_elements], workspace); + constexpr const unsigned int n_compute_elements = FFT::shared_memory_size / sizeof(complex_compute_t); + FFT( ).execute(thread_data, &shared_mem[threadIdx.y * n_compute_elements], workspace); __syncthreads( ); // TODO: is this needed? // memory is at least large enough to hold the output with padding. synchronizing @@ -1246,20 +1104,20 @@ __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ io::store_r2c_transposed_xz_strided_Z(shared_mem, output_values); } -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_R2C_INCREASE_XY(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { + void block_fft_kernel_R2C_INCREASE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ scalar_type shared_input[]; - complex_type* shared_mem = (complex_type*)&shared_input[mem_offsets.shared_input]; + extern __shared__ scalar_compute_t shared_input[]; + complex_compute_t* shared_mem = (complex_compute_t*)&shared_input[mem_offsets.shared_input]; // Memory used by FFT - complex_type twiddle; - complex_type thread_data[FFT::storage_size]; + complex_compute_t twiddle; + complex_compute_t thread_data[FFT::storage_size]; // To re-map the thread index to the data ... these really could be short ints, but I don't know how that will perform. TODO benchmark // It is also questionable whether storing these vs, recalculating makes more sense. @@ -1307,31 +1165,31 @@ __launch_bounds__(FFT::max_threads_per_block) __global__ io::store_r2c_transposed_xy(thread_data, &output_values[ReturnZplane(blockDim.y, mem_offsets.physical_x_output)], output_MAP, gridDim.y, mem_offsets.physical_x_output); } -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_R2C_INCREASE_XZ(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { + void block_fft_kernel_R2C_INCREASE_XZ(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ scalar_type shared_input[]; - complex_type* shared_mem = (complex_type*)&shared_input[XZ_STRIDE * mem_offsets.shared_input]; + extern __shared__ scalar_compute_t shared_input[]; + complex_compute_t* shared_mem = (complex_compute_t*)&shared_input[XZ_STRIDE * mem_offsets.shared_input]; // Memory used by FFT - complex_type twiddle; - complex_type thread_data[FFT::storage_size]; + complex_compute_t twiddle; + complex_compute_t thread_data[FFT::storage_size]; float twiddle_factor_args[FFT::storage_size]; // Note: Q is used to calculate the strided output, which in this use, will end up being an offest in Z, so // we multiply by the NXY physical mem size of the OUTPUT array (which will be ZY') Then in the sub_fft loop, instead of adding one // we add NXY io::load_r2c_shared(&input_values[Return1DFFTAddress_strided_Z(mem_offsets.physical_x_input)], - &shared_input[threadIdx.z * mem_offsets.shared_input], + &shared_input[threadIdx.y * mem_offsets.shared_input], thread_data, twiddle_factor_args, twiddle_in); - FFT( ).execute(thread_data, &shared_mem[threadIdx.z * FFT::shared_memory_size / sizeof(complex_type)], workspace); + FFT( ).execute(thread_data, &shared_mem[threadIdx.y * FFT::shared_memory_size / sizeof(complex_compute_t)], workspace); __syncthreads( ); // Now we have a partial strided output due to the transform decomposition. In the 2D case we either write it out, or coalsece it in to shared memory // until we have the full output. Here, we are working on a tile, so we can transpose the data, and write it out partially coalesced. @@ -1342,7 +1200,7 @@ __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ // Now we need to loop over the remaining fragments. // For the other fragments we need the initial twiddle for ( int sub_fft = 1; sub_fft < Q; sub_fft++ ) { - io::copy_from_shared(&shared_input[threadIdx.z * mem_offsets.shared_input], thread_data); + io::copy_from_shared(&shared_input[threadIdx.y * mem_offsets.shared_input], thread_data); for ( int i = 0; i < FFT::elements_per_thread; i++ ) { // Pre shift with twiddle SINCOS(twiddle_factor_args[i] * sub_fft, &twiddle.y, &twiddle.x); @@ -1350,14 +1208,14 @@ __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ // increment the output mapping. } - FFT( ).execute(thread_data, &shared_mem[threadIdx.z * FFT::shared_memory_size / sizeof(complex_type)], workspace); // FIXME the workspace is probably not going to work with the batched, look at the examples to see what to do. + FFT( ).execute(thread_data, &shared_mem[threadIdx.y * FFT::shared_memory_size / sizeof(complex_compute_t)], workspace); // FIXME the workspace is probably not going to work with the batched, look at the examples to see what to do. __syncthreads( ); io::transpose_r2c_in_shared_XZ(shared_mem, thread_data); io::store_r2c_transposed_xz_strided_Z(shared_mem, output_values, Q, sub_fft); } // // For the last fragment we need to also do a bounds check. FIXME where does this happen - // io::copy_from_shared(&shared_input[threadIdx.z * mem_offsets.shared_input], thread_data); + // io::copy_from_shared(&shared_input[threadIdx.y * mem_offsets.shared_input], thread_data); // for (int i = 0; i < FFT::elements_per_thread; i++) { // // Pre shift with twiddle // SINCOS(twiddle_factor_args[i]*(Q-1),&twiddle.y,&twiddle.x); @@ -1365,23 +1223,23 @@ __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ // // increment the output mapping. // } - // FFT().execute(thread_data, &shared_mem[threadIdx.z * FFT::shared_memory_size/sizeof(complex_type)], workspace); // FIXME the workspace is not setup for tiled approach + // FFT().execute(thread_data, &shared_mem[threadIdx.y * FFT::shared_memory_size/sizeof(complex_compute_t)], workspace); // FIXME the workspace is not setup for tiled approach // __syncthreads(); // io::transpose_r2c_in_shared_XZ(shared_mem, thread_data); // io::store_r2c_transposed_xz_strided_Z(shared_mem, output_values, Q, 0); } // __launch_bounds__(FFT::max_threads_per_block) we don't know this because it is threadDim.x * threadDim.z - this could be templated if it affects performance significantly -template -__global__ void block_fft_kernel_R2C_DECREASE_XY(const ScalarType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { +template +__global__ void block_fft_kernel_R2C_DECREASE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; // The shared memory is used for storage, shuffling and fft ops at different stages and includes room for bank padding. - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; // Load in natural order io::load_r2c_shared_and_pad(&input_values[Return1DFFTAddress(mem_offsets.physical_x_input)], shared_mem); @@ -1389,31 +1247,29 @@ __global__ void block_fft_kernel_R2C_DECREASE_XY(const ScalarType* __restrict__ // DIT shuffle, bank conflict free io::copy_from_shared(shared_mem, thread_data, Q); - // The FFT operator has no idea we are using threadIdx.z to get multiple sub transforms, so we need to + // The FFT operator has no idea we are using threadIdx.y to get multiple sub transforms, so we need to // segment the shared memory it accesses to avoid conflicts. - constexpr const unsigned int fft_shared_mem_num_elements = FFT::shared_memory_size / sizeof(complex_type); - FFT( ).execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.z], workspace); + constexpr const unsigned int fft_shared_mem_num_elements = FFT::shared_memory_size / sizeof(complex_compute_t); + FFT( ).execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.y], workspace); __syncthreads( ); // Full twiddle multiply and store in natural order in shared memory io::reduce_block_fft(thread_data, shared_mem, twiddle_in, Q); // Reduce from shared memory into registers, ending up with only P valid outputs. - io::store_r2c_reduced(thread_data, &output_values[mem_offsets.physical_x_output * threadIdx.z], gridDim.y, mem_offsets.physical_x_output); - -} // end of block_fft_kernel_R2C_DECREASE_XY - -// decomposed with conj multiplication + io::store_r2c_reduced(thread_data, &output_values[mem_offsets.physical_x_output * threadIdx.y], gridDim.y, mem_offsets.physical_x_output); +} -template -__global__ void thread_fft_kernel_C2C_decomposed_ConjMul(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { +template +__global__ void thread_fft_kernel_C2C_decomposed_ConjMul(const ExternalImage_t* __restrict__ image_to_search, const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { - using complex_type = ComplexType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; // Memory used by FFT - for Thread() type, FFT::storage_size == FFT::elements_per_thread == size_of::value - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io_thread::load_c2c(&input_values[Return1DFFTAddress(size_of::value) * Q], thread_data, Q); @@ -1436,108 +1292,32 @@ __global__ void thread_fft_kernel_C2C_decomposed_ConjMul(const ComplexType* __re io_thread::store_c2c(shared_mem, &output_values[Return1DFFTAddress(size_of::value * Q)], Q); } -// C2C with conj multiplication +template +__global__ void block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul(const ExternalImage_t* __restrict__ image_to_search, const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, + Offsets mem_offsets, float twiddle_in, int apparent_Q, typename FFT::workspace_type workspace_fwd, typename invFFT::workspace_type workspace_inv) { -template -__launch_bounds__(invFFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, - Offsets mem_offsets, int apparent_Q, typename FFT::workspace_type workspace_fwd, typename invFFT::workspace_type workspace_inv) { + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - // // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; + extern __shared__ complex_compute_t shared_mem[]; - // __shared__ complex_type shared_mem[invFFT::shared_memory_size/sizeof(complex_type)]; // Storage for the input data that is re-used each blcok - extern __shared__ complex_type shared_mem[]; // Storage for the input data that is re-used each blcok + complex_compute_t thread_data[FFT::storage_size]; - complex_type thread_data[FFT::storage_size]; + // Load in natural order + io::load(&input_values[Return1DFFTAddress(size_of::value)], thread_data); - // For simplicity, we explicitly zeropad the input data to the size of the FFT. - // It may be worth trying to use threadIdx.z as in the DECREASE methods. - // Until then, this - io::load(&input_values[Return1DFFTAddress(size_of::value / apparent_Q)], thread_data, size_of::value / apparent_Q); + // io::load_c2c_shared_and_pad(&input_values[Return1DFFTAddress(mem_offsets.physical_x_input)], shared_mem); - // In the first FFT the modifying twiddle factor is 1 so the data are reeal + // // DIT shuffle, bank conflict free + // io::copy_from_shared(shared_mem, thread_data, Q); + + // constexpr const unsigned int fft_shared_mem_num_elements = FFT::shared_memory_size / sizeof(complex_compute_t); + // FFT().execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.y], workspace_fwd); + // __syncthreads(); FFT( ).execute(thread_data, shared_mem, workspace_fwd); - -#if FFT_DEBUG_STAGE > 3 - // * apparent_Q - io::load_shared_and_conj_multiply(&image_to_search[Return1DFFTAddress(size_of::value)], thread_data); -#endif - -#if FFT_DEBUG_STAGE > 4 - invFFT( ).execute(thread_data, shared_mem, workspace_inv); -#endif - - // * apparent_Q - io::store(thread_data, &output_values[Return1DFFTAddress(size_of::value)]); -} - -template -__launch_bounds__(invFFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul_SwapRealSpaceQuadrants(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace_fwd, typename invFFT::workspace_type workspace_inv) { - - // // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; - - // __shared__ complex_type shared_mem[invFFT::shared_memory_size/sizeof(complex_type)]; // Storage for the input data that is re-used each blcok - extern __shared__ complex_type shared_mem[]; // Storage for the input data that is re-used each blcok - - complex_type thread_data[FFT::storage_size]; - - io::load(&input_values[Return1DFFTAddress(size_of::value)], thread_data, size_of::value); - - // In the first FFT the modifying twiddle factor is 1 so the data are reeal - FFT( ).execute(thread_data, shared_mem, workspace_fwd); - -#if FFT_DEBUG_STAGE > 3 - // Swap real space quadrants using a phase shift by N/2 pixels - const unsigned int stride = io::stride_size( ); - int logical_y; - for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - logical_y = threadIdx.x + i * stride; - // FIXME, not sure the physical_x_output is updated to replace the previous terms appropriately. This is supposed to be setting the conjugate terms. - if ( logical_y >= mem_offsets.physical_x_output ) - logical_y -= mem_offsets.physical_x_output; - if ( (int(blockIdx.y) + logical_y) % 2 != 0 ) - thread_data[i] *= -1.f; // FIXME TYPE - } - - io::load_shared_and_conj_multiply(&image_to_search[Return1DFFTAddress(size_of::value * Q)], thread_data); -#endif - -#if FFT_DEBUG_STAGE > 4 - invFFT( ).execute(thread_data, shared_mem, workspace_inv); -#endif - - io::store(thread_data, &output_values[Return1DFFTAddress(size_of::value * Q)]); - -} // - -template -__global__ void block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul(const ComplexType* __restrict__ image_to_search, const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, - Offsets mem_offsets, float twiddle_in, int apparent_Q, typename FFT::workspace_type workspace_fwd, typename invFFT::workspace_type workspace_inv) { - - using complex_type = ComplexType; - - extern __shared__ complex_type shared_mem[]; - - complex_type thread_data[FFT::storage_size]; - - // Load in natural order - io::load(&input_values[Return1DFFTAddress(size_of::value)], thread_data); - - // io::load_c2c_shared_and_pad(&input_values[Return1DFFTAddress(mem_offsets.physical_x_input)], shared_mem); - - // // DIT shuffle, bank conflict free - // io::copy_from_shared(shared_mem, thread_data, Q); - - // constexpr const unsigned int fft_shared_mem_num_elements = FFT::shared_memory_size / sizeof(complex_type); - // FFT().execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.z], workspace_fwd); - // __syncthreads(); - FFT( ).execute(thread_data, shared_mem, workspace_fwd); - - // // Full twiddle multiply and store in natural order in shared memory - // io::reduce_block_fft(thread_data, shared_mem, twiddle_in, Q); + + // // Full twiddle multiply and store in natural order in shared memory + // io::reduce_block_fft(thread_data, shared_mem, twiddle_in, Q); #if FFT_DEBUG_STAGE > 3 // Load in imageFFT to search @@ -1546,12 +1326,12 @@ __global__ void block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul(const Complex #if FFT_DEBUG_STAGE > 4 // Run the inverse FFT - // invFFT().execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.z], workspace_inv); + // invFFT().execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.y], workspace_inv); invFFT( ).execute(thread_data, shared_mem, workspace_inv); #endif -// // The reduced store considers threadIdx.z to ignore extra threads +// // The reduced store considers threadIdx.y to ignore extra threads // io::store_c2c_reduced(thread_data, &output_values[blockIdx.y * gridDim.y]); #if FFT_DEBUG_STAGE < 5 // There is no size reduction for this debug stage, so we need to use the pixel_pitch of the input array. @@ -1565,15 +1345,17 @@ __global__ void block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul(const Complex // C2C -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_NONE(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { + void block_fft_kernel_C2C_NONE(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; - extern __shared__ complex_type shared_mem[]; // Storage for the input data that is re-used each blcok + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; + + extern __shared__ complex_compute_t shared_mem[]; // Storage for the input data that is re-used each blcok // Memory used by FFT - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; // No need to __syncthreads as each thread only accesses its own shared mem anyway io::load(&input_values[Return1DFFTAddress(size_of::value)], thread_data); @@ -1584,22 +1366,23 @@ __launch_bounds__(FFT::max_threads_per_block) __global__ io::store(thread_data, &output_values[Return1DFFTAddress(size_of::value)]); } -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_NONE_XZ(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { + void block_fft_kernel_C2C_NONE_XZ(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { // // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - extern __shared__ complex_type shared_mem[]; // Storage for the input data that is re-used each blcok + extern __shared__ complex_compute_t shared_mem[]; // Storage for the input data that is re-used each blcok // Memory used by FFT - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; // No need to __syncthreads as each thread only accesses its own shared mem anyway io::load(&input_values[Return1DFFTAddress_strided_Z(size_of::value)], thread_data); - FFT( ).execute(thread_data, &shared_mem[threadIdx.z * FFT::shared_memory_size / sizeof(complex_type)], workspace); + FFT( ).execute(thread_data, &shared_mem[threadIdx.y * FFT::shared_memory_size / sizeof(complex_compute_t)], workspace); __syncthreads( ); // Now we need to transpose in shared mem, fix bank conflicts later. TODO @@ -1607,9 +1390,9 @@ __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ const unsigned int stride = io::stride_size( ); unsigned int index = threadIdx.x; for ( unsigned int i = 0; i < FFT::elements_per_thread; i++ ) { - // return (XZ_STRIDE*blockIdx.z + threadIdx.z) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + X * gridDim.y ); + // return (XZ_STRIDE*blockIdx.z + threadIdx.y) + (XZ_STRIDE*gridDim.z) * ( blockIdx.y + X * gridDim.y ); // XZ_STRIDE == blockDim.z - shared_mem[threadIdx.z + index * XZ_STRIDE] = thread_data[i]; + shared_mem[threadIdx.y + index * XZ_STRIDE] = thread_data[i]; index += stride; } } @@ -1620,14 +1403,15 @@ __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ } // __launch_bounds__(FFT::max_threads_per_block) we don't know this because it is threadDim.x * threadDim.z - this could be templated if it affects performance significantly -template -__global__ void block_fft_kernel_C2C_DECREASE(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { +template +__global__ void block_fft_kernel_C2C_DECREASE(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { // // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; // Load in natural order io::load_c2c_shared_and_pad(&input_values[Return1DFFTAddress(size_of::value * Q)], shared_mem); @@ -1635,8 +1419,8 @@ __global__ void block_fft_kernel_C2C_DECREASE(const ComplexType* __restrict__ in // DIT shuffle, bank conflict free io::copy_from_shared(shared_mem, thread_data, Q); - constexpr const unsigned int fft_shared_mem_num_elements = FFT::shared_memory_size / sizeof(complex_type); - FFT( ).execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.z], workspace); + constexpr const unsigned int fft_shared_mem_num_elements = FFT::shared_memory_size / sizeof(complex_compute_t); + FFT( ).execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.y], workspace); __syncthreads( ); // Full twiddle multiply and store in natural order in shared memory @@ -1647,18 +1431,21 @@ __global__ void block_fft_kernel_C2C_DECREASE(const ComplexType* __restrict__ in } // __launch_bounds__(FFT::max_threads_per_block) we don't know this because it is threadDim.x * threadDim.z - this could be templated if it affects performance significantly -template -__global__ void block_fft_kernel_C2C_INCREASE(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { +template +__global__ void block_fft_kernel_C2C_INCREASE(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; - extern __shared__ complex_type shared_input_complex[]; // Storage for the input data that is re-used each blcok - complex_type* shared_output = (complex_type*)&shared_input_complex[mem_offsets.shared_input]; // storage for the coalesced output data. This may grow too large, - complex_type* shared_mem = (complex_type*)&shared_output[mem_offsets.shared_output]; + + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; + + extern __shared__ complex_compute_t shared_input_complex[]; // Storage for the input data that is re-used each blcok + complex_compute_t* shared_output = (complex_compute_t*)&shared_input_complex[mem_offsets.shared_input]; // storage for the coalesced output data. This may grow too large, + complex_compute_t* shared_mem = (complex_compute_t*)&shared_output[mem_offsets.shared_output]; // Memory used by FFT - complex_type thread_data[FFT::storage_size]; - float twiddle_factor_args[FFT::storage_size]; - complex_type twiddle; + complex_compute_t thread_data[FFT::storage_size]; + float twiddle_factor_args[FFT::storage_size]; + complex_compute_t twiddle; // No need to __syncthreads as each thread only accesses its own shared mem anyway io::load_shared(&input_values[Return1DFFTAddress(size_of::value)], shared_input_complex, thread_data, twiddle_factor_args, twiddle_in); @@ -1687,20 +1474,21 @@ __global__ void block_fft_kernel_C2C_INCREASE(const ComplexType* __restrict__ in } } -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_INCREASE_SwapRealSpaceQuadrants(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { + void block_fft_kernel_C2C_INCREASE_SwapRealSpaceQuadrants(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { // // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - extern __shared__ complex_type shared_input_complex[]; // Storage for the input data that is re-used each blcok - complex_type* shared_output = (complex_type*)&shared_input_complex[mem_offsets.shared_input]; // storage for the coalesced output data. This may grow too large, - complex_type* shared_mem = (complex_type*)&shared_output[mem_offsets.shared_output]; + extern __shared__ complex_compute_t shared_input_complex[]; // Storage for the input data that is re-used each blcok + complex_compute_t* shared_output = (complex_compute_t*)&shared_input_complex[mem_offsets.shared_input]; // storage for the coalesced output data. This may grow too large, + complex_compute_t* shared_mem = (complex_compute_t*)&shared_output[mem_offsets.shared_output]; // Memory used by FFT - complex_type twiddle; - complex_type thread_data[FFT::storage_size]; + complex_compute_t twiddle; + complex_compute_t thread_data[FFT::storage_size]; // To re-map the thread index to the data int input_MAP[FFT::storage_size]; @@ -1745,15 +1533,17 @@ __launch_bounds__(FFT::max_threads_per_block) __global__ } } -template -__global__ void thread_fft_kernel_C2C_decomposed(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { +template +__global__ void thread_fft_kernel_C2C_decomposed(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { + + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - using complex_type = ComplexType; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; // Memory used by FFT - for Thread() type, FFT::storage_size == FFT::elements_per_thread == size_of::value - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io_thread::load_c2c(&input_values[Return1DFFTAddress(size_of::value)], thread_data, Q); @@ -1766,22 +1556,23 @@ __global__ void thread_fft_kernel_C2C_decomposed(const ComplexType* __restrict__ io_thread::store_c2c(shared_mem, &output_values[Return1DFFTAddress(size_of::value * Q)], Q); } -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_NONE_XYZ(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { + void block_fft_kernel_C2C_NONE_XYZ(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { // // Initialize the shared memory, assuming everyting matches the input data X size in - using complex_type = ComplexType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - extern __shared__ complex_type shared_mem[]; // Storage for the input data that is re-used each blcok + extern __shared__ complex_compute_t shared_mem[]; // Storage for the input data that is re-used each blcok // Memory used by FFT - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; // No need to __syncthreads as each thread only accesses its own shared mem anyway io::load(&input_values[Return1DFFTColumn_XYZ_transpose(size_of::value)], thread_data); - FFT( ).execute(thread_data, &shared_mem[threadIdx.z * FFT::shared_memory_size / sizeof(complex_type)], workspace); + FFT( ).execute(thread_data, &shared_mem[threadIdx.y * FFT::shared_memory_size / sizeof(complex_compute_t)], workspace); __syncthreads( ); io::transpose_in_shared_XZ(shared_mem, thread_data); @@ -1789,25 +1580,26 @@ __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ io::store_Z(shared_mem, output_values); } -template +template __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2C_INCREASE_XYZ(const ComplexType* __restrict__ input_values, ComplexType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { + void block_fft_kernel_C2C_INCREASE_XYZ(const ComplexData_t* __restrict__ input_values, ComplexData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q, typename FFT::workspace_type workspace) { - using complex_type = ComplexType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - extern __shared__ complex_type shared_input_complex[]; // Storage for the input data that is re-used each blcok - complex_type* shared_mem = (complex_type*)&shared_input_complex[XZ_STRIDE * mem_offsets.shared_input]; // storage for computation and transposition (alternating) + extern __shared__ complex_compute_t shared_input_complex[]; // Storage for the input data that is re-used each blcok + complex_compute_t* shared_mem = (complex_compute_t*)&shared_input_complex[XZ_STRIDE * mem_offsets.shared_input]; // storage for computation and transposition (alternating) // Memory used by FFT - complex_type thread_data[FFT::storage_size]; - complex_type twiddle; - float twiddle_factor_args[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; + complex_compute_t twiddle; + float twiddle_factor_args[FFT::storage_size]; // No need to __syncthreads as each thread only accesses its own shared mem anyway io::load_shared(&input_values[Return1DFFTColumn_XYZ_transpose(size_of::value)], - &shared_input_complex[threadIdx.z * mem_offsets.shared_input], thread_data, twiddle_factor_args, twiddle_in); + &shared_input_complex[threadIdx.y * mem_offsets.shared_input], thread_data, twiddle_factor_args, twiddle_in); - FFT( ).execute(thread_data, &shared_mem[threadIdx.z * FFT::shared_memory_size / sizeof(complex_type)], workspace); + FFT( ).execute(thread_data, &shared_mem[threadIdx.y * FFT::shared_memory_size / sizeof(complex_compute_t)], workspace); __syncthreads( ); io::transpose_in_shared_XZ(shared_mem, thread_data); @@ -1815,28 +1607,28 @@ __launch_bounds__(XZ_STRIDE* FFT::max_threads_per_block) __global__ // For the other fragments we need the initial twiddle for ( int sub_fft = 1; sub_fft < Q; sub_fft++ ) { - io::copy_from_shared(&shared_input_complex[threadIdx.z * mem_offsets.shared_input], thread_data); + io::copy_from_shared(&shared_input_complex[threadIdx.y * mem_offsets.shared_input], thread_data); for ( int i = 0; i < FFT::elements_per_thread; i++ ) { // Pre shift with twiddle SINCOS(twiddle_factor_args[i] * sub_fft, &twiddle.y, &twiddle.x); thread_data[i] *= twiddle; } - FFT( ).execute(thread_data, &shared_mem[threadIdx.z * FFT::shared_memory_size / sizeof(complex_type)], workspace); + FFT( ).execute(thread_data, &shared_mem[threadIdx.y * FFT::shared_memory_size / sizeof(complex_compute_t)], workspace); io::transpose_in_shared_XZ(shared_mem, thread_data); io::store_Z(shared_mem, output_values, Q, sub_fft); } } -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2R_NONE(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { + void block_fft_kernel_C2R_NONE(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io::load_c2r(&input_values[Return1DFFTAddress(mem_offsets.physical_x_input)], thread_data); @@ -1846,16 +1638,16 @@ __launch_bounds__(FFT::max_threads_per_block) __global__ io::store_c2r(thread_data, &output_values[Return1DFFTAddress(mem_offsets.physical_x_output)], size_of::value); } -template +template __launch_bounds__(FFT::max_threads_per_block) __global__ - void block_fft_kernel_C2R_NONE_XY(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { + void block_fft_kernel_C2R_NONE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, typename FFT::workspace_type workspace) { - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io::load_c2r_transposed(&input_values[ReturnZplane(gridDim.y, mem_offsets.physical_x_input)], thread_data, gridDim.y); @@ -1863,18 +1655,17 @@ __launch_bounds__(FFT::max_threads_per_block) __global__ FFT( ).execute(thread_data, shared_mem, workspace); io::store_c2r(thread_data, &output_values[Return1DFFTAddress(mem_offsets.physical_x_output)], size_of::value); +} -} // end of block_fft_kernel_C2R_NONE_XY - -template -__global__ void block_fft_kernel_C2R_DECREASE_XY(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, const float twiddle_in, const unsigned int Q, typename FFT::workspace_type workspace) { +template +__global__ void block_fft_kernel_C2R_DECREASE_XY(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, const float twiddle_in, const unsigned int Q, typename FFT::workspace_type workspace) { - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; - extern __shared__ complex_type shared_mem[]; + extern __shared__ complex_compute_t shared_mem[]; - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io::load_c2r_transposed(&input_values[ReturnZplane(gridDim.y, mem_offsets.physical_x_input)], thread_data, gridDim.y); @@ -1889,8 +1680,8 @@ __global__ void block_fft_kernel_C2R_DECREASE_XY(const ComplexType* __restrict__ // // DIT shuffle, bank conflict free // io::copy_from_shared(shared_mem, thread_data, Q); - // constexpr const unsigned int fft_shared_mem_num_elements = FFT::shared_memory_size / sizeof(complex_type); - // FFT().execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.z], workspace); + // constexpr const unsigned int fft_shared_mem_num_elements = FFT::shared_memory_size / sizeof(complex_compute_t); + // FFT().execute(thread_data, &shared_mem[fft_shared_mem_num_elements * threadIdx.y], workspace); // __syncthreads(); // // Full twiddle multiply and store in natural order in shared memory @@ -1898,21 +1689,19 @@ __global__ void block_fft_kernel_C2R_DECREASE_XY(const ComplexType* __restrict__ // // Reduce from shared memory into registers, ending up with only P valid outputs. // io::store_c2r_reduced(thread_data, &output_values[Return1DFFTAddress(mem_offsets.physical_x_output)]); +} -} // end of block_fft_kernel_C2R_DECREASE_XY - -// C2R decomposed - -template -__global__ void thread_fft_kernel_C2R_decomposed(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { - using complex_type = ComplexType; - using scalar_type = ScalarType; +template +__global__ void thread_fft_kernel_C2R_decomposed(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; + ; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ scalar_type shared_mem_C2R_decomposed[]; + extern __shared__ scalar_compute_t shared_mem_C2R_decomposed[]; // Memory used by FFT - for Thread() type, FFT::storage_size == FFT::elements_per_thread == size_of::value - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io_thread::load_c2r(&input_values[Return1DFFTAddress(mem_offsets.physical_x_input)], thread_data, Q, mem_offsets.physical_x_input); @@ -1925,17 +1714,17 @@ __global__ void thread_fft_kernel_C2R_decomposed(const ComplexType* __restrict__ io_thread::store_c2r(shared_mem_C2R_decomposed, &output_values[Return1DFFTAddress(mem_offsets.physical_x_output)], Q); } -template -__global__ void thread_fft_kernel_C2R_decomposed_transposed(const ComplexType* __restrict__ input_values, ScalarType* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { +template +__global__ void thread_fft_kernel_C2R_decomposed_transposed(const InputData_t* __restrict__ input_values, OutputData_t* __restrict__ output_values, Offsets mem_offsets, float twiddle_in, int Q) { - using complex_type = ComplexType; - using scalar_type = ScalarType; + using complex_compute_t = typename FFT::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; // The data store is non-coalesced, so don't aggregate the data in shared mem. - extern __shared__ scalar_type shared_mem_transposed[]; + extern __shared__ scalar_compute_t shared_mem_transposed[]; // Memory used by FFT - for Thread() type, FFT::storage_size == FFT::elements_per_thread == size_of::value - complex_type thread_data[FFT::storage_size]; + complex_compute_t thread_data[FFT::storage_size]; io_thread::load_c2r_transposed(&input_values[ReturnZplane(blockDim.y, mem_offsets.physical_x_input)], thread_data, Q, gridDim.y, mem_offsets.physical_x_input); @@ -1948,24 +1737,9 @@ __global__ void thread_fft_kernel_C2R_decomposed_transposed(const ComplexType* _ io_thread::store_c2r(shared_mem_transposed, &output_values[Return1DFFTAddress(mem_offsets.physical_x_output)], Q); } -template -void FourierTransformer::ClipIntoTopLeft( ) { - // TODO add some checks and logic. - - // Assuming we are calling this from R2C_Transposed and that the launch bounds are not set. - dim3 local_threadsPerBlock = dim3(512, 1, 1); - dim3 local_gridDims = dim3((fwd_dims_out.x + local_threadsPerBlock.x - 1) / local_threadsPerBlock.x, 1, 1); - - const short4 area_to_clip_from = make_short4(fwd_dims_in.x, fwd_dims_in.y, fwd_dims_in.w * 2, fwd_dims_out.w * 2); - - precheck; - clip_into_top_left_kernel<<>>(d_ptr.position_space, d_ptr.position_space, area_to_clip_from); - postcheck; -} - // FIXME assumed FWD -template -__global__ void clip_into_top_left_kernel(InputType* input_values, OutputType* output_values, const short4 dims) { +template +__global__ void clip_into_top_left_kernel(InputType* input_values, InputType* output_values, const short4 dims) { int x = blockIdx.x * blockDim.x + threadIdx.x; if ( x > dims.w ) @@ -1973,12 +1747,12 @@ __global__ void clip_into_top_left_kernel(InputType* input_values, OutputType* o // dims.w is the pitch of the output array if ( blockIdx.y > dims.y ) { - output_values[blockIdx.y * dims.w + x] = OutputType(0); + output_values[blockIdx.y * dims.w + x] = OtherImageType(0); return; } if ( threadIdx.x > dims.x ) { - output_values[blockIdx.y * dims.w + x] = OutputType(0); + output_values[blockIdx.y * dims.w + x] = OtherImageType(0); return; } else { @@ -1988,35 +1762,30 @@ __global__ void clip_into_top_left_kernel(InputType* input_values, OutputType* o } } -template -void FourierTransformer::ClipIntoReal(int wanted_coordinate_of_box_center_x, int wanted_coordinate_of_box_center_y, int wanted_coordinate_of_box_center_z) { +template +void FourierTransformer::ClipIntoTopLeft(InputType* input_ptr) { // TODO add some checks and logic. // Assuming we are calling this from R2C_Transposed and that the launch bounds are not set. - dim3 threadsPerBlock; - dim3 gridDims; - int3 wanted_center = make_int3(wanted_coordinate_of_box_center_x, wanted_coordinate_of_box_center_y, wanted_coordinate_of_box_center_z); - threadsPerBlock = dim3(32, 32, 1); - gridDims = dim3((fwd_dims_out.x + threadsPerBlock.x - 1) / threadsPerBlock.x, - (fwd_dims_out.y + threadsPerBlock.y - 1) / threadsPerBlock.y, - 1); + dim3 local_threadsPerBlock = dim3(512, 1, 1); + dim3 local_gridDims = dim3((fwd_dims_out.x + local_threadsPerBlock.x - 1) / local_threadsPerBlock.x, 1, 1); - const short4 area_to_clip_from = make_short4(fwd_dims_in.x, fwd_dims_in.y, fwd_dims_in.w * 2, fwd_dims_out.w * 2); - float wanted_padding_value = 0.f; + const short4 area_to_clip_from = make_short4(fwd_dims_in.x, fwd_dims_in.y, fwd_dims_in.w * 2, fwd_dims_out.w * 2); precheck; - clip_into_real_kernel<<>>(d_ptr.position_space, d_ptr.position_space, fwd_dims_in, fwd_dims_out, wanted_center, wanted_padding_value); + clip_into_top_left_kernel<<>>(input_ptr, (InputType*)d_ptr.buffer_1, area_to_clip_from); postcheck; + current_buffer = fastfft_internal_buffer_1; } // Modified from GpuImage::ClipIntoRealKernel -template -__global__ void clip_into_real_kernel(InputType* real_values_gpu, - OutputType* other_image_real_values_gpu, - short4 dims, - short4 other_dims, - int3 wanted_coordinate_of_box_center, - OutputType wanted_padding_value) { +template +__global__ void clip_into_real_kernel(InputType* real_values_gpu, + InputType* other_image_real_values_gpu, + short4 dims, + short4 other_dims, + int3 wanted_coordinate_of_box_center, + InputType wanted_padding_value) { int3 other_coord = make_int3(blockIdx.x * blockDim.x + threadIdx.x, blockIdx.y * gridDim.y + threadIdx.y, blockIdx.z); @@ -2047,72 +1816,115 @@ __global__ void clip_into_real_kernel(InputType* real_values_gpu, } } // end of bounds check +} + +template +void FourierTransformer::ClipIntoReal(InputType* input_ptr, int wanted_coordinate_of_box_center_x, int wanted_coordinate_of_box_center_y, int wanted_coordinate_of_box_center_z) { + // TODO add some checks and logic. -} // end of ClipIntoRealKernel + // Assuming we are calling this from R2C_Transposed and that the launch bounds are not set. + dim3 threadsPerBlock; + dim3 gridDims; + int3 wanted_center = make_int3(wanted_coordinate_of_box_center_x, wanted_coordinate_of_box_center_y, wanted_coordinate_of_box_center_z); + threadsPerBlock = dim3(32, 32, 1); + gridDims = dim3((fwd_dims_out.x + threadsPerBlock.x - 1) / threadsPerBlock.x, + (fwd_dims_out.y + threadsPerBlock.y - 1) / threadsPerBlock.y, + 1); + + const short4 area_to_clip_from = make_short4(fwd_dims_in.x, fwd_dims_in.y, fwd_dims_in.w * 2, fwd_dims_out.w * 2); + float wanted_padding_value = 0.f; + + precheck; + clip_into_real_kernel<<>>(input_ptr, (InputType*)d_ptr.buffer_1, fwd_dims_in, fwd_dims_out, wanted_center, wanted_padding_value); + postcheck; + current_buffer = fastfft_internal_buffer_1; +} -template -template -void FourierTransformer::SetPrecisionAndExectutionMethod(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor) { +template +template +EnableIf> +FourierTransformer::SetPrecisionAndExectutionMethod(OtherImageType* other_image_ptr, + KernelType kernel_type, + PreOpType pre_op_functor, + IntraOpType intra_op_functor, + PostOpType post_op_functor) { // For kernels with fwd and inv transforms, we want to not set the direction yet. - static const bool is_half = std::is_same_v; - static const bool is_float = std::is_same_v; - static_assert(is_half || is_float, "FourierTransformer::SetPrecisionAndExectutionMethod: Unsupported ComputeType"); + static const bool is_half = std::is_same_v; // FIXME: This should be done in the constructor + static const bool is_float = std::is_same_v; + static_assert(is_half || is_float, "FourierTransformer::SetPrecisionAndExectutionMethod: Unsupported ComputeBaseType"); + if constexpr ( FFT_ALGO_t == Generic_Fwd_Image_Inv_FFT ) { + static_assert(IS_IKF_t( ), "FourierTransformer::SetPrecisionAndExectutionMethod: Unsupported IntraOpType"); + } if constexpr ( use_thread_method ) { - using FFT = decltype(Thread( ) + Size<32>( ) + Precision( )); - SetIntraKernelFunctions(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + using FFT = decltype(Thread( ) + Size<32>( ) + Precision( )); + SetIntraKernelFunctions(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); } else { - using FFT = decltype(Block( ) + Precision( ) + FFTsPerBlock<1>( )); - SetIntraKernelFunctions(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + using FFT = decltype(Block( ) + Precision( ) + FFTsPerBlock<1>( )); + SetIntraKernelFunctions(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); } } -template -template -void FourierTransformer::SetIntraKernelFunctions(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor) { +template +template +void FourierTransformer::SetIntraKernelFunctions(OtherImageType* other_image_ptr, + KernelType kernel_type, + PreOpType pre_op_functor, + IntraOpType intra_op_functor, + PostOpType post_op_functor) { if constexpr ( ! detail::has_any_block_operator::value ) { - // SelectSizeAndType(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + // SelectSizeAndType(kernel_type, pre_op_functor, intra_op_functor, post_op_functor); } else { if constexpr ( Rank == 3 ) { - SelectSizeAndType(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + SelectSizeAndType(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); } else { // TODO: 8192 will fail for sm75 if wanted need some extra logic ... , 8192, 16 - SelectSizeAndType(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + SelectSizeAndType(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); } } } -template -template -void FourierTransformer::SelectSizeAndType(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor) { +template +template +void FourierTransformer::SelectSizeAndType(OtherImageType* other_image_ptr, + KernelType kernel_type, + PreOpType pre_op_functor, + IntraOpType intra_op_functor, + PostOpType post_op_functor) { // This provides both a termination point for the recursive version needed for the block transform case as well as the actual function for thread transform with fixed size 32 GetTransformSize(kernel_type); if constexpr ( ! detail::has_any_block_operator::value ) { - elements_per_thread_complex = 8; + SetEptForUseInLaunchParameters(8); switch ( device_properties.device_arch ) { case 700: { using FFT = decltype(FFT_base( ) + SM<700>( ) + ElementsPerThread<8>( )); - SetAndLaunchKernel(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); break; } case 750: { using FFT = decltype(FFT_base( ) + SM<750>( ) + ElementsPerThread<8>( )); - SetAndLaunchKernel(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); break; } case 800: { using FFT = decltype(FFT_base( ) + SM<800>( ) + ElementsPerThread<8>( )); - SetAndLaunchKernel(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); break; } case 860: { using FFT = decltype(FFT_base( ) + SM<700>( ) + ElementsPerThread<8>( )); - SetAndLaunchKernel(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); + break; + } + case 890: { + // FIXME: on migrating to cufftDx 1.1.1 + using FFT = decltype(FFT_base( ) + SM<700>( ) + ElementsPerThread<8>( )); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); break; } default: { @@ -2123,35 +1935,56 @@ void FourierTransformer::SelectSizeAnd } } -template -template -void FourierTransformer::SelectSizeAndType(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor) { +// TODO: see if you can replace this with a fold expression? +template +template +void FourierTransformer::SelectSizeAndType(OtherImageType* other_image_ptr, + KernelType kernel_type, + PreOpType pre_op_functor, + IntraOpType intra_op_functor, + PostOpType post_op_functor) { // Use recursion to step through the allowed sizes. GetTransformSize(kernel_type); + // Note: the size of the input/output may not match the size of the transform, i.e. transform_size.L <= transform_size.P if ( SizeValue == transform_size.P ) { - elements_per_thread_complex = Ept; switch ( device_properties.device_arch ) { case 700: { - using FFT = decltype(FFT_base( ) + Size( ) + SM<700>( ) + ElementsPerThread<8>( )); - SetAndLaunchKernel(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + SetEptForUseInLaunchParameters(Ept); + using FFT = decltype(FFT_base( ) + Size( ) + SM<700>( ) + ElementsPerThread( )); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); break; } case 750: { + SetEptForUseInLaunchParameters(Ept); if constexpr ( SizeValue <= 4096 ) { - using FFT = decltype(FFT_base( ) + Size( ) + SM<750>( ) + ElementsPerThread<8>( )); - SetAndLaunchKernel(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + using FFT = decltype(FFT_base( ) + Size( ) + SM<750>( ) + ElementsPerThread( )); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); } break; } case 800: { - using FFT = decltype(FFT_base( ) + Size( ) + SM<800>( ) + ElementsPerThread<8>( )); - SetAndLaunchKernel(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + SetEptForUseInLaunchParameters(Ept); + using FFT = decltype(FFT_base( ) + Size( ) + SM<800>( ) + ElementsPerThread( )); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); break; } case 860: { - using FFT = decltype(FFT_base( ) + Size( ) + SM<700>( ) + ElementsPerThread<8>( )); - SetAndLaunchKernel(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + // TODO: confirm that this is needed (over 860) which at the time just redirects to 700 + // if maintining this, we could save some time on compilation by combining with the 700 case + SetEptForUseInLaunchParameters(Ept); + using FFT = decltype(FFT_base( ) + Size( ) + SM<700>( ) + ElementsPerThread( )); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); + break; + } + case 890: { + // TODO: confirm that this is needed (over 860) which at the time just redirects to 700 + // if maintining this, we could save some time on compilation by combining with the 700 case + // FIXME: on migrating to cufftDx 1.1.1 + + SetEptForUseInLaunchParameters(Ept); + using FFT = decltype(FFT_base( ) + Size( ) + SM<700>( ) + ElementsPerThread( )); + SetAndLaunchKernel(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); break; } default: { @@ -2161,189 +1994,217 @@ void FourierTransformer::SelectSizeAnd } } - SelectSizeAndType(kernel_type, do_forward_transform, pre_op_functor, intra_op_functor, post_op_functor); + SelectSizeAndType(other_image_ptr, kernel_type, pre_op_functor, intra_op_functor, post_op_functor); } -template -template -void FourierTransformer::SetAndLaunchKernel(KernelType kernel_type, bool do_forward_transform, PreOpType pre_op_functor, IntraOpType intra_op_functor, PostOpType post_op_functor) { - - using complex_type = typename FFT_base_arch::value_type; - using scalar_type = typename complex_type::value_type; - - complex_type* complex_input; - complex_type* complex_output; - scalar_type* scalar_input; - scalar_type* scalar_output; - - // Make sure we are in the right chunk of the memory pool. - if ( is_in_buffer_memory ) { - complex_input = (complex_type*)d_ptr.momentum_space_buffer; - complex_output = (complex_type*)d_ptr.momentum_space; - - scalar_input = (scalar_type*)d_ptr.position_space_buffer; - scalar_output = (scalar_type*)d_ptr.position_space; - - is_in_buffer_memory = false; +template +template +void FourierTransformer::SetAndLaunchKernel(OtherImageType* other_image_ptr, + KernelType kernel_type, + PreOpType pre_op_functor, + IntraOpType intra_op_functor, + PostOpType post_op_functor) { + + // Used to determine shared memory requirements + using complex_compute_t = typename FFT_base_arch::value_type; + using scalar_compute_t = typename complex_compute_t::value_type; + // Determined by InputType as complex version, i.e., half half2 or float float2 + using data_buffer_t = std::remove_pointer_t; + // Allowed half, float (real type image) half2 float2 (complex type image) so with typical case + // as real valued image, data_io_t != data_buffer_t + using data_io_t = std::remove_pointer_t; + // Could match data_io_t, but need not, will be converted in kernels to match complex_compute_t as needed. + using external_image_t = OtherImageType; + + // If the user passed in a different exerternal pointer for the image, set it here, and if not, + // we just alias the external input + data_io_t* external_output_ptr; + buffer_location aliased_output_buffer; + if ( d_ptr.external_output != nullptr ) { + external_output_ptr = d_ptr.external_output; + aliased_output_buffer = fastfft_external_output; } else { - complex_input = (complex_type*)d_ptr.momentum_space; - complex_output = (complex_type*)d_ptr.momentum_space_buffer; - - scalar_input = (scalar_type*)d_ptr.position_space; - scalar_output = (scalar_type*)d_ptr.position_space_buffer; - - is_in_buffer_memory = true; + external_output_ptr = d_ptr.external_input; + aliased_output_buffer = fastfft_external_input; } - // if constexpr (detail::is_operator::value) { if constexpr ( ! detail::has_any_block_operator::value ) { + MyFFTRunTimeAssertFalse(true, "thread_fft_kernel is currently broken"); switch ( kernel_type ) { case r2c_decomposed: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, r2c_decomposed); + LaunchParams LP = SetLaunchParameters(r2c_decomposed); - int shared_mem = LP.mem_offsets.shared_output * sizeof(complex_type); - CheckSharedMemory(shared_mem, device_properties); - cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_R2C_decomposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_mem)); + int shared_mem = LP.mem_offsets.shared_output * sizeof(complex_compute_t); + CheckSharedMemory(shared_mem, device_properties); #if FFT_DEBUG_STAGE > 0 - precheck; - thread_fft_kernel_R2C_decomposed<<>>(scalar_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_R2C_decomposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_mem)); + precheck; + thread_fft_kernel_R2C_decomposed<<>>( + d_ptr.external_input, reinterpret_cast(external_output_ptr), LP.mem_offsets, LP.twiddle_in, LP.Q); + postcheck; + current_buffer = aliased_output_buffer; + } #endif + } + break; } case r2c_decomposed_transposed: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, r2c_decomposed_transposed); + LaunchParams LP = SetLaunchParameters(r2c_decomposed_transposed); - int shared_mem = LP.mem_offsets.shared_output * sizeof(complex_type); - CheckSharedMemory(shared_mem, device_properties); - cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_R2C_decomposed_transposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_mem)); + int shared_mem = LP.mem_offsets.shared_output * sizeof(complex_compute_t); + CheckSharedMemory(shared_mem, device_properties); #if FFT_DEBUG_STAGE > 0 - precheck; - thread_fft_kernel_R2C_decomposed_transposed<<>>(scalar_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; + if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_R2C_decomposed_transposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_mem)); + precheck; + thread_fft_kernel_R2C_decomposed_transposed<<>>( + d_ptr.external_input, d_ptr.buffer_1, LP.mem_offsets, LP.twiddle_in, LP.Q); + postcheck; + current_buffer = fastfft_internal_buffer_1; + } #endif - + } break; } case c2r_decomposed: { - // Note that unlike the block C2R we require a C2C sub xform. - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - // TODO add completeness check. + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + // Note that unlike the block C2R we require a C2C sub xform. + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + // TODO add completeness check. - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2r_decomposed); - int shared_memory = LP.mem_offsets.shared_output * sizeof(scalar_type); - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2R_decomposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + LaunchParams LP = SetLaunchParameters(c2r_decomposed); + int shared_memory = LP.mem_offsets.shared_output * sizeof(scalar_compute_t); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 6 - precheck; - thread_fft_kernel_C2R_decomposed<<>>(complex_input, scalar_output, LP.mem_offsets, LP.twiddle_in, LP.Q); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; + if constexpr ( Rank == 1 ) { + cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2R_decomposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + precheck; + thread_fft_kernel_C2R_decomposed<<>>( + reinterpret_cast(d_ptr.external_input), external_output_ptr, LP.mem_offsets, LP.twiddle_in, LP.Q); + postcheck; + current_buffer = aliased_output_buffer; + } #endif - + } break; } case c2r_decomposed_transposed: { - // Note that unlike the block C2R we require a C2C sub xform. - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + // Note that unlike the block C2R we require a C2C sub xform. + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2r_decomposed_transposed); - int shared_memory = LP.mem_offsets.shared_output * sizeof(scalar_type); - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2R_decomposed_transposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + LaunchParams LP = SetLaunchParameters(c2r_decomposed_transposed); + int shared_memory = LP.mem_offsets.shared_output * sizeof(scalar_compute_t); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 6 - precheck; - thread_fft_kernel_C2R_decomposed_transposed<<>>(complex_input, scalar_output, LP.mem_offsets, LP.twiddle_in, LP.Q); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; + cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2R_decomposed_transposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1, "current_buffer != fastfft_internal_buffer_1"); + precheck; + thread_fft_kernel_C2R_decomposed_transposed<<>>( + d_ptr.buffer_1, external_output_ptr, LP.mem_offsets, LP.twiddle_in, LP.Q); + postcheck; + current_buffer = aliased_output_buffer; + } #endif - + } break; } case xcorr_decomposed: { + // TODO: FFT_ALGO_t + // TODO: unused + // using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); + // using invFFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - using invFFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); + // LaunchParams LP = SetLaunchParameters( xcorr_decomposed); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, xcorr_decomposed); + // int shared_memory = LP.mem_offsets.shared_output * sizeof(complex_compute_t); + // CheckSharedMemory(shared_memory, device_properties); - int shared_memory = LP.mem_offsets.shared_output * sizeof(complex_type); - CheckSharedMemory(shared_memory, device_properties); + // #if FFT_DEBUG_STAGE > 2 + // cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2C_decomposed_ConjMul, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - // FIXME - bool swap_real_space_quadrants = false; + // // the image_to_search pointer is set during call to CrossCorrelate, + // precheck; + // thread_fft_kernel_C2C_decomposed_ConjMul<<>>((external_image_t*)other_image_ptr, intra_complex_input, intra_complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q); + // postcheck; + // is_in_buffer_memory = ! is_in_buffer_memory; + // #endif - if ( swap_real_space_quadrants ) { - MyFFTRunTimeAssertTrue(false, "decomposed xcorr with swap real space quadrants is not implemented."); - // cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul_SwapRealSpaceQuadrants, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + // break; + } - // precheck; - // block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul_SwapRealSpaceQuadrants<< > > - // ( (complex_type*) image_to_search, (complex_type*) d_ptr.momentum_space_buffer, (complex_type*) d_ptr.momentum_space, LP.mem_offsets, LP.twiddle_in,LP.Q, workspace_fwd, workspace_inv); - // postcheck; - } - else { - cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2C_decomposed_ConjMul, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + case c2c_fwd_decomposed: { + // TODO: FFT_ALGO_t + using FFT_nodir = decltype(FFT_base_arch( ) + Type( )); + LaunchParams LP = SetLaunchParameters(c2c_fwd_decomposed); + using FFT = decltype(FFT_nodir( ) + Direction( )); + int shared_memory = LP.mem_offsets.shared_output * sizeof(complex_compute_t); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 2 - // the image_to_search pointer is set during call to CrossCorrelate, + cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2C_decomposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + if constexpr ( Rank == 1 ) { + // Should only be used when d_ptr.external_input is complex valued input precheck; - thread_fft_kernel_C2C_decomposed_ConjMul<<>>((complex_type*)d_ptr.image_to_search, complex_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q); + thread_fft_kernel_C2C_decomposed<<>>( + d_ptr.external_input, external_output_ptr, LP.mem_offsets, LP.twiddle_in, LP.Q); postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; -#endif } + else if constexpr ( Rank == 2 ) { + precheck; + thread_fft_kernel_C2C_decomposed<<>>( + d_ptr.buffer_1, external_output_ptr, LP.mem_offsets, LP.twiddle_in, LP.Q); + postcheck; + } + current_buffer = aliased_output_buffer; +#endif + break; } + case c2c_inv_decomposed: { - case c2c_decomposed: { using FFT_nodir = decltype(FFT_base_arch( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_decomposed, do_forward_transform); + LaunchParams LP = SetLaunchParameters(c2c_inv_decomposed); - if ( do_forward_transform ) { - using FFT = decltype(FFT_nodir( ) + Direction( )); - int shared_memory = LP.mem_offsets.shared_output * sizeof(complex_type); - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2C_decomposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); -#if FFT_DEBUG_STAGE > 2 + using FFT = decltype(FFT_nodir( ) + Direction( )); + int shared_memory = LP.mem_offsets.shared_output * sizeof(complex_compute_t); + CheckSharedMemory(shared_memory, device_properties); +#if FFT_DEBUG_STAGE > 4 + cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2C_decomposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + if constexpr ( Rank == 1 ) { + // This should only be called when d_ptr.external_input is complex valued input precheck; - thread_fft_kernel_C2C_decomposed<<>>(complex_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q); + thread_fft_kernel_C2C_decomposed<<>>( + d_ptr.external_input, external_output_ptr, LP.mem_offsets, LP.twiddle_in, LP.Q); postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; -#endif + current_buffer = aliased_output_buffer; } - else { - - using FFT = decltype(FFT_nodir( ) + Direction( )); - int shared_memory = LP.mem_offsets.shared_output * sizeof(complex_type); - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)thread_fft_kernel_C2C_decomposed, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); -#if FFT_DEBUG_STAGE > 4 + else if constexpr ( Rank == 2 ) { precheck; - thread_fft_kernel_C2C_decomposed<<>>(complex_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q); + thread_fft_kernel_C2C_decomposed<<>>( + reinterpret_cast(d_ptr.external_input), d_ptr.buffer_1, LP.mem_offsets, LP.twiddle_in, LP.Q); postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; -#endif + current_buffer = fastfft_internal_buffer_1; } +#endif + break; } } // switch on (thread) kernel_type @@ -2351,576 +2212,726 @@ void FourierTransformer::SetAndLaunchK else { switch ( kernel_type ) { case r2c_none_XY: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, r2c_none_XY); - - int shared_memory = FFT::shared_memory_size; - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_NONE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - - // cudaErr(cudaSetDevice(0)); - // cudaErr(cudaFuncSetCacheConfig( (void*)block_fft_kernel_R2C_NONE_XY,cudaFuncCachePreferShared )); - // cudaFuncSetSharedMemConfig ( (void*)block_fft_kernel_R2C_NONE_XY, cudaSharedMemBankSizeEightByte ); - -#if FFT_DEBUG_STAGE > 0 - precheck; - block_fft_kernel_R2C_NONE_XY<<>>(scalar_input, complex_output, LP.mem_offsets, workspace); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; -#endif - break; - } - - case r2c_none_XZ: { - if constexpr ( Rank == 3 ) { + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); cudaError_t error_code = cudaSuccess; auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, r2c_none_XZ); + LaunchParams LP = SetLaunchParameters(r2c_none_XY); - int shared_memory = std::max(LP.threadsPerBlock.z * FFT::shared_memory_size, LP.threadsPerBlock.z * LP.mem_offsets.physical_x_output * (unsigned int)sizeof(complex_type)); + int shared_memory = FFT::shared_memory_size; CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_NONE_XZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - #if FFT_DEBUG_STAGE > 0 - precheck; - block_fft_kernel_R2C_NONE_XZ<<>>(scalar_input, complex_output, LP.mem_offsets, workspace); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_NONE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + + // If impl a round trip, the output will need to be data_io_t + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_R2C_NONE_XY<<>>( + d_ptr.external_input, reinterpret_cast(external_output_ptr), LP.mem_offsets, workspace); + postcheck; + current_buffer = aliased_output_buffer; + } + else if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_R2C_NONE_XY<<>>( + d_ptr.external_input, d_ptr.buffer_1, LP.mem_offsets, workspace); + postcheck; + } + current_buffer = fastfft_internal_buffer_1; #endif } break; } - case r2c_decrease: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, r2c_decrease); + case r2c_none_XZ: { + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); - // the shared mem is mixed between storage, shuffling and FFT. For this kernel we need to add padding to avoid bank conlicts (N/32) - int shared_memory = std::max(FFT::shared_memory_size * LP.threadsPerBlock.z, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input / 32) * (unsigned int)sizeof(complex_type)); + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + LaunchParams LP = SetLaunchParameters(r2c_none_XZ); - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_DECREASE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + int shared_memory = std::max(LP.threadsPerBlock.y * FFT::shared_memory_size, LP.threadsPerBlock.y * LP.mem_offsets.physical_x_output * (unsigned int)sizeof(complex_compute_t)); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 0 - precheck; - block_fft_kernel_R2C_DECREASE_XY<<>>(scalar_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_NONE_XZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + precheck; + block_fft_kernel_R2C_NONE_XZ<<>>( + d_ptr.external_input, d_ptr.buffer_1, LP.mem_offsets, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_1; #endif + } + } break; } - case r2c_increase: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, r2c_increase); + case r2c_decrease_XY: { + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + LaunchParams LP = SetLaunchParameters(r2c_decrease_XY); - int shared_memory = LP.mem_offsets.shared_input * sizeof(scalar_type) + FFT::shared_memory_size; - - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_INCREASE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + // the shared mem is mixed between storage, shuffling and FFT. For this kernel we need to add padding to avoid bank conlicts (N/32) + int shared_memory = std::max(FFT::shared_memory_size * LP.threadsPerBlock.y, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input / 32) * (unsigned int)sizeof(complex_compute_t)); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 0 - precheck; - block_fft_kernel_R2C_INCREASE_XY<<>>(scalar_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; -#endif + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_DECREASE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + // PrintState( ); + // PrintLaunchParameters(LP); + // std::cerr << "shared mem req " << shared_memory << std::endl; + // std::cerr << "FFT max tbp " << FFT::max_threads_per_block << std::endl; + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_R2C_DECREASE_XY<<>>( + d_ptr.external_input, reinterpret_cast(external_output_ptr), LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + current_buffer = aliased_output_buffer; + } + else if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + // PrintState( ); + // PrintLaunchParameters(LP); + // std::cerr << "shared mem req " << shared_memory << std::endl; + // int numBlocks; + // size_t block_size = LP.threadsPerBlock.x * LP.threadsPerBlock.y * LP.threadsPerBlock.y; + // cudaErr(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&numBlocks, (void*)block_fft_kernel_R2C_DECREASE_XY, block_size, size_t(shared_memory))); + + // std::cerr << "BLock size " << block_size << " shared mem " << shared_memory << " numBlocks " << numBlocks << std::endl; + // std::cerr << "pointer " << d_ptr.external_input << " is in memory " << is_pointer_in_device_memory(d_ptr.external_input) << std::endl; + // std::cerr << "pointer " << d_ptr.buffer_1 << " is in memory " << is_pointer_in_device_memory(d_ptr.buffer_1) << std::endl; + // std::cerr << "FFT::shared_memory_size " << FFT::shared_memory_size << std::endl; + // std::cerr << "size_of::value " << size_of::value << std::endl; + // std::cerr << "batch_size_of::value " << batch_size_of::value << std::endl; + // std::cerr << "FFT::elements_per_thread " << FFT::elements_per_thread << std::endl; + block_fft_kernel_R2C_DECREASE_XY<<>>( + d_ptr.external_input, d_ptr.buffer_1, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_1; + } +#endif + } break; } - case r2c_increase_XZ: { - if constexpr ( Rank == 3 ) { + case r2c_increase_XY: { + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // FIXME: I don't think this is right when XZ_STRIDE is used - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, r2c_increase_XZ); + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + LaunchParams LP = SetLaunchParameters(r2c_increase_XY); - // We need shared memory to hold the input array(s) that is const through the kernel. - // We alternate using additional shared memory for the computation and the transposition of the data. - int shared_memory = std::max(XZ_STRIDE * FFT::shared_memory_size, LP.mem_offsets.physical_x_output / LP.Q * (unsigned int)sizeof(complex_type)); - shared_memory += XZ_STRIDE * LP.mem_offsets.shared_input * (unsigned int)sizeof(scalar_type); + int shared_memory = LP.mem_offsets.shared_input * sizeof(scalar_compute_t) + FFT::shared_memory_size; CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_INCREASE_XZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); +#if FFT_DEBUG_STAGE > 0 + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_INCREASE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_R2C_INCREASE_XY<<>>( + d_ptr.external_input, reinterpret_cast(external_output_ptr), LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + current_buffer = aliased_output_buffer; + } + else if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_R2C_INCREASE_XY<<>>( + d_ptr.external_input, d_ptr.buffer_1, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_1; + } +#endif + } + break; + } + case r2c_increase_XZ: { + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // FIXME: I don't think this is right when XZ_STRIDE is used + LaunchParams LP = SetLaunchParameters(r2c_increase_XZ); + + // We need shared memory to hold the input array(s) that is const through the kernel. + // We alternate using additional shared memory for the computation and the transposition of the data. + int shared_memory = std::max(XZ_STRIDE * FFT::shared_memory_size, LP.mem_offsets.physical_x_output / LP.Q * (unsigned int)sizeof(complex_compute_t)); + shared_memory += XZ_STRIDE * LP.mem_offsets.shared_input * (unsigned int)sizeof(scalar_compute_t); + + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 0 - precheck; - block_fft_kernel_R2C_INCREASE_XZ<<>>(scalar_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_R2C_INCREASE_XZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + precheck; + block_fft_kernel_R2C_INCREASE_XZ<<>>( + d_ptr.external_input, d_ptr.buffer_1, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_1; #endif + } } break; } case c2c_fwd_none: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_fwd_none); + LaunchParams LP = SetLaunchParameters(c2c_fwd_none); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - int shared_memory = FFT::shared_memory_size; + cudaError_t error_code = cudaSuccess; + DebugUnused auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + DebugUnused int shared_memory = FFT::shared_memory_size; #if FFT_DEBUG_STAGE > 2 - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - precheck; - block_fft_kernel_C2C_NONE<<>>(complex_input, complex_output, LP.mem_offsets, workspace); - postcheck; -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; + CheckSharedMemory(shared_memory, device_properties); + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_C2C_NONE<<>>( + d_ptr.external_input, reinterpret_cast(external_output_ptr), LP.mem_offsets, workspace); + postcheck; + } + else if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1, "current_buffer != fastfft_internal_buffer_1"); + precheck; + block_fft_kernel_C2C_NONE<<>>( + d_ptr.buffer_1, reinterpret_cast(external_output_ptr), LP.mem_offsets, workspace); + postcheck; + } + else if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_2, "current_buffer != fastfft_internal_buffer_2"); + precheck; + block_fft_kernel_C2C_NONE<<>>( + d_ptr.buffer_2, reinterpret_cast(external_output_ptr), LP.mem_offsets, workspace); + postcheck; + } + current_buffer = aliased_output_buffer; #endif - + } break; } - case c2c_fwd_none_Z: { - if constexpr ( Rank == 3 ) { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + case c2c_fwd_none_XYZ: { + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1, "current_buffer != fastfft_internal_buffer_1"); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_fwd_none_Z); + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - int shared_memory = std::max(XZ_STRIDE * FFT::shared_memory_size, size_of::value * (unsigned int)sizeof(complex_type) * XZ_STRIDE); + LaunchParams LP = SetLaunchParameters(c2c_fwd_none_XYZ); + + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + int shared_memory = std::max(XZ_STRIDE * FFT::shared_memory_size, size_of::value * (unsigned int)sizeof(complex_compute_t) * XZ_STRIDE); #if FFT_DEBUG_STAGE > 1 - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE_XYZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - precheck; - block_fft_kernel_C2C_NONE_XYZ<<>>(complex_input, complex_output, LP.mem_offsets, workspace); - postcheck; -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; + CheckSharedMemory(shared_memory, device_properties); + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE_XYZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + precheck; + block_fft_kernel_C2C_NONE_XYZ<<>>( + d_ptr.buffer_1, d_ptr.buffer_2, LP.mem_offsets, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_2; #endif + } } break; } case c2c_fwd_decrease: { - - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_fwd_decrease); + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + LaunchParams LP = SetLaunchParameters(c2c_fwd_decrease); #if FFT_DEBUG_STAGE > 2 - // the shared mem is mixed between storage, shuffling and FFT. For this kernel we need to add padding to avoid bank conlicts (N/32) - // For decrease methods, the shared_input > shared_output - int shared_memory = std::max(FFT::shared_memory_size * LP.threadsPerBlock.z, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input / 32) * (unsigned int)sizeof(complex_type)); + // the shared mem is mixed between storage, shuffling and FFT. For this kernel we need to add padding to avoid bank conlicts (N/32) + // For decrease methods, the shared_input > shared_output + int shared_memory = std::max(FFT::shared_memory_size * LP.threadsPerBlock.y, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input / 32) * (unsigned int)sizeof(complex_compute_t)); - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_DECREASE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - - precheck; - block_fft_kernel_C2C_DECREASE<<>>(complex_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); - postcheck; -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; + CheckSharedMemory(shared_memory, device_properties); + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_DECREASE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_C2C_DECREASE<<>>( + d_ptr.external_input, reinterpret_cast(external_output_ptr), LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + } + else if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1, "current_buffer != fastfft_internal_buffer_1"); + precheck; + block_fft_kernel_C2C_DECREASE<<>>( + d_ptr.buffer_1, reinterpret_cast(external_output_ptr), LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + } + // Rank 3 not yet implemented + current_buffer = aliased_output_buffer; #endif - + } break; } - case c2c_fwd_increase_Z: { - if constexpr ( Rank == 3 ) { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + case c2c_fwd_increase_XYZ: { + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1, "current_buffer != fastfft_internal_buffer_1"); + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_fwd_increase_Z); + LaunchParams LP = SetLaunchParameters(c2c_fwd_increase_XYZ); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - // We need shared memory to hold the input array(s) that is const through the kernel. - // We alternate using additional shared memory for the computation and the transposition of the data. - int shared_memory = std::max(XZ_STRIDE * FFT::shared_memory_size, XZ_STRIDE * LP.mem_offsets.physical_x_output / LP.Q * (unsigned int)sizeof(complex_type)); - shared_memory += XZ_STRIDE * LP.mem_offsets.shared_input * (unsigned int)sizeof(complex_type); + // We need shared memory to hold the input array(s) that is const through the kernel. + // We alternate using additional shared memory for the computation and the transposition of the data. + int shared_memory = std::max(XZ_STRIDE * FFT::shared_memory_size, XZ_STRIDE * LP.mem_offsets.physical_x_output / LP.Q * (unsigned int)sizeof(complex_compute_t)); + shared_memory += XZ_STRIDE * LP.mem_offsets.shared_input * (unsigned int)sizeof(complex_compute_t); #if FFT_DEBUG_STAGE > 1 - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_INCREASE_XYZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - precheck; - block_fft_kernel_C2C_INCREASE_XYZ<<>>(complex_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); - postcheck; -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; + CheckSharedMemory(shared_memory, device_properties); + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_INCREASE_XYZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + precheck; + block_fft_kernel_C2C_INCREASE_XYZ<<>>( + d_ptr.buffer_1, d_ptr.buffer_2, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_2; #endif + } } break; } case c2c_fwd_increase: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + if constexpr ( FFT_ALGO_t == Generic_Fwd_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_fwd_increase); + LaunchParams LP = SetLaunchParameters(c2c_fwd_increase); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - int shared_memory = FFT::shared_memory_size + (unsigned int)sizeof(complex_type) * (LP.mem_offsets.shared_input + LP.mem_offsets.shared_output); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + int shared_memory = FFT::shared_memory_size + (unsigned int)sizeof(complex_compute_t) * (LP.mem_offsets.shared_input + LP.mem_offsets.shared_output); #if FFT_DEBUG_STAGE > 2 - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_INCREASE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - - precheck; - block_fft_kernel_C2C_INCREASE<<>>(complex_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); - postcheck; -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; + CheckSharedMemory(shared_memory, device_properties); + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_INCREASE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_C2C_INCREASE<<>>( + d_ptr.external_input, reinterpret_cast(external_output_ptr), LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + } + else if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1, "current_buffer != fastfft_internal_buffer_1"); + precheck; + block_fft_kernel_C2C_INCREASE<<>>( + d_ptr.buffer_1, reinterpret_cast(external_output_ptr), LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + } + else if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_2, "current_buffer != fastfft_internal_buffer_2"); + precheck; + block_fft_kernel_C2C_INCREASE<<>>( + d_ptr.buffer_2, reinterpret_cast(external_output_ptr), LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + } + current_buffer = aliased_output_buffer; #endif - + } break; } case c2c_inv_none: { - using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_inv_none); + LaunchParams LP = SetLaunchParameters(c2c_inv_none); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - int shared_memory = FFT::shared_memory_size; + int shared_memory = FFT::shared_memory_size; - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 4 - precheck; - block_fft_kernel_C2C_NONE<<>>(complex_input, complex_output, LP.mem_offsets, workspace); - postcheck; -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_C2C_NONE<<>>( + reinterpret_cast(d_ptr.external_input), external_output_ptr, LP.mem_offsets, workspace); + postcheck; + current_buffer = aliased_output_buffer; + } + else if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_C2C_NONE<<>>( + reinterpret_cast(d_ptr.external_input), d_ptr.buffer_1, LP.mem_offsets, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_1; + } #endif - // do something + // do something + } break; } case c2c_inv_none_XZ: { - if constexpr ( Rank == 3 ) { - using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_inv_none_XZ); + LaunchParams LP = SetLaunchParameters(c2c_inv_none_XZ); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - int shared_memory = std::max(FFT::shared_memory_size * XZ_STRIDE, size_of::value * (unsigned int)sizeof(complex_type) * XZ_STRIDE); + int shared_memory = std::max(FFT::shared_memory_size * XZ_STRIDE, size_of::value * (unsigned int)sizeof(complex_compute_t) * XZ_STRIDE); - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE_XZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 4 - precheck; - block_fft_kernel_C2C_NONE_XZ<<>>(complex_input, complex_output, LP.mem_offsets, workspace); - postcheck; -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE_XZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + precheck; + block_fft_kernel_C2C_NONE_XZ<<>>( + reinterpret_cast(d_ptr.external_input), d_ptr.buffer_1, LP.mem_offsets, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_1; #endif + } + // do something } - // do something break; } - case c2c_inv_none_Z: { - if constexpr ( Rank == 3 ) { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + case c2c_inv_none_XYZ: { + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1, "current_buffer != fastfft_internal_buffer_1"); + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_inv_none_Z); + LaunchParams LP = SetLaunchParameters(c2c_inv_none_XYZ); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - int shared_memory = std::max(XZ_STRIDE * FFT::shared_memory_size, size_of::value * (unsigned int)sizeof(complex_type) * XZ_STRIDE); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + int shared_memory = std::max(XZ_STRIDE * FFT::shared_memory_size, size_of::value * (unsigned int)sizeof(complex_compute_t) * XZ_STRIDE); #if FFT_DEBUG_STAGE > 5 - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE_XYZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - precheck; - block_fft_kernel_C2C_NONE_XYZ<<>>(complex_input, complex_output, LP.mem_offsets, workspace); - postcheck; -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; + CheckSharedMemory(shared_memory, device_properties); + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_NONE_XYZ, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + precheck; + block_fft_kernel_C2C_NONE_XYZ<<>>( + d_ptr.buffer_1, d_ptr.buffer_2, LP.mem_offsets, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_2; #endif + } } break; } case c2c_inv_decrease: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2c_inv_decrease); + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; + LaunchParams LP = SetLaunchParameters(c2c_inv_decrease); #if FFT_DEBUG_STAGE > 4 - // the shared mem is mixed between storage, shuffling and FFT. For this kernel we need to add padding to avoid bank conlicts (N/32) - // For decrease methods, the shared_input > shared_output - int shared_memory = std::max(FFT::shared_memory_size * LP.threadsPerBlock.z, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input / 32) * (unsigned int)sizeof(complex_type)); - - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_DECREASE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + // the shared mem is mixed between storage, shuffling and FFT. For this kernel we need to add padding to avoid bank conlicts (N/32) + // For decrease methods, the shared_input > shared_output + int shared_memory = std::max(FFT::shared_memory_size * LP.threadsPerBlock.y, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input / 32) * (unsigned int)sizeof(complex_compute_t)); - precheck; - block_fft_kernel_C2C_DECREASE<<>>(complex_input, complex_output, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); - postcheck; -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; + CheckSharedMemory(shared_memory, device_properties); + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_DECREASE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_C2C_DECREASE<<>>( + reinterpret_cast(d_ptr.external_input), external_output_ptr, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + current_buffer = aliased_output_buffer; + } + else if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_C2C_DECREASE<<>>( + reinterpret_cast(d_ptr.external_input), d_ptr.buffer_1, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + current_buffer = fastfft_internal_buffer_1; + } + // 3D not yet implemented #endif - + } break; } case c2c_inv_increase: { - MyFFTRunTimeAssertTrue(false, "c2c_inv_increase is not yet implemented."); + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + MyFFTRunTimeAssertTrue(false, "c2c_inv_increase is not yet implemented."); #if FFT_DEBUG_STAGE > 4 -#else - // Since we skip the memory ops, unlike the other kernels, we need to flip the buffer pinter - is_in_buffer_memory = ! is_in_buffer_memory; +// TODO; #endif - + } break; } case c2r_none: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2r_none); + LaunchParams LP = SetLaunchParameters(c2r_none); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; cudaErr(error_code); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; cudaErr(error_code); - int shared_memory = FFT::shared_memory_size; + int shared_memory = FFT::shared_memory_size; - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2R_NONE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 6 - precheck; - block_fft_kernel_C2R_NONE<<>>(complex_input, scalar_output, LP.mem_offsets, workspace); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; -#endif + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2R_NONE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + if constexpr ( Rank == 1 ) { + // TODO: + // precheck; + // block_fft_kernel_C2R_NONE<<>>(external_input, external_data_ptr, LP.mem_offsets, workspace); + // postcheck; + } + else if constexpr ( Rank == 2 ) { + // TODO: + // precheck; + // block_fft_kernel_C2R_NONE<<>>( + // intra_complex_input, external_data_ptr, LP.mem_offsets, workspace); + // postcheck; + } + else if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_2, "current_buffer != fastfft_internal_buffer_2"); + precheck; + block_fft_kernel_C2R_NONE<<>>( + d_ptr.buffer_2, external_output_ptr, LP.mem_offsets, workspace); + postcheck; + current_buffer = aliased_output_buffer; + } + +#endif + } break; } case c2r_none_XY: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2r_none_XY); + LaunchParams LP = SetLaunchParameters(c2r_none_XY); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; cudaErr(error_code); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; cudaErr(error_code); - int shared_memory = FFT::shared_memory_size; + int shared_memory = FFT::shared_memory_size; - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2R_NONE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 6 - precheck; - block_fft_kernel_C2R_NONE_XY<<>>(complex_input, scalar_output, LP.mem_offsets, workspace); - postcheck; -#else - is_in_buffer_memory = ! is_in_buffer_memory; + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2R_NONE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + + if constexpr ( Rank == 1 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_external_input, "current_buffer != fastfft_external_input"); + precheck; + block_fft_kernel_C2R_NONE_XY<<>>( + reinterpret_cast(d_ptr.external_input), external_output_ptr, LP.mem_offsets, workspace); + postcheck; + } + else if constexpr ( Rank == 2 ) { + // This could be the last step after a partial InvFFT or partial FwdImgInv, and right now, the only way to tell the difference is if the + // current buffer is set correctly. + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1 || current_buffer == fastfft_internal_buffer_2, "current_buffer != fastfft_internal_buffer_1/2"); + if ( current_buffer == fastfft_internal_buffer_1 ) { + // Presumably this is intended to be the second step of an InvFFT + precheck; + block_fft_kernel_C2R_NONE_XY<<>>( + d_ptr.buffer_1, external_output_ptr, LP.mem_offsets, workspace); + postcheck; + } + else if ( current_buffer == fastfft_internal_buffer_2 ) { + // Presumably this is intended to be the last step in a FwdImgInv + precheck; + block_fft_kernel_C2R_NONE_XY<<>>( + d_ptr.buffer_2, external_output_ptr, LP.mem_offsets, workspace); + postcheck; + } + // DebugAssert will fail if current_buffer is not set correctly to end up in an else clause + } + current_buffer = aliased_output_buffer; + // 3D not yet implemented #endif - + } break; } - case c2r_decrease: { - using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); + case c2r_decrease_XY: { + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + using FFT = decltype(FFT_base_arch( ) + Direction( ) + Type( )); - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, c2r_decrease); + LaunchParams LP = SetLaunchParameters(c2r_decrease_XY); - cudaError_t error_code = cudaSuccess; - auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; cudaErr(error_code); + cudaError_t error_code = cudaSuccess; + auto workspace = make_workspace(error_code); // std::cout << " EPT: " << FFT::elements_per_thread << "kernel " << KernelName[kernel_type] << std::endl; cudaErr(error_code); - int shared_memory = std::max(FFT::shared_memory_size * LP.gridDims.z, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input / 32) * (unsigned int)sizeof(complex_type)); + int shared_memory = std::max(FFT::shared_memory_size * LP.gridDims.z, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input / 32) * (unsigned int)sizeof(complex_compute_t)); - CheckSharedMemory(shared_memory, device_properties); - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2R_DECREASE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 6 - precheck; - block_fft_kernel_C2R_DECREASE_XY<<>>(complex_input, scalar_output, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); - postcheck; + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2R_DECREASE_XY, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + if constexpr ( Rank == 1 ) { + precheck; + block_fft_kernel_C2R_DECREASE_XY<<>>( + d_ptr.external_input, external_output_ptr, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + } + else if constexpr ( Rank == 2 ) { + // This could be the last step after a partial InvFFT or partial FwdImgInv, and right now, the only way to tell the difference is if the + // current buffer is set correctly. + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1 || current_buffer == fastfft_internal_buffer_2, "current_buffer != fastfft_internal_buffer_1/2"); + if ( current_buffer == fastfft_internal_buffer_1 ) { + precheck; + block_fft_kernel_C2R_DECREASE_XY<<>>( + d_ptr.buffer_1, external_output_ptr, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + } + else if ( current_buffer == fastfft_internal_buffer_2 ) { + precheck; + block_fft_kernel_C2R_DECREASE_XY<<>>( + d_ptr.buffer_2, external_output_ptr, LP.mem_offsets, LP.twiddle_in, LP.Q, workspace); + postcheck; + } + } + current_buffer = aliased_output_buffer; - transform_stage_completed = TransformStageCompleted::inv; -#else - is_in_buffer_memory = ! is_in_buffer_memory; #endif - + } break; } case c2r_increase: { - MyFFTRunTimeAssertTrue(false, "c2r_increase is not yet implemented."); - break; - } - - case xcorr_fwd_increase_inv_none: { - using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - using invFFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, xcorr_fwd_increase_inv_none); - - cudaError_t error_code = cudaSuccess; - auto workspace_fwd = make_workspace(error_code); // presumably larger of the two - cudaErr(error_code); - error_code = cudaSuccess; - auto workspace_inv = make_workspace(error_code); // presumably larger of the two - cudaErr(error_code); - - int shared_memory = invFFT::shared_memory_size; - CheckSharedMemory(shared_memory, device_properties); - - // FIXME -#if FFT_DEBUG_STAGE > 2 - bool swap_real_space_quadrants = false; - if ( swap_real_space_quadrants ) { - MyFFTRunTimeAssertTrue(false, "Swapping real space quadrants is not yet implemented."); - // cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul_SwapRealSpaceQuadrants, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - - // precheck; - // block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul_SwapRealSpaceQuadrants<< > > - // ( (complex_type *)d_ptr.image_to_search, complex_input, complex_output, LP.mem_offsets, LP.twiddle_in,LP.Q, workspace_fwd, workspace_inv); - // postcheck; - } - else { - - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - precheck; - - // Right now, because of the n_threads == size_of requirement, we are explicitly zero padding, so we need to send an "apparent Q" to know the input size. - // Could send the actual size, but later when converting to use the transform decomp with different sized FFTs this will be a more direct conversion. - int apparent_Q = size_of::value / fwd_dims_in.y; - - block_fft_kernel_C2C_FWD_INCREASE_INV_NONE_ConjMul<<>>((complex_type*)d_ptr.image_to_search, complex_input, complex_output, LP.mem_offsets, apparent_Q, workspace_fwd, workspace_inv); - postcheck; + if constexpr ( FFT_ALGO_t == Generic_Inv_FFT ) { + MyFFTRunTimeAssertTrue(false, "c2r_increase is not yet implemented."); } -#else - is_in_buffer_memory = ! is_in_buffer_memory; -#endif - - // do something break; } case xcorr_fwd_none_inv_decrease: { - using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - using invFFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, xcorr_fwd_none_inv_decrease); - - cudaError_t error_code = cudaSuccess; - auto workspace_fwd = make_workspace(error_code); // presumably larger of the two - cudaErr(error_code); - error_code = cudaSuccess; - auto workspace_inv = make_workspace(error_code); // presumably larger of the two - cudaErr(error_code); - - // Max shared memory needed to store the full 1d fft remaining on the forward transform - unsigned int shared_memory = FFT::shared_memory_size + (unsigned int)sizeof(complex_type) * LP.mem_offsets.physical_x_input; - // shared_memory = std::max( shared_memory, std::max( invFFT::shared_memory_size * LP.threadsPerBlock.z, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input/32) * (unsigned int)sizeof(complex_type))); - - CheckSharedMemory(shared_memory, device_properties); - -// FIXME + if constexpr ( FFT_ALGO_t == Generic_Fwd_Image_Inv_FFT ) { + if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1, "current_buffer != fastfft_internal_buffer_1"); + using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); + using invFFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); + + LaunchParams LP = SetLaunchParameters(xcorr_fwd_none_inv_decrease); + + cudaError_t error_code = cudaSuccess; + auto workspace_fwd = make_workspace(error_code); // presumably larger of the two + cudaErr(error_code); + error_code = cudaSuccess; + auto workspace_inv = make_workspace(error_code); // presumably larger of the two + cudaErr(error_code); + + // Max shared memory needed to store the full 1d fft remaining on the forward transform + unsigned int shared_memory = FFT::shared_memory_size + (unsigned int)sizeof(complex_compute_t) * LP.mem_offsets.physical_x_input; + // shared_memory = std::max( shared_memory, std::max( invFFT::shared_memory_size * LP.threadsPerBlock.y, (LP.mem_offsets.shared_input + LP.mem_offsets.shared_input/32) * (unsigned int)sizeof(complex_compute_t))); + + CheckSharedMemory(shared_memory, device_properties); #if FFT_DEBUG_STAGE > 2 - bool swap_real_space_quadrants = false; - if ( swap_real_space_quadrants ) { - // cudaErr(cudaFuncSetAttribute((void*)_INV_DECREASE_ConjMul_SwapRealSpaceQuadrants, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - MyFFTDebugAssertFalse(swap_real_space_quadrants, "Swap real space quadrants not yet implemented in xcorr_fwd_none_inv_decrease."); - // precheck; - // _INV_DECREASE_ConjMul_SwapRealSpaceQuadrants<< > > - // ( (complex_type *)d_ptr.image_to_search, complex_input, complex_output, LP.mem_offsets, LP.twiddle_in,LP.Q, workspace_fwd, workspace_inv); - // postcheck; - } - else { - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - // Right now, because of the n_threads == size_of requirement, we are explicitly zero padding, so we need to send an "apparent Q" to know the input size. - // Could send the actual size, but later when converting to use the transform decomp with different sized FFTs this will be a more direct conversion. - int apparent_Q = size_of::value / inv_dims_out.y; - precheck; - block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul<<>>((complex_type*)d_ptr.image_to_search, complex_input, complex_output, LP.mem_offsets, LP.twiddle_in, apparent_Q, workspace_fwd, workspace_inv); - postcheck; - } - transform_stage_completed = TransformStageCompleted::fwd; - -#else - is_in_buffer_memory = ! is_in_buffer_memory; + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + // Right now, because of the n_threads == size_of requirement, we are explicitly zero padding, so we need to send an "apparent Q" to know the input size. + // Could send the actual size, but later when converting to use the transform decomp with different sized FFTs this will be a more direct conversion. + int apparent_Q = size_of::value / inv_dims_out.y; + precheck; + block_fft_kernel_C2C_FWD_NONE_INV_DECREASE_ConjMul<<>>( + (external_image_t*)other_image_ptr, d_ptr.buffer_1, d_ptr.buffer_2, LP.mem_offsets, LP.twiddle_in, apparent_Q, workspace_fwd, workspace_inv); + postcheck; + current_buffer = fastfft_internal_buffer_2; #endif - + } + } // do something + break; - } // end case xcorr_fwd_none_inv_decrease + } case generic_fwd_increase_op_inv_none: { + if constexpr ( FFT_ALGO_t == Generic_Fwd_Image_Inv_FFT ) { + // For convenience, we are explicitly zero-padding. This is lazy. FIXME + using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); + using invFFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - // For convenience, we are explicitly zero-padding. This is lazy. FIXME - using FFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - using invFFT = decltype(FFT_base_arch( ) + Type( ) + Direction( )); - - LaunchParams LP = SetLaunchParameters(elements_per_thread_complex, generic_fwd_increase_op_inv_none); + LaunchParams LP = SetLaunchParameters(generic_fwd_increase_op_inv_none); - cudaError_t error_code = cudaSuccess; - auto workspace_fwd = make_workspace(error_code); // presumably larger of the two - cudaErr(error_code); - error_code = cudaSuccess; - auto workspace_inv = make_workspace(error_code); // presumably larger of the two - cudaErr(error_code); - - int shared_memory = invFFT::shared_memory_size; - CheckSharedMemory(shared_memory, device_properties); + cudaError_t error_code = cudaSuccess; + auto workspace_fwd = make_workspace(error_code); // presumably larger of the two + cudaErr(error_code); + error_code = cudaSuccess; + auto workspace_inv = make_workspace(error_code); // presumably larger of the two + cudaErr(error_code); - // __nv_is_extended_device_lambda_closure_type(type); - // __nv_is_extended_host_device_lambda_closure_type(type) - if constexpr ( IS_IKF_t( ) ) { + int shared_memory = invFFT::shared_memory_size; + CheckSharedMemory(shared_memory, device_properties); + // __nv_is_extended_device_lambda_closure_type(type); + // __nv_is_extended_host_device_lambda_closure_type(type) + if constexpr ( IS_IKF_t( ) ) { // FIXME #if FFT_DEBUG_STAGE > 2 - // Right now, because of the n_threads == size_of requirement, we are explicitly zero padding, so we need to send an "apparent Q" to know the input size. - // Could send the actual size, but later when converting to use the transform decomp with different sized FFTs this will be a more direct conversion. - int apparent_Q = size_of::value / fwd_dims_in.y; - - cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); - precheck; - block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE<<>>((complex_type*)d_ptr.image_to_search, complex_input, complex_output, LP.mem_offsets, apparent_Q, workspace_fwd, workspace_inv, pre_op_functor, intra_op_functor, post_op_functor); - postcheck; + // Right now, because of the n_threads == size_of requirement, we are explicitly zero padding, so we need to send an "apparent Q" to know the input size. + // Could send the actual size, but later when converting to use the transform decomp with different sized FFTs this will be a more direct conversion. + int apparent_Q = size_of::value / fwd_dims_in.y; + cudaErr(cudaFuncSetAttribute((void*)block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE, cudaFuncAttributeMaxDynamicSharedMemorySize, shared_memory)); + if constexpr ( Rank == 2 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_1, "current_buffer != fastfft_internal_buffer_1"); + precheck; + block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE<<>>( + (external_image_t*)other_image_ptr, d_ptr.buffer_1, d_ptr.buffer_2, LP.mem_offsets, apparent_Q, workspace_fwd, workspace_inv, pre_op_functor, intra_op_functor, post_op_functor); + postcheck; + current_buffer = fastfft_internal_buffer_2; + } + else if constexpr ( Rank == 3 ) { + MyFFTDebugAssertTrue(current_buffer == fastfft_internal_buffer_2, "current_buffer != fastfft_internal_buffer_2"); + precheck; + block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE<<>>( + (external_image_t*)other_image_ptr, d_ptr.buffer_2, d_ptr.buffer_1, LP.mem_offsets, apparent_Q, workspace_fwd, workspace_inv, pre_op_functor, intra_op_functor, post_op_functor); + postcheck; + current_buffer = fastfft_internal_buffer_1; + } - // FIXME: this is set in the public method calls for other functions. Since it will be changed to 0-7 to match FFT_DEBUG_STAGE, fix it then. - transform_stage_completed = TransformStageCompleted::fwd; -#else - is_in_buffer_memory = ! is_in_buffer_memory; #endif - } + } - // do something + // do something + } break; } default: { @@ -2937,20 +2948,20 @@ void FourierTransformer::SetAndLaunchK ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// // Some helper functions that are annoyingly long to have in the header. ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// -template -void FourierTransformer::GetTransformSize(KernelType kernel_type) { +template +void FourierTransformer::GetTransformSize(KernelType kernel_type) { // Set member variable transform_size.N (.P .L .Q) if ( IsR2CType(kernel_type) ) { - AssertDivisibleAndFactorOf2(std::max(fwd_dims_in.x, fwd_dims_out.x), std::min(fwd_dims_in.x, fwd_dims_out.x)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(fwd_dims_in.x, fwd_dims_out.x), std::min(fwd_dims_in.x, fwd_dims_out.x)); } else if ( IsC2RType(kernel_type) ) { // FIXME - if ( kernel_type == c2r_decrease ) { - AssertDivisibleAndFactorOf2(std::max(inv_dims_in.x, inv_dims_out.x), std::max(inv_dims_in.x, inv_dims_out.x)); + if ( kernel_type == c2r_decrease_XY ) { + AssertDivisibleAndFactorOf2(kernel_type, std::max(inv_dims_in.x, inv_dims_out.x), std::max(inv_dims_in.x, inv_dims_out.x)); } else { - AssertDivisibleAndFactorOf2(std::max(inv_dims_in.x, inv_dims_out.x), std::min(inv_dims_in.x, inv_dims_out.x)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(inv_dims_in.x, inv_dims_out.x), std::min(inv_dims_in.x, inv_dims_out.x)); } } else { @@ -2958,25 +2969,25 @@ void FourierTransformer::GetTransformS if ( IsForwardType(kernel_type) ) { switch ( transform_dimension ) { case 1: { - AssertDivisibleAndFactorOf2(std::max(fwd_dims_in.x, fwd_dims_out.x), std::min(fwd_dims_in.x, fwd_dims_out.x)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(fwd_dims_in.x, fwd_dims_out.x), std::min(fwd_dims_in.x, fwd_dims_out.x)); break; } case 2: { - if ( kernel_type == xcorr_fwd_increase_inv_none || kernel_type == generic_fwd_increase_op_inv_none ) { + if ( kernel_type == generic_fwd_increase_op_inv_none ) { // FIXME - AssertDivisibleAndFactorOf2(std::max(fwd_dims_in.y, fwd_dims_out.y), std::max(fwd_dims_in.y, fwd_dims_out.y)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(fwd_dims_in.y, fwd_dims_out.y), std::max(fwd_dims_in.y, fwd_dims_out.y)); } else { - AssertDivisibleAndFactorOf2(std::max(fwd_dims_in.y, fwd_dims_out.y), std::min(fwd_dims_in.y, fwd_dims_out.y)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(fwd_dims_in.y, fwd_dims_out.y), std::min(fwd_dims_in.y, fwd_dims_out.y)); } break; } case 3: { if ( IsTransormAlongZ(kernel_type) ) { - AssertDivisibleAndFactorOf2(std::max(fwd_dims_in.z, fwd_dims_out.z), std::min(fwd_dims_in.z, fwd_dims_out.z)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(fwd_dims_in.z, fwd_dims_out.z), std::min(fwd_dims_in.z, fwd_dims_out.z)); } else { - AssertDivisibleAndFactorOf2(std::max(fwd_dims_in.y, fwd_dims_out.y), std::min(fwd_dims_in.y, fwd_dims_out.y)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(fwd_dims_in.y, fwd_dims_out.y), std::min(fwd_dims_in.y, fwd_dims_out.y)); } break; @@ -2990,25 +3001,25 @@ void FourierTransformer::GetTransformS else { switch ( transform_dimension ) { case 1: { - AssertDivisibleAndFactorOf2(std::max(inv_dims_in.x, inv_dims_out.x), std::min(inv_dims_in.x, inv_dims_out.x)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(inv_dims_in.x, inv_dims_out.x), std::min(inv_dims_in.x, inv_dims_out.x)); break; } case 2: { if ( kernel_type == xcorr_fwd_none_inv_decrease ) { // FIXME, for now using full transform - AssertDivisibleAndFactorOf2(std::max(inv_dims_in.y, inv_dims_out.y), std::max(inv_dims_in.y, inv_dims_out.y)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(inv_dims_in.y, inv_dims_out.y), std::max(inv_dims_in.y, inv_dims_out.y)); } else { - AssertDivisibleAndFactorOf2(std::max(inv_dims_in.y, inv_dims_out.y), std::min(inv_dims_in.y, inv_dims_out.y)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(inv_dims_in.y, inv_dims_out.y), std::min(inv_dims_in.y, inv_dims_out.y)); } break; } case 3: { if ( IsTransormAlongZ(kernel_type) ) { - AssertDivisibleAndFactorOf2(std::max(inv_dims_in.z, inv_dims_out.z), std::min(inv_dims_in.z, inv_dims_out.z)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(inv_dims_in.z, inv_dims_out.z), std::min(inv_dims_in.z, inv_dims_out.z)); } else { - AssertDivisibleAndFactorOf2(std::max(inv_dims_in.y, inv_dims_out.y), std::min(inv_dims_in.y, inv_dims_out.y)); + AssertDivisibleAndFactorOf2(kernel_type, std::max(inv_dims_in.y, inv_dims_out.y), std::min(inv_dims_in.y, inv_dims_out.y)); } break; @@ -3023,8 +3034,8 @@ void FourierTransformer::GetTransformS } // end GetTransformSize function -template -void FourierTransformer::GetTransformSize_thread(KernelType kernel_type, int thread_fft_size) { +template +void FourierTransformer::GetTransformSize_thread(KernelType kernel_type, int thread_fft_size) { transform_size.P = thread_fft_size; @@ -3035,7 +3046,14 @@ void FourierTransformer::GetTransformS case r2c_decomposed_transposed: transform_size.N = fwd_dims_in.x; break; - case c2c_decomposed: + case c2c_fwd_decomposed: + // FIXME fwd vs inv + if ( fwd_dims_in.y == 1 ) + transform_size.N = fwd_dims_in.x; + else + transform_size.N = fwd_dims_in.y; + break; + case c2c_inv_decomposed: // FIXME fwd vs inv if ( fwd_dims_in.y == 1 ) transform_size.N = fwd_dims_in.x; @@ -3068,8 +3086,8 @@ void FourierTransformer::GetTransformS transform_size.Q = transform_size.N / transform_size.P; } // end GetTransformSize_thread function -template -LaunchParams FourierTransformer::SetLaunchParameters(const int& ept, KernelType kernel_type, bool do_forward_transform) { +template +LaunchParams FourierTransformer::SetLaunchParameters(KernelType kernel_type) { /* Assuming: 1) r2c/c2r imply forward/inverse transform. @@ -3085,6 +3103,8 @@ LaunchParams FourierTransformer::SetLa twiddle_in = +/- 2*PI/Largest dimension : + for the inverse transform Q = number of sub-transforms */ + MyFFTDebugAssertTrue(elements_per_thread_complex > 1 && elements_per_thread_complex < 33 && IsAPowerOfTwo(elements_per_thread_complex), "elements_per_thead_complex must be a power of 2 and > 2."); + const int ept = elements_per_thread_complex; LaunchParams L; // This is the same for all kernels as set in AssertDivisibleAndFactorOf2() @@ -3109,7 +3129,7 @@ LaunchParams FourierTransformer::SetLa } else { if ( size_change_type == SizeChangeType::decrease ) { - L.threadsPerBlock = dim3(transform_size.P / ept, 1, transform_size.Q); + L.threadsPerBlock = dim3(transform_size.P / ept, transform_size.Q, 1); } else { // In the current xcorr methods that have INCREASE, explicit zero padding is used, so this will be overridden (overrode?) with transform_size.N @@ -3164,7 +3184,7 @@ LaunchParams FourierTransformer::SetLa L.mem_offsets.physical_x_output = fwd_dims_out.w; if ( kernel_type == r2c_none_XZ || kernel_type == r2c_increase_XZ ) { - L.threadsPerBlock.z = XZ_STRIDE; + L.threadsPerBlock.y = XZ_STRIDE; L.gridDims.z /= XZ_STRIDE; } } @@ -3230,15 +3250,14 @@ LaunchParams FourierTransformer::SetLa // Over ride for partial output coalescing if ( kernel_type == c2c_inv_none_XZ ) { - L.threadsPerBlock.z = XZ_STRIDE; + L.threadsPerBlock.y = XZ_STRIDE; L.gridDims.z /= XZ_STRIDE; } - if ( kernel_type == c2c_fwd_none_Z || kernel_type == c2c_inv_none_Z || kernel_type == c2c_fwd_increase_Z ) { - L.threadsPerBlock.z = XZ_STRIDE; + if ( kernel_type == c2c_fwd_none_XYZ || kernel_type == c2c_inv_none_XYZ || kernel_type == c2c_fwd_increase_XYZ ) { + L.threadsPerBlock.y = XZ_STRIDE; L.gridDims.y /= XZ_STRIDE; } } - // FIXME // Some shared memory over-rides if ( kernel_type == c2c_inv_decrease || kernel_type == c2c_inv_increase ) { @@ -3247,7 +3266,7 @@ LaunchParams FourierTransformer::SetLa // FIXME // Some xcorr overrides TODO try the DECREASE approcae - if ( kernel_type == xcorr_fwd_increase_inv_none || kernel_type == generic_fwd_increase_op_inv_none ) { + if ( kernel_type == generic_fwd_increase_op_inv_none ) { // FIXME not correct for 3D L.threadsPerBlock = dim3(transform_size.N / ept, 1, 1); } @@ -3261,7 +3280,6 @@ LaunchParams FourierTransformer::SetLa L.mem_offsets.physical_x_input = inv_dims_in.y; L.mem_offsets.physical_x_output = inv_dims_out.y; } - return L; } @@ -3275,7 +3293,7 @@ void GetCudaDeviceProps(DeviceProps& dp) { dp.device_arch = major * 100 + minor * 10; - MyFFTRunTimeAssertTrue(dp.device_arch == 700 || dp.device_arch == 750 || dp.device_arch == 800 || dp.device_arch == 860, "FastFFT currently only supports compute capability [7.0, 7.5, 8.0, 8.6]."); + MyFFTRunTimeAssertTrue(dp.device_arch == 700 || dp.device_arch == 750 || dp.device_arch == 800 || dp.device_arch == 860 || dp.device_arch == 890, "FastFFT currently only supports compute capability [7.0, 7.5, 8.0, 8.6, 8.9]."); cudaErr(cudaDeviceGetAttribute(&dp.max_shared_memory_per_block, cudaDevAttrMaxSharedMemoryPerBlock, dp.device_id)); cudaErr(cudaDeviceGetAttribute(&dp.max_shared_memory_per_SM, cudaDevAttrMaxSharedMemoryPerMultiprocessor, dp.device_id)); @@ -3302,51 +3320,62 @@ using namespace FastFFT::KernelFunction; // 2d explicit instantiations -template class FourierTransformer; - -template void FourierTransformer::CopyDeviceToDevice(float*, bool, int); -template void FourierTransformer::CopyDeviceToDevice(float2*, bool, int); -template void FourierTransformer::CopyDeviceToDeviceAndSynchronize(float*, bool, int); -template void FourierTransformer::CopyDeviceToDeviceAndSynchronize(float2*, bool, int); - -template void FourierTransformer::Generic_Fwd, - my_functor>(my_functor, - my_functor); - -template void FourierTransformer::Generic_Inv, - my_functor>(my_functor, - my_functor); - -template void FourierTransformer::Generic_Fwd(std::nullptr_t, std::nullptr_t); -template void FourierTransformer::Generic_Inv(std::nullptr_t, std::nullptr_t); - -template void FourierTransformer::Generic_Fwd_Image_Inv, - my_functor, - my_functor>(float2*, - my_functor, - my_functor, - my_functor); - -// 3d explicit instantiations - -template class FourierTransformer; - -template void FourierTransformer::Generic_Fwd, - my_functor>(my_functor, - my_functor); - -template void FourierTransformer::Generic_Inv, - my_functor>(my_functor, - my_functor); - -template void FourierTransformer::Generic_Fwd(std::nullptr_t, std::nullptr_t); -template void FourierTransformer::Generic_Inv(std::nullptr_t, std::nullptr_t); - -template void FourierTransformer::Generic_Fwd_Image_Inv, - my_functor, - my_functor>(float2*, - my_functor, - my_functor, - my_functor); +// TODO: Pass in functor types +// TODO: Take another look at the explicit NOOP vs nullptr and determine if it is really needed +#define INSTANTIATE(COMPUTE_BASE_TYPE, INPUT_TYPE, OTHER_IMAGE_TYPE, RANK) \ + template class FourierTransformer; \ + \ + template void FourierTransformer::FwdFFT(INPUT_TYPE*, INPUT_TYPE*, std::nullptr_t, std::nullptr_t); \ + \ + template void FourierTransformer::InvFFT(INPUT_TYPE*, INPUT_TYPE*, std::nullptr_t, std::nullptr_t); \ + \ + template void FourierTransformer::FwdFFT, \ + my_functor>(INPUT_TYPE*, \ + INPUT_TYPE*, \ + my_functor, \ + my_functor); \ + \ + template void FourierTransformer::InvFFT, \ + my_functor>(INPUT_TYPE*, \ + INPUT_TYPE*, \ + my_functor, \ + my_functor); \ + \ + template void FourierTransformer::FwdImageInvFFT, \ + my_functor, \ + my_functor>(INPUT_TYPE*, \ + OTHER_IMAGE_TYPE*, \ + INPUT_TYPE*, \ + my_functor, \ + my_functor, \ + my_functor); \ + template void FourierTransformer::FwdImageInvFFT, \ + my_functor, \ + my_functor>(INPUT_TYPE*, \ + OTHER_IMAGE_TYPE*, \ + INPUT_TYPE*, \ + my_functor, \ + my_functor, \ + my_functor); \ + template void FourierTransformer::FwdImageInvFFT, \ + my_functor, \ + my_functor>(INPUT_TYPE*, \ + OTHER_IMAGE_TYPE*, \ + INPUT_TYPE*, \ + my_functor, \ + my_functor, \ + my_functor); + +INSTANTIATE(float, float, float2, 2); +INSTANTIATE(float, __half, float2, 2); +INSTANTIATE(float, float, __half2, 2); +INSTANTIATE(float, __half, __half2, 2); +#ifdef FastFFT_3d_instantiation +INSTANTIATE(float, float, float2, 3); +INSTANTIATE(float, __half, __half2, 3); +#endif +#undef INSTANTIATE } // namespace FastFFT diff --git a/src/fastfft/Image.cu b/src/fastfft/Image.cu index 8bdaa4d..facc286 100644 --- a/src/fastfft/Image.cu +++ b/src/fastfft/Image.cu @@ -13,15 +13,21 @@ Image::Image(short4 wanted_size) { size.w = (size.x + padding_jump_value) / 2; - is_in_memory = false; - is_in_real_space = true; - is_cufft_planned = false; - is_fftw_planned = false; + is_in_memory = false; + is_in_real_space = true; + is_cufft_planned = false; + is_fftw_planned = false; + data_is_fp16 = false; + real_memory_allocated = size.w * size.y * size.z * 2; + n_bytes_allocated = real_memory_allocated * sizeof(wanted_real_type); + is_registered = false; } template Image::~Image( ) { + UnRegisterPageLockedMemory( ); + if ( is_in_memory ) { delete[] real_values; // fftw_free((wanted_real_type *)real_values); @@ -102,11 +108,13 @@ void Image::Allocate( ) { complex_values = (wanted_complex_type*)real_values; // Set the complex_values to point at the newly allocated real values; is_fftw_planned = false; is_in_memory = true; + + RegisterPageLockedMemory( ); } template void Image::Allocate(bool set_fftw_plan) { - real_values = new wanted_real_type[real_memory_allocated]; + real_values = new wanted_real_type[real_memory_allocated + 2]; // real_values = (wanted_real_type *) fftw_malloc(sizeof(wanted_real_type) * real_memory_allocated); complex_values = (wanted_complex_type*)real_values; // Set the complex_values to point at the newly allocated real values; @@ -118,6 +126,8 @@ void Image::Allocate(bool set_fftw_plan) } is_in_memory = true; + + RegisterPageLockedMemory( ); } template @@ -340,9 +350,29 @@ void Image::print_values_complex(float* i } } -// Return sum of real values +// // Return sum of real values +// template +// float Image::ReturnSumOfReal(float* input, short4 size, bool print_val) { +// double temp_sum = 0; +// long address = 0; +// int padding_jump_val = size.w * 2 - size.x; +// for ( int k = 0; k < size.z; k++ ) { +// for ( int j = 0; j < size.y; j++ ) { +// for ( int i = 0; i < size.x; i++ ) { + +// temp_sum += double(input[address]); +// address++; +// } +// address += padding_jump_val; +// } +// } + +// return float(temp_sum); +// } + template -float Image::ReturnSumOfReal(float* input, short4 size, bool print_val) { +template +float Image::ReturnSumOfReal(T* input, short4 size, bool print_val) { double temp_sum = 0; long address = 0; int padding_jump_val = size.w * 2 - size.x; @@ -360,6 +390,10 @@ float Image::ReturnSumOfReal(float* input return float(temp_sum); } +template float Image::ReturnSumOfReal(float* input, short4 size, bool print_val); +template float Image::ReturnSumOfReal(half_float::half* input, short4 size, bool print_val); +template float Image::ReturnSumOfReal<__half>(__half* input, short4 size, bool print_val); + // Return the sum of the complex values template @@ -447,5 +481,59 @@ void Image::ClipInto(const float* array_t } // end of clip into +template +void Image::ConvertFP32ToFP16( ) { + if ( data_is_fp16 ) { + std::cerr << "Error: Image is already in FP16." << std::endl; + exit(1); + } + if ( ! is_in_memory ) { + std::cerr << "Error: Image is not in memory." << std::endl; + exit(1); + } + // We can just do this in place as the new values are smaller than the old ones. + for ( int i = 0; i < real_memory_allocated; i++ ) { + reinterpret_cast(real_values)[i] = (half_float::half)real_values[i]; + } + data_is_fp16 = true; +} + +template +void Image::ConvertFP16ToFP32( ) { + if ( ! data_is_fp16 ) { + std::cerr << "Error: Image is not already in FP16." << std::endl; + exit(1); + } + if ( ! is_in_memory ) { + std::cerr << "Error: Image is not in memory." << std::endl; + exit(1); + } + // We can just do this in place as the new values are smaller than the old ones. + float* tmp = new float[real_memory_allocated]; + for ( int i = 0; i < real_memory_allocated; i++ ) { + tmp[i] = float(reinterpret_cast(real_values)[i]); + } + for ( int i = 0; i < real_memory_allocated; i++ ) { + real_values[i] = tmp[i]; + } + delete[] tmp; + data_is_fp16 = false; +} + +template +void Image::RegisterPageLockedMemory( ) { + if ( ! is_registered ) { + cudaErr(cudaHostRegister(real_values, sizeof(wanted_real_type) * real_memory_allocated, cudaHostRegisterDefault)); + is_registered = true; + } +} + +template +void Image::UnRegisterPageLockedMemory( ) { + if ( is_registered ) { + cudaErr(cudaHostUnregister(real_values)); + } +} + template class Image; // template Image::Image(short4); \ No newline at end of file diff --git a/src/fastfft/Image.cuh b/src/fastfft/Image.cuh index b6c9275..a98595b 100644 --- a/src/fastfft/Image.cuh +++ b/src/fastfft/Image.cuh @@ -17,6 +17,8 @@ #include #include +#include "../../include/ieee-754-half/half.hpp" + // A simple class to represent image objects needed for testing FastFFT. template @@ -33,6 +35,7 @@ class Image { short4 size; int real_memory_allocated; + size_t n_bytes_allocated; int padding_jump_value; float fftw_epsilon; @@ -86,15 +89,27 @@ class Image { void SetClipIntoMask(short4 input_size, short4 output_size); bool is_set_clip_into_mask = false; // void SetClipIntoCallback(cufftReal* image_to_insert, int image_to_insert_size_x, int image_to_insert_size_y,int image_to_insert_pitch); - void SetComplexConjMultiplyAndLoadCallBack(cufftComplex* search_image_FT, cufftReal FT_normalization_factor); - void MultiplyConjugateImage(wanted_complex_type* other_image); - void print_values_complex(float* input, std::string msg, int n_to_print); - float ReturnSumOfReal(float* input, short4 size, bool print_val = false); + void SetComplexConjMultiplyAndLoadCallBack(cufftComplex* search_image_FT, cufftReal FT_normalization_factor); + void MultiplyConjugateImage(wanted_complex_type* other_image); + void print_values_complex(float* input, std::string msg, int n_to_print); + // float ReturnSumOfReal(float* input, short4 size, bool print_val = false); + template + float ReturnSumOfReal(T* input, short4 size, bool print_val = false); + float2 ReturnSumOfComplex(float2* input, int n_to_print); float ReturnSumOfComplexAmplitudes(float2* input, int n_to_print); void ClipInto(const float* array_to_paste, float* array_to_paste_into, short4 size_from, short4 size_into, short4 wanted_center, float wanted_padding_value); + bool data_is_fp16; + void ConvertFP32ToFP16( ); + void ConvertFP16ToFP32( ); + private: + // Note; this is not thread safe + bool is_registered; + + void RegisterPageLockedMemory( ); + void UnRegisterPageLockedMemory( ); }; #endif // SRC_CPP_IMAGE_CUH_ \ No newline at end of file diff --git a/src/python/FastFFT_binding/FastFFT_python_binding.cu b/src/python/FastFFT_binding/FastFFT_python_binding.cu index ce5fb40..3575092 100644 --- a/src/python/FastFFT_binding/FastFFT_python_binding.cu +++ b/src/python/FastFFT_binding/FastFFT_python_binding.cu @@ -14,52 +14,50 @@ #include "../../../include/cufftdx/include/cufftdx.hpp" #include "../../../include/FastFFT.h" #include "../../../include/FastFFT.cuh" -#include "../../FastFFT.cu" +#include "../fastfft/FastFFT.cu" namespace py = pybind11; +template +void declare_array(py::module& m, const std::string& typestr) { -template -void declare_array(py::module &m, const std::string &typestr) { - - - using FT_t = FastFFT::FourierTransformer; + using FT_t = FastFFT::FourierTransformer; std::string pyclass_name = std::string("FourierTransformer") + typestr; - py::class_(m, pyclass_name.c_str()) - // Constructor and initialization functions - .def(py::init<>()) - // TODO: I have a virtual destructor, check what should be done here. - .def("SetForwardFFTPlan", &FT_t::SetForwardFFTPlan) - .def("SetInverseFFTPlan", &FT_t::SetInverseFFTPlan) - .def("SetInputPointer" , py::overload_cast(&FT_t::SetInputPointer)) - - // Memory operations: For now, assume user has cupy or torch to handle getting the data to and from the GPU. - // TODO: may need to check on whether the data block is reliably contiguous. - // CopyHostToDevice, CopyDeviceToHost, CopyDeviceToHost - - // FFT operations - .def("FwdFFT", &FT_t::FwdFFT) - .def("InvFFT", &FT_t::InvFFT) - - // Cross-correlation operations - // TODO: confirm overload resolution is working (r/n float2* and __half2* are the first args) - // I think there is a float2 built in to numpy but how to get pybind11 to recognize it? - // .def("CrossCorrelate", &FT_t::CrossCorrelate) - - // Getters - .def("ReturnInputMemorySize", &FT_t::ReturnInputMemorySize) - .def("ReturnFwdOutputMemorySize", &FT_t::ReturnFwdOutputMemorySize) - .def("ReturnInvOutputMemorySize", &FT_t::ReturnInvOutputMemorySize) - - .def("ReturnFwdInputDimensions", &FT_t::ReturnFwdInputDimensions) - .def("ReturnInvInputDimensions", &FT_t::ReturnInvInputDimensions) - .def("ReturnFwdOutputDimensions", &FT_t::ReturnFwdOutputDimensions) - .def("ReturnInvOutputDimensions", &FT_t::ReturnInvOutputDimensions) - - // Debugging info - .def("PrintState", &FT_t::PrintState) - .def("Wait", &FT_t::Wait); + py::class_(m, pyclass_name.c_str( )) + // Constructor and initialization functions + .def(py::init<>( )) + // TODO: I have a virtual destructor, check what should be done here. + .def("SetForwardFFTPlan", &FT_t::SetForwardFFTPlan) + .def("SetInverseFFTPlan", &FT_t::SetInverseFFTPlan) + .def("SetInputPointerFromPython", py::overload_cast(&FT_t::SetInputPointerFromPython)) + + // Memory operations: For now, assume user has cupy or torch to handle getting the data to and from the GPU. + // TODO: may need to check on whether the data block is reliably contiguous. + // CopyHostToDevice, CopyDeviceToHost, CopyDeviceToHost + + // FFT operations + .def("FwdFFT", &FT_t::FwdFFT) + .def("InvFFT", &FT_t::InvFFT) + + // Cross-correlation operations + // TODO: confirm overload resolution is working (r/n float2* and __half2* are the first args) + // I think there is a float2 built in to numpy but how to get pybind11 to recognize it? + // .def("CrossCorrelate", &FT_t::CrossCorrelate) + + // Getters + .def("ReturnInputMemorySize", &FT_t::ReturnInputMemorySize) + .def("ReturnFwdOutputMemorySize", &FT_t::ReturnFwdOutputMemorySize) + .def("ReturnInvOutputMemorySize", &FT_t::ReturnInvOutputMemorySize) + + .def("ReturnFwdInputDimensions", &FT_t::ReturnFwdInputDimensions) + .def("ReturnInvInputDimensions", &FT_t::ReturnInvInputDimensions) + .def("ReturnFwdOutputDimensions", &FT_t::ReturnFwdOutputDimensions) + .def("ReturnInvOutputDimensions", &FT_t::ReturnInvOutputDimensions) + + // Debugging info + .def("PrintState", &FT_t::PrintState) + .def("Wait", &FT_t::Wait); // py::enum_(FourierTransformer, "OriginType") // .value("natural", FastFFT::FourierTransformer::OriginType::natural) @@ -69,7 +67,6 @@ void declare_array(py::module &m, const std::string &typestr) { } PYBIND11_MODULE(FastFFT, m) { - - declare_array(m, "_float_float_float"); + declare_array(m, "_float_float_float"); } diff --git a/src/python/FastFFT_binding/test_cupy.py b/src/python/FastFFT_binding/test_cupy.py index cd2d965..d009553 100644 --- a/src/python/FastFFT_binding/test_cupy.py +++ b/src/python/FastFFT_binding/test_cupy.py @@ -11,11 +11,12 @@ print("Cupy array is {}".format(a)) # Setup the plans -FT.SetForwardFFTPlan(16,16,1,16,16,1, True) +FT. +(16,16,1,16,16,1, True) FT.Wait() FT.SetInverseFFTPlan(16,16,1,16,16,1, True) FT.Wait() -FT.SetInputPointer(a.data.ptr) +FT.SetInputPointerFromPython(a.data.ptr) FT.Wait() # These bools are defaults that are deprecated FT.FwdFFT(False, True) diff --git a/src/tests/constant_image_test.cu b/src/tests/constant_image_test.cu index 438f2c0..deccabd 100644 --- a/src/tests/constant_image_test.cu +++ b/src/tests/constant_image_test.cu @@ -1,7 +1,7 @@ #include "tests.h" -template +template bool const_image_test(std::vector& size) { bool all_passed = true; @@ -9,9 +9,17 @@ bool const_image_test(std::vector& size) { std::vector FFTW_passed(size.size( ), true); std::vector FastFFT_forward_passed(size.size( ), true); std::vector FastFFT_roundTrip_passed(size.size( ), true); + float* output_buffer_fp32 = nullptr; + __half* output_buffer_fp16 = nullptr; for ( int n = 0; n < size.size( ); n++ ) { + // FIXME: In the current implementation, any 2d size > 128 will overflow in fp16. + if constexpr ( use_fp16_io_buffers ) { + if ( size[n] > 128 ) + continue; + } + short4 input_size; short4 output_size; long full_sum = long(size[n]); @@ -26,8 +34,7 @@ bool const_image_test(std::vector& size) { full_sum = full_sum * full_sum * full_sum * full_sum; } - float sum; - + float sum; Image host_input(input_size); Image host_output(output_size); Image device_output(output_size); @@ -36,12 +43,18 @@ bool const_image_test(std::vector& size) { // We just make one instance of the FourierTransformer class, with calc type float. // For the time being input and output are also float. TODO calc optionally either fp16 or nv_bloat16, TODO inputs at lower precision for bandwidth improvement. - FastFFT::FourierTransformer FT; + FastFFT::FourierTransformer FT; + FastFFT::FourierTransformer FT_fp16; // This is similar to creating an FFT/CUFFT plan, so set these up before doing anything on the GPU FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); FT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + if constexpr ( use_fp16_io_buffers ) { + FT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + FT_fp16.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + } + // The padding (dims.w) is calculated based on the setup short4 dims_in = FT.ReturnFwdInputDimensions( ); short4 dims_out = FT.ReturnFwdOutputDimensions( ); @@ -64,10 +77,6 @@ bool const_image_test(std::vector& size) { // Set our input host memory to a constant. Then FFT[0] = host_input_memory_allocated FT.SetToConstant(host_output.real_values, host_output.real_memory_allocated, 1.0f); - - // Now we want to associate the host memory with the device memory. The method here asks if the host pointer is pinned (in page locked memory) which - // ensures faster transfer. If false, it will be pinned for you. - FT.SetInputPointer(host_output.real_values, false); sum = host_output.ReturnSumOfReal(host_output.real_values, dims_out); if ( sum != long(dims_in.x) * long(dims_in.y) * long(dims_in.z) ) { @@ -75,11 +84,6 @@ bool const_image_test(std::vector& size) { init_passed[n] = false; } - // MyFFTDebugAssertTestTrue( sum == dims_out.x*dims_out.y*dims_out.z,"Unit impulse Init "); - - // This copies the host memory into the device global memory. If needed, it will also allocate the device memory first. - FT.CopyHostToDevice(host_output.real_values); - host_output.FwdFFT( ); bool test_passed = true; @@ -96,17 +100,46 @@ bool const_image_test(std::vector& size) { all_passed = false; FFTW_passed[n] = false; } - // MyFFTDebugAssertTestTrue( test_passed, "FFTW unit impulse forward FFT"); + + FT.SetToConstant(host_output.real_values, host_output.real_memory_allocated, 1.0f); + + if constexpr ( use_fp16_io_buffers ) { + // We need to allocate memory for the output buffer. + cudaErr(cudaMalloc((void**)&output_buffer_fp16, sizeof(__half) * host_output.real_memory_allocated)); + // This is an in-place operation so when copying to device, just use half the memory. + host_output.ConvertFP32ToFP16( ); + // Now we want to associate the host memory with the device memory. The method here asks if the host pointer is pinned (in page locked memory) which + // ensures faster transfer. If false, it will be pinned for you. + sum = host_output.ReturnSumOfReal(reinterpret_cast<__half*>(host_output.real_values), dims_out); + + cudaErr(cudaMemcpyAsync(output_buffer_fp16, host_output.real_values, sizeof(__half) * host_output.real_memory_allocated, cudaMemcpyHostToDevice, cudaStreamPerThread)); + } + else { + cudaErr(cudaMalloc((void**)&output_buffer_fp32, sizeof(float) * host_output.real_memory_allocated)); + + // Now we want to associate the host memory with the device memory. The method here asks if the host pointer is pinned (in page locked memory) which + // ensures faster transfer. If false, it will be pinned for you. + sum = host_output.ReturnSumOfReal(host_output.real_values, dims_out); + // This copies the host memory into the device global memory. If needed, it will also allocate the device memory first. + cudaErr(cudaMemcpy(output_buffer_fp32, host_output.real_values, sizeof(float) * host_output.real_memory_allocated, cudaMemcpyHostToDevice)); + } // Just to make sure we don't get a false positive, set the host memory to some undesired value. FT.SetToConstant(host_output.real_values, host_output.real_memory_allocated, 2.0f); // This method will call the regular FFT kernels given the input/output dimensions are equal when the class is instantiated. // bool swap_real_space_quadrants = false; - FT.FwdFFT( ); + if constexpr ( use_fp16_io_buffers ) { + // Recast the position space buffer and pass it in as if it were an external, device, __half* pointer. + FT_fp16.FwdFFT(output_buffer_fp16); + FT_fp16.CopyDeviceToHostAndSynchronize(reinterpret_cast<__half*>(host_output.real_values)); + host_output.ConvertFP16ToFP32( ); + } + else { + FT.FwdFFT(output_buffer_fp32); + FT.CopyDeviceToHostAndSynchronize(host_output.real_values); + } - // in buffer, do not deallocate, do not unpin memory - FT.CopyDeviceToHostAndSynchronize(host_output.real_values, false); test_passed = true; // FIXME: centralized test conditions for ( long index = 1; index < host_output.real_memory_allocated / 2; index++ ) { @@ -117,11 +150,12 @@ bool const_image_test(std::vector& size) { if ( host_output.complex_values[0].x != (float)dims_out.x * (float)dims_out.y * (float)dims_out.z ) test_passed = false; - bool continue_debugging; + bool continue_debugging = true; // We don't want this to break compilation of other tests, so only check at runtime. if constexpr ( FFT_DEBUG_STAGE < 5 ) { continue_debugging = debug_partial_fft(host_output, dims_in, dims_out, dims_in, dims_out, __LINE__); } + MyTestPrintAndExit(continue_debugging, "Partial FFT debug stage " + std::to_string(FFT_DEBUG_STAGE)); if ( test_passed == false ) { all_passed = false; @@ -130,12 +164,23 @@ bool const_image_test(std::vector& size) { // MyFFTDebugAssertTestTrue( test_passed, "FastFFT unit impulse forward FFT"); FT.SetToConstant(host_input.real_values, host_input.real_memory_allocated, 2.0f); - FT.InvFFT( ); - FT.CopyDeviceToHostAndSynchronize(host_output.real_values, true); + if constexpr ( use_fp16_io_buffers ) { + + FT_fp16.InvFFT(output_buffer_fp16); + FT_fp16.CopyDeviceToHostAndSynchronize(reinterpret_cast<__half*>(host_output.real_values)); + host_output.data_is_fp16 = true; // we need to over-ride this as we already convertted but are overwriting. + host_output.ConvertFP16ToFP32( ); + } + else { + FT.InvFFT(output_buffer_fp32); + FT.CopyDeviceToHostAndSynchronize(host_output.real_values); + } if constexpr ( FFT_DEBUG_STAGE > 4 ) { continue_debugging = debug_partial_fft(host_output, dims_in, dims_out, dims_in, dims_out, __LINE__); } + if ( ! continue_debugging ) + std::abort( ); // Assuming the outputs are always even dimensions, padding_jump_val is always 2. sum = host_output.ReturnSumOfReal(host_output.real_values, dims_out, true); @@ -145,6 +190,13 @@ bool const_image_test(std::vector& size) { FastFFT_roundTrip_passed[n] = false; } MyFFTDebugAssertTestTrue(sum == full_sum, "FastFFT constant image round trip for size " + std::to_string(dims_in.x)); + + if constexpr ( use_fp16_io_buffers ) { + cudaErr(cudaFree(output_buffer_fp16)); + } + else { + cudaErr(cudaFree(output_buffer_fp32)); + } } // loop over sizes if ( all_passed ) { @@ -179,13 +231,17 @@ int main(int argc, char** argv) { FastFFT::CheckInputArgs(argc, argv, text_line, run_2d_unit_tests, run_3d_unit_tests); if ( run_2d_unit_tests ) { - if ( ! const_image_test<2>(FastFFT::test_size) ) + constexpr bool start_with_fp16 = false; + constexpr bool start_with_fp32 = ! start_with_fp16; + if ( ! const_image_test<2, start_with_fp16>(FastFFT::test_size) ) + return 1; + if ( ! const_image_test<2, start_with_fp32>(FastFFT::test_size) ) return 1; } if ( run_3d_unit_tests ) { - if ( ! const_image_test<3>(FastFFT::test_size_3d) ) - return 1; + // if ( ! const_image_test<3, false>(FastFFT::test_size_3d) ) + // return 1; // if (! unit_impulse_test(test_size_3d, true, true)) return 1; } diff --git a/src/tests/fuck-preop_functor_scale_test.cu-fuck b/src/tests/fuck-preop_functor_scale_test.cu-fuck new file mode 100644 index 0000000..6bcfc83 --- /dev/null +++ b/src/tests/fuck-preop_functor_scale_test.cu-fuck @@ -0,0 +1,435 @@ +#include "tests.h" +#include +#include + +template +void preop_functor_scale(std::vector size, FastFFT::SizeChangeType::Enum size_change_type, bool do_rectangle) { + + using SCT = FastFFT::SizeChangeType::Enum; + + constexpr bool print_out_time = true; + // bool set_padding_callback = false; // the padding callback is slower than pasting in b/c the read size of the pointers is larger than the actual data. do not use. + bool is_size_change_decrease = false; + const bool set_conjMult_callback = true; + + if ( size_change_type == SCT::decrease ) { + std::cerr << "Decreasing the size is not yet implemented." << std::endl; + std::exit(1); + is_size_change_decrease = true; + } + + // For an increase or decrease in size, we have to shrink the loop by one, + // for a no_change, we don't because every size is compared to itself. + int loop_limit = 1; + if ( size_change_type == SCT::no_change ) + loop_limit = 0; + + // Currently, to test a non-square input, the fixed input sizes are used + // and the input x size is reduced by input_x / make_rect_x + int make_rect_x; + int make_rect_y = 1; + if ( do_rectangle ) + make_rect_x = 2; + else + make_rect_x = 1; + + if ( Rank == 3 && do_rectangle ) { + std::cout << "ERROR: cannot do 3d and rectangle at the same time" << std::endl; + return; + } + + short4 input_size; + short4 output_size; + for ( int iSize = 0; iSize < size.size( ) - loop_limit; iSize++ ) { + int oSize; + int loop_size; + // TODO: the logic here is confusing, clean it up + if ( size_change_type != SCT::no_change ) { + oSize = iSize + 1; + loop_size = size.size( ); + } + else { + oSize = iSize; + loop_size = oSize + 1; + } + + while ( oSize < loop_size ) { + + if ( is_size_change_decrease ) { + output_size = make_short4(size[iSize] / make_rect_x, size[iSize] / make_rect_y, 1, 0); + input_size = make_short4(size[oSize] / make_rect_x, size[oSize] / make_rect_y, 1, 0); + if ( Rank == 3 ) { + output_size.z = size[iSize]; + input_size.z = size[oSize]; + } + } + else { + input_size = make_short4(size[iSize] / make_rect_x, size[iSize] / make_rect_y, 1, 0); + output_size = make_short4(size[oSize] / make_rect_x, size[oSize] / make_rect_y, 1, 0); + if ( Rank == 3 ) { + input_size.z = size[iSize]; + output_size.z = size[oSize]; + } + } + if ( print_out_time ) { + printf("Testing padding from %i,%i,%i to %i,%i,%i\n", input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + } + + if ( (input_size.x == output_size.x && input_size.y == output_size.y && input_size.z == output_size.z) ) { + std::cerr << "This test is only setup for block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE" << std::endl; + std::exit(1); + } + + // bool test_passed = true; + + Image FT_input(input_size); + Image FT_output(output_size); + Image FT_fp16_input(input_size); + Image FT_fp16_output(output_size); + + short4 target_size; + + if ( is_size_change_decrease ) + target_size = input_size; // assuming xcorr_fwd_NOOP_inv_DECREASE + else + target_size = output_size; + + Image target_search_image(target_size); + Image target_search_image_fp16(target_size); + Image positive_control(target_size); + + // We just make one instance of the FourierTransformer class, with calc type float. + // For the time being input and output are also float. TODO caFlc optionally either fp16 or nv_bloat16, TODO inputs at lower precision for bandwidth improvement. + FastFFT::FourierTransformer FT; + FastFFT::FourierTransformer targetFT; + + // Create an instance to copy memory also for the cufft tests. + FastFFT::FourierTransformer FT_fp16; + FastFFT::FourierTransformer targetFT_fp16; + + float* FT_buffer; + float* targetFT_buffer; + __half* FT_fp16_buffer; + __half* targetFT_fp16_buffer; + + if ( is_size_change_decrease ) { + FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + FT.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + targetFT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + targetFT.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + + FT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + FT_fp16.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + targetFT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + targetFT_fp16.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + } + else { + FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + FT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT.SetForwardFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + + FT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + FT_fp16.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT_fp16.SetForwardFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT_fp16.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + } + + short4 fwd_dims_in = FT.ReturnFwdInputDimensions( ); + short4 fwd_dims_out = FT.ReturnFwdOutputDimensions( ); + short4 inv_dims_in = FT.ReturnInvInputDimensions( ); + short4 inv_dims_out = FT.ReturnInvOutputDimensions( ); + + FT_input.real_memory_allocated = FT.ReturnInputMemorySize( ); + FT_output.real_memory_allocated = FT.ReturnInvOutputMemorySize( ); + + size_t device_memory = std::max(FT_input.real_memory_allocated, FT_output.real_memory_allocated); + cudaErr(cudaMallocAsync((void**)&FT_buffer, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMallocAsync((void**)&targetFT_buffer, device_memory * sizeof(float), cudaStreamPerThread)); + // Set to zero + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + + cudaErr(cudaMallocAsync((void**)&FT_fp16_buffer, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMallocAsync((void**)&targetFT_fp16_buffer, device_memory * sizeof(__half), cudaStreamPerThread)); + // Set to zero + cudaErr(cudaMemsetAsync(FT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + if ( is_size_change_decrease ) + target_search_image.real_memory_allocated = targetFT.ReturnInputMemorySize( ); + else + target_search_image.real_memory_allocated = targetFT.ReturnInvOutputMemorySize( ); // the larger of the two. + + positive_control.real_memory_allocated = target_search_image.real_memory_allocated; // this won't change size + + bool set_fftw_plan = false; + FT_input.Allocate(set_fftw_plan); + FT_output.Allocate(set_fftw_plan); + FT_fp16_input.Allocate(set_fftw_plan); + FT_fp16_output.Allocate(set_fftw_plan); + + target_search_image.Allocate(true); + target_search_image_fp16.Allocate(true); + positive_control.Allocate(true); + + // Set a unit impulse at the center of the input array. + // For now just considering the real space image to have been implicitly quadrant swapped so the center is at the origin. + FT.SetToConstant(FT_input.real_values, FT_input.real_memory_allocated, 0.0f); + FT.SetToConstant(FT_output.real_values, FT_output.real_memory_allocated, 0.0f); + FT.SetToConstant(FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated, 0.0f); + FT.SetToConstant(FT_fp16_output.real_values, FT_fp16_output.real_memory_allocated, 0.0f); + FT.SetToConstant(target_search_image.real_values, target_search_image.real_memory_allocated, 0.0f); + FT.SetToConstant(target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated, 0.0f); + FT.SetToConstant(positive_control.real_values, target_search_image.real_memory_allocated, 0.0f); + + // Place these values at the origin of the image and after convolution, should be at 0,0,0. + float testVal_1 = 2.f; + float testVal_2 = set_conjMult_callback ? 3.f : 1.0; // This way the test conditions are the same, the 1. indicating no conj + FT_input.real_values[0] = testVal_1; + FT_fp16_input.real_values[0] = testVal_1; + target_search_image.real_values[0] = testVal_2; + target_search_image_fp16.real_values[0] = testVal_2; + positive_control.real_values[0] = testVal_1; + + // Transform the target on the host prior to transfer. + target_search_image.FwdFFT( ); + target_search_image_fp16.FwdFFT( ); + + cudaErr(cudaMemcpyAsync(FT_buffer, FT_input.real_values, FT_input.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_buffer, target_search_image.real_values, target_search_image.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + // sync before converting to fp16 + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + FT_fp16_input.ConvertFP32ToFP16( ); + target_search_image_fp16.ConvertFP32ToFP16( ); + + cudaErr(cudaMemcpyAsync(FT_fp16_buffer, FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_fp16_buffer, target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + + // Positive control on the host. + // After both forward FFT's we should constant values in each pixel = testVal_1 and testVal_2. + // After the Conjugate multiplication, we should have a constant value of testVal_1*testVal_2. + // After the inverse FFT, we should have a constant value of testVal_1*testVal_2 in the center pixel and 0 everywhere else. + positive_control.FwdFFT( ); + positive_control.MultiplyConjugateImage(target_search_image.complex_values); + positive_control.InvFFT( ); + + CheckUnitImpulseRealImage(positive_control, __LINE__); + + float scale_factor = positive_control.size.x * positive_control.size.y * positive_control.size.z * testVal_1 * testVal_2; + if ( positive_control.real_values[0] == scale_factor ) { + if ( print_out_time ) { + std::cout << "Test passed for FFTW positive control." << std::endl; + } + } + else { + MyTestPrintAndExit(false, "Test failed for FFTW positive control. Value at zero is " + std::to_string(positive_control.real_values[0])); + } + + FastFFT::KernelFunction::my_functor noop; + FastFFT::KernelFunction::my_functor scale(1.f / scale_factor); + FastFFT::KernelFunction::my_functor conj_mul; + FastFFT::KernelFunction::my_functor conj_mul_then_scale(1.f / scale_factor); + + // Do first round without scaling + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, noop, conj_mul, noop); + FT_fp16.FwdImageInvFFT(FT_fp16_buffer, reinterpret_cast<__half2*>(targetFT_fp16_buffer), FT_fp16_buffer, noop, conj_mul, noop); + + // Copy back to the host and check the results + if constexpr ( FFT_DEBUG_STAGE < 7 ) { + FT.CopyDeviceToHostAndSynchronize(FT_output.real_values); + FT_fp16.CopyDeviceToHostAndSynchronize(reinterpret_cast<__half*>(FT_fp16_output.real_values)); + } + else { + // Copy back to the host and check the results + cudaErr(cudaMemcpyAsync(FT_output.real_values, FT_buffer, FT_output.real_memory_allocated * sizeof(float), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(FT_fp16_output.real_values, FT_fp16_buffer, FT_fp16_output.real_memory_allocated * sizeof(__half), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + } + FT_fp16_output.data_is_fp16 = true; + FT_fp16_output.ConvertFP16ToFP32( ); + + if ( FT_output.real_values[0] == scale_factor ) { + if ( print_out_time ) { + std::cout << "Test passed for FP32 no scale." << std::endl; + } + } + else { + debug_partial_fft(FT_output, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); + debug_partial_fft(FT_fp16_output, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); + MyTestPrintAndExit(false, "Test failed for FP32 positive control. Value at zero is " + std::to_string(FT_output.real_values[0])); + } + + if ( FT_fp16_output.real_values[0] == scale_factor ) { + if ( print_out_time ) { + std::cout << "Test passed for FP16 no scale." << std::endl; + } + } + else { + debug_partial_fft(FT_output, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); + debug_partial_fft(FT_fp16_output, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); + MyTestPrintAndExit(false, "Test failed for FP16 positive control. Value at zero is " + std::to_string(FT_fp16_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + } + + // Restore the buffers and do the second round with preop scaling + // Set to zero + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(FT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + + cudaErr(cudaMemcpyAsync(FT_buffer, FT_input.real_values, FT_input.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_buffer, target_search_image.real_values, target_search_image.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + + cudaErr(cudaMemcpyAsync(FT_fp16_buffer, FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_fp16_buffer, target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, scale, conj_mul, noop); + FT_fp16.FwdImageInvFFT(FT_fp16_buffer, reinterpret_cast<__half2*>(targetFT_fp16_buffer), FT_fp16_buffer, scale, conj_mul, noop); + + // Copy back to the host and check the results + if constexpr ( FFT_DEBUG_STAGE < 7 ) { + FT.CopyDeviceToHostAndSynchronize(FT_output.real_values); + FT_fp16.CopyDeviceToHostAndSynchronize(reinterpret_cast<__half*>(FT_fp16_output.real_values)); + } + else { + // Copy back to the host and check the results + cudaErr(cudaMemcpyAsync(FT_output.real_values, FT_buffer, FT_output.real_memory_allocated * sizeof(float), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(FT_fp16_output.real_values, FT_fp16_buffer, FT_fp16_output.real_memory_allocated * sizeof(__half), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + } + FT_fp16_output.data_is_fp16 = true; + FT_fp16_output.ConvertFP16ToFP32( ); + + if ( FT_output.real_values[0] == 1.0f ) { + if ( print_out_time ) { + std::cout << "Test passed for FP32 with scale." << std::endl; + } + } + else + MyTestPrintAndExit(false, "Test failed for FP32 positive control. Value at zero is " + std::to_string(FT_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + + if ( FT_fp16_output.real_values[0] == 1.0f ) { + if ( print_out_time ) { + std::cout << "Test passed for FP16 with scale." << std::endl; + } + } + else + MyTestPrintAndExit(false, "Test failed for FP16 positive control. Value at zero is " + std::to_string(FT_fp16_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + + // Restore the buffers and do the second round with intra_op scaling + // Set to zero + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(FT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + + cudaErr(cudaMemcpyAsync(FT_buffer, FT_input.real_values, FT_input.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_buffer, target_search_image.real_values, target_search_image.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + + cudaErr(cudaMemcpyAsync(FT_fp16_buffer, FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_fp16_buffer, target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, noop, conj_mul_then_scale, noop); + FT_fp16.FwdImageInvFFT(FT_fp16_buffer, reinterpret_cast<__half2*>(targetFT_fp16_buffer), FT_fp16_buffer, noop, conj_mul_then_scale, noop); + + // Copy back to the host and check the results + if constexpr ( FFT_DEBUG_STAGE < 7 ) { + FT.CopyDeviceToHostAndSynchronize(FT_output.real_values); + FT_fp16.CopyDeviceToHostAndSynchronize(reinterpret_cast<__half*>(FT_fp16_output.real_values)); + } + else { + // Copy back to the host and check the results + cudaErr(cudaMemcpyAsync(FT_output.real_values, FT_buffer, FT_output.real_memory_allocated * sizeof(float), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(FT_fp16_output.real_values, FT_fp16_buffer, FT_fp16_output.real_memory_allocated * sizeof(__half), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + } + FT_fp16_output.data_is_fp16 = true; + FT_fp16_output.ConvertFP16ToFP32( ); + + if ( FT_output.real_values[0] == 1.0f ) { + if ( print_out_time ) { + std::cout << "Test passed for FP32 with scale." << std::endl; + } + } + else + MyTestPrintAndExit(false, "Test failed for FP32 positive control. Value at zero is " + std::to_string(FT_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + + if ( FT_fp16_output.real_values[0] == 1.0f ) { + if ( print_out_time ) { + std::cout << "Test passed for FP16 with scale." << std::endl; + } + } + else + MyTestPrintAndExit(false, "Test failed for FP16 positive control. Value at zero is " + std::to_string(FT_fp16_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + + oSize++; + // We don't want to loop if the size is not actually changing. + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + cudaErr(cudaFree(FT_buffer)); + cudaErr(cudaFree(targetFT_buffer)); + cudaErr(cudaFree(FT_fp16_buffer)); + cudaErr(cudaFree(targetFT_fp16_buffer)); + + // Right now, we would overflow with larger arrays in fp16 so just exit + break; + } // while loop over pad to size + + } // for loop over pad from size +} + +int main(int argc, char** argv) { + + using SCT = FastFFT::SizeChangeType::Enum; + + std::string test_name; + // Default to running all tests + bool run_2d_performance_tests = false; + bool run_3d_performance_tests = false; + + const std::string_view text_line = "simple convolution"; + FastFFT::CheckInputArgs(argc, argv, text_line, run_2d_performance_tests, run_3d_performance_tests); + + // TODO: size decrease + if ( run_2d_performance_tests ) { +#ifdef HEAVYERRORCHECKING_FFT + std::cout << "Running performance tests with heavy error checking.\n"; + std::cout << "This doesn't make sense as the synchronizations are invalidating.\n"; +// exit(1); +#endif + SCT size_change_type; + // Set the SCT to no_change, increase, or decrease + // size_change_type = SCT::no_change; + // preop_functor_scale<2>(FastFFT::test_size, size_change_type, false); + // preop_functor_scale<2>(test_size_rectangle, do_3d, size_change_type, true); + + size_change_type = SCT::increase; + preop_functor_scale<2>(FastFFT::test_size, size_change_type, false); + // preop_functor_scale<2>(test_size_rectangle, do_3d, size_change_type, true); + + // size_change_type = SCT::decrease; + // preop_functor_scale<2>(FastFFT::test_size, size_change_type, false); + } + + if ( run_3d_performance_tests ) { +#ifdef HEAVYERRORCHECKING_FFT + std::cout << "Running performance tests with heavy error checking.\n"; + std::cout << "This doesn't make sense as the synchronizations are invalidating.\n"; +#endif + + SCT size_change_type; + + size_change_type = SCT::no_change; + + preop_functor_scale<3>(FastFFT::test_size_3d, size_change_type, false); + + // TODO: These are not yet completed. + // size_change_type = SCT::increase; + // preop_functor_scale<3>(FastFFT::test_size, do_3d, size_change_type, false); + + // size_change_type = SCT::decrease; + // preop_functor_scale(FastFFT::test_size, do_3d, size_change_type, false); + } + + return 0; +}; \ No newline at end of file diff --git a/src/tests/helper_functions.cuh b/src/tests/helper_functions.cuh index 71f26a2..96f8170 100644 --- a/src/tests/helper_functions.cuh +++ b/src/tests/helper_functions.cuh @@ -15,7 +15,7 @@ #include "../../include/FastFFT.cuh" // clang-format off -#define MyTestPrintAndExit(...) { std::cerr << __VA_ARGS__ << " From: " << __FILE__ << " " << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; exit(-1); } +#define MyTestPrintAndExit(cond, ...) { if(! cond) {std::cerr << __VA_ARGS__ << " From: " << __FILE__ << ":" << __LINE__ << " " << __PRETTY_FUNCTION__ << std::endl; exit(-1); }} // clang-format on @@ -34,7 +34,7 @@ void PrintArray(float2* array, short NX, short NY, short NZ, int line_wrapping = std::cout << std::endl; } // line wrapping } - std::cout << "] " << std::endl; + std::cout << "] " << NY << std::endl; n = 0; } if ( NZ > 0 ) @@ -56,7 +56,7 @@ void PrintArray(float* array, short NX, short NY, short NZ, short NW, int line_w std::cout << std::endl; } // line wrapping } - std::cout << "] " << std::endl; + std::cout << "] " << NY << std::endl; n = 0; } if ( NZ > 0 ) @@ -79,7 +79,7 @@ void PrintArray_XZ(float2* array, short NX, short NY, short NZ, int line_wrappin std::cout << std::endl; } // line wrapping } - std::cout << "] " << std::endl; + std::cout << "] " << NY << std::endl; n = 0; } if ( NZ > 0 ) @@ -98,7 +98,7 @@ void CheckUnitImpulseRealImage(Image& positive_control, i // Only check the address if we have too. if ( positive_control.real_values[address] != 0.0f && address != 0 ) { PrintArray(positive_control.real_values, positive_control.size.x, positive_control.size.y, positive_control.size.z, positive_control.size.w); - MyTestPrintAndExit(" "); + MyTestPrintAndExit(false, "Check Unit Impulse Real control val " + std::to_string(positive_control.real_values[address]) + " different from zero at line " + std::to_string(input_line)); } address++; } @@ -108,9 +108,20 @@ void CheckUnitImpulseRealImage(Image& positive_control, i return; } -// For debugging the individual stages of the xforms -// Note: for some reason, passing by value altered the values while passing by reference did not. (Opposite?) +/** + * @brief For debugging the individual stages of the xforms + * Note: for some reason, passing by value altered the values while passing by reference did not. (Opposite?) // eg. + + * @param test_image + * @param fwd_dims_in + * @param fwd_dims_out + * @param inv_dims_in + * @param inv_dims_out + * @param input_line + * @return true + * @return false + */ template bool debug_partial_fft(Image& test_image, short4 fwd_dims_in, @@ -179,13 +190,11 @@ bool debug_partial_fft(Image& test_image, debug_stage_is_8 = true; } else - MyTestPrintAndExit("FFT_DEBUG_STAGE not recognized " + std::to_string(FFT_DEBUG_STAGE)); + MyTestPrintAndExit(false, "FFT_DEBUG_STAGE not recognized " + std::to_string(FFT_DEBUG_STAGE)); // std::cerr << "Debug stage " << fft_debug_stage << " passed." << std::endl; - return debug_stage_is_8; - if ( ! debug_stage_is_8 ) - std::cerr << " Failed Assert at " << __FILE__ << " " << input_line << " " << __PRETTY_FUNCTION__ << std::endl; + return debug_stage_is_8; } #endif \ No newline at end of file diff --git a/src/tests/non_cuda_compilation_unit.cu b/src/tests/non_cuda_compilation_unit.cu index 65660fb..a3d3259 100644 --- a/src/tests/non_cuda_compilation_unit.cu +++ b/src/tests/non_cuda_compilation_unit.cu @@ -2,16 +2,26 @@ // The purpose of this test is to ensure that we can build a "pure" cpp file and only link against the CUDA business at the end +// Note FFT_DEBUG_STAGE is not handled here. + #include "../../include/FastFFT.h" int main(int argc, char** argv) { +#if ( FFT_DEBUG_STAGE != 8 ) + std::cout << "Error: FFT_DEBUG_STAGE must be set to 8 when running this test." << std::endl; + std::exit(1); +#endif + const int input_size = 64; - FastFFT::FourierTransformer FT; + FastFFT::FourierTransformer FT; + + float* d_input = nullptr; + // This is similar to creating an FFT/CUFFT plan, so set these up before doing anything on the GPU - FT.SetForwardFFTPlan(input_size, input_size, 1, input_size, input_size, 1, true); - FT.SetInverseFFTPlan(input_size, input_size, 1, input_size, input_size, 1, false); + FT.SetForwardFFTPlan(input_size, input_size, 1, input_size, input_size, 1); + FT.SetInverseFFTPlan(input_size, input_size, 1, input_size, input_size, 1); // The padding (dims.w) is calculated based on the setup short4 dims_in = FT.ReturnFwdInputDimensions( ); @@ -23,6 +33,8 @@ int main(int argc, char** argv) { int host_input_real_memory_allocated = FT.ReturnInputMemorySize( ); int host_output_real_memory_allocated = FT.ReturnInvOutputMemorySize( ); + cudaErr(cudaMallocAsync(&d_input, sizeof(float) * host_input_real_memory_allocated, cudaStreamPerThread)); + if ( host_input_real_memory_allocated != host_output_real_memory_allocated ) { std::cout << "Error: input and output memory sizes do not match" << std::endl; std::cout << "Input: " << host_input_real_memory_allocated << " Output: " << host_output_real_memory_allocated << std::endl; @@ -39,10 +51,6 @@ int main(int argc, char** argv) { host_input.fill(-1.0f); host_output.fill(-1.0f); - // Now we want to associate the host memory with the device memory. The method here asks if the host pointer is pinned (in page locked memory) which - // ensures faster transfer. If false, it will be pinned for you. - FT.SetInputPointer(host_input.data( ), false); - // Check basic initialization function FT.SetToConstant(host_input.data( ), host_output_real_memory_allocated, 3.14f); for ( auto& val : host_input ) { @@ -57,14 +65,14 @@ int main(int argc, char** argv) { host_input.at(0) = 1.0f; // Copy to the device - FT.CopyHostToDevice(host_input.data( )); - + cudaErr(cudaMemcpyAsync(d_input, host_input.data( ), sizeof(float) * host_input_real_memory_allocated, cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); // Do a round trip FFT - FT.FwdFFT( ); - FT.InvFFT( ); + FT.FwdFFT(d_input); + FT.InvFFT(d_input); // Now copy back to the output array (still set to -1) - FT.CopyDeviceToHost(host_output.data( ), true, host_input_real_memory_allocated); + cudaErr(cudaMemcpyAsync(host_output.data( ), d_input, sizeof(float) * host_output_real_memory_allocated, cudaMemcpyDeviceToHost, cudaStreamPerThread)); if ( host_output.at(0) == input_size * input_size ) { std::cout << "Success: output memory copied back correctly after fft/ifft pair" << std::endl; } diff --git a/src/tests/padded_convolution_FastFFT_vs_cuFFT.cu b/src/tests/padded_convolution_FastFFT_vs_cuFFT.cu index 3e0d359..e152535 100644 --- a/src/tests/padded_convolution_FastFFT_vs_cuFFT.cu +++ b/src/tests/padded_convolution_FastFFT_vs_cuFFT.cu @@ -97,11 +97,15 @@ void compare_libraries(std::vector size, FastFFT::SizeChangeType::Enum size Image positive_control(target_size); // We just make one instance of the FourierTransformer class, with calc type float. - // For the time being input and output are also float. TODO calc optionally either fp16 or nv_bloat16, TODO inputs at lower precision for bandwidth improvement. - FastFFT::FourierTransformer FT; + // For the time being input and output are also float. TODO caFlc optionally either fp16 or nv_bloat16, TODO inputs at lower precision for bandwidth improvement. + FastFFT::FourierTransformer FT; // Create an instance to copy memory also for the cufft tests. - FastFFT::FourierTransformer cuFFT; - FastFFT::FourierTransformer targetFT; + FastFFT::FourierTransformer cuFFT; + FastFFT::FourierTransformer targetFT; + + float* FT_buffer; + float* cuFFT_buffer; + float* targetFT_buffer; if ( is_size_change_decrease ) { FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); @@ -136,6 +140,15 @@ void compare_libraries(std::vector size, FastFFT::SizeChangeType::Enum size cuFFT_input.real_memory_allocated = cuFFT.ReturnInputMemorySize( ); cuFFT_output.real_memory_allocated = cuFFT.ReturnInvOutputMemorySize( ); + size_t device_memory = std::max(FT_input.n_bytes_allocated, FT_output.n_bytes_allocated); + cudaErr(cudaMallocAsync((void**)&FT_buffer, device_memory, cudaStreamPerThread)); + cudaErr(cudaMallocAsync((void**)&cuFFT_buffer, device_memory, cudaStreamPerThread)); + cudaErr(cudaMallocAsync((void**)&targetFT_buffer, device_memory, cudaStreamPerThread)); + // Set to zero + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_memory, cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(cuFFT_buffer, 0, device_memory, cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_buffer, 0, device_memory, cudaStreamPerThread)); + if ( is_size_change_decrease ) target_search_image.real_memory_allocated = targetFT.ReturnInputMemorySize( ); else @@ -153,12 +166,6 @@ void compare_libraries(std::vector size, FastFFT::SizeChangeType::Enum size target_search_image.Allocate(true); positive_control.Allocate(true); - // Now we want to associate the host memory with the device memory. The method here asks if the host pointer is pinned (in page locked memory) which - // ensures faster transfer. If false, it will be pinned for you. - FT.SetInputPointer(FT_input.real_values, false); - cuFFT.SetInputPointer(cuFFT_input.real_values, false); - targetFT.SetInputPointer(target_search_image.real_values, false); - // Set a unit impulse at the center of the input array. // For now just considering the real space image to have been implicitly quadrant swapped so the center is at the origin. FT.SetToConstant(FT_input.real_values, FT_input.real_memory_allocated, 0.0f); @@ -179,14 +186,9 @@ void compare_libraries(std::vector size, FastFFT::SizeChangeType::Enum size // Transform the target on the host prior to transfer. target_search_image.FwdFFT( ); - // This copies the host memory into the device global memory. If needed, it will also allocate the device memory first. - FT.CopyHostToDevice(FT_input.real_values); - - cuFFT.CopyHostToDevice(cuFFT_input.real_values); - - targetFT.CopyHostToDevice(target_search_image.real_values); - - // Wait on the transfers to finish. + cudaErr(cudaMemcpyAsync(FT_buffer, FT_input.real_values, FT_input.n_bytes_allocated, cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(cuFFT_buffer, cuFFT_input.real_values, cuFFT_input.n_bytes_allocated, cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_buffer, target_search_image.real_values, target_search_image.n_bytes_allocated, cudaMemcpyHostToDevice, cudaStreamPerThread)); cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); // Positive control on the host. @@ -206,8 +208,7 @@ void compare_libraries(std::vector size, FastFFT::SizeChangeType::Enum size } } else { - std::cout << "Test failed for FFTW positive control. Value at zero is " << positive_control.real_values[0] << std::endl; - MyTestPrintAndExit(" "); + MyTestPrintAndExit(false, "Test failed for FFTW positive control. Value at zero is " + std::to_string(positive_control.real_values[0])); } cuFFT_output.create_timing_events( ); @@ -223,10 +224,8 @@ void compare_libraries(std::vector size, FastFFT::SizeChangeType::Enum size cuFFT_output.MakeCufftPlan( ); } - std::cout << "Test lambda" << std::endl; - FastFFT::KernelFunction::my_functor noop; - FastFFT::KernelFunction::my_functor conj_mul; + FastFFT::KernelFunction::my_functor conj_mul; ////////////////////////////////////////// ////////////////////////////////////////// @@ -235,31 +234,28 @@ void compare_libraries(std::vector size, FastFFT::SizeChangeType::Enum size if ( set_conjMult_callback || is_size_change_decrease ) { // FT.CrossCorrelate(targetFT.d_ptr.momentum_space, false); // Will type deduction work here? - MyFFTDebugPrintWithDetails("Calling Generic_Fwd_Image_Inv"); - FT.Generic_Fwd_Image_Inv(targetFT.d_ptr.momentum_space, noop, conj_mul, noop); + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, noop, conj_mul, noop); } else { - MyFFTDebugPrintWithDetails("Calling Generic_Fwd_Image_Inv"); - FT.FwdFFT( ); - FT.InvFFT( ); + FT.FwdFFT(FT_buffer); + FT.InvFFT(FT_buffer); } bool continue_debugging; if ( is_size_change_decrease ) { // Because the output is smaller than the input, we just copy to FT input. - // FIXME: In reality, we didn't need to allocate FT_output at all in this case - FT.CopyDeviceToHostAndSynchronize(FT_input.real_values, false); + // TODO: In reality, we didn't need to allocate FT_output at all in this case + + FT.CopyDeviceToHostAndSynchronize(FT_input.real_values); continue_debugging = debug_partial_fft(FT_input, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); } else { + // the output is equal or > the input, so we can always copy there. - FT.CopyDeviceToHostAndSynchronize(FT_output.real_values, false, false); + FT.CopyDeviceToHostAndSynchronize(FT_output.real_values); continue_debugging = debug_partial_fft(FT_output, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); } - - if ( ! continue_debugging ) { - MyTestPrintAndExit(" "); - } + MyTestPrintAndExit(continue_debugging, "Partial FFT debug stage " + std::to_string(FFT_DEBUG_STAGE)); if ( is_size_change_decrease ) { CheckUnitImpulseRealImage(FT_input, __LINE__); @@ -305,99 +301,101 @@ void compare_libraries(std::vector size, FastFFT::SizeChangeType::Enum size if ( set_conjMult_callback || is_size_change_decrease ) { // FT.CrossCorrelate(targetFT.d_ptr.momentum_space_buffer, false); // Will type deduction work here? - FT.Generic_Fwd_Image_Inv(targetFT.d_ptr.momentum_space, noop, conj_mul, noop); + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, noop, conj_mul, noop); } else { - FT.FwdFFT( ); - FT.InvFFT( ); + FT.FwdFFT(FT_buffer); + FT.InvFFT(FT_buffer); } } cuFFT_output.record_stop( ); cuFFT_output.synchronize( ); cuFFT_output.print_time("FastFFT", print_out_time); + MyFFTPrintWithDetails(""); float FastFFT_time = cuFFT_output.elapsed_gpu_ms; - // if (set_padding_callback) - // { - // precheck; - // cufftReal* overlap_pointer; - // overlap_pointer = cuFFT.d_ptr.position_space; - // cuFFT_output.SetClipIntoCallback(overlap_pointer, cuFFT_input.size.x, cuFFT_input.size.y, cuFFT_input.size.w*2); - // postcheck; - // } - if ( set_conjMult_callback ) { precheck; - // FIXME scaling factor - cuFFT_output.SetComplexConjMultiplyAndLoadCallBack((cufftComplex*)targetFT.d_ptr.momentum_space_buffer, 1.0f); + cuFFT_output.SetComplexConjMultiplyAndLoadCallBack((cufftComplex*)cuFFT_buffer, 1.0f); postcheck; } + MyFFTPrintWithDetails(""); if ( ! skip_cufft_for_profiling ) { ////////////////////////////////////////// ////////////////////////////////////////// // Warm up and check for accuracy + MyFFTPrintWithDetails(""); if ( is_size_change_decrease ) { - + MyFFTPrintWithDetails(""); precheck; - cudaErr(cufftExecR2C(cuFFT_input.cuda_plan_forward, (cufftReal*)cuFFT.d_ptr.position_space, (cufftComplex*)cuFFT.d_ptr.momentum_space_buffer)); + cudaErr(cufftExecR2C(cuFFT_input.cuda_plan_forward, (cufftReal*)cuFFT_buffer, (cufftComplex*)cuFFT_buffer)); postcheck; precheck; - cudaErr(cufftExecC2R(cuFFT_input.cuda_plan_inverse, (cufftComplex*)cuFFT.d_ptr.momentum_space_buffer, (cufftReal*)cuFFT.d_ptr.position_space)); + cudaErr(cufftExecC2R(cuFFT_input.cuda_plan_inverse, (cufftComplex*)cuFFT_buffer, (cufftReal*)cuFFT_buffer)); postcheck; } else { // cuFFT.ClipIntoTopLeft(); // cuFFT.ClipIntoReal(cuFFT_output.size.x/2, cuFFT_output.size.y/2, cuFFT_output.size.z/2); // cuFFT.CopyDeviceToHostAndSynchronize(cuFFT_output.real_values,false); - + MyFFTPrintWithDetails(""); precheck; - cudaErr(cufftExecR2C(cuFFT_output.cuda_plan_forward, (cufftReal*)cuFFT.d_ptr.position_space, (cufftComplex*)cuFFT.d_ptr.momentum_space_buffer)); + cudaErr(cufftExecR2C(cuFFT_output.cuda_plan_forward, (cufftReal*)cuFFT_buffer, (cufftComplex*)cuFFT_buffer)); postcheck; precheck; - cudaErr(cufftExecC2R(cuFFT_output.cuda_plan_inverse, (cufftComplex*)cuFFT.d_ptr.momentum_space_buffer, (cufftReal*)cuFFT.d_ptr.position_space)); + cudaErr(cufftExecC2R(cuFFT_output.cuda_plan_inverse, (cufftComplex*)cuFFT_buffer, (cufftReal*)cuFFT_buffer)); postcheck; } - + MyFFTPrintWithDetails(""); cuFFT_output.record_start( ); for ( int i = 0; i < n_loops; ++i ) { // std::cout << i << "i / " << n_loops << "n_loops" << std::endl; if ( set_conjMult_callback ) - cuFFT.ClipIntoTopLeft( ); + cuFFT.ClipIntoTopLeft(cuFFT_buffer); // cuFFT.ClipIntoReal(input_size.x/2, input_size.y/2, input_size.z/2); if ( is_size_change_decrease ) { precheck; - cudaErr(cufftExecR2C(cuFFT_input.cuda_plan_forward, (cufftReal*)cuFFT.d_ptr.position_space, (cufftComplex*)cuFFT.d_ptr.momentum_space_buffer)); + cudaErr(cufftExecR2C(cuFFT_input.cuda_plan_forward, (cufftReal*)cuFFT_buffer, (cufftComplex*)cuFFT_buffer)); postcheck; precheck; - cudaErr(cufftExecC2R(cuFFT_input.cuda_plan_inverse, (cufftComplex*)cuFFT.d_ptr.momentum_space_buffer, (cufftReal*)cuFFT.d_ptr.position_space)); + cudaErr(cufftExecC2R(cuFFT_input.cuda_plan_inverse, (cufftComplex*)cuFFT_buffer, (cufftReal*)cuFFT_buffer)); postcheck; } else { precheck; - cudaErr(cufftExecR2C(cuFFT_output.cuda_plan_forward, (cufftReal*)cuFFT.d_ptr.position_space, (cufftComplex*)cuFFT.d_ptr.momentum_space_buffer)); + cudaErr(cufftExecR2C(cuFFT_output.cuda_plan_forward, (cufftReal*)cuFFT_buffer, (cufftComplex*)cuFFT_buffer)); postcheck; precheck; - cudaErr(cufftExecC2R(cuFFT_output.cuda_plan_inverse, (cufftComplex*)cuFFT.d_ptr.momentum_space_buffer, (cufftReal*)cuFFT.d_ptr.position_space)); + cudaErr(cufftExecC2R(cuFFT_output.cuda_plan_inverse, (cufftComplex*)cuFFT_buffer, (cufftReal*)cuFFT_buffer)); postcheck; } } + MyFFTPrintWithDetails(""); cuFFT_output.record_stop( ); cuFFT_output.synchronize( ); cuFFT_output.print_time("cuFFT", print_out_time); + MyFFTPrintWithDetails(""); } // end of if (! skip_cufft_for_profiling) + MyFFTPrintWithDetails(""); + std::cout << "For size " << input_size.x << " to " << output_size.x << ": "; std::cout << "Ratio cuFFT/FastFFT : " << cuFFT_output.elapsed_gpu_ms / FastFFT_time << "\n\n" << std::endl; oSize++; // We don't want to loop if the size is not actually changing. + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + cudaErr(cudaFree(FT_buffer)); + cudaErr(cudaFree(cuFFT_buffer)); + cudaErr(cudaFree(targetFT_buffer)); } // while loop over pad to size + } // for loop over pad from size } @@ -443,7 +441,7 @@ int main(int argc, char** argv) { SCT size_change_type; size_change_type = SCT::no_change; - compare_libraries<3>(FastFFT::test_size, size_change_type, false); + compare_libraries<3>(FastFFT::test_size_3d, size_change_type, false); // TODO: These are not yet completed. // size_change_type = SCT::increase; diff --git a/src/tests/padded_convolution_fp32_vs_fp16_io.cu b/src/tests/padded_convolution_fp32_vs_fp16_io.cu new file mode 100644 index 0000000..ea6bced --- /dev/null +++ b/src/tests/padded_convolution_fp32_vs_fp16_io.cu @@ -0,0 +1,381 @@ +#include "tests.h" +#include +#include + +template +void compare_libraries(std::vector size, FastFFT::SizeChangeType::Enum size_change_type, bool do_rectangle) { + + using SCT = FastFFT::SizeChangeType::Enum; + + constexpr bool print_out_time = true; + // bool set_padding_callback = false; // the padding callback is slower than pasting in b/c the read size of the pointers is larger than the actual data. do not use. + bool set_conjMult_callback = true; + bool is_size_change_decrease = false; + + if ( size_change_type == SCT::decrease ) { + is_size_change_decrease = true; + } + + // For an increase or decrease in size, we have to shrink the loop by one, + // for a no_change, we don't because every size is compared to itself. + int loop_limit = 1; + if ( size_change_type == SCT::no_change ) + loop_limit = 0; + + // Currently, to test a non-square input, the fixed input sizes are used + // and the input x size is reduced by input_x / make_rect_x + int make_rect_x; + int make_rect_y = 1; + if ( do_rectangle ) + make_rect_x = 2; + else + make_rect_x = 1; + + if ( Rank == 3 && do_rectangle ) { + std::cout << "ERROR: cannot do 3d and rectangle at the same time" << std::endl; + return; + } + + short4 input_size; + short4 output_size; + for ( int iSize = 0; iSize < size.size( ) - loop_limit; iSize++ ) { + int oSize; + int loop_size; + // TODO: the logic here is confusing, clean it up + if ( size_change_type != SCT::no_change ) { + oSize = iSize + 1; + loop_size = size.size( ); + } + else { + oSize = iSize; + loop_size = oSize + 1; + } + + while ( oSize < loop_size ) { + + if ( is_size_change_decrease ) { + output_size = make_short4(size[iSize] / make_rect_x, size[iSize] / make_rect_y, 1, 0); + input_size = make_short4(size[oSize] / make_rect_x, size[oSize] / make_rect_y, 1, 0); + if ( Rank == 3 ) { + output_size.z = size[iSize]; + input_size.z = size[oSize]; + } + } + else { + input_size = make_short4(size[iSize] / make_rect_x, size[iSize] / make_rect_y, 1, 0); + output_size = make_short4(size[oSize] / make_rect_x, size[oSize] / make_rect_y, 1, 0); + if ( Rank == 3 ) { + input_size.z = size[iSize]; + output_size.z = size[oSize]; + } + } + if ( print_out_time ) { + printf("Testing padding from %i,%i,%i to %i,%i,%i\n", input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + } + + if ( (input_size.x == output_size.x && input_size.y == output_size.y && input_size.z == output_size.z) ) { + // Also will change the path called in FastFFT to just be fwd/inv xform. + set_conjMult_callback = false; + } + + // bool test_passed = true; + + Image FT_input(input_size); + Image FT_output(output_size); + Image FT_fp16_input(input_size); + Image FT_fp16_output(output_size); + + short4 target_size; + + if ( is_size_change_decrease ) + target_size = input_size; // assuming xcorr_fwd_NOOP_inv_DECREASE + else + target_size = output_size; + + Image target_search_image(target_size); + Image target_search_image_fp16(input_size); + Image positive_control(target_size); + + // We just make one instance of the FourierTransformer class, with calc type float. + // For the time being input and output are also float. TODO caFlc optionally either fp16 or nv_bloat16, TODO inputs at lower precision for bandwidth improvement. + FastFFT::FourierTransformer FT; + FastFFT::FourierTransformer targetFT; + + // Create an instance to copy memory also for the cufft tests. + FastFFT::FourierTransformer FT_fp16; + FastFFT::FourierTransformer targetFT_fp16; + + float* FT_buffer; + float* targetFT_buffer; + __half* FT_fp16_buffer; + __half* targetFT_fp16_buffer; + + if ( is_size_change_decrease ) { + FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + FT.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + targetFT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + targetFT.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + + FT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + FT_fp16.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + targetFT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + targetFT_fp16.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + } + else { + FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + FT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT.SetForwardFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + + FT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + FT_fp16.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT_fp16.SetForwardFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT_fp16.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + } + + short4 fwd_dims_in = FT.ReturnFwdInputDimensions( ); + short4 fwd_dims_out = FT.ReturnFwdOutputDimensions( ); + short4 inv_dims_in = FT.ReturnInvInputDimensions( ); + short4 inv_dims_out = FT.ReturnInvOutputDimensions( ); + + FT_input.real_memory_allocated = FT.ReturnInputMemorySize( ); + FT_output.real_memory_allocated = FT.ReturnInvOutputMemorySize( ); + + size_t device_memory = std::max(FT_input.real_memory_allocated, FT_output.real_memory_allocated); + cudaErr(cudaMallocAsync((void**)&FT_buffer, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMallocAsync((void**)&targetFT_buffer, device_memory * sizeof(float), cudaStreamPerThread)); + // Set to zero + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + + cudaErr(cudaMallocAsync((void**)&FT_fp16_buffer, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMallocAsync((void**)&targetFT_fp16_buffer, device_memory * sizeof(__half), cudaStreamPerThread)); + // Set to zero + cudaErr(cudaMemsetAsync(FT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + if ( is_size_change_decrease ) + target_search_image.real_memory_allocated = targetFT.ReturnInputMemorySize( ); + else + target_search_image.real_memory_allocated = targetFT.ReturnInvOutputMemorySize( ); // the larger of the two. + + positive_control.real_memory_allocated = target_search_image.real_memory_allocated; // this won't change size + + bool set_fftw_plan = false; + FT_input.Allocate(set_fftw_plan); + FT_output.Allocate(set_fftw_plan); + FT_fp16_input.Allocate(set_fftw_plan); + FT_fp16_output.Allocate(set_fftw_plan); + + target_search_image.Allocate(true); + target_search_image_fp16.Allocate(true); + positive_control.Allocate(true); + + // Set a unit impulse at the center of the input array. + // For now just considering the real space image to have been implicitly quadrant swapped so the center is at the origin. + FT.SetToConstant(FT_input.real_values, FT_input.real_memory_allocated, 0.0f); + FT.SetToConstant(FT_output.real_values, FT_output.real_memory_allocated, 0.0f); + FT.SetToConstant(FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated, 0.0f); + FT.SetToConstant(FT_fp16_output.real_values, FT_fp16_output.real_memory_allocated, 0.0f); + FT.SetToConstant(target_search_image.real_values, target_search_image.real_memory_allocated, 0.0f); + FT.SetToConstant(target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated, 0.0f); + FT.SetToConstant(positive_control.real_values, target_search_image.real_memory_allocated, 0.0f); + + // Place these values at the origin of the image and after convolution, should be at 0,0,0. + float testVal_1 = 2.0f; + float testVal_2 = set_conjMult_callback ? 3.0f : 1.0; // This way the test conditions are the same, the 1. indicating no conj + FT_input.real_values[0] = testVal_1; + FT_fp16_input.real_values[0] = testVal_1; + target_search_image.real_values[0] = testVal_2; + target_search_image_fp16.real_values[0] = testVal_2; + positive_control.real_values[0] = testVal_1; + + // Transform the target on the host prior to transfer. + target_search_image.FwdFFT( ); + target_search_image_fp16.FwdFFT( ); + + cudaErr(cudaMemcpyAsync(FT_buffer, FT_input.real_values, FT_input.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_buffer, target_search_image.real_values, target_search_image.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + FT_fp16_input.ConvertFP32ToFP16( ); + target_search_image_fp16.ConvertFP32ToFP16( ); + cudaErr(cudaMemcpyAsync(FT_fp16_buffer, FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_fp16_buffer, target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + + // Positive control on the host. + // After both forward FFT's we should constant values in each pixel = testVal_1 and testVal_2. + // After the Conjugate multiplication, we should have a constant value of testVal_1*testVal_2. + // After the inverse FFT, we should have a constant value of testVal_1*testVal_2 in the center pixel and 0 everywhere else. + positive_control.FwdFFT( ); + if ( set_conjMult_callback ) + positive_control.MultiplyConjugateImage(target_search_image.complex_values); + positive_control.InvFFT( ); + + CheckUnitImpulseRealImage(positive_control, __LINE__); + + if ( positive_control.real_values[0] == positive_control.size.x * positive_control.size.y * positive_control.size.z * testVal_1 * testVal_2 ) { + if ( print_out_time ) { + std::cout << "Test passed for FFTW positive control." << std::endl; + } + } + else { + MyTestPrintAndExit(false, "Test failed for FFTW positive control. Value at zero is " + std::to_string(positive_control.real_values[0])); + } + + FT_output.create_timing_events( ); + + FastFFT::KernelFunction::my_functor noop; + FastFFT::KernelFunction::my_functor conj_mul; + + ////////////////////////////////////////// + ////////////////////////////////////////// + // Warm up and check for accuracy + // we set set_conjMult_callback = false + if ( set_conjMult_callback || is_size_change_decrease ) { + // FT.CrossCorrelate(targetFT.d_ptr.momentum_space, false); + // Will type deduction work here? + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, noop, conj_mul, noop); + FT_fp16.FwdImageInvFFT(FT_fp16_buffer, reinterpret_cast<__half2*>(targetFT_fp16_buffer), FT_fp16_buffer, noop, conj_mul, noop); + } + else { + FT.FwdFFT(FT_buffer); + FT.InvFFT(FT_buffer); + FT_fp16.FwdFFT(FT_fp16_buffer); + FT_fp16.InvFFT(FT_fp16_buffer); + } + + int n_loops; + if ( Rank == 3 ) { + int max_size = std::max(fwd_dims_in.x, fwd_dims_out.x); + if ( max_size < 128 ) { + n_loops = 1000; + } + else if ( max_size <= 256 ) { + n_loops = 400; + } + else if ( max_size <= 512 ) { + n_loops = 150; + } + else { + n_loops = 50; + } + } + else { + int max_size = std::max(fwd_dims_in.x, fwd_dims_out.x); + if ( max_size < 256 ) { + n_loops = 10000; + } + else if ( max_size <= 512 ) { + n_loops = 5000; + } + else if ( max_size <= 2048 ) { + n_loops = 2500; + } + else { + n_loops = 1000; + } + } + + FT_output.record_start( ); + for ( int i = 0; i < n_loops; ++i ) { + if ( set_conjMult_callback || is_size_change_decrease ) { + // FT.CrossCorrelate(targetFT.d_ptr.momentum_space_buffer, false); + // Will type deduction work here? + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, noop, conj_mul, noop); + } + else { + FT.FwdFFT(FT_buffer); + FT.InvFFT(FT_buffer); + } + } + FT_output.record_stop( ); + FT_output.synchronize( ); + FT_output.print_time("FastFFT", print_out_time); + float FastFFT_time = FT_output.elapsed_gpu_ms; + + FT_output.record_start( ); + for ( int i = 0; i < n_loops; ++i ) { + if ( set_conjMult_callback || is_size_change_decrease ) { + // FT.CrossCorrelate(targetFT.d_ptr.momentum_space_buffer, false); + // Will type deduction work here? + FT_fp16.FwdImageInvFFT(FT_fp16_buffer, reinterpret_cast<__half2*>(targetFT_fp16_buffer), FT_fp16_buffer, noop, conj_mul, noop); + } + else { + FT_fp16.FwdFFT(FT_fp16_buffer); + FT_fp16.InvFFT(FT_fp16_buffer); + } + } + FT_output.record_stop( ); + FT_output.synchronize( ); + FT_output.print_time("FastFFT_fp16", print_out_time); + float FastFFT_time_fp16 = FT_output.elapsed_gpu_ms; + + std::cout << "For size " << input_size.x << " to " << output_size.x << ": "; + std::cout << "Ratio FP32/FP16 : " << FastFFT_time / FastFFT_time_fp16 << "\n\n" + << std::endl; + + oSize++; + // We don't want to loop if the size is not actually changing. + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + cudaErr(cudaFree(FT_buffer)); + cudaErr(cudaFree(targetFT_buffer)); + cudaErr(cudaFree(FT_fp16_buffer)); + cudaErr(cudaFree(targetFT_fp16_buffer)); + } // while loop over pad to size + + } // for loop over pad from size +} + +int main(int argc, char** argv) { + + using SCT = FastFFT::SizeChangeType::Enum; + + std::string test_name; + // Default to running all tests + bool run_2d_performance_tests = false; + bool run_3d_performance_tests = false; + + const std::string_view text_line = "simple convolution"; + FastFFT::CheckInputArgs(argc, argv, text_line, run_2d_performance_tests, run_3d_performance_tests); + + // TODO: size decrease + if ( run_2d_performance_tests ) { +#ifdef HEAVYERRORCHECKING_FFT + std::cout << "Running performance tests with heavy error checking.\n"; + std::cout << "This doesn't make sense as the synchronizations are invalidating.\n"; +// exit(1); +#endif + SCT size_change_type; + // Set the SCT to no_change, increase, or decrease + size_change_type = SCT::no_change; + compare_libraries<2>(FastFFT::test_size, size_change_type, false); + // compare_libraries<2>(test_size_rectangle, do_3d, size_change_type, true); + + size_change_type = SCT::increase; + compare_libraries<2>(FastFFT::test_size, size_change_type, false); + // compare_libraries<2>(test_size_rectangle, do_3d, size_change_type, true); + + size_change_type = SCT::decrease; + compare_libraries<2>(FastFFT::test_size, size_change_type, false); + } + + if ( run_3d_performance_tests ) { +#ifdef HEAVYERRORCHECKING_FFT + std::cout << "Running performance tests with heavy error checking.\n"; + std::cout << "This doesn't make sense as the synchronizations are invalidating.\n"; +#endif + + SCT size_change_type; + + size_change_type = SCT::no_change; + compare_libraries<3>(FastFFT::test_size_3d, size_change_type, false); + + // TODO: These are not yet completed. + // size_change_type = SCT::increase; + // compare_libraries<3>(FastFFT::test_size, do_3d, size_change_type, false); + + // size_change_type = SCT::decrease; + // compare_libraries(FastFFT::test_size, do_3d, size_change_type, false); + } + + return 0; +}; \ No newline at end of file diff --git a/src/tests/preop_functor_scale_test.cu b/src/tests/preop_functor_scale_test.cu new file mode 100644 index 0000000..6bcfc83 --- /dev/null +++ b/src/tests/preop_functor_scale_test.cu @@ -0,0 +1,435 @@ +#include "tests.h" +#include +#include + +template +void preop_functor_scale(std::vector size, FastFFT::SizeChangeType::Enum size_change_type, bool do_rectangle) { + + using SCT = FastFFT::SizeChangeType::Enum; + + constexpr bool print_out_time = true; + // bool set_padding_callback = false; // the padding callback is slower than pasting in b/c the read size of the pointers is larger than the actual data. do not use. + bool is_size_change_decrease = false; + const bool set_conjMult_callback = true; + + if ( size_change_type == SCT::decrease ) { + std::cerr << "Decreasing the size is not yet implemented." << std::endl; + std::exit(1); + is_size_change_decrease = true; + } + + // For an increase or decrease in size, we have to shrink the loop by one, + // for a no_change, we don't because every size is compared to itself. + int loop_limit = 1; + if ( size_change_type == SCT::no_change ) + loop_limit = 0; + + // Currently, to test a non-square input, the fixed input sizes are used + // and the input x size is reduced by input_x / make_rect_x + int make_rect_x; + int make_rect_y = 1; + if ( do_rectangle ) + make_rect_x = 2; + else + make_rect_x = 1; + + if ( Rank == 3 && do_rectangle ) { + std::cout << "ERROR: cannot do 3d and rectangle at the same time" << std::endl; + return; + } + + short4 input_size; + short4 output_size; + for ( int iSize = 0; iSize < size.size( ) - loop_limit; iSize++ ) { + int oSize; + int loop_size; + // TODO: the logic here is confusing, clean it up + if ( size_change_type != SCT::no_change ) { + oSize = iSize + 1; + loop_size = size.size( ); + } + else { + oSize = iSize; + loop_size = oSize + 1; + } + + while ( oSize < loop_size ) { + + if ( is_size_change_decrease ) { + output_size = make_short4(size[iSize] / make_rect_x, size[iSize] / make_rect_y, 1, 0); + input_size = make_short4(size[oSize] / make_rect_x, size[oSize] / make_rect_y, 1, 0); + if ( Rank == 3 ) { + output_size.z = size[iSize]; + input_size.z = size[oSize]; + } + } + else { + input_size = make_short4(size[iSize] / make_rect_x, size[iSize] / make_rect_y, 1, 0); + output_size = make_short4(size[oSize] / make_rect_x, size[oSize] / make_rect_y, 1, 0); + if ( Rank == 3 ) { + input_size.z = size[iSize]; + output_size.z = size[oSize]; + } + } + if ( print_out_time ) { + printf("Testing padding from %i,%i,%i to %i,%i,%i\n", input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + } + + if ( (input_size.x == output_size.x && input_size.y == output_size.y && input_size.z == output_size.z) ) { + std::cerr << "This test is only setup for block_fft_kernel_C2C_FWD_INCREASE_OP_INV_NONE" << std::endl; + std::exit(1); + } + + // bool test_passed = true; + + Image FT_input(input_size); + Image FT_output(output_size); + Image FT_fp16_input(input_size); + Image FT_fp16_output(output_size); + + short4 target_size; + + if ( is_size_change_decrease ) + target_size = input_size; // assuming xcorr_fwd_NOOP_inv_DECREASE + else + target_size = output_size; + + Image target_search_image(target_size); + Image target_search_image_fp16(target_size); + Image positive_control(target_size); + + // We just make one instance of the FourierTransformer class, with calc type float. + // For the time being input and output are also float. TODO caFlc optionally either fp16 or nv_bloat16, TODO inputs at lower precision for bandwidth improvement. + FastFFT::FourierTransformer FT; + FastFFT::FourierTransformer targetFT; + + // Create an instance to copy memory also for the cufft tests. + FastFFT::FourierTransformer FT_fp16; + FastFFT::FourierTransformer targetFT_fp16; + + float* FT_buffer; + float* targetFT_buffer; + __half* FT_fp16_buffer; + __half* targetFT_fp16_buffer; + + if ( is_size_change_decrease ) { + FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + FT.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + targetFT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + targetFT.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + + FT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + FT_fp16.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + targetFT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, input_size.x, input_size.y, input_size.z); + targetFT_fp16.SetInverseFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + } + else { + FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + FT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT.SetForwardFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + + FT_fp16.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); + FT_fp16.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT_fp16.SetForwardFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + targetFT_fp16.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); + } + + short4 fwd_dims_in = FT.ReturnFwdInputDimensions( ); + short4 fwd_dims_out = FT.ReturnFwdOutputDimensions( ); + short4 inv_dims_in = FT.ReturnInvInputDimensions( ); + short4 inv_dims_out = FT.ReturnInvOutputDimensions( ); + + FT_input.real_memory_allocated = FT.ReturnInputMemorySize( ); + FT_output.real_memory_allocated = FT.ReturnInvOutputMemorySize( ); + + size_t device_memory = std::max(FT_input.real_memory_allocated, FT_output.real_memory_allocated); + cudaErr(cudaMallocAsync((void**)&FT_buffer, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMallocAsync((void**)&targetFT_buffer, device_memory * sizeof(float), cudaStreamPerThread)); + // Set to zero + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + + cudaErr(cudaMallocAsync((void**)&FT_fp16_buffer, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMallocAsync((void**)&targetFT_fp16_buffer, device_memory * sizeof(__half), cudaStreamPerThread)); + // Set to zero + cudaErr(cudaMemsetAsync(FT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + if ( is_size_change_decrease ) + target_search_image.real_memory_allocated = targetFT.ReturnInputMemorySize( ); + else + target_search_image.real_memory_allocated = targetFT.ReturnInvOutputMemorySize( ); // the larger of the two. + + positive_control.real_memory_allocated = target_search_image.real_memory_allocated; // this won't change size + + bool set_fftw_plan = false; + FT_input.Allocate(set_fftw_plan); + FT_output.Allocate(set_fftw_plan); + FT_fp16_input.Allocate(set_fftw_plan); + FT_fp16_output.Allocate(set_fftw_plan); + + target_search_image.Allocate(true); + target_search_image_fp16.Allocate(true); + positive_control.Allocate(true); + + // Set a unit impulse at the center of the input array. + // For now just considering the real space image to have been implicitly quadrant swapped so the center is at the origin. + FT.SetToConstant(FT_input.real_values, FT_input.real_memory_allocated, 0.0f); + FT.SetToConstant(FT_output.real_values, FT_output.real_memory_allocated, 0.0f); + FT.SetToConstant(FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated, 0.0f); + FT.SetToConstant(FT_fp16_output.real_values, FT_fp16_output.real_memory_allocated, 0.0f); + FT.SetToConstant(target_search_image.real_values, target_search_image.real_memory_allocated, 0.0f); + FT.SetToConstant(target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated, 0.0f); + FT.SetToConstant(positive_control.real_values, target_search_image.real_memory_allocated, 0.0f); + + // Place these values at the origin of the image and after convolution, should be at 0,0,0. + float testVal_1 = 2.f; + float testVal_2 = set_conjMult_callback ? 3.f : 1.0; // This way the test conditions are the same, the 1. indicating no conj + FT_input.real_values[0] = testVal_1; + FT_fp16_input.real_values[0] = testVal_1; + target_search_image.real_values[0] = testVal_2; + target_search_image_fp16.real_values[0] = testVal_2; + positive_control.real_values[0] = testVal_1; + + // Transform the target on the host prior to transfer. + target_search_image.FwdFFT( ); + target_search_image_fp16.FwdFFT( ); + + cudaErr(cudaMemcpyAsync(FT_buffer, FT_input.real_values, FT_input.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_buffer, target_search_image.real_values, target_search_image.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + // sync before converting to fp16 + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + FT_fp16_input.ConvertFP32ToFP16( ); + target_search_image_fp16.ConvertFP32ToFP16( ); + + cudaErr(cudaMemcpyAsync(FT_fp16_buffer, FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_fp16_buffer, target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + + // Positive control on the host. + // After both forward FFT's we should constant values in each pixel = testVal_1 and testVal_2. + // After the Conjugate multiplication, we should have a constant value of testVal_1*testVal_2. + // After the inverse FFT, we should have a constant value of testVal_1*testVal_2 in the center pixel and 0 everywhere else. + positive_control.FwdFFT( ); + positive_control.MultiplyConjugateImage(target_search_image.complex_values); + positive_control.InvFFT( ); + + CheckUnitImpulseRealImage(positive_control, __LINE__); + + float scale_factor = positive_control.size.x * positive_control.size.y * positive_control.size.z * testVal_1 * testVal_2; + if ( positive_control.real_values[0] == scale_factor ) { + if ( print_out_time ) { + std::cout << "Test passed for FFTW positive control." << std::endl; + } + } + else { + MyTestPrintAndExit(false, "Test failed for FFTW positive control. Value at zero is " + std::to_string(positive_control.real_values[0])); + } + + FastFFT::KernelFunction::my_functor noop; + FastFFT::KernelFunction::my_functor scale(1.f / scale_factor); + FastFFT::KernelFunction::my_functor conj_mul; + FastFFT::KernelFunction::my_functor conj_mul_then_scale(1.f / scale_factor); + + // Do first round without scaling + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, noop, conj_mul, noop); + FT_fp16.FwdImageInvFFT(FT_fp16_buffer, reinterpret_cast<__half2*>(targetFT_fp16_buffer), FT_fp16_buffer, noop, conj_mul, noop); + + // Copy back to the host and check the results + if constexpr ( FFT_DEBUG_STAGE < 7 ) { + FT.CopyDeviceToHostAndSynchronize(FT_output.real_values); + FT_fp16.CopyDeviceToHostAndSynchronize(reinterpret_cast<__half*>(FT_fp16_output.real_values)); + } + else { + // Copy back to the host and check the results + cudaErr(cudaMemcpyAsync(FT_output.real_values, FT_buffer, FT_output.real_memory_allocated * sizeof(float), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(FT_fp16_output.real_values, FT_fp16_buffer, FT_fp16_output.real_memory_allocated * sizeof(__half), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + } + FT_fp16_output.data_is_fp16 = true; + FT_fp16_output.ConvertFP16ToFP32( ); + + if ( FT_output.real_values[0] == scale_factor ) { + if ( print_out_time ) { + std::cout << "Test passed for FP32 no scale." << std::endl; + } + } + else { + debug_partial_fft(FT_output, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); + debug_partial_fft(FT_fp16_output, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); + MyTestPrintAndExit(false, "Test failed for FP32 positive control. Value at zero is " + std::to_string(FT_output.real_values[0])); + } + + if ( FT_fp16_output.real_values[0] == scale_factor ) { + if ( print_out_time ) { + std::cout << "Test passed for FP16 no scale." << std::endl; + } + } + else { + debug_partial_fft(FT_output, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); + debug_partial_fft(FT_fp16_output, fwd_dims_in, fwd_dims_out, inv_dims_in, inv_dims_out, __LINE__); + MyTestPrintAndExit(false, "Test failed for FP16 positive control. Value at zero is " + std::to_string(FT_fp16_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + } + + // Restore the buffers and do the second round with preop scaling + // Set to zero + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(FT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + + cudaErr(cudaMemcpyAsync(FT_buffer, FT_input.real_values, FT_input.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_buffer, target_search_image.real_values, target_search_image.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + + cudaErr(cudaMemcpyAsync(FT_fp16_buffer, FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_fp16_buffer, target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, scale, conj_mul, noop); + FT_fp16.FwdImageInvFFT(FT_fp16_buffer, reinterpret_cast<__half2*>(targetFT_fp16_buffer), FT_fp16_buffer, scale, conj_mul, noop); + + // Copy back to the host and check the results + if constexpr ( FFT_DEBUG_STAGE < 7 ) { + FT.CopyDeviceToHostAndSynchronize(FT_output.real_values); + FT_fp16.CopyDeviceToHostAndSynchronize(reinterpret_cast<__half*>(FT_fp16_output.real_values)); + } + else { + // Copy back to the host and check the results + cudaErr(cudaMemcpyAsync(FT_output.real_values, FT_buffer, FT_output.real_memory_allocated * sizeof(float), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(FT_fp16_output.real_values, FT_fp16_buffer, FT_fp16_output.real_memory_allocated * sizeof(__half), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + } + FT_fp16_output.data_is_fp16 = true; + FT_fp16_output.ConvertFP16ToFP32( ); + + if ( FT_output.real_values[0] == 1.0f ) { + if ( print_out_time ) { + std::cout << "Test passed for FP32 with scale." << std::endl; + } + } + else + MyTestPrintAndExit(false, "Test failed for FP32 positive control. Value at zero is " + std::to_string(FT_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + + if ( FT_fp16_output.real_values[0] == 1.0f ) { + if ( print_out_time ) { + std::cout << "Test passed for FP16 with scale." << std::endl; + } + } + else + MyTestPrintAndExit(false, "Test failed for FP16 positive control. Value at zero is " + std::to_string(FT_fp16_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + + // Restore the buffers and do the second round with intra_op scaling + // Set to zero + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_buffer, 0, device_memory * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(FT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(targetFT_fp16_buffer, 0, device_memory * sizeof(__half), cudaStreamPerThread)); + + cudaErr(cudaMemcpyAsync(FT_buffer, FT_input.real_values, FT_input.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_buffer, target_search_image.real_values, target_search_image.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + + cudaErr(cudaMemcpyAsync(FT_fp16_buffer, FT_fp16_input.real_values, FT_fp16_input.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(targetFT_fp16_buffer, target_search_image_fp16.real_values, target_search_image_fp16.real_memory_allocated * sizeof(__half), cudaMemcpyHostToDevice, cudaStreamPerThread)); + + FT.FwdImageInvFFT(FT_buffer, reinterpret_cast(targetFT_buffer), FT_buffer, noop, conj_mul_then_scale, noop); + FT_fp16.FwdImageInvFFT(FT_fp16_buffer, reinterpret_cast<__half2*>(targetFT_fp16_buffer), FT_fp16_buffer, noop, conj_mul_then_scale, noop); + + // Copy back to the host and check the results + if constexpr ( FFT_DEBUG_STAGE < 7 ) { + FT.CopyDeviceToHostAndSynchronize(FT_output.real_values); + FT_fp16.CopyDeviceToHostAndSynchronize(reinterpret_cast<__half*>(FT_fp16_output.real_values)); + } + else { + // Copy back to the host and check the results + cudaErr(cudaMemcpyAsync(FT_output.real_values, FT_buffer, FT_output.real_memory_allocated * sizeof(float), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaMemcpyAsync(FT_fp16_output.real_values, FT_fp16_buffer, FT_fp16_output.real_memory_allocated * sizeof(__half), cudaMemcpyDeviceToHost, cudaStreamPerThread)); + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + } + FT_fp16_output.data_is_fp16 = true; + FT_fp16_output.ConvertFP16ToFP32( ); + + if ( FT_output.real_values[0] == 1.0f ) { + if ( print_out_time ) { + std::cout << "Test passed for FP32 with scale." << std::endl; + } + } + else + MyTestPrintAndExit(false, "Test failed for FP32 positive control. Value at zero is " + std::to_string(FT_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + + if ( FT_fp16_output.real_values[0] == 1.0f ) { + if ( print_out_time ) { + std::cout << "Test passed for FP16 with scale." << std::endl; + } + } + else + MyTestPrintAndExit(false, "Test failed for FP16 positive control. Value at zero is " + std::to_string(FT_fp16_output.real_values[0]) + " and should be " + std::to_string(scale_factor)); + + oSize++; + // We don't want to loop if the size is not actually changing. + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); + cudaErr(cudaFree(FT_buffer)); + cudaErr(cudaFree(targetFT_buffer)); + cudaErr(cudaFree(FT_fp16_buffer)); + cudaErr(cudaFree(targetFT_fp16_buffer)); + + // Right now, we would overflow with larger arrays in fp16 so just exit + break; + } // while loop over pad to size + + } // for loop over pad from size +} + +int main(int argc, char** argv) { + + using SCT = FastFFT::SizeChangeType::Enum; + + std::string test_name; + // Default to running all tests + bool run_2d_performance_tests = false; + bool run_3d_performance_tests = false; + + const std::string_view text_line = "simple convolution"; + FastFFT::CheckInputArgs(argc, argv, text_line, run_2d_performance_tests, run_3d_performance_tests); + + // TODO: size decrease + if ( run_2d_performance_tests ) { +#ifdef HEAVYERRORCHECKING_FFT + std::cout << "Running performance tests with heavy error checking.\n"; + std::cout << "This doesn't make sense as the synchronizations are invalidating.\n"; +// exit(1); +#endif + SCT size_change_type; + // Set the SCT to no_change, increase, or decrease + // size_change_type = SCT::no_change; + // preop_functor_scale<2>(FastFFT::test_size, size_change_type, false); + // preop_functor_scale<2>(test_size_rectangle, do_3d, size_change_type, true); + + size_change_type = SCT::increase; + preop_functor_scale<2>(FastFFT::test_size, size_change_type, false); + // preop_functor_scale<2>(test_size_rectangle, do_3d, size_change_type, true); + + // size_change_type = SCT::decrease; + // preop_functor_scale<2>(FastFFT::test_size, size_change_type, false); + } + + if ( run_3d_performance_tests ) { +#ifdef HEAVYERRORCHECKING_FFT + std::cout << "Running performance tests with heavy error checking.\n"; + std::cout << "This doesn't make sense as the synchronizations are invalidating.\n"; +#endif + + SCT size_change_type; + + size_change_type = SCT::no_change; + + preop_functor_scale<3>(FastFFT::test_size_3d, size_change_type, false); + + // TODO: These are not yet completed. + // size_change_type = SCT::increase; + // preop_functor_scale<3>(FastFFT::test_size, do_3d, size_change_type, false); + + // size_change_type = SCT::decrease; + // preop_functor_scale(FastFFT::test_size, do_3d, size_change_type, false); + } + + return 0; +}; \ No newline at end of file diff --git a/src/tests/test.cu b/src/tests/test.cu deleted file mode 100644 index f934565..0000000 --- a/src/tests/test.cu +++ /dev/null @@ -1,307 +0,0 @@ -#include "tests.h" - -// Define an enum for size change type to indecate a decrease, no change or increase - -// The Fourier transform of a constant should be a unit impulse, and on back fft, without normalization, it should be a constant * N. -// It is assumed the input/output have the same dimension (i.e. no padding) - -template -bool random_image_test(std::vector size, bool do_3d = false) { - - bool all_passed = true; - std::vector init_passed(size.size( ), true); - std::vector FFTW_passed(size.size( ), true); - std::vector FastFFT_forward_passed(size.size( ), true); - std::vector FastFFT_roundTrip_passed(size.size( ), true); - - for ( int n = 0; n < size.size( ); n++ ) { - - short4 input_size; - short4 output_size; - long full_sum = long(size[n]); - if ( do_3d ) { - input_size = make_short4(size[n], size[n], size[n], 0); - output_size = make_short4(size[n], size[n], size[n], 0); - full_sum = full_sum * full_sum * full_sum * full_sum * full_sum * full_sum; - } - else { - input_size = make_short4(size[n], size[n], 1, 0); - output_size = make_short4(size[n], size[n], 1, 0); - full_sum = full_sum * full_sum * full_sum * full_sum; - } - - float sum; - - Image host_input(input_size); - Image host_output(output_size); - Image host_copy(output_size); - Image device_output(output_size); - - // Pointers to the arrays on the host -- maybe make this a struct of some sort? I'm sure there is a parallel in cuda, look into cuarray/texture code - - // We just make one instance of the FourierTransformer class, with calc type float. - // For the time being input and output are also float. TODO calc optionally either fp16 or nv_bloat16, TODO inputs at lower precision for bandwidth improvement. - FastFFT::FourierTransformer FT; - - // This is similar to creating an FFT/CUFFT plan, so set these up before doing anything on the GPU - FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); - FT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); - - // The padding (dims.w) is calculated based on the setup - short4 dims_in = FT.ReturnFwdInputDimensions( ); - short4 dims_out = FT.ReturnFwdOutputDimensions( ); - - // Determine how much memory we need, working with FFTW/CUDA style in place transform padding. - // Note: there is no reason we really need this, because the xforms will always be out of place. - // For now, this is just in place because all memory in cisTEM is allocated accordingly. - host_input.real_memory_allocated = FT.ReturnInputMemorySize( ); - host_output.real_memory_allocated = FT.ReturnInvOutputMemorySize( ); - host_copy.real_memory_allocated = FT.ReturnInvOutputMemorySize( ); - - // On the device, we will always allocate enough memory for the larger of input/output including the buffer array. - // Minmize the number of calls to malloc which are slow and can lead to fragmentation. - device_output.real_memory_allocated = std::max(host_input.real_memory_allocated, host_output.real_memory_allocated); - - // In your own programs, you will be handling this memory allocation yourself. We'll just make something here. - // I think fftwf_malloc may potentially create a different alignment than new/delete, but kinda doubt it. For cisTEM consistency... - bool set_fftw_plan = true; - host_input.Allocate(set_fftw_plan); - host_output.Allocate(set_fftw_plan); - host_copy.Allocate(set_fftw_plan); - - // Set our input host memory to a constant. Then FFT[0] = host_input_memory_allocated - FT.SetToRandom(host_output.real_values, host_output.real_memory_allocated, 0.0f, 1.0f); - - // Now we want to associate the host memory with the device memory. The method here asks if the host pointer is pinned (in page locked memory) which - // ensures faster transfer. If false, it will be pinned for you. - FT.SetInputPointer(host_output.real_values, false); - - // This copies the host memory into the device global memory. If needed, it will also allocate the device memory first. - FT.CopyHostToDevice(host_output.real_values); - -#if FFT_DEBUG_STAGE > 0 - host_output.FwdFFT( ); -#endif - - for ( long i = 0; i < host_output.real_memory_allocated / 2; i++ ) { - host_copy.complex_values[i] = host_output.complex_values[i]; - } - - // This method will call the regular FFT kernels given the input/output dimensions are equal when the class is instantiated. - bool swap_real_space_quadrants = false; - FT.FwdFFT( ); - - // in buffer, do not deallocate, do not unpin memory - FT.CopyDeviceToHostAndSynchronize(host_output.real_values, false); - bool test_passed = true; - -#if FFT_DEBUG_STAGE == 0 - PrintArray(host_output.real_values, dims_out.x, dims_in.y, dims_in.z, dims_out.w); - PrintArray(host_copy.real_values, dims_out.x, dims_in.y, dims_in.z, dims_out.w); - MyTestPrintAndExit("stage 0 "); -#elif FFT_DEBUG_STAGE == 1 - std::cout << " For random_image_test partial transforms aren't supported, b/c we need to compare to the cpu output." << std::endl; - MyTestPrintAndExit("stage 1 "); -#elif FFT_DEBUG_STAGE == 2 - std::cout << " For random_image_test partial transforms aren't supported, b/c we need to compare to the cpu output." << std::endl; - MyTestPrintAndExit("stage 2 "); -#elif FFT_DEBUG_STAGE == 3 - PrintArray(host_output.complex_values, dims_in.y, dims_out.w, dims_out.z); - PrintArray(host_copy.complex_values, dims_in.y, dims_out.w, dims_out.z); - - // std::cout << "Distance between FastFFT and CPU: " << distance << std::endl; - MyTestPrintAndExit("stage 3 "); -#endif - - double distance = 0.0; - for ( long index = 0; index < host_output.real_memory_allocated / 2; index++ ) { - distance += sqrt((host_output.complex_values[index].x - host_copy.complex_values[index].x) * (host_output.complex_values[index].x - host_copy.complex_values[index].x) + - (host_output.complex_values[index].y - host_copy.complex_values[index].y) * (host_output.complex_values[index].y - host_copy.complex_values[index].y)); - } - distance /= (host_output.real_memory_allocated / 2); - - std::cout << "Distance between FastFFT and CPU: " << distance << std::endl; - exit(0); - if ( test_passed == false ) { - all_passed = false; - FastFFT_forward_passed[n] = false; - } - // MyFFTDebugAssertTestTrue( test_passed, "FastFFT unit impulse forward FFT"); - FT.SetToConstant(host_input.real_values, host_input.real_memory_allocated, 2.0f); - - FT.InvFFT( ); - FT.CopyDeviceToHostAndSynchronize(host_output.real_values, true); - -#if FFT_DEBUG_STAGE == 4 - PrintArray(host_output.complex_values, dims_out.y, dims_out.w, dims_out.z); - MyTestPrintAndExit("stage 4 "); -#elif FFT_DEBUG_STAGE == 5 - PrintArray(host_output.complex_values, dims_out.y, dims_out.w, dims_out.z); - MyTestPrintAndExit("stage 5 "); -#elif FFT_DEBUG_STAGE == 6 - if ( do_3d ) { - std::cout << " in 3d print inv " << dims_out.w << "w" << std::endl; - PrintArray(host_output.complex_values, dims_out.w, dims_out.y, dims_out.z); - } - else - PrintArray(host_output.complex_values, dims_out.y, dims_out.w, dims_out.z); - MyTestPrintAndExit("stage 6 "); -#elif FFT_DEBUG_STAGE == 7 - PrintArray(host_output.real_values, dims_out.x, dims_out.y, dims_out.z, dims_out.w); - MyTestPrintAndExit("stage 7 "); -#elif FFT_DEBUG_STAGE > 7 - // No debug, keep going -#else - MyTestPrintAndExit(" This block is only valid for FFT_DEBUG_STAGE == 4, 5, 7 "); -#endif - - // Assuming the outputs are always even dimensions, padding_jump_val is always 2. - sum = host_output.ReturnSumOfReal(host_output.real_values, dims_out, true); - - if ( sum != full_sum ) { - all_passed = false; - FastFFT_roundTrip_passed[n] = false; - } - MyFFTDebugAssertTestTrue(sum == full_sum, "FastFFT constant image round trip for size " + std::to_string(dims_in.x)); - } // loop over sizes - - if ( all_passed ) { - if ( do_3d ) - std::cout << " All 3d const_image tests passed!" << std::endl; - else - std::cout << " All 2d const_image tests passed!" << std::endl; - } - else { - for ( int n = 0; n < size.size( ); n++ ) { - if ( ! init_passed[n] ) - std::cout << " Initialization failed for size " << size[n] << std::endl; - if ( ! FFTW_passed[n] ) - std::cout << " FFTW failed for size " << size[n] << std::endl; - if ( ! FastFFT_forward_passed[n] ) - std::cout << " FastFFT failed for forward transform size " << size[n] << std::endl; - if ( ! FastFFT_roundTrip_passed[n] ) - std::cout << " FastFFT failed for roundtrip transform size " << size[n] << std::endl; - } - } - return all_passed; -} - -template -void run_oned(std::vector size) { - - // Override the size to be one dimensional in x - std::cout << "Running one-dimensional tests\n" - << std::endl; - - for ( int n : size ) { - short4 input_size = make_short4(n, 1, 1, 0); - short4 output_size = make_short4(n, 1, 1, 0); - - Image FT_input(input_size); - Image FT_output(output_size); - Image FT_input_complex(input_size); - Image FT_output_complex(output_size); - - // We just make one instance of the FourierTransformer class, with calc type float. - // For the time being input and output are also float. TODO calc optionally either fp16 or nv_bloat16, TODO inputs at lower precision for bandwidth improvement. - FastFFT::FourierTransformer FT; - FastFFT::FourierTransformer FT_complex; - - // This is similar to creating an FFT/CUFFT plan, so set these up before doing anything on the GPU - FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); - FT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); - - FT_complex.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); - FT_complex.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); - - FT_input.real_memory_allocated = FT.ReturnInputMemorySize( ); - FT_output.real_memory_allocated = FT.ReturnInvOutputMemorySize( ); - - FT_input_complex.real_memory_allocated = FT_complex.ReturnInputMemorySize( ); - FT_output_complex.real_memory_allocated = FT_complex.ReturnInvOutputMemorySize( ); - - bool set_fftw_plan = true; - FT_input.Allocate(set_fftw_plan); - FT_output.Allocate(set_fftw_plan); - - FT_input_complex.Allocate( ); - FT_output_complex.Allocate( ); - - // Now we want to associate the host memory with the device memory. The method here asks if the host pointer is pinned (in page locked memory) which - // ensures faster transfer. If false, it will be pinned for you. - FT.SetInputPointer(FT_input.real_values, false); - FT_complex.SetInputPointer(FT_input_complex.complex_values, false); - - FT.SetToConstant(FT_input.real_values, FT_input.real_memory_allocated, 1.f); - - // Set a unit impulse at the center of the input array. - // FT.SetToConstant(FT_input.real_values, FT_input.real_memory_allocated, 1.0f); - float2 const_val = make_float2(1.0f, 0.0f); - FT_complex.SetToConstant(FT_input_complex.complex_values, FT_input.real_memory_allocated, const_val); - for ( int i = 0; i < 10; i++ ) { - std::cout << FT_input_complex.complex_values[i].x << "," << FT_input_complex.complex_values[i].y << std::endl; - } - - FT.CopyHostToDevice(FT_input.real_values); - FT_complex.CopyHostToDevice(FT_input_complex.complex_values); - cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); - - // Set the outputs to a clearly wrong answer. - FT.SetToConstant(FT_output.real_values, FT_input.real_memory_allocated, 2.0f); - const_val = make_float2(2.0f, 2.0f); - FT_complex.SetToConstant(FT_output_complex.complex_values, FT_output.real_memory_allocated, const_val); - - FT_input.FwdFFT( ); - - bool transpose_output = false; - bool swap_real_space_quadrants = false; - FT.FwdFFT( ); - FT_complex.FwdFFT( ); - - FT.CopyDeviceToHostAndSynchronize(FT_output.real_values, false, false); - FT_complex.CopyDeviceToHostAndSynchronize(FT_output_complex.real_values, false, false); - - FT_input.InvFFT( ); - - for ( int i = 0; i < 5; ++i ) { - std::cout << "FFTW inv " << FT_input.real_values[i] << std::endl; - } - std::cout << std::endl; - - FT.InvFFT( ); - FT_complex.InvFFT( ); - FT.CopyDeviceToHostAndSynchronize(FT_output.real_values, true); - FT_complex.CopyDeviceToHostAndSynchronize(FT_output_complex.real_values, true); - - for ( int i = 0; i < 10; i++ ) { - std::cout << "Ft inv " << FT_output.real_values[i] << std::endl; - } - for ( int i = 0; i < 10; i++ ) { - std::cout << "Ft complex inv " << FT_output_complex.real_values[i].x << "," << FT_output_complex.real_values[i].y << std::endl; - } - } -} - -int main(int argc, char** argv) { - - using SCT = FastFFT::SizeChangeType::Enum; - - if ( argc != 2 ) { - return 1; - } - std::string test_name = argv[1]; - std::printf("Standard is %li\n\n", __cplusplus); - - // Input size vectors to be tested. - std::vector test_size = {32, 64, 128, 256, 512, 1024, 2048, 4096}; - std::vector test_size_rectangle = {64, 128, 256, 512, 1024, 2048, 4096}; - std::vector test_size_3d = {32, 64, 128, 256, 512}; - // std::vector test_size_3d ={512}; - - // The launch parameters fail for 4096 -> < 64 for r2c_decrease, not sure if it is the elements_per_thread or something else. - // For now, just over-ride these small sizes - std::vector test_size_for_decrease = {64, 128, 256, 512, 1024, 2048, 4096}; - - // If we get here, all tests passed. - return 0; -}; diff --git a/src/tests/test_half_precision_buffer.cu b/src/tests/test_half_precision_buffer.cu new file mode 100644 index 0000000..d8671ab --- /dev/null +++ b/src/tests/test_half_precision_buffer.cu @@ -0,0 +1,71 @@ + +#include "tests.h" + +template +bool half_precision_buffer_test(int size) { + + bool all_passed = true; + + short4 input_size; + + input_size = make_short4(size, size, 1, 0); + + Image host_input_fp32(input_size); + Image host_output_fp16(input_size); + + FastFFT::FourierTransformer FT; + + host_input_fp32.Allocate(false); + host_output_fp16.Allocate(false); + + FT.SetToRandom(host_input_fp32.real_values, host_input_fp32.real_memory_allocated, 0.f, 1.0f); + for ( int i = 0; i < host_input_fp32.real_memory_allocated; i++ ) { + host_output_fp16.real_values[i] = host_input_fp32.real_values[i]; + } + + float fp32sum = host_input_fp32.ReturnSumOfReal(host_input_fp32.real_values, input_size, false); + float fp16sum = host_output_fp16.ReturnSumOfReal(host_output_fp16.real_values, input_size, false); + std::cout << "Sum of real values in fp16 buffer: " << fp16sum << std::endl; + std::cout << "Sum of real values in fp32 buffer: " << fp32sum << std::endl; + if ( fp32sum != fp16sum ) { + std::cerr << "Error: Sum of real values in input and output buffers do not match." << std::endl; + std::cout << "Sum of real values in fp16 buffer: " << fp16sum << std::endl; + std::cout << "Sum of real values in fp32 buffer: " << fp32sum << std::endl; + all_passed = false; + } + + // Now convert one buffer to fp 16 + host_output_fp16.ConvertFP32ToFP16( ); + // And convert back, results should almost be the same + host_output_fp16.ConvertFP16ToFP32( ); + float diff_value; + for ( int i = 0; i < host_input_fp32.real_memory_allocated; i++ ) { + // Check that the floats are the same up to the third decimal point but different past it + diff_value = std::abs(host_input_fp32.real_values[i] - host_output_fp16.real_values[i]); + if ( diff_value > 0.001f && diff_value < 0.0001 ) { + std::cerr << "fp32 " << host_input_fp32.real_values[i] << std::endl; + std::cerr << " fp16 " << host_output_fp16.real_values[i] << std::endl; + all_passed = false; + } + } + + return all_passed; +} + +int main(int argc, char** argv) { + + std::string test_name; + // Default to running all tests + bool run_2d_unit_tests = false; + bool run_3d_unit_tests = false; + + const std::string_view text_line = "half precision buffers"; + FastFFT::CheckInputArgs(argc, argv, text_line, run_2d_unit_tests, run_3d_unit_tests); + + if ( run_2d_unit_tests ) { + if ( ! half_precision_buffer_test<2>(64) ) + return 1; + } + + return 0; +}; \ No newline at end of file diff --git a/src/tests/tests.h b/src/tests/tests.h index 3818d50..da1d3c8 100644 --- a/src/tests/tests.h +++ b/src/tests/tests.h @@ -1,20 +1,21 @@ #ifndef _SRC_TESTS_TESTS_H #define _SRC_TESTS_TESTS_H +#include #include "../fastfft/Image.cuh" #include "../../include/FastFFT.cuh" #include "helper_functions.cuh" -#include - namespace FastFFT { // Input size vectors to be tested. -std::vector test_size = {32, 64, 128, 256, 512, 1024, 2048, 4096}; +std::vector test_size = {32, 64, 128, 256, 512, 1024, 2048, 4096}; +// std::vector test_size = {32, 64, 128, 256, 512, 1024, 2048, 4096}; + std::vector test_size_rectangle = {64, 128, 256, 512, 1024, 2048, 4096}; std::vector test_size_3d = {32, 64, 128, 256, 512}; // std::vector test_size_3d ={512}; -// The launch parameters fail for 4096 -> < 64 for r2c_decrease, not sure if it is the elements_per_thread or something else. +// The launch parameters fail for 4096 -> < 64 for r2c_decrease_XY, not sure if it is the elements_per_thread or something else. // For now, just over-ride these small sizes std::vector test_size_for_decrease = {64, 128, 256, 512, 1024, 2048, 4096}; diff --git a/src/tests/unit_impulse_test.cu b/src/tests/unit_impulse_test.cu index 94fe6fb..4153e10 100644 --- a/src/tests/unit_impulse_test.cu +++ b/src/tests/unit_impulse_test.cu @@ -2,7 +2,7 @@ #include "tests.h" template -bool unit_impulse_test(std::vector size, bool do_increase_size) { +bool unit_impulse_test(std::vector size, bool do_increase_size, bool inplace_transform = true) { bool all_passed = true; std::vector init_passed(size.size( ), true); @@ -46,14 +46,20 @@ bool unit_impulse_test(std::vector size, bool do_increase_size) { // We just make one instance of the FourierTransformer class, with calc type float. // For the time being input and output are also float. TODO calc optionally either fp16 or nv_bloat16, TODO inputs at lower precision for bandwidth improvement. - FastFFT::FourierTransformer FT; + FastFFT::FourierTransformer FT; + + float* FT_buffer; + // This is only used for the out of place test. + float* FT_buffer_output; // This is similar to creating an FFT/CUFFT plan, so set these up before doing anything on the GPU FT.SetForwardFFTPlan(input_size.x, input_size.y, input_size.z, output_size.x, output_size.y, output_size.z); FT.SetInverseFFTPlan(output_size.x, output_size.y, output_size.z, output_size.x, output_size.y, output_size.z); // The padding (dims.w) is calculated based on the setup - short4 dims_in = FT.ReturnFwdInputDimensions( ); - short4 dims_out = FT.ReturnFwdOutputDimensions( ); + short4 dims_fwd_in = FT.ReturnFwdInputDimensions( ); + short4 dims_fwd_out = FT.ReturnFwdOutputDimensions( ); + short4 dims_inv_in = FT.ReturnInvInputDimensions( ); + short4 dims_inv_out = FT.ReturnInvOutputDimensions( ); // Determine how much memory we need, working with FFTW/CUDA style in place transform padding. // Note: there is no reason we really need this, because the xforms will always be out of place. // For now, this is just in place because all memory in cisTEM is allocated accordingly. @@ -63,7 +69,12 @@ bool unit_impulse_test(std::vector size, bool do_increase_size) { // On the device, we will always allocate enough memory for the larger of input/output including the buffer array. // Minmize the number of calls to malloc which are slow and can lead to fragmentation. device_output.real_memory_allocated = std::max(host_input.real_memory_allocated, host_output.real_memory_allocated); - + cudaErr(cudaMallocAsync((void**)&FT_buffer, device_output.real_memory_allocated * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_output.real_memory_allocated * sizeof(float), cudaStreamPerThread)); + if ( ! inplace_transform ) { + cudaErr(cudaMallocAsync((void**)&FT_buffer_output, device_output.real_memory_allocated * sizeof(float), cudaStreamPerThread)); + cudaErr(cudaMemsetAsync(FT_buffer_output, 0, device_output.real_memory_allocated * sizeof(float), cudaStreamPerThread)); + } // In your own programs, you will be handling this memory allocation yourself. We'll just make something here. // I think fftwf_malloc may potentially create a different alignment than new/delete, but kinda doubt it. For cisTEM consistency... bool set_fftw_plan = true; @@ -72,7 +83,8 @@ bool unit_impulse_test(std::vector size, bool do_increase_size) { // Now we want to associate the host memory with the device memory. The method here asks if the host pointer is pinned (in page locked memory) which // ensures faster transfer. If false, it will be pinned for you. - FT.SetInputPointer(host_input.real_values, false); + // FIXME: + // FT.SetInputPointer(host_input.real_values); // Set a unit impulse at the center of the input array. FT.SetToConstant(host_input.real_values, host_input.real_memory_allocated, 0.0f); @@ -84,15 +96,11 @@ bool unit_impulse_test(std::vector size, bool do_increase_size) { // This will exit if fail, so the following bools are not really needed any more. CheckUnitImpulseRealImage(host_output, __LINE__); - // TODO: remove me - // if ( sum != 1 ) { - // all_passed = true; - // init_passed[iSize] = true; - // } - - // This copies the host memory into the device global memory. If needed, it will also allocate the device memory first. - FT.CopyHostToDevice(host_input.real_values); + // It doesn't really matter which one we copy here, it would make sense to do the smaller one though. + cudaErr(cudaMemcpyAsync(FT_buffer, host_output.real_values, host_output.real_memory_allocated * sizeof(float), cudaMemcpyHostToDevice, cudaStreamPerThread)); + // We need to wait for the copy to finish before we can do the FFT on the host. + cudaErr(cudaStreamSynchronize(cudaStreamPerThread)); host_output.FwdFFT( ); host_output.fftw_epsilon = host_output.ReturnSumOfComplexAmplitudes(host_output.complex_values, host_output.real_memory_allocated / 2); @@ -112,21 +120,28 @@ bool unit_impulse_test(std::vector size, bool do_increase_size) { // This method will call the regular FFT kernels given the input/output dimensions are equal when the class is instantiated. // bool swap_real_space_quadrants = true; + if ( inplace_transform ) { + FT.FwdFFT(FT_buffer); + } + else { + FT.FwdFFT(FT_buffer, FT_buffer_output); + // To make sure we are not getting a false positive, set the input to some undesired value. + cudaErr(cudaMemsetAsync(FT_buffer, 0, device_output.real_memory_allocated * sizeof(float), cudaStreamPerThread)); + } - FT.FwdFFT( ); - - bool continue_debugging; + bool continue_debugging = true; // We don't want this to break compilation of other tests, so only check at runtime. if constexpr ( FFT_DEBUG_STAGE < 5 ) { if ( do_increase_size ) { - FT.CopyDeviceToHostAndSynchronize(host_output.real_values, false); + + FT.CopyDeviceToHostAndSynchronize(host_output.real_values); // Right now, only testing a size change on the forward transform, - continue_debugging = debug_partial_fft(host_output, input_size, output_size, output_size, output_size, __LINE__); + continue_debugging = debug_partial_fft(host_output, dims_fwd_in, dims_fwd_out, dims_inv_in, dims_inv_out, __LINE__); sum = host_output.ReturnSumOfComplexAmplitudes(host_output.complex_values, host_output.real_memory_allocated / 2); } else { FT.CopyDeviceToHostAndSynchronize(host_input.real_values, FT.ReturnInputMemorySize( )); - continue_debugging = debug_partial_fft(host_input, input_size, output_size, output_size, output_size, __LINE__); + continue_debugging = debug_partial_fft(host_input, dims_fwd_in, dims_fwd_out, dims_inv_in, dims_inv_out, __LINE__); sum = host_input.ReturnSumOfComplexAmplitudes(host_input.complex_values, host_input.real_memory_allocated / 2); } @@ -138,46 +153,65 @@ bool unit_impulse_test(std::vector size, bool do_increase_size) { FastFFT_forward_passed[iSize] = false; } } + + MyTestPrintAndExit(continue_debugging, "Partial FFT debug stage " + std::to_string(FFT_DEBUG_STAGE)); // MyFFTDebugAssertTestTrue( abs(sum - host_output.fftw_epsilon) < 1e-8, "FastFFT unit impulse forward FFT"); FT.SetToConstant(host_output.real_values, host_output.real_memory_allocated, 2.0f); - FT.InvFFT( ); - FT.CopyDeviceToHostAndSynchronize(host_output.real_values, true); + if ( inplace_transform ) { + FT.InvFFT(FT_buffer); + } + else { + // Switch the role of the buffers + FT.InvFFT(FT_buffer_output, FT_buffer); + cudaErr(cudaMemsetAsync(FT_buffer_output, 0, device_output.real_memory_allocated * sizeof(float), cudaStreamPerThread)); + } + FT.CopyDeviceToHostAndSynchronize(host_output.real_values); if constexpr ( FFT_DEBUG_STAGE > 4 ) { // Right now, only testing a size change on the forward transform, - continue_debugging = debug_partial_fft(host_output, input_size, output_size, output_size, output_size, __LINE__); + // continue_debugging = debug_partial_fft(host_output, dims_fwd_in, dims_fwd_out, dims_inv_in, dims_inv_out, __LINE__); - sum = host_output.ReturnSumOfReal(host_output.real_values, dims_out); - if ( sum != dims_out.x * dims_out.y * dims_out.z ) { + sum = host_output.ReturnSumOfReal(host_output.real_values, dims_fwd_out); + if ( sum != dims_fwd_out.x * dims_fwd_out.y * dims_fwd_out.z ) { all_passed = false; FastFFT_roundTrip_passed[iSize] = false; } } + // MyTestPrintAndExit(continue_debugging, "Partial FFT debug stage " + std::to_string(FFT_DEBUG_STAGE)); - // std::cout << "size in/out " << dims_in.x << ", " << dims_out.x << std::endl; - // MyFFTDebugAssertTestTrue( sum == dims_out.x*dims_out.y*dims_out.z,"FastFFT unit impulse round trip FFT"); + // std::cout << "size in/out " << dims_fwd_in.x << ", " << dims_fwd_out.x << std::endl; + // MyFFTDebugAssertTestTrue( sum == dims_fwd_out.x*dims_fwd_out.y*dims_fwd_out.z,"FastFFT unit impulse round trip FFT"); oSize++; + cudaErr(cudaFreeAsync(FT_buffer, cudaStreamPerThread)); + if ( ! inplace_transform ) + cudaErr(cudaFreeAsync(FT_buffer_output, cudaStreamPerThread)); } // while loop over pad to size } // for loop over pad from size + std::string is_in_place; + if ( inplace_transform ) + is_in_place = "in place"; + else + is_in_place = "out of place"; + if ( all_passed ) { if ( ! do_increase_size ) - std::cout << " All rank " << Rank << " size_decrease unit impulse tests passed!" << std::endl; + std::cout << " All rank " << Rank << " size_decrease unit impulse, " << is_in_place << " tests passed!" << std::endl; else - std::cout << " All rank " << Rank << " size_increase unit impulse tests passed!" << std::endl; + std::cout << " All rank " << Rank << " size_increase unit impulse, " << is_in_place << " tests passed!" << std::endl; } else { for ( int n = 0; n < size.size( ); n++ ) { if ( ! init_passed[n] ) - std::cout << " Initialization failed for size " << size[n] << " rank " << Rank << std::endl; + std::cout << " Initialization failed for size " << size[n] << " rank " << Rank << " " << is_in_place << std::endl; if ( ! FFTW_passed[n] ) - std::cout << " FFTW failed for size " << size[n] << " rank " << Rank << std::endl; + std::cout << " FFTW failed for size " << size[n] << " rank " << Rank << " " << is_in_place << std::endl; if ( ! FastFFT_forward_passed[n] ) - std::cout << " FastFFT failed for forward transform size " << size[n] << " rank " << Rank << std::endl; + std::cout << " FastFFT failed for forward transform size " << size[n] << " rank " << Rank << " " << is_in_place << " increase " << do_increase_size << std::endl; if ( ! FastFFT_roundTrip_passed[n] ) - std::cout << " FastFFT failed for roundtrip transform size " << size[n] << " rank " << Rank << std::endl; + std::cout << " FastFFT failed for roundtrip transform size " << size[n] << " rank " << Rank << " " << is_in_place << " increase " << do_increase_size << std::endl; } } return all_passed; @@ -193,17 +227,25 @@ int main(int argc, char** argv) { const std::string_view text_line = "unit impulse"; FastFFT::CheckInputArgs(argc, argv, text_line, run_2d_unit_tests, run_3d_unit_tests); - constexpr bool do_increase_size = true; // TODO: size decrease if ( run_2d_unit_tests ) { - if ( ! unit_impulse_test<2>(FastFFT::test_size, do_increase_size) ) + constexpr bool do_increase_size_first = true; + constexpr bool second_round = ! do_increase_size_first; + if ( ! unit_impulse_test<2>(FastFFT::test_size, do_increase_size_first) ) + return 1; + if ( ! unit_impulse_test<2>(FastFFT::test_size, second_round) ) + return 1; + // Also test case where the external pointer is different on output + if ( ! unit_impulse_test<2>(FastFFT::test_size, true, false) ) return 1; } if ( run_3d_unit_tests ) { // FIXME: tests are failing for 3d - if ( ! unit_impulse_test<3>(FastFFT::test_size_3d, do_increase_size) ) - return 1; + // constexpr bool do_increase_size_first = true; + // constexpr bool second_round = ! do_increase_size_first; + // if ( ! unit_impulse_test<3>(FastFFT::test_size_3d, do_increase_size_first) ) + // return 1; // if (! unit_impulse_test(test_size_3d, true, true)) return 1; }