diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 373d5bebd67..40dfd4c7544 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,3 +1,6 @@ +default_language_version: + python: python3.6 + exclude: '^tools/(prettify/fprettify|build_utils/fypp)' fail_fast: false repos: @@ -5,13 +8,27 @@ repos: rev: v0.24.0 hooks: - id: yapf + language_version: "python3" - repo: https://github.com/pre-commit/pre-commit-hooks rev: v2.0.0 hooks: - id: flake8 + language_version: "python2" + exclude: '^src/acc/libsmm_acc/libcusmm/(tune|predict)_' + - id: flake8 + language_version: "python3" - id: check-ast stages: [manual] language_version: "python2" + exclude: '^src/acc/libsmm_acc/libcusmm/(tune|predict)_' - id: check-ast stages: [manual] language_version: "python3" +- repo: local + hooks: + - id: check-header + name: check file headers + entry: ./.pre-commit/check_header.py --verbose + language: script + types: [text] + exclude: '^tools/' diff --git a/.pre-commit/check_header.py b/.pre-commit/check_header.py new file mode 100755 index 00000000000..77df69c617d --- /dev/null +++ b/.pre-commit/check_header.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import argparse +import re +import mmap +import sys +from os import path +from contextlib import contextmanager + +TYPES = { + 'c_cpp': ['.c', 'h', '.cc', '.hh', '.cxx', '.hpp', '.cu'], + 'python': ['.py'], + 'fortran': ['.F', '.f', '.f90', '.f03'], +} + +# max number of lines allowed between header and top of file +ALLOWED_LINES = 5 + +# some assumed max line length to terminate early for large files +MAX_LINE_LENGTH = 128 + + +@contextmanager +def mmap_open(name, mode='r'): + access = mmap.ACCESS_READ if mode == 'r' else mmap.ACCESS_WRITE + with open(name, mode + 'b') as fhandle: + fmapped = mmap.mmap(fhandle.fileno(), 0, access=access) + yield fmapped + fmapped.close() + + +def check_header(header_dir, files, verbose=False): + retval = 0 + header_re = {} + header_len = {} + + for headertype in TYPES: + with open(path.join(header_dir, headertype), 'rb') as fhandle: + header_content = fhandle.read() + header_re[headertype] = re.compile(re.escape(header_content)) + header_len[headertype] = len(header_content) + + ext_map = {e: t for t, exts in TYPES.items() for e in exts} + + for fpath in files: + _, fext = path.splitext(fpath) + + if fext not in ext_map: + if verbose: + print("? {} ... unknown file type, ignoring".format(fpath)) + continue + + with mmap_open(fpath) as fmapped: + header_type = ext_map[fext] + match = header_re[header_type].search(fmapped, 0, ALLOWED_LINES * MAX_LINE_LENGTH + header_len[header_type]) + + if not match: + print("✗ {} ... required header not found".format(fpath)) + retval = 1 + continue + + lines_above = fmapped[0:match.start()].splitlines() + if len(lines_above) > ALLOWED_LINES: + print("✗ {} ... header not within first {} lines".format(fpath, ALLOWED_LINES)) + retval = 1 + continue + + if verbose: + print("✓ {}".format(fpath)) + + sys.exit(retval) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="Check files for header presence") + parser.add_argument('files', metavar='FILE', type=str, nargs='+', help="files to check") + parser.add_argument('--verbose', '-v', action='store_true', default=False) + args = parser.parse_args() + + header_dir = path.join(path.dirname(path.abspath(__file__)), 'headers') + check_header(header_dir, args.files, args.verbose) diff --git a/.pre-commit/headers/c_cpp b/.pre-commit/headers/c_cpp new file mode 100644 index 00000000000..ea04de621a5 --- /dev/null +++ b/.pre-commit/headers/c_cpp @@ -0,0 +1,8 @@ +/*------------------------------------------------------------------------------------------------* + * Copyright (C) by the DBCSR developers group - All rights reserved * + * This file is part of the DBCSR library. * + * * + * For information on the license, see the LICENSE file. * + * For further information please visit https://dbcsr.cp2k.org * + * SPDX-License-Identifier: GPL-2.0+ * + *------------------------------------------------------------------------------------------------*/ diff --git a/.pre-commit/headers/fortran b/.pre-commit/headers/fortran new file mode 100644 index 00000000000..8d93d5eb502 --- /dev/null +++ b/.pre-commit/headers/fortran @@ -0,0 +1,8 @@ +!--------------------------------------------------------------------------------------------------! +! Copyright (C) by the DBCSR developers group - All rights reserved ! +! This file is part of the DBCSR library. ! +! ! +! For information on the license, see the LICENSE file. ! +! For further information please visit https://dbcsr.cp2k.org ! +! SPDX-License-Identifier: GPL-2.0+ ! +!--------------------------------------------------------------------------------------------------! diff --git a/.pre-commit/headers/python b/.pre-commit/headers/python new file mode 100644 index 00000000000..94a7e161f87 --- /dev/null +++ b/.pre-commit/headers/python @@ -0,0 +1,8 @@ +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### diff --git a/.travis.yml b/.travis.yml index 1f7e9f990d7..390351da248 100644 --- a/.travis.yml +++ b/.travis.yml @@ -37,6 +37,11 @@ env: - USE_MPI=OFF USE_OPENMP=ON USE_SMM=libxsmm - USE_MPI=OFF USE_OPENMP=OFF USE_SMM=libxsmm +matrix: + include: + - os: linux + python: 3.6 + matrix: exclude: - os: osx @@ -87,7 +92,12 @@ jobs: # the following jobs inherit only the first OS - name: "Run pre-commit hook on changed files" script: - set -o pipefail - - git diff --name-only --diff-filter=AM HEAD...$TRAVIS_BRANCH | xargs pre-commit run --files + # fetch remotes first to ensure that referenced branches (other than the default) are available + - git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" + - git fetch + # yapf requires python3.6.4+ + - pyenv global 3.6.7 2.7.15 + - git diff --name-only --diff-filter=AM HEAD "origin/${TRAVIS_BRANCH}" | xargs pre-commit run --files - name: "Run pre-commit hook 'check-ast' on all files" script: - pre-commit run --hook-stage manual --all-files check-ast @@ -96,7 +106,9 @@ jobs: # the following jobs inherit only the first OS if: tag IS present script: # do an in-source config for simplicity since we are not building anything here - - cmake . + - mkdir -p build + - cd build + - cmake .. - make dist deploy: provider: releases diff --git a/CMakeLists.txt b/CMakeLists.txt index a93d8671cd8..68516078694 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -88,6 +88,14 @@ endif () get_filename_component(Fortran_COMPILER_NAME ${CMAKE_Fortran_COMPILER} NAME) +# if the fortran compiler command is a CRAY wrapper, find out what the underlying compiler is +if (Fortran_COMPILER_NAME MATCHES "ftn") + execute_process(COMMAND ftn -craype-verbose OUTPUT_VARIABLE ftn_verbose_OUT ERROR_QUIET) + separate_arguments(ftn_verbose_OUT NATIVE_COMMAND "${ftn_verbose_OUT}") + list(GET ftn_verbose_OUT 0 Fortran_COMPILER_NAME) +endif () + +# Depending on the fortran compiler, set the appropriate compiler flags if (Fortran_COMPILER_NAME MATCHES "gfortran.*") set(CMAKE_CXX_FLAGS "-std=c++11") set(CMAKE_CXX_FLAGS_RELEASE "-O3 -funroll-loops") diff --git a/Makefile b/Makefile index 972803985fc..943a445c8e4 100644 --- a/Makefile +++ b/Makefile @@ -84,6 +84,7 @@ endif dirs makedep \ default_target $(LIBRARY) all \ toolversions \ + toolflags \ doxify doxifyclean \ pretty prettyclean doxygen/clean doxygen \ install clean realclean help \ @@ -144,7 +145,7 @@ dirs: @mkdir -p $(LIBDIR) version: - @echo "DCBSR Version: "$(MAJOR)"."$(MINOR)"."$(PATCH)" ("$(DATE)")" + @echo "DBCSR Version: "$(MAJOR)"."$(MINOR)"."$(PATCH)" ("$(DATE)")" OTHER_HELP += "version : Print DBCSR version" toolversions: @@ -158,6 +159,11 @@ else $(FC) --version endif endif +ifneq ($(CXX),) + @echo "========== CXX ==========" + $(CXX) --version + @echo "" +endif ifneq ($(CC),) @echo "=========== CC ===========" ifeq (Cray,$(shell $(CC) -V 2>&1 | head -n1 | cut -d' ' -f1)) @@ -168,6 +174,11 @@ else $(CC) --version endif endif +ifneq ($(LD),) + @echo "========== LD ==========" + $(LD) --version + @echo "" +endif ifneq ($(NVCC),) @echo "========== NVCC ==========" $(NVCC) --version @@ -186,6 +197,40 @@ endif OTHER_HELP += "toolversions : Print versions of build tools" +toolflags: +ifneq ($(FCFLAGS),) + @echo "========== FCFLAGS ==========" + @echo $(FCFLAGS) + @echo "" +endif +ifneq ($(CXXFLAGS),) + @echo "========== CXXFLAGS ==========" + @echo $(CXXFLAGS) + @echo "" +endif +ifneq ($(CFLAGS),) + @echo "========== CFLAGS ==========" + @echo $(CFLAGS) + @echo "" +endif +ifneq ($(LDFLAGS),) + @echo "========== LDFLAGS ==========" + @echo $(LDFLAGS) + @echo "" +endif +ifneq ($(NVFLAGS),) + @echo "========== NVFLAGS ==========" + @echo $(NVFLAGS) + @echo "" +endif +ifneq ($(GPUVER),) + @echo "========== GPUVER ==========" + @echo $(GPUVER) + @echo "" +endif + +OTHER_HELP += "toolflags : Print flags used with build tools" + else # stage 2: Include $(OBJDIR)/all.dep, expand target all, and get list of dependencies. all: $(foreach e, $(BIN_NAMES), $(e)) @@ -222,6 +267,14 @@ help: @echo "================= Other Targets =================" @printf "%s\n" $(OTHER_HELP) | awk -F ':' '{printf "%-28s%s\n", $$1, $$2}' @echo "help Print this help text" + @echo "================= Variables =====================" + @echo "For convenience, some variables can be set during compilation," + @echo "e.g. make VARIABLE=value (multiple variables are possible):" + @echo "MPI=0 : disable MPI compilation" + @echo "GNU=0 : disable GNU compiler compilation and enable Intel compiler compilation" + @echo "CHECKS=1 : enable GNU compiler checks and DBCSR asserts" + @echo "CINT=1 : generate the C interface" + @echo "GPU=1 : enable GPU support" ifeq ($(INCLUDE_DEPS),) install: $(LIBRARY) @@ -239,12 +292,12 @@ install: $(LIBRARY) else install: @printf " ... modules ..." - @if [[ -n "$(wildcard $(addprefix $(OBJDIR)/, $(PUBLICFILES:.F=.mod)))" ]] ; then \ + @if [ -n "$(wildcard $(addprefix $(OBJDIR)/, $(PUBLICFILES:.F=.mod)))" ] ; then \ cp $(addprefix $(OBJDIR)/, $(PUBLICFILES:.F=.mod)) $(PREFIX)/include ; \ echo " done." ; \ else echo " no modules were installed!" ; fi @printf " ... headers ..." - @if [[ -n "$(PUBLICHEADERS)" ]] ; then \ + @if [ -n "$(PUBLICHEADERS)" ] ; then \ cp $(PUBLICHEADERS) $(PREFIX)/include ; \ echo " done." ; \ else echo " no headers were installed!" ; fi @@ -266,6 +319,8 @@ test: OTHER_HELP += "test : Run the unittests available in tests/" clean: + rm -f $(TESTSDIR)/libcusmm_libcusmm_unittest_multiply.cu + rm -f $(TESTSDIR)/libcusmm_timer_multiply.cu rm -rf $(OBJDIR) rm -f $(LIBCUSMM_ABS_DIR)/parameters.h $(LIBCUSMM_ABS_DIR)/cusmm_kernels.h $(LIBCUSMM_ABS_DIR)/*.so OTHER_HELP += "clean : Remove intermediate object and mod files, but not the libraries and executables" @@ -439,7 +494,7 @@ FYPPFLAGS ?= -n $(CXX) -c $(CXXFLAGS) $< libcusmm.o: libcusmm.cpp parameters.h cusmm_kernels.h - $(CXX) -c $(CXXFLAGS) -DARCH_NUMBER=$(ARCH_NUMBER) $< + $(CXX) -c $(CXXFLAGS) $(CXXOMPFLAGS) -DARCH_NUMBER=$(ARCH_NUMBER) $< %.o: %.cu $(NVCC) -c $(NVFLAGS) -I'$(SRCDIR)' $< diff --git a/Makefile.inc b/Makefile.inc index 1ff95022f91..498ee6c5965 100644 --- a/Makefile.inc +++ b/Makefile.inc @@ -34,11 +34,12 @@ # # For convenience, some variables can be set during compilation, # e.g. make `VARIABLE=value` (multiple variables are possible): -# 1) MPI=0 : disable MPI compilation -# 2) GNU=0 : disable GNU compiler compilation and enable Intel compiler compilation -# 3) CHECKS=1 : enable GNU compiler checks and DBCSR asserts -# 4) CINT=1 : generate the C interface -# 5) GPU=1 : enable GPU support +# 1) OMP=0 : disable OpenMP compilation +# 2) MPI=0 : disable MPI compilation +# 3) GNU=0 : disable GNU compiler compilation and enable Intel compiler compilation +# 4) CHECKS=1 : enable GNU compiler checks and DBCSR asserts +# 5) CINT=1 : generate the C interface +# 6) GPU=1 : enable GPU support # ####################################################################################### @@ -136,15 +137,20 @@ endif ifneq (0,$(GNU)) # DEFAULT, just edit... CXXFLAGS += -std=c++11 - FCFLAGS += -fopenmp -std=f2003 -ffree-form -fimplicit-none -ffree-line-length-512 + FCFLAGS += -std=f2003 -ffree-form -fimplicit-none -ffree-line-length-512 OPTFLAGS += -O3 -g -fno-omit-frame-pointer -funroll-loops else # Intel compiler flags CXXFLAGS += -std=c++11 - FCFLAGS += -fopenmp -free + FCFLAGS += -free OPTFLAGS += -O2 -g endif +ifneq (0,$(OMP)) # using OpenMP + CXXOMPFLAGS = -fopenmp + FCFLAGS += -fopenmp +endif + OPTFLAGS += CFLAGS += $(OPTFLAGS) CXXFLAGS += $(OPTFLAGS) diff --git a/README.md b/README.md index 7066066b201..49c0982d5bb 100644 --- a/README.md +++ b/README.md @@ -15,19 +15,19 @@ You absolutely need: * a Fortran compiler which supports at least Fortran 2003 (respectively 2008+TS when using the C-bindings) * a LAPACK implementation (reference, OpenBLAS-bundled and MKL have been tested) * a BLAS implementation (reference, OpenBLAS-bundled and MKL have been tested) -* a Python version installed (2.7 or 3.6+ have been tested) +* a Python version installed (2.7 or 3.6+ have been tested) with Numpy Optionally: * [libxsmm](https://github.com/hfp/libxsmm) (1.8.2+ with make-only, 1.10+ with cmake) for Small Matrix Multiplication acceleration * [CMake](https://cmake.org/) (3.10+) -To build with CUDA support you further need: +To build [libcusmm](src/acc/libsmm_acc/libcusmm) (DBCSR's CUDA backend), you further need: * CUDA Toolkit * a C++ compiler which supports at least C++11 standard -We test against GNU and Intel compilers on Linux systems. +We test against GNU and Intel compilers on Linux systems, GNU compiler on MacOS systems. ## Getting started diff --git a/VERSION b/VERSION index 70884fe13fd..71f8756cd5e 100644 --- a/VERSION +++ b/VERSION @@ -1,6 +1,6 @@ MAJOR = 1 -MINOR = 0 -PATCH = 0 +MINOR = 1 +PATCH = 0.rc.0 # A specific DATE (Month/Year) fixes an official release, otherwise # it is considered Development version. -DATE = 2019-01-03 +DATE = 04/2019 diff --git a/docs/images/README.md b/docs/images/README.md new file mode 100644 index 00000000000..e464001970f --- /dev/null +++ b/docs/images/README.md @@ -0,0 +1,3 @@ +#### libcusmm_predictive_modelling_features + +The XML file `libcusmm_predictive_modelling_features.xml` can be opened in www.draw.io to be edited diff --git a/docs/images/libcusmm_parameters_and_memory.png b/docs/images/libcusmm_parameters_and_memory.png new file mode 100644 index 00000000000..d5db1603679 Binary files /dev/null and b/docs/images/libcusmm_parameters_and_memory.png differ diff --git a/docs/images/libcusmm_predictive_modeling_features.png b/docs/images/libcusmm_predictive_modeling_features.png new file mode 100644 index 00000000000..69df06ea0eb Binary files /dev/null and b/docs/images/libcusmm_predictive_modeling_features.png differ diff --git a/docs/images/libcusmm_predictive_modeling_features.xml b/docs/images/libcusmm_predictive_modeling_features.xml new file mode 100644 index 00000000000..1a3b13b6908 --- /dev/null +++ b/docs/images/libcusmm_predictive_modeling_features.xml @@ -0,0 +1 @@ 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x+BblsJ0WMhlMdWLzitWkhxAnYZXp8ZA59n5HiiyPM7Csb7uee5SeyRe7vHRlpnIToa7LZBmmgI/4MtwfF0+ZAS3XUwfaXpv+m8RxWnvvU+Ju7r/Ens+O+As= \ No newline at end of file diff --git a/src/acc/libsmm_acc/libcusmm/README.md b/src/acc/libsmm_acc/libcusmm/README.md new file mode 100644 index 00000000000..6a5d47a9c5b --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/README.md @@ -0,0 +1,90 @@ +# libcusmm: GPU Accelerated Small Matrix Multiplications + +`libcusmm` is a **lib**rary using **cu**da for **s**mall **m**atrix-**m**atrix multiplication on the GPU. Stacks of matrix-matrix multiplication indices are passed from DBCSR to `libcusmm` which performs the multiplications on the GPU. + +![libcusmm parameters](../../../../docs/images/libcusmm_parameters_and_memory.png) + +For a description of the library (some details are outdated, but this nevertheless provides a very good introduction), see Chapter 8.4 of: + +> WALKER, R. C., & GOETZ, A. W. (2016). Electronic structure calculations on graphics processing units: from quantum chemistry to condensed matter physics. +> +> Available at https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118670712. + +### Compilation + +`libcusmm` is compiled from within DBCSR, there is no separate compilation. + +## Directory Organization + +- [`kernels/`](kernels/): CUDA kernels for matrix-matrix multiplication and python interface to autotuning and predictive code. +- [`notebooks/`](notebooks/): jupyter notebooks for exploring data generated from autotuning and prediction. +- `generate_*.py`: utility scripts for `libcusmm` compilation +- `libcusmm_*`: libcusmm C++ and CUDA code +- `parameters_*.json`: sets of matrix-matrix multiplication parameters for different (m, n, k)-triplets optimized for a given GPU card. You can explore these parameters interactively using the [provided jupyter notebook](notebooks/inspect_autotuned_parameters.ipynb) +- `predict_*.py`: scripts for prediction of optimal parameter sets, see [predictive modelling of kernel parameters](#predictive-modelling-of-kernel-parameters) +- `tune_*.py`: scripts for autotuning of optimal parameter sets, see [autotuning of kernel parameters](#autotuning-procedure) + +## Matrix-matrix Multiplication Kernels and Parameters + +For a given matrix-matrix multiplication **triplet** characterized by dimensions + +- **m** +- **n** +- **k**, + +`libcusmm` can run 5 different matrix-matrix multiplication **kernels**: + +- [tiny](kernels/cusmm_dnt_tiny.h) +- [small](kernels/cusmm_dnt_small.h) +- [medium](kernels/cusmm_dnt_medium.h) +- [largeDB1](kernels/cusmm_dnt_largeDB1.h) ("large double-buffering 1") +- [largeDB2](kernels/cusmm_dnt_largeDB2.h) ("large double-buffering 2") + +which take between 3 - 7 **parameters** (see figure at the top): + +- **threads**: number of threads per block in the execution configuration of the CUDA kernels +- **grouping**: how many stack entries are grouped together into a CUDA thread block (if `grouping` is bigger, less blocks are launched) +- **minblocks**: specifies the desired minimum number of resident blocks per multiprocessor +- **tile_m**: (on the figure: **M**), tile_m * tile_n = dimensions of the result block `T` +- **tile_n** : (on the figure: **N**) +- **w**: input slab width (width of slab `P_A` and `P_B`) +- **v**: output slab width (width of slab `P_C`) + +The performance of the matrix-matrix multiplication kernels is highly dependant on the choice of algorithm and parameters. For this reason, `libcusmm` provides lists of optimal parameters for different GPU cards and different (m, n, k)-triplets. These sets of optimal parameters can be found either through *autotuning* or *predictive modelling*. + +## Contributing to libcusmm + +#### Autotuning procedure + +Follow the [autotuning procedure](https://www.cp2k.org/howto:libcusmm) + +#### Predictive modelling of kernel parameters + +Follow the [predictive modelling procedure](predict.md) + +#### Adding a new kernel + +1. Choose a kernel `name` + +2. Add the kernel's CUDA code in file `kernels/cusmm_dnt_name.h` + +3. Add python kernel class inheriting from base class `kernels/cusmm_dnt_name.py` + +4. Add the new kernel to the `kernel_algorithm` data structure in [`kernels/cusmm_predict.py`](kernels/cusmm_predict.py) + +#### Adding support for a new GPU card + +1. Add the GPU's compute architecture properties to [`kernels/gpu_properties.json`](kernels/gpu_properties.json). For more information on where to find these properties, please refer to the "info" field of [`kernels/gpu_properties.json`](kernels/gpu_properties.json). + +2. Add the GPU to the `arch_number` data structure in [`kernels/cusmm_predict.py`](kernels/cusmm_predict.py) + +4. Add the necessary code for setting `ARCH_NUMBER` correctly in the [`Makefile`](../../../../Makefile) and in the [`CMakeListst`](CMakeLists.txt) + +5. Add a minimal JSON file `parameters_GPU.json`, containing: + +```json +{ +} +``` + +then add matrix-matrix multiplication parameters for this GPU using *autotuning* and *predictive modelling* diff --git a/src/acc/libsmm_acc/libcusmm/collect.py b/src/acc/libsmm_acc/libcusmm/collect.py deleted file mode 100755 index 6a790e5f47d..00000000000 --- a/src/acc/libsmm_acc/libcusmm/collect.py +++ /dev/null @@ -1,88 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -#################################################################################################### -# Copyright (C) by the DBCSR developers group - All rights reserved # -# This file is part of the DBCSR library. # -# # -# For information on the license, see the LICENSE file. # -# For further information please visit https://dbcsr.cp2k.org # -# SPDX-License-Identifier: GPL-2.0+ # -#################################################################################################### - -import sys -import os -from glob import glob -import re -import json -from kernels.cusmm_dnt_helper import descr_to_kernel - -re_mnk = re.compile(r"tune_(\d+)x(\d+)x(\d+)_") -re_winner = re.compile(r"\nWINNER: \d+ (.+)\n") -re_gflops = re.compile(r"# ([0-9.]+) GFlop/s") -re_errors = re.compile(r"Number of errors: (\d+)\n") - - -#=============================================================================== -def main(): - winners = dict() - - for d in glob("tune_*"): - if not os.path.isdir(d): - continue - - for exe_fn in glob(d+"/tune_*main.cu"): - mnk = tuple([int(i) for i in re_mnk.search(exe_fn).groups()]) - log_fn = exe_fn.replace("_main.cu", ".log") - if not os.path.exists(log_fn): - winners[mnk] = "log missing: "+log_fn - continue - - process_log(log_fn, mnk, winners) - - # Get kernel objects from list of strings - kernels = [descr_to_kernel(kernel_descr) for kernel_descr in winners.values()] - with open("parameters.json", 'w') as f: - s = json.dumps([kernel.as_dict for kernel in kernels]) - s = s.replace('}, ', '},\n') - s = s.replace('[', '[\n') - s = s.replace(']', '\n]') - f.write(s) - - print("Wrote parameters.json") - -#=============================================================================== -def process_log(log_fn, mnk, winners): - print("Reading: "+log_fn) - - with open(log_fn) as f: - content = f.read() - - m = re_errors.search(content) - if not m: - winners[mnk] = "log incomplete: "+log_fn - return - - n_errors = int(m.group(1)) - if n_errors != 0: - winners[mnk] = "errors: "+log_fn - return - - old_gflops = 0.0 - if mnk in winners.keys(): - m = re_gflops.search(winners[mnk]) - if not m: - return - old_gflops = float(m.group(1)) - - new_winner = re_winner.search(content).group(1).strip().replace("GFlops","GFlop/s") - new_gflops = float(re_gflops.search(new_winner).group(1)) - - if new_gflops > old_gflops : - winners[mnk] = new_winner - - -#=============================================================================== -if len(sys.argv)==2 and sys.argv[-1]=="--selftest": - pass #TODO implement selftest -else: - main() diff --git a/src/acc/libsmm_acc/libcusmm/generate_kernels.py b/src/acc/libsmm_acc/libcusmm/generate_kernels.py index 1a8a632c1f3..b374ab107bf 100755 --- a/src/acc/libsmm_acc/libcusmm/generate_kernels.py +++ b/src/acc/libsmm_acc/libcusmm/generate_kernels.py @@ -16,34 +16,31 @@ import re import argparse -#=============================================================================== +# =============================================================================== # Helper variables separator = "//===========================================================================\n" line_in_string = "{:<70}\\n\\" -variable_declaration = "std::string {var_name} = \" \\n\\" -end_string = "\";" -commented_line = r'\s*(//|/\*.*/*/)' -open_comment = r'\s*/\*' -close_comment = r'.*\*/' -cusmm_header = '/*------------------------------------------------------------------------------------------------*\n' \ - + ' * Copyright (C) by the DBCSR developers group - All rights reserved *\n' \ - + ' * This file is part of the DBCSR library. *\n' \ - + ' * *\n' \ - + ' * For information on the license, see the LICENSE file. *\n' \ - + ' * For further information please visit https://dbcsr.cp2k.org *\n' \ - + ' * SPDX-License-Identifier: GPL-2.0+ *\n' \ - + ' *------------------------------------------------------------------------------------------------*/\n' \ - + '\n' \ - + '/*****************************************************************************\n' \ - + ' * FILE GENERATED BY SCRIPT \'generate_kernels.py\' DO NOT EDIT *\n' \ - + ' *****************************************************************************/\n' \ - + '\n' \ - + '#ifndef CUSMM_H\n' \ - + '#define CUSMM_H\n' \ - + '#include \n' +variable_declaration = 'std::string {var_name} = " \\n\\' +end_string = '";' +commented_line = r"\s*(//|/\*.*/*/)" +open_comment = r"\s*/\*" +close_comment = r".*\*/" +cusmm_header = ( + "/*------------------------------------------------------------------------------------------------*\n" + + " * Copyright (C) by the DBCSR developers group - All rights reserved *\n" + + " * This file is part of the DBCSR library. *\n" + + " * *\n" + + " * For information on the license, see the LICENSE file. *\n" + + " * For further information please visit https://dbcsr.cp2k.org *\n" + + " * SPDX-License-Identifier: GPL-2.0+ *\n" + + " *------------------------------------------------------------------------------------------------*/\n" + "\n" + + "/*****************************************************************************\n" + + " * FILE GENERATED BY SCRIPT 'generate_kernels.py' DO NOT EDIT *\n" + + " *****************************************************************************/\n" + "\n" + "#ifndef CUSMM_H\n" + + "#define CUSMM_H\n" + "#include \n") -#=============================================================================== +# =============================================================================== def main(kernels_folder): """ Find files corresponding to CUDA kernels and write them as strings into a @@ -55,8 +52,8 @@ def main(kernels_folder): for f in kernels_folder_files: if f[:6] == "cusmm_" and f[-2:] == ".h": kernel_files.append(os.path.join(kernels_folder, f)) - print("Found", len(kernel_files), "kernel files:") - print(*("<- {}".format(kf) for kf in kernel_files), sep='\n') + print("Found {} kernel files:".format(len(kernel_files))) + print(*("<- {}".format(kf) for kf in kernel_files), sep="\n") # Read kernels_h = dict() # key: path to kernel file (string), value: file content (list of string) @@ -69,18 +66,18 @@ def main(kernels_folder): file_h = cusmm_header for kernel_file, kernel in kernels_h.items(): kernel_name, _ = path.splitext(path.basename(kernel_file)) # use the filename as name for the kernel - file_h += '\n' + separator + cpp_function_to_string(kernel, kernel_name) + '\n' - file_h += '#endif\n' - file_h += '//EOF' + file_h += "\n" + separator + cpp_function_to_string(kernel, kernel_name) + "\n" + file_h += "#endif\n" + file_h += "//EOF" # Write file_h_path = "cusmm_kernels.h" - with open(file_h_path, 'w') as f: + with open(file_h_path, "w") as f: f.write(file_h) - print("Wrote kernel string to file\n->", file_h_path) + print("Wrote kernel string to file\n-> {}".format(file_h_path)) -#=============================================================================== +# =============================================================================== def cpp_function_to_string(cpp_file, kernel_name): """ Transform a C++ function into a char array @@ -89,15 +86,14 @@ def cpp_function_to_string(cpp_file, kernel_name): :param kernel_name: name of the kernel (string, must be usable as a C++ variable name) :return: string containing the kernel written as a C++ char array """ - assert re.match(r'^[a-zA-Z]\w*', kernel_name), "kernel_name must be a valid C/C++ variable name" + assert re.match(r"^[a-zA-Z]\w*", kernel_name), "kernel_name must be a valid C/C++ variable name" out = variable_declaration.format(var_name=kernel_name) + "\n" in_comment = False for l in cpp_file: if not in_comment: # ignore comments and empty lines - if re.match(commented_line, l) is not None or len(l) == 0 \ - or "#include \"cusmm_common.h\"" in l: + if re.match(commented_line, l) is not None or len(l) == 0 or '#include "cusmm_common.h"' in l: pass elif re.match(open_comment, l) is not None: in_comment = True @@ -112,10 +108,16 @@ def cpp_function_to_string(cpp_file, kernel_name): return out + end_string -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser(description="Pack CUDA kernels for JIT'ing") - parser.add_argument('kernels_folder', metavar='KERNELS_FOLDER', type=str, nargs='?', default="./kernels", - help="directory with the kernel header files. Default: %(default)s") + parser.add_argument( + "kernels_folder", + metavar="KERNELS_FOLDER", + type=str, + nargs="?", + default="./kernels", + help="directory with the kernel header files. Default: %(default)s", + ) args = parser.parse_args() main(args.kernels_folder) diff --git a/src/acc/libsmm_acc/libcusmm/generate_parameters.py b/src/acc/libsmm_acc/libcusmm/generate_parameters.py index fe541471d7e..158f534de54 100755 --- a/src/acc/libsmm_acc/libcusmm/generate_parameters.py +++ b/src/acc/libsmm_acc/libcusmm/generate_parameters.py @@ -15,33 +15,30 @@ import argparse from os import path -from kernels.cusmm_dnt_helper import params_dict_to_kernel +from kernels.cusmm_predict import params_dict_to_kernel +# =============================================================================== def main(gpu_version, base_dir): # Read existing parameters - print("GPU version:\n", gpu_version) + print("GPU version: {}".format(gpu_version)) param_fn = path.join(base_dir, "parameters_{}.json".format(gpu_version)) with open(param_fn) as f: all_kernels = [params_dict_to_kernel(**params) for params in json.load(f)] - print("About to process", len(all_kernels), "kernels from file", param_fn) - parameters = dict() - for kernel in all_kernels: - (m, n, k), pars = kernel.as_key_value - parameters[(m, n, k)] = pars + print("About to process {:,} kernels from file {}".format(len(all_kernels), param_fn)) # Construct output - out, all_pars = write_parameters_file(parameters) + out, all_pars = write_parameters_file(all_kernels) # Write to c++ header-file file_h = "parameters.h" - print('Found', len(parameters), 'kernels in', param_fn) - print('Printing them to file', file_h) - with open(file_h, 'w') as f: + print("Found {:,} kernels in file {}".format(len(all_kernels), param_fn)) + print("Printing them to file {}".format(file_h)) + with open(file_h, "w") as f: f.write(out) -#=============================================================================== +# =============================================================================== def write_parameters_file(all_pars): # Header @@ -65,7 +62,8 @@ def write_parameters_file(all_pars): #include "parameters_utils.h" /* - * Lookup table: given a triplet (m, n, k) describing a matrix-matrix multiplication, look up its optimal kernel parameters + * Lookup table: given a triplet (m, n, k) describing a matrix-matrix multiplication, + * look up its optimal kernel parameters * * Keys: * (m, n, k) @@ -80,7 +78,7 @@ def write_parameters_file(all_pars): * 6: grouping * 7: minblocks * - * Note: for the matrix matrix multiplication algorithms which take less than 8 parameters (i.e. "tiny", "small" and "medium"), + * Note: for the matrix matrix multiplication algorithms which take less than 8 parameters (i.e. tiny, small, medium), * the superfluous parameters are set to 0 */ @@ -88,11 +86,12 @@ def write_parameters_file(all_pars): """ # Initializer list body print("Get parameters and write to file") - init_list_line = \ - " {{ {{{{{m:3}, {n:3}, {k:3}}}}}, {{{{ {algo}, {tile_m}, {tile_n}, {w}, {v}, {threads}, {grouping}, {minblocks} }}}} }},\n" - for (m, n, k), pars in sorted(all_pars.items()): - out += init_list_line.format(algo=pars[0], tile_m=pars[1], tile_n=pars[2], w=pars[3], v=pars[4], - threads=pars[5], grouping=pars[6], minblocks=pars[7], m=m, n=n, k=k) + init_list_line = ( + " {{ {{{{{m:3}, {n:3}, {k:3}}}}}," + + " {{{{ {algorithm:1}, {tile_m:2}, {tile_n:2}, {w:2}, {v:2}, {threads:3}, {grouping:2}, {minblocks:2} }}}} }}," + + " // perf: {perf} {source}\n") + for pars in all_pars: + out += init_list_line.format(**pars.as_dict_for_parameters_h) # Footer out += """\ @@ -105,12 +104,24 @@ def write_parameters_file(all_pars): return out, all_pars -if __name__ == '__main__': +# =============================================================================== +if __name__ == "__main__": parser = argparse.ArgumentParser( - description="Generator of LibCuSMM. The Library for Cuda Small Matrix Multiplications.") - parser.add_argument("-g", "--gpu_version", metavar="GPU_VERSION", default="P100", - help="GPU card version, used to select the appropriate libcusmm parameters file. Default: %(default)s") - parser.add_argument("-d", "--base_dir", metavar="BASE_DIR", default=".", - help="Set the base directory to look for the parameter files. Default: %(default)s") + description="Generator of LibCuSMM. The Library for Cuda Small Matrix Multiplications.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + "-g", + "--gpu_version", + metavar="GPU_VERSION", + default="P100", + help="GPU card version, used to select the appropriate libcusmm parameters file. Default: %(default)s", + ) + parser.add_argument( + "-d", + "--base_dir", + metavar="BASE_DIR", + default=".", + help="Set the base directory to look for the parameter files. Default: %(default)s", + ) args = parser.parse_args() main(args.gpu_version, args.base_dir) diff --git a/src/acc/libsmm_acc/libcusmm/kernels/README.md b/src/acc/libsmm_acc/libcusmm/kernels/README.md new file mode 100644 index 00000000000..e2f88c8c40f --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/kernels/README.md @@ -0,0 +1,19 @@ +# libcusmm/kernels + +## Directory Organization + +* [`autotuning_properties.json`](autotuning_properties.json) Properties of the autotuning procedure, read from [DBCSR source code](../libcusmm_benchmark.cu) + +* [`cusmm_common.h`](cusmm_common.h) Functionalities common to kernel CUDA codes + +* [`cusmm_dnt_base.py`](cusmm_dnt_base.py) Kernel base class + + * `cusmm_dnt_ALGORITHM.py` Kernel class + + * `cusmm_dnt_ALGORITHM.h` Kernel CUDA code + +* [`cusmm_predict.py`](cusmm_predict.py) Class and helper functions for parameter prediction procedure + +* [`cusmm_transpose.h`](cusmm_transpose.h) Transposition CUDA code + +* [`gpu_properties.json`](gpu_properties.json) GPU card properties diff --git a/src/acc/libsmm_acc/libcusmm/kernels/__init__.py b/src/acc/libsmm_acc/libcusmm/kernels/__init__.py index 46c2af8240f..3939d4628b1 100644 --- a/src/acc/libsmm_acc/libcusmm/kernels/__init__.py +++ b/src/acc/libsmm_acc/libcusmm/kernels/__init__.py @@ -7,3 +7,4 @@ # For further information please visit https://dbcsr.cp2k.org # # SPDX-License-Identifier: GPL-2.0+ # #################################################################################################### +"""Manage libcusmm kernels: their CUDA code, their parameters, and predictive features""" diff --git a/src/acc/libsmm_acc/libcusmm/kernels/autotuning_properties.json b/src/acc/libsmm_acc/libcusmm/kernels/autotuning_properties.json new file mode 100644 index 00000000000..05b48a31049 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/kernels/autotuning_properties.json @@ -0,0 +1,13 @@ +{ + "info": { + "header": "Autotuning characteristics", + "stack_size": "Number of block multiplications computed in one DBCSR stack", + "npar": "Number of parameters in one DBCSR stack entry", + "sizeof_int": "size of an int (bytes)", + "sizeof_double": "size of a double (bytes)" + }, + "stack_size": 16005, + "npars": 3, + "sizeof_int": 4, + "sizeof_double": 8 +} diff --git a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt.py b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt.py deleted file mode 100644 index 3ca145183ac..00000000000 --- a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- -#################################################################################################### -# Copyright (C) by the DBCSR developers group - All rights reserved # -# This file is part of the DBCSR library. # -# # -# For information on the license, see the LICENSE file. # -# For further information please visit https://dbcsr.cp2k.org # -# SPDX-License-Identifier: GPL-2.0+ # -#################################################################################################### - -#=============================================================================== -# Helper variables/functions used in all kernels - -# Number of parameters in one stack entry -stack_size = 16005 -npar = 3 - -# Sizes in bytes -sizeof_int = 4 -sizeof_double = 8 - - -# utility functions -def round_up_to_multiple(x, step): - if x % step == 0: - return x - else: - return x + step - x % step - - -#=============================================================================== -class Kernel: - - def __repr__(self): - return "<%s>" % self.name - - def can_handle(self, m, n, k): - return self.m == m and self.n == n and self.k == k - - @property - def include(self): - return "cusmm_dnt_" + self.algorithm + ".h" - - @property - def name(self): - return "cusmm_dnt_" + self.algorithm + "_" + \ - "_".join([str(self.__dict__[k]) for k in sorted(self.launch_parameters)]) - - @property - def as_dict(self): - return dict(algorithm=self.algorithm, **self.__dict__) - - @property - def as_key_value(self): - key = (self.m, self.n, self.k) - self_props = self.as_dict - self_props['algorithm'] = self.algorithm_num - value_list = ['algorithm', 'tile_m', 'tile_n', 'w', 'v', 'threads', 'grouping', 'minblocks'] - value = [self_props[prop] if prop in self_props.keys() else 0 for prop in value_list] - return key, value - - @property - def launcher_code(self): - output = "int launch_" + self.name + "(int *param_stack, int stack_size, " - output += "cudaStream_t stream, int m_max, int n_max, int k_max, " - output += "double *a_data, double *b_data, double *c_data){\n" - output += "int shared_size = 0;\n" - output += "//%s\n" % str(self.__dict__) - output += "typedef void (*kernel)(const int*, int, const double*, const double*, double*);\n" - output += "static kernel kern_func = " + self.func_signature - output += "static bool configured = false;\n" - output += "if(configured == false){\n" - output += " cudaError_t err = cudaFuncSetSharedMemConfig(kern_func, cudaSharedMemBankSizeEightByte);\n" - output += " if(err != cudaSuccess) return(-1);\n" - output += " configured = true;\n" - output += "}\n" - output += "kern_func<<< ((stack_size + %(grouping)d - 1) / %(grouping)d), %(threads)d, shared_size, stream >>>\n" \ - % self.__dict__ - output += "(param_stack, stack_size, \n" - output += "a_data, b_data, c_data);\n" - output += "return(0);\n" - output += "}\n" - return output - - @staticmethod - def promising_parameters(m, n, k, gpu): - raise NotImplementedError() diff --git a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_base.py b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_base.py new file mode 100644 index 00000000000..88735b56a53 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_base.py @@ -0,0 +1,132 @@ +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +# =============================================================================== +# Computing helpers +import numpy as np + + +def round_up_to_nearest_multiple(x, step): + result = np.where(x % step == 0, x, x + step - x % step).astype(float) + if result.size == 1: + result = result.item() # extract single element of numpy array + return result + + +def round_down_to_nearest_multiple(x, step): + result = np.where(x % step == 0, x, x - x % step).astype(float) + if result.size == 1: + result = result.item() # extract single element of numpy array + return result + + +# =============================================================================== +class Kernel: + """ + Base class for libcusmm's kernels + """ + + def __repr__(self): + return "<%s>" % self.name + + def can_handle(self, m, n, k): + return self.m == m and self.n == n and self.k == k + + @property + def include(self): + return "cusmm_dnt_" + self.algorithm + ".h" + + @property + def name(self): + return ("cusmm_dnt_" + self.algorithm + "_" + "_".join([str(self.__dict__[k]) for k in self.launch_parameters])) + + @property + def autotuned(self): + return True if self.source == "autotuned" else False + + @property + def as_dict(self): + return dict(algorithm=self.algorithm, **self.__dict__) + + @property + def as_dict_for_parameters_json(self): + """ + Return the kernel as a dictionary in such a way that it is convenient to write in a JSON file and parameters + always appear in the same order + """ + # Add common fields + fields = ["m", "n", "k", "tile_m", "tile_n", "w", "v", "threads", "grouping", "minblocks", "algorithm"] + d = dict() + for f in fields: + if f in self.as_dict.keys(): + d[f] = self.as_dict[f] + + # Only add the performance if it was autotuned. If it was predicted, we only have a value scaled in (0,1) + if self.as_dict["source"] == "autotuned": + d["perf"] = self.as_dict["perf"] + + # Add the source of this parameter set (autotuned or predicted) + d["source"] = self.as_dict["source"] + + return d + + @property + def as_dict_for_parameters_h(self): + """ + Return the kernel as a dictionary in such a way that it is convenient to write into the parameters.h file and + parameters always appear in the same order + """ + # Add common fields + fields = ["m", "n", "k", "tile_m", "tile_n", "w", "v", "threads", "grouping", "minblocks", "perf", "source"] + d = dict() + for f in fields: + d[f] = self.as_dict[f] if f in self.as_dict.keys() else 0 + + # Add algorithm and source + d["algorithm"] = self.algorithm_num + d["source"] = "(predicted)" if not self.autotuned else "" + return d + + @property + def launcher_code(self): + output = "int launch_" + self.name + "(int *param_stack, int stack_size, " + output += "cudaStream_t stream, int m_max, int n_max, int k_max, " + output += "double *a_data, double *b_data, double *c_data){\n" + output += " int shared_size = 0;\n" + output += " //%s\n" % str(self.__dict__) + output += " typedef void (*kernel)(const int*, int, const double*, const double*, double*);\n" + output += " static kernel kern_func = " + self.func_signature + output += " static bool configured = false;\n" + output += " if(configured == false){\n" + output += " cudaError_t err = cudaFuncSetSharedMemConfig(kern_func, cudaSharedMemBankSizeEightByte);\n" + output += " if(err != cudaSuccess) return(-1);\n" + output += " configured = true;\n" + output += " }\n" + output += ( + " kern_func<<< ((stack_size + %(grouping)d - 1) / %(grouping)d), %(threads)d, shared_size, stream >>>\n" % + self.__dict__) + output += " (param_stack, stack_size, \n" + output += " a_data, b_data, c_data);\n" + output += " return(0);\n" + output += "}\n" + return output + + @property + def func_signature(self): + raise NotImplementedError("func_signature must be implemented in subclass") + + @staticmethod + def promising_parameters(m, n, k, gpu, autotuning): + raise NotImplementedError("promising_parameters must be implemented in subclass") + + @staticmethod + def baseline(m, n, k, gpu, autotuning): + """Compute a baseline parameter set, whose performance can be compared against""" + raise NotImplementedError("baseline must be implemented in subclass") diff --git a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_helper.py b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_helper.py deleted file mode 100644 index 3a5ab422541..00000000000 --- a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_helper.py +++ /dev/null @@ -1,38 +0,0 @@ - - -#=============================================================================== -from kernels.cusmm_dnt_largeDB1 import Kernel_dnt_largeDB1 -from kernels.cusmm_dnt_largeDB2 import Kernel_dnt_largeDB2 -from kernels.cusmm_dnt_medium import Kernel_dnt_medium -from kernels.cusmm_dnt_small import Kernel_dnt_small -from kernels.cusmm_dnt_tiny import Kernel_dnt_tiny - -kernel_algorithm = { - 'tiny': Kernel_dnt_tiny, - 'small': Kernel_dnt_small, - 'medium': Kernel_dnt_medium, - 'largeDB1': Kernel_dnt_largeDB1, - 'largeDB2': Kernel_dnt_largeDB2 -} - - -#=============================================================================== -def params_dict_to_kernel(**params): - return kernel_algorithm[params.pop('algorithm')](**params) - - -def descr_to_kernel(kernel_descr): - import re - from ast import literal_eval - re_kernel_descr = re.compile(r"Kernel_dnt_(\w+)(\(.*\)) , # (\d+\.\d+) GFlop/s") - match = re_kernel_descr.search(kernel_descr).groups() - algo = match[0] - m = match[1].replace('=', '\':') - m = m.replace(', ', ', \'') - m = m.replace('(', '{\'') - m = m.replace(')', '}') - params = dict(literal_eval(m)) - params['perf'] = float(match[2]) - return kernel_algorithm[algo](**params) - - diff --git a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_largeDB1.py b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_largeDB1.py index c761f069424..5f1e1869cea 100644 --- a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_largeDB1.py +++ b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_largeDB1.py @@ -8,16 +8,17 @@ # SPDX-License-Identifier: GPL-2.0+ # #################################################################################################### -from kernels import cusmm_dnt as cu +from kernels.cusmm_dnt_base import Kernel, round_up_to_nearest_multiple -class Kernel_dnt_largeDB1(cu.Kernel): +class Kernel_dnt_largeDB1(Kernel): + """Kernel 'large double-buffering' 1""" algorithm = "largeDB1" algorithm_num = 1 - launch_parameters = ['m', 'n', 'k', 'tile_m', 'tile_n', 'w', 'v', 'threads', 'grouping', 'minblocks'] + launch_parameters = ["m", "n", "k", "tile_m", "tile_n", "w", "v", "threads", "grouping", "minblocks"] - def __init__(self, m, n, k, threads, tile_m, tile_n, w, v, grouping, minblocks, perf): + def __init__(self, m, n, k, threads, tile_m, tile_n, w, v, grouping, minblocks, perf, source): self.m = m self.n = n self.k = k @@ -29,31 +30,50 @@ def __init__(self, m, n, k, threads, tile_m, tile_n, w, v, grouping, minblocks, self.grouping = grouping self.minblocks = minblocks self.perf = perf + self.source = source assert self.threads * self.minblocks <= 2048 - min_threads = ((self.m+self.tile_m-1)//self.tile_m) * ((self.n+self.tile_n-1)//self.tile_n) + min_threads = ((self.m + self.tile_m - 1) // self.tile_m) * ((self.n + self.tile_n - 1) // self.tile_n) assert min_threads <= self.threads assert self.tile_m <= self.v assert self.tile_n <= self.w @property def func_signature(self): - return "cusmm_dnt_largeDB1<%(m)d,%(n)d,%(k)d,%(tile_m)d,%(tile_n)d,%(w)d,%(v)d,%(threads)d,%(grouping)d,%(minblocks)d>;\n" \ - % self.__dict__ + return ("cusmm_dnt_largeDB1" + + "<%(m)d,%(n)d,%(k)d,%(tile_m)d,%(tile_n)d,%(w)d,%(v)d,%(threads)d,%(grouping)d,%(minblocks)d>;\n" % + self.__dict__) @staticmethod - def promising_parameters(m, n, k, gpu): + def promising_parameters(m, + n, + k, + gpu, + autotuning, + threads=None, + grouping=None, + minblocks=None, + tile_m=None, + tile_n=None, + w=None, + v=None): + """ + Given a certain (m,n,k)-triplet, GPU properties and autotuning properties, return a list of all possible + kernel parameters + """ params = [] grouping = 16 - for minblocks in (1, 2, 4, 8, 12): # for exhaustive search, it should be: range(1, gpu.maxBLOCKSperSM + 1): - # but heuristically reduce the search space - for threads in range(gpu["warp_size"], gpu["maxTHREADSperBLOCK"] + 1, gpu["warp_size"]): + for minblocks_ in (1, 2, 4, 8, 12) if minblocks is None else [minblocks]: + # for exhaustive search, it should be: range(1, gpu["Thread_Blocks_/_Multiprocessor"] + 1): + # but heuristically reduce the search space + for threads_ in (range(gpu["Threads_/_Warp"], gpu["Max_Thread_Block_Size"] + 1, gpu["Threads_/_Warp"]) + if threads is None else [threads]): - if threads * minblocks > gpu["maxTHREADSperSM"]: + if threads_ * minblocks_ > gpu["Threads_/_Multiprocessor"]: continue - for tm in range(1, min(12, m + 1)): - for tn in range(1, min(12, n + 1)): + for tm in range(1, min(12, m + 1)) if tile_m is None else [tile_m]: + for tn in range(1, min(12, n + 1)) if tile_n is None else [tile_n]: if tm * tn > 49: continue # heuristic: performance decreases for very large tiles @@ -65,45 +85,94 @@ def promising_parameters(m, n, k, gpu): # Minimum number of threads required to have one thread per tile, # i.e., cover the result matrix min_threads = cmax * rmax - if threads < min_threads: + if threads_ < min_threads: continue - if min_threads < (threads - 32): + if min_threads < (threads_ - 32): continue # heuristic: too many threads unused during calculation - for w in range(4, (k + 1)//2, 2): # heuristic: even numbers yield better performance - if w < tn: + for w_ in range(4, (k + 1) // 2, 2) if w is None else [w]: + # heuristic: even numbers yield better performance + if w_ < tn: continue # invalid: input slap too small - if 2 * w > k: + if 2 * w_ > k: continue # heuristic: do at least one double-buffering step - for v in range(2, n + 1, 2): # heuristic: even numbers yield better performance + for v_ in range(2, n + 1, 2) if v is None else [v]: + # heuristic: even numbers yield better performance - if v < tm: + if v_ < tm: continue # invalid: output slab too small # Number of registers - n_regs = tm * tn + (w * m + threads - 1) // threads + (w * n + threads - 1) // threads - if n_regs * threads * minblocks > 15000: + n_regs = ( + tm * tn + (w_ * m + threads_ - 1) // threads_ + (w_ * n + threads_ - 1) // threads_) + if n_regs * threads_ * minblocks_ > 15000: continue # heuristic: too many registers used # Max work ("operations") which can be run concurrently - max_concurrent_work = max(grouping, m*w, w*n, m*v, cmax*rmax) - if threads > cu.round_up_to_multiple(max_concurrent_work, gpu["warp_size"]): + max_concurrent_work = max(grouping, m * w_, w_ * n, m * v_, cmax * rmax) + if threads_ > round_up_to_nearest_multiple(max_concurrent_work, gpu["Threads_/_Warp"]): continue # heuristics: too much concurrency harms performance # Shared memory buffer size - buf_sz = max((w - 1) * m + rmax * tm, m * w + (w - 1) * n + cmax * tn, v * m) - smem_tot = buf_sz * cu.sizeof_double + cu.npar * grouping * cu.sizeof_int - if smem_tot > gpu["SMEMperBLOCK"]: + buf_sz = max((w_ - 1) * m + rmax * tm, m * w_ + (w_ - 1) * n + cmax * tn, v_ * m) + smem_tot = (buf_sz * autotuning["sizeof_double"] + + autotuning["npars"] * grouping * autotuning["sizeof_int"]) + if smem_tot > gpu["Max_Shared_Memory_/_Block_(bytes)"]: continue # invalid: uses too much shared memory - if smem_tot * minblocks > gpu["SMEMperSM"]: + if smem_tot * minblocks_ > gpu["Shared_Memory_/_Multiprocessor_(bytes)"]: continue # invalid: uses too much shared memory - params.append({'m': m, 'n': n, 'k': k, - 'tile_m': tm, 'tile_n': tn, - 'w': w, 'v': v, - 'threads': threads, - 'grouping': grouping, - 'minblocks': minblocks, - 'perf': 0}) + params.append({ + "m": m, + "n": n, + "k": k, + "tile_m": tm, + "tile_n": tn, + "w": w_, + "v": v_, + "threads": threads_, + "grouping": grouping, + "minblocks": minblocks_, + }) return params + + @staticmethod + def baseline(m, n, k, gpu, autotuning): + """ + Given an (m, n, k)-triplet and GPu and autotuning properties, return a set of parameters corresponding to a + baseline ("educated guess") of the kernel's optimal parameters + """ + + grouping = 16 + minblk = 2 + tm = 2 + tn = 2 + cmax = (n + tn - 1) // tn + rmax = (m + tm - 1) // tm + min_threads = cmax * rmax + w = 8 + v = 8 + + while True: + base = { + "threads": round_up_to_nearest_multiple(min_threads, 32), + "grouping": grouping, + "minblocks": minblk, + "tile_m": tn, + "tile_n": tn, + "w": w, + "v": v, + } + if len(Kernel_dnt_largeDB1.promising_parameters(m, n, k, gpu, autotuning, **base)) > 0: + break + else: + if w > 1: + w /= 2 + else: + base = Kernel_dnt_largeDB1.promising_parameters(m, n, k, gpu, autotuning)[0] + break + + base.update( + dict([("m", m), ("n", n), ("k", k), ("algorithm", "largeDB1"), ("perf", 0), ("source", "predicted")])) + return base diff --git a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_largeDB2.py b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_largeDB2.py index 34bf5f23ef3..653934420b4 100644 --- a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_largeDB2.py +++ b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_largeDB2.py @@ -8,16 +8,17 @@ # SPDX-License-Identifier: GPL-2.0+ # #################################################################################################### -from kernels import cusmm_dnt as cu +from kernels.cusmm_dnt_base import Kernel, round_up_to_nearest_multiple -class Kernel_dnt_largeDB2(cu.Kernel): +class Kernel_dnt_largeDB2(Kernel): + """Kernel 'large double-buffering' 2""" algorithm = "largeDB2" algorithm_num = 2 - launch_parameters = ['m', 'n', 'k', 'tile_m', 'tile_n', 'w', 'v', 'threads', 'grouping', 'minblocks'] + launch_parameters = ["m", "n", "k", "tile_m", "tile_n", "w", "v", "threads", "grouping", "minblocks"] - def __init__(self, m, n, k, threads, tile_m, tile_n, w, v, grouping, minblocks, perf): + def __init__(self, m, n, k, threads, tile_m, tile_n, w, v, grouping, minblocks, perf, source): self.m = m self.n = n self.k = k @@ -29,82 +30,150 @@ def __init__(self, m, n, k, threads, tile_m, tile_n, w, v, grouping, minblocks, self.grouping = grouping self.minblocks = minblocks self.perf = perf + self.source = source assert self.threads * self.minblocks <= 2048 - min_threads = ((self.m+self.tile_m-1)//self.tile_m) * ((self.n+self.tile_n-1)//self.tile_n) + min_threads = ((self.m + self.tile_m - 1) // self.tile_m) * ((self.n + self.tile_n - 1) // self.tile_n) assert min_threads <= self.threads assert self.tile_m <= self.v assert self.tile_n <= self.w @property def func_signature(self): - return "cusmm_dnt_largeDB2<%(m)d,%(n)d,%(k)d,%(tile_m)d,%(tile_n)d,%(w)d,%(v)d,%(threads)d,%(grouping)d,%(minblocks)d>;\n"\ - % self.__dict__ + return ("cusmm_dnt_largeDB2" + + "<%(m)d,%(n)d,%(k)d,%(tile_m)d,%(tile_n)d,%(w)d,%(v)d,%(threads)d,%(grouping)d,%(minblocks)d>;\n" % + self.__dict__) @staticmethod - def promising_parameters(m, n, k, gpu): + def promising_parameters(m, + n, + k, + gpu, + autotuning, + threads=None, + grouping=None, + minblocks=None, + tile_m=None, + tile_n=None, + w=None, + v=None): + """ + Given a certain (m,n,k)-triplet, GPU properties and autotuning properties, return a list of all possible + kernel parameters + """ params = [] grouping = 16 - for minblocks in (1, 2, 4, 8, 12): # for exhaustive search, it should be: range(1, gpu.maxBLOCKSperSM + 1): - # but heuristically reduce the search space - for threads in range(gpu["warp_size"], gpu["maxTHREADSperBLOCK"] + 1, gpu["warp_size"]): + for minblocks_ in (1, 2, 4, 8, 12) if minblocks is None else [minblocks]: + # for exhaustive search, it should be: range(1, gpu.maxBLOCKSperSM + 1): + # but heuristically reduce the search space + for threads_ in (range(gpu["Threads_/_Warp"], gpu["Max_Thread_Block_Size"] + 1, gpu["Threads_/_Warp"]) + if threads is None else [threads]): - if threads * minblocks > gpu["maxTHREADSperSM"]: + if threads_ * minblocks_ > gpu["Threads_/_Multiprocessor"]: continue - for tm in range(1, min(12, m + 1)): - for tn in range(1, min(12, n + 1)): + for tm in range(1, min(12, m + 1)) if tile_m is None else [tile_m]: + for tn in range(1, min(12, n + 1)) if tile_n is None else [tile_n]: if tm * tn > 49: continue # heuristic: performance decreases for very large tiles - # Number of tiled columns, rows + # Number of tiled columns, rows cmax = (n + tn - 1) // tn rmax = (m + tm - 1) // tm # Minimum number of threads required to have one thread per tile # i.e., cover the result matrix min_threads = cmax * rmax - if threads < min_threads: + if threads_ < min_threads: continue - if min_threads < (threads - 32): + if min_threads < (threads_ - 32): continue # heuristic: too many threads unused during calculation - for w in range(4, (k + 1)//2, 2): # heuristic: even numbers yield better performance + for w_ in range(4, (k + 1) // 2, 2) if w is None else [w]: + # heuristic: even numbers yield better performance - if w < tn: + if w_ < tn: continue # invalid: input slap too small - if 2 * w > k: + if 2 * w_ > k: continue # heuristic: do at least one double-buffering step - for v in range(4, n + 1, 2): # heuristic: even numbers yield better performance + for v_ in range(4, n + 1, 2) if v is None else [v]: + # heuristic: even numbers yield better performance - if v < tm: - continue # invalid: output slab too small + if v_ < tm: + continue # invalid: output slab too small # Number of registers - n_regs = tm * tn + (w * m + threads - 1) // threads + (w * n + threads - 1) // threads - if n_regs * threads * minblocks > 15000: + n_regs = ( + tm * tn + (w_ * m + threads_ - 1) // threads_ + (w_ * n + threads_ - 1) // threads_) + if n_regs * threads_ * minblocks_ > 15000: continue # heuristic: too many registers used # Max work ("operations") which can be run concurrently - max_concurrent_work = max(grouping, m*w, w*n, m*v, cmax*rmax) - if threads > cu.round_up_to_multiple(max_concurrent_work, gpu["warp_size"]): + max_concurrent_work = max(grouping, m * w_, w_ * n, m * v_, cmax * rmax) + if threads_ > round_up_to_nearest_multiple(max_concurrent_work, gpu["Threads_/_Warp"]): continue # heuristics: too much concurrency harms performance # Shared memory buffer size - buf_sz = max((w - 1) * m + rmax * tm, m * w + (w - 1) * n + cmax * tn, v * m) - smem_tot = buf_sz * cu.sizeof_double + cu.npar * grouping * cu.sizeof_int - if smem_tot > gpu["SMEMperBLOCK"]: + buf_sz = max((w_ - 1) * m + rmax * tm, m * w_ + (w_ - 1) * n + cmax * tn, v_ * m) + smem_tot = (buf_sz * autotuning["sizeof_double"] + + autotuning["npars"] * grouping * autotuning["sizeof_int"]) + if smem_tot > gpu["Max_Shared_Memory_/_Block_(bytes)"]: continue # invalid: uses too much shared memory - if smem_tot * minblocks > gpu["SMEMperSM"]: + if smem_tot * minblocks_ > gpu["Shared_Memory_/_Multiprocessor_(bytes)"]: continue # invalid: uses too much shared memory - params.append({'m': m, 'n': n, 'k': k, - 'tile_m': tm, 'tile_n': tn, - 'w': w, 'v': v, - 'threads': threads, - 'grouping': grouping, - 'minblocks': minblocks, - 'perf': 0}) + params.append({ + "m": m, + "n": n, + "k": k, + "tile_m": tm, + "tile_n": tn, + "w": w_, + "v": v_, + "threads": threads_, + "grouping": grouping, + "minblocks": minblocks_, + }) return params + + @staticmethod + def baseline(m, n, k, gpu, autotuning): + """ + Given an (m, n, k)-triplet and GPu and autotuning properties, return a set of parameters corresponding to a + baseline ("educated guess") of the kernel's optimal parameters + """ + + grouping = 16 + minblk = 2 + tm = 2 + tn = 2 + cmax = (n + tn - 1) // tn + rmax = (m + tm - 1) // tm + min_threads = cmax * rmax + w = 8 + v = 8 + + while True: + base = { + "threads": round_up_to_nearest_multiple(min_threads, 32), + "grouping": grouping, + "minblocks": minblk, + "tile_m": tn, + "tile_n": tn, + "w": w, + "v": v, + } + if len(Kernel_dnt_largeDB2.promising_parameters(m, n, k, gpu, autotuning, **base)) > 0: + break + else: + if w > 1: + w /= 2 + else: + base = Kernel_dnt_largeDB2.promising_parameters(m, n, k, gpu, autotuning)[0] + break + + base.update( + dict([("m", m), ("n", n), ("k", k), ("algorithm", "largeDB1"), ("perf", 0), ("source", "predicted")])) + return base diff --git a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_medium.py b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_medium.py index 95d9325f1df..ee5494e4b21 100644 --- a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_medium.py +++ b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_medium.py @@ -8,16 +8,17 @@ # SPDX-License-Identifier: GPL-2.0+ # #################################################################################################### -from kernels import cusmm_dnt as cu +import numpy as np +from kernels.cusmm_dnt_base import Kernel, round_up_to_nearest_multiple -class Kernel_dnt_medium(cu.Kernel): +class Kernel_dnt_medium(Kernel): - algorithm = 'medium' + algorithm = "medium" algorithm_num = 3 - launch_parameters = ['m', 'n', 'k', 'tile_m', 'tile_n', 'threads', 'grouping', 'minblocks'] + launch_parameters = ["m", "n", "k", "tile_m", "tile_n", "threads", "grouping", "minblocks"] - def __init__(self, m, n, k, threads, tile_m, tile_n, grouping, minblocks, perf): + def __init__(self, m, n, k, threads, tile_m, tile_n, grouping, minblocks, perf, source): self.m = m self.n = n self.k = k @@ -27,27 +28,48 @@ def __init__(self, m, n, k, threads, tile_m, tile_n, grouping, minblocks, perf): self.grouping = grouping self.minblocks = minblocks self.perf = perf + self.source = source assert self.threads * self.minblocks <= 2048 min_threads = ((self.m + self.tile_m - 1) // self.tile_m) * ((self.n + self.tile_n - 1) // self.tile_n) assert min_threads <= self.threads @property def func_signature(self): - return "cusmm_dnt_medium<%(m)d,%(n)d,%(k)d,%(tile_m)d,%(tile_n)d,%(threads)d,%(grouping)d,%(minblocks)d>;\n" \ - % self.__dict__ + return ("cusmm_dnt_medium<%(m)d,%(n)d,%(k)d,%(tile_m)d,%(tile_n)d,%(threads)d,%(grouping)d,%(minblocks)d>;\n" % + self.__dict__) @staticmethod - def promising_parameters(m, n, k, gpu): + def promising_parameters(m, + n, + k, + gpu, + autotuning, + threads=None, + grouping=None, + minblocks=None, + tile_m=None, + tile_n=None, + w=None, + v=None): + """ + Given a certain (m,n,k)-triplet, GPU properties and autotuning properties, return a list of all possible + kernel parameters + """ params = [] - for minblocks in range(1, 28): # for exhaustive search: range(1, gpu.maxBLOCKSperSM + 1): - # heuristic: the optimal minblocks is never > 28 - if m >= 28: # heuristic: investigate a smaller search space of grouping for large matrices - grouping_range = (3, 4, 5, 24, 26, 29, 32,) - else: - grouping_range = range(1, 32 + 1, 1) - for grouping in grouping_range: - for tm in range(1, min(12, m) + 1): # heuristic: the optimal tile_m is never above 12 - for tn in range(1, min(12, n) + 1): # heuristic: the optimal tile_m is never above 12 + for minblocks_ in range(1, 28) if minblocks is None else [minblocks]: + # for exhaustive search: range(1, gpu["Thread_Blocks_/_Multiprocessor"] + 1): + # heuristic: the optimal minblocks is never > 28 + for grouping_ in range(1, 32 + 1, 1) if grouping is None else [grouping]: + + if m >= 28 and grouping_ not in (3, 4, 5, 24, 26, 29, 32): + continue # heuristic: investigate a smaller search space of grouping for large matrices + + for tm in (range(1, + min(12, m) + 1) + if tile_m is None else [tile_m]): # heuristic: the optimal tile_m is never above 12 + for tn in (range(1, + min(12, n) + 1) + if tile_n is None else [tile_n]): # heuristic: the optimal tile_m is never above 12 if tm * tn > 16: continue # heuristic: performance decreases for very large tiles @@ -57,34 +79,79 @@ def promising_parameters(m, n, k, gpu): rmax = (m + tm - 1) // tm # Max work ("operations") which can be run concurrently - max_concurrent_work = max(grouping, m*k, k*n, m*n, cmax*rmax) + max_concurrent_work = max(grouping_, m * k, k * n, m * n, cmax * rmax) # Minimum number of threads required to have one thread per tile # i.e., cover the result matrix min_threads = cmax * rmax # Shared memory buffer size - buf_sz = max(m*n, m*k + k*tn*cmax, tm*rmax*k + 1) - smem_tot = buf_sz * cu.sizeof_double + cu.npar * grouping * cu.sizeof_int - if smem_tot > gpu["SMEMperBLOCK"]: + buf_sz = max(m * n, m * k + k * tn * cmax, tm * rmax * k + 1) + smem_tot = (buf_sz * autotuning["sizeof_double"] + + autotuning["npars"] * grouping_ * autotuning["sizeof_int"]) + if smem_tot > gpu["Max_Shared_Memory_/_Block_(bytes)"]: continue - if smem_tot * minblocks > gpu["SMEMperSM"]: + if smem_tot * minblocks_ > gpu["Shared_Memory_/_Multiprocessor_(bytes)"]: continue # Use all concurrency available: fill warps - for threads in range(gpu["warp_size"], gpu["maxTHREADSperBLOCK"] + 1, gpu["warp_size"]): + for threads_ in (range(gpu["Threads_/_Warp"], gpu["Max_Thread_Block_Size"] + 1, + gpu["Threads_/_Warp"]) if threads is None else [threads]): - if threads > cu.round_up_to_multiple(max_concurrent_work, gpu["warp_size"]): + if threads_ > round_up_to_nearest_multiple(max_concurrent_work, gpu["Threads_/_Warp"]): continue # soft: too much concurrency harms performance - if threads * minblocks > gpu["maxTHREADSperSM"]: + if threads_ * minblocks_ > gpu["Threads_/_Multiprocessor"]: continue - if threads < min_threads: + if threads_ < min_threads: continue - params.append({'m': m, 'n': n, 'k': k, - 'tile_m': tm, 'tile_n': tn, - 'threads': threads, - 'grouping': grouping, - 'minblocks': minblocks, - 'perf': 0}) + params.append({ + "m": m, + "n": n, + "k": k, + "tile_m": tm, + "tile_n": tn, + "threads": threads_, + "grouping": grouping_, + "minblocks": minblocks_, + }) return params + + @staticmethod + def baseline(m, n, k, gpu, autotuning): + """ + Given an (m, n, k)-triplet and GPu and autotuning properties, return a set of parameters corresponding to a + baseline ("educated guess") of the kernel's optimal parameters + """ + + grp = 16 + minblk = 2 + tm = 2 + tn = 2 + cmax = (n + tn - 1) // tn + rmax = (m + tm - 1) // tm + min_threads = cmax * rmax + + while True: + + base = { + "threads": round_up_to_nearest_multiple(min_threads, 32), + "grouping": grp, + "minblocks": minblk, + "tile_m": tn, + "tile_n": tn, + "w": np.NaN, + "v": np.NaN, + } + + if len(Kernel_dnt_medium.promising_parameters(m, n, k, gpu, autotuning, **base)) > 0: + break + else: + grp -= 1 + if grp == 0: + base = Kernel_dnt_medium.promising_parameters(m, n, k, gpu, autotuning)[0] + base.update(dict([("w", np.NaN), ("v", np.NaN)])) + break + + base.update(dict([("m", m), ("n", n), ("k", k), ("algorithm", "medium"), ("perf", 0), ("source", "predicted")])) + return base diff --git a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_small.py b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_small.py index 550d827c86f..8a12a08c3f6 100644 --- a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_small.py +++ b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_small.py @@ -8,16 +8,17 @@ # SPDX-License-Identifier: GPL-2.0+ # #################################################################################################### -from kernels import cusmm_dnt as cu +import numpy as np +from kernels.cusmm_dnt_base import Kernel, round_up_to_nearest_multiple -class Kernel_dnt_small(cu.Kernel): +class Kernel_dnt_small(Kernel): algorithm = "small" algorithm_num = 4 - launch_parameters = ['m', 'n', 'k', 'tile_m', 'tile_n', 'threads', 'grouping', 'minblocks'] + launch_parameters = ["m", "n", "k", "tile_m", "tile_n", "threads", "grouping", "minblocks"] - def __init__(self, m, n, k, threads, tile_m, tile_n, grouping, minblocks, perf): + def __init__(self, m, n, k, threads, tile_m, tile_n, grouping, minblocks, perf, source): self.m = m self.n = n self.k = k @@ -27,21 +28,42 @@ def __init__(self, m, n, k, threads, tile_m, tile_n, grouping, minblocks, perf): self.grouping = grouping self.minblocks = minblocks self.perf = perf + self.source = source @property def func_signature(self): - return "cusmm_dnt_small<%(m)d,%(n)d,%(k)d,%(tile_m)d,%(tile_n)d,%(threads)d,%(grouping)d,%(minblocks)d>;\n" \ - % self.__dict__ + return ("cusmm_dnt_small<%(m)d,%(n)d,%(k)d,%(tile_m)d,%(tile_n)d,%(threads)d,%(grouping)d,%(minblocks)d>;\n" % + self.__dict__) @staticmethod - def promising_parameters(m, n, k, gpu): + def promising_parameters(m, + n, + k, + gpu, + autotuning, + threads=None, + grouping=None, + minblocks=None, + tile_m=None, + tile_n=None, + w=None, + v=None): + """ + Given a certain (m,n,k)-triplet, GPU properties and autotuning properties, return a list of all possible + kernel parameters + """ # Parameter space: params = [] - for minblocks in range(1, gpu["maxBLOCKSperSM"] + 1): - for grouping in range(2, 32 + 1, 1): # heuristic: never seen optimal=1 hence start from 2 - for tm in range(1, min(12, m) + 1): # heuristic: the optimal tile_m is never above 12 - for tn in range(1, min(12, n) + 1): # heuristic: the optimal tile_n is never above 12 + for minblocks_ in range(1, gpu["Thread_Blocks_/_Multiprocessor"] + 1) if minblocks is None else [minblocks]: + for grouping_ in (range(2, 32 + 1, 1) if grouping is None else + [grouping]): # heuristic: never seen optimal=1 hence start from 2 + for tm in (range(1, + min(12, m) + 1) + if tile_m is None else [tile_m]): # heuristic: the optimal tile_m is never above 12 + for tn in (range(1, + min(12, n) + 1) + if tile_n is None else [tile_n]): # heuristic: the optimal tile_n is never above 12 if tm * tn > 16: continue # heuristic: performance decreases for very large tiles @@ -54,30 +76,68 @@ def promising_parameters(m, n, k, gpu): min_threads = cmax * rmax # Max work ("operations") which can be run concurrently - max_concurrent_work = max(grouping, m*k, k*n, m*n, min_threads) + max_concurrent_work = max(grouping_, m * k, k * n, m * n, min_threads) # Shared memory buffer size - buf_sz = max(m*n, m*k + k*tn*cmax, tm*rmax*k + 1) - smem_tot = buf_sz * cu.sizeof_double + cu.npar * grouping * cu.sizeof_int - if smem_tot > gpu["SMEMperBLOCK"]: + buf_sz = max(m * n, m * k + k * tn * cmax, tm * rmax * k + 1) + smem_tot = (buf_sz * autotuning["sizeof_double"] + + autotuning["npars"] * grouping_ * autotuning["sizeof_int"]) + if smem_tot > gpu["Max_Shared_Memory_/_Block_(bytes)"]: continue - if smem_tot * minblocks > gpu["SMEMperSM"]: + if smem_tot * minblocks_ > gpu["Shared_Memory_/_Multiprocessor_(bytes)"]: continue # Use all concurrency available: fill warps - for threads in range(gpu["warp_size"], gpu["maxTHREADSperBLOCK"] + 1, gpu["warp_size"]): - - if threads > cu.round_up_to_multiple(max_concurrent_work, gpu["warp_size"]): + for threads_ in (range(gpu["Threads_/_Warp"], gpu["Max_Thread_Block_Size"] + 1, + gpu["Threads_/_Warp"]) if threads is None else [threads]): + + if threads_ > round_up_to_nearest_multiple(max_concurrent_work, gpu["Threads_/_Warp"]): continue # soft: too much concurrency harms performance - if threads * minblocks > gpu["maxTHREADSperSM"]: + if threads_ * minblocks_ > gpu["Threads_/_Multiprocessor"]: continue - if threads < min_threads: + if threads_ < min_threads: continue - params.append({'m': m, 'n': n, 'k': k, - 'tile_m': tm, 'tile_n': tn, - 'threads': threads, - 'grouping': grouping, - 'minblocks': minblocks, - 'perf': 0}) + params.append({ + "m": m, + "n": n, + "k": k, + "tile_m": tm, + "tile_n": tn, + "threads": threads_, + "grouping": grouping_, + "minblocks": minblocks_, + }) return params + + @staticmethod + def baseline(m, n, k, gpu, autotuning): + """ + Given an (m, n, k)-triplet and GPu and autotuning properties, return a set of parameters corresponding to a + baseline ("educated guess") of the kernel's optimal parameters + """ + + grp = 16 + minblk = 2 + tm = 2 + tn = 2 + cmax = (n + tn - 1) // tn + rmax = (m + tm - 1) // tm + min_threads = cmax * rmax + + base = { + "threads": round_up_to_nearest_multiple(min_threads, 32), + "grouping": grp, + "minblocks": minblk, + "tile_m": tn, + "tile_n": tn, + "w": np.NaN, + "v": np.NaN, + } + + if len(Kernel_dnt_small.promising_parameters(m, n, k, gpu, autotuning, **base)) > 0: + base = Kernel_dnt_small.promising_parameters(m, n, k, gpu, autotuning)[0] + base.update(dict([("tile_m", np.NaN), ("tile_n", np.NaN), ("w", np.NaN), ("v", np.NaN)])) + + base.update(dict([("m", m), ("n", n), ("k", k), ("algorithm", "small"), ("perf", 0), ("source", "predicted")])) + return base diff --git a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_tiny.py b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_tiny.py index bb7891370a2..927370cdc62 100644 --- a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_tiny.py +++ b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_dnt_tiny.py @@ -8,16 +8,17 @@ # SPDX-License-Identifier: GPL-2.0+ # #################################################################################################### -from kernels import cusmm_dnt as cu +import numpy as np +from kernels.cusmm_dnt_base import Kernel, round_up_to_nearest_multiple -class Kernel_dnt_tiny(cu.Kernel): +class Kernel_dnt_tiny(Kernel): algorithm = "tiny" algorithm_num = 5 - launch_parameters = ['m', 'n', 'k', 'threads', 'grouping', 'minblocks'] + launch_parameters = ["m", "n", "k", "threads", "grouping", "minblocks"] - def __init__(self, m, n, k, threads, grouping, minblocks, perf): + def __init__(self, m, n, k, threads, grouping, minblocks, perf, source): self.m = m self.n = n self.k = k @@ -25,6 +26,7 @@ def __init__(self, m, n, k, threads, grouping, minblocks, perf): self.grouping = grouping self.minblocks = minblocks self.perf = perf + self.source = source assert self.m * self.n <= self.threads @property @@ -32,42 +34,91 @@ def func_signature(self): return "cusmm_dnt_tiny<%(m)d,%(n)d,%(k)d,%(threads)d,%(grouping)d,%(minblocks)d>;\n" % self.__dict__ @staticmethod - def promising_parameters(m, n, k, gpu): + def promising_parameters(m, + n, + k, + gpu, + autotuning, + threads=None, + grouping=None, + minblocks=None, + tile_m=None, + tile_n=None, + w=None, + v=None): + """ + Given a certain (m,n,k)-triplet, GPU properties and autotuning properties, return a list of all possible + kernel parameters + """ # Shared memory buffer size - buf_sz = k * (m + n) # number of elements in the a_block buffer = mk, and in the b_block buffer = kn + buf_sz = k * (m + n) # number of elements in the a_block buffer = mk, and in the b_block buffer = kn # Minimum number of threads required to cover the result matrix c - min_threads = m*n + min_threads = m * n - # Parameter space: + # Parameter space: params = [] - for minblocks in range(1, gpu["maxBLOCKSperSM"] + 1): - for grouping in range(2, 32 + 1, 1): # heuristic: never seen optimal=1 hence start from 2 - + for minblocks_ in range(1, gpu["Thread_Blocks_/_Multiprocessor"] + 1) if minblocks is None else [minblocks]: + # heuristic: never seen optimal=1 hence start from 2 + for grouping_ in range(2, 32 + 1, 1) if grouping is None else [grouping]: + # Max work ("operations") which can be run concurrently - max_concurrent_work = max(grouping, m*k, k*n, m*n) + max_concurrent_work = max(grouping_, m * k, k * n, m * n) # Shared memory utilisation (bytes) - smem_tot = buf_sz * cu.sizeof_double + cu.npar * grouping * cu.sizeof_int - if smem_tot > gpu["SMEMperBLOCK"]: + smem_tot = ( + buf_sz * autotuning["sizeof_double"] + autotuning["npars"] * grouping_ * autotuning["sizeof_int"]) + if smem_tot > gpu["Max_Shared_Memory_/_Block_(bytes)"]: continue - if smem_tot * minblocks > gpu["SMEMperSM"]: + if smem_tot * minblocks_ > gpu["Max_Shared_Memory_/_Block_(bytes)"]: continue # Use all concurrency available: fill warps - for threads in range(gpu["warp_size"], gpu["maxTHREADSperBLOCK"] + 1, gpu["warp_size"]): + for threads_ in (range(gpu["Threads_/_Warp"], gpu["Max_Thread_Block_Size"] + 1, gpu["Threads_/_Warp"]) + if threads is None else [threads]): - if threads > cu.round_up_to_multiple(max_concurrent_work, gpu["warp_size"]): + if threads_ > round_up_to_nearest_multiple(max_concurrent_work, gpu["Threads_/_Warp"]): continue # soft: too much concurrency harms performance - if threads * minblocks > gpu["maxTHREADSperSM"]: + if threads_ * minblocks_ > gpu["Threads_/_Multiprocessor"]: continue - if threads < min_threads: + if threads_ < min_threads: continue - params.append({'m': m, 'n': n, 'k': k, - 'threads': threads, - 'grouping': grouping, - 'minblocks': minblocks, - 'perf': 0}) + params.append({ + "m": m, + "n": n, + "k": k, + "threads": threads_, + "grouping": grouping_, + "minblocks": minblocks_ + }) return params + + @staticmethod + def baseline(m, n, k, gpu, autotuning): + """ + Given an (m, n, k)-triplet and GPu and autotuning properties, return a set of parameters corresponding to a + baseline ("educated guess") of the kernel's optimal parameters + """ + + grp = 16 + minblk = 2 + min_threads = m * n + + base = { + "threads": round_up_to_nearest_multiple(min_threads, 32), + "grouping": grp, + "minblocks": minblk, + "tile_m": np.NaN, + "tile_n": np.NaN, + "w": np.NaN, + "v": np.NaN, + } + + if len(Kernel_dnt_tiny.promising_parameters(m, n, k, gpu, autotuning, **base)) > 0: + base = Kernel_dnt_tiny.promising_parameters(m, n, k, gpu, autotuning)[0] + base.update(dict([("tile_m", np.NaN), ("tile_n", np.NaN), ("w", np.NaN), ("v", np.NaN)])) + + base.update(dict([("m", m), ("n", n), ("k", k), ("algorithm", "tiny"), ("perf", 0), ("source", "predicted")])) + return base diff --git a/src/acc/libsmm_acc/libcusmm/kernels/cusmm_predict.py b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_predict.py new file mode 100644 index 00000000000..8fe3cccb77e --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/kernels/cusmm_predict.py @@ -0,0 +1,708 @@ +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import re +import numpy as np + +# =============================================================================== +# Dictionary of available kernel algorithms +# keys: kernel name +# values: kernel implementation class +from kernels.cusmm_dnt_largeDB1 import Kernel_dnt_largeDB1 +from kernels.cusmm_dnt_largeDB2 import Kernel_dnt_largeDB2 +from kernels.cusmm_dnt_medium import Kernel_dnt_medium +from kernels.cusmm_dnt_small import Kernel_dnt_small +from kernels.cusmm_dnt_tiny import Kernel_dnt_tiny + +kernel_algorithm = { + "tiny": Kernel_dnt_tiny, + "small": Kernel_dnt_small, + "medium": Kernel_dnt_medium, + "largeDB1": Kernel_dnt_largeDB1, + "largeDB2": Kernel_dnt_largeDB2, +} + +# =============================================================================== +# Dictionary of available GPU architectures. +# keys: parameter_file +# values: CUDA compute versions +arch_number = { + "parameters_K20X.json": 35, + "parameters_K40.json": 35, + "parameters_K80.json": 37, + "parameters_P100.json": 60, +} + + +# =============================================================================== +def compatible_mnk(algo, m, n, k): + """Determine whether a given algorithm is compatible with given m, n, k values""" + + max_sizes = max(m * k, n * k, m * n) + compatible = True + if algo == "tiny": + if max_sizes > 64: + compatible = False + elif algo == "small": + if max_sizes > 128: + compatible = False + elif algo in ["largeDB1", "largeDB2"]: + if max_sizes < 250: + compatible = False + else: + if algo != "medium": + assert False, "Cannot identify algorithm:" + str(algo) + + return compatible + + +# =============================================================================== +def params_dict_to_kernel(**params): + """Given a dictionary of parameters, return the corresponding Kernel class instance""" + + # Get the 'algorithm' field + algo = params.pop("algorithm") + + # Get the list of fields needed to initialize a Kernel instance of this given algorithm + kernel_init_params = kernel_algorithm[algo].launch_parameters + ["perf", "source"] + + # Fill in dictionary fields + kernel_init_params_dict = dict() + for k in kernel_init_params: + if k == 'perf' and params["source"] == 'predicted': + # the performance of predicted parameter sets is not given + kernel_init_params_dict["perf"] = None + else: + kernel_init_params_dict[k] = params[k] + + return kernel_algorithm[algo](**kernel_init_params_dict) + + +def descr_to_kernel(kernel_descr, source="autotuned"): + """Given a kernel description from the autotuning output, return the corresponding Kernel class instance""" + + from ast import literal_eval + + re_kernel_descr = re.compile(r"Kernel_dnt_(\w+)(\(.*\)) , # (\d+\.\d+) GFlop/s") + match = re_kernel_descr.search(kernel_descr).groups() + algo = match[0] + m = match[1].replace("=", "':") + m = m.replace(", ", ", '") + m = m.replace("(", "{'") + m = m.replace(")", "}") + params = dict(literal_eval(m)) + params["perf"] = float(match[2]) + params["source"] = source + return kernel_algorithm[algo](**params) + + +def to_string(*iterable): + """ + Given a (list of) m,n,k-triplet(s), return the corresponding (list of) string(s) "mxnxk" + """ + mnk_string = "{}x{}x{}" + if len(iterable) == 3 and isinstance(iterable[0], int): + m, n, k = iterable + iterable_to_string = mnk_string.format(m, n, k) + else: + iterable_to_string = [mnk_string.format(m, n, k) for m, n, k in iterable] + if len(iterable_to_string) == 1: + iterable_to_string = iterable_to_string[0] + return iterable_to_string + + +def to_tuple(*iterable): + """ + Given a (list of) string(s) "mxnxk", return the corresponding (list of) m,n,k-triplet(s) + """ + mnk_pattern = re.compile(r"(\d+)x(\d+)x(\d+)") + tuple_mnks = list() + for mnk in iterable: + m, n, k = mnk_pattern.match(mnk).groups() + tuple_mnks.append((int(m), int(n), int(k))) + if len(tuple_mnks) == 1: + tuple_mnks = tuple_mnks[0] + return tuple_mnks + + +# =============================================================================== +# Lists of derived parameters to use as training data for the predictive modelling +# Some of the computable features are commented out because they are constant or (almost) linearly dependent on another +# feature, and therefore they do not contribute to the decision tree model. +raw_parameters = [ + "m", + "n", + "k", + "threads_per_blk", + 'grouping', + "minblocks", + "tile_m", + "tile_n", + "w", + "v", + "perf (Gflop/s)", +] +derived_parameters = { + "common": [ + "perf_scaled", + # 'Gflops', # linearly dependent on mxnxk + "mxnxk", + "size_a", + "size_b", + "size_c", + # 'nblks', 'nthreads', # constant value for largeDB, since the grouping is always = 16 + "sm_desired" + # 'warps_per_blk', 'nwarps', # linearly dependent on threads, nthreads + # 'ru_param_stack_unroll_factor', # constant values for each algo + ], + "tiny": [ + "nblks", + "nthreads", + # tiny, small, medium: resource occupation + "ru_tinysmallmed_unroll_factor_a", + "ru_tinysmallmed_unroll_factor_a_total", + "ru_tinysmallmed_unroll_factor_b", + "ru_tinysmallmed_unroll_factor_b_total", + "ru_tinysmallmed_unroll_factor_c_total", + # tiny: resource occupation + "ru_tiny_max_parallel_work", + "ru_tiny_smem_per_block", + # 'ru_tiny_min_threads', # equal to size_c + # 'ru_tiny_nblks_per_sm', # always = 32, so also removing: 'ru_tiny_nwarps_per_sm', 'ru_tiny_occupancy' + "ru_tiny_nsm", # 'ru_tiny_ngpu', # highly correlated with ru_tiny_n_sm + ], + "small": [ + "nblks", + "nthreads", + # tiny, small, medium: resource occupation + "ru_tinysmallmed_unroll_factor_a", + "ru_tinysmallmed_unroll_factor_a_total", + "ru_tinysmallmed_unroll_factor_b", + "ru_tinysmallmed_unroll_factor_b_total", + "ru_tinysmallmed_unroll_factor_c_total", + # small, medium: resource occupation + "ru_smallmed_unroll_factor_c", + "ru_smallmed_loop_matmul", + "ru_smallmed_max_parallel_work", + # 'ru_smallmed_buf_size', # highly correlated with ru_smallmed_smem_per_block + "ru_smallmed_smem_per_block", + "ru_smallmed_regs_per_thread", + # small, medium, large: resource occupation + "ru_smallmedlarge_cmax", + "ru_smallmedlarge_rmax", + "ru_smallmedlarge_T", + "ru_smallmedlarge_min_threads", + ], + "medium": [ + "nblks", + "nthreads", + # tiny, small, medium: resource occupation + "ru_tinysmallmed_unroll_factor_a", + "ru_tinysmallmed_unroll_factor_a_total", + "ru_tinysmallmed_unroll_factor_b", + "ru_tinysmallmed_unroll_factor_b_total", + "ru_tinysmallmed_unroll_factor_c_total", + # medium: resource occupation + # 'load_unroll_factor_1', 'load_unroll_factor_2', # highly correlated with ru_tinysmallmed_unroll_factor_a,b + # 'n_mkloads', 'n_knloads', # constant value + # small, medium: resource occupation + "ru_smallmed_unroll_factor_c", + "ru_smallmed_loop_matmul", + "ru_smallmed_max_parallel_work", + # 'ru_smallmed_buf_size', # highly correlated with ru_smallmed_smem_per_block + "ru_smallmed_smem_per_block", + "ru_smallmed_regs_per_thread", + # small, medium, large: resource occupation + "ru_smallmedlarge_cmax", + "ru_smallmedlarge_rmax", + "ru_smallmedlarge_T", + "ru_smallmedlarge_min_threads", + ], + "largeDB1": [ + # largeDB: resource occupation + "ru_large_Pa", + "ru_large_Pb", + "ru_large_unroll_factor_a", + "ru_large_unroll_factor_b", + "ru_large_unroll_factor_c", + "ru_large_loop_matmul", + "ru_large_max_concurrent_work", + "ru_large_n_DB_iter", + "ru_large_regs_per_thread", + # 'ru_large_buf_size', # highly correlated with ru_large_smem_per_block + "ru_large_smem_per_block", + # small, medium, large: resource occupation + "ru_smallmedlarge_cmax", + "ru_smallmedlarge_rmax", + "ru_smallmedlarge_T", + "ru_smallmedlarge_min_threads", + ], + "largeDB2": [ + # largeDB: resource occupation + "ru_large_Pa", + "ru_large_Pb", + "ru_large_unroll_factor_a", + "ru_large_unroll_factor_b", + "ru_large_unroll_factor_c", + "ru_large_loop_matmul", + "ru_large_max_concurrent_work", + "ru_large_n_DB_iter", + "ru_large_regs_per_thread", + # 'ru_large_buf_size', # highly correlated with ru_large_smem_per_block + "ru_large_smem_per_block", + # small, medium, large: resource occupation + "ru_smallmedlarge_cmax", + "ru_smallmedlarge_rmax", + "ru_smallmedlarge_T", + "ru_smallmedlarge_min_threads", + ], +} + + +# =============================================================================== +def get_max_performances_per_mnk(data): + """ + Construct dictionary: + keys: (m, n, k)-tuple, + values: maximum performance found over all algorithms for this given (m, n, k) + """ + # Get list of different (m, n, k)s occurring in this instance + data["mnk"] = list(zip(data["m"], data["n"], data["k"])) + mnks = np.unique(data["mnk"]) + + # Get max. performance per (m, n, k) + max_perf = dict() + + for mnk in mnks: + # Get indices corresponding to this mnk + idx_mnk = np.where(data["mnk"] == mnk)[0].tolist() + + # Get performances per mnk + perf_mnk_algo = data["perf (Gflop/s)"].values[idx_mnk] + + # Store maxperf + maxperf = float(perf_mnk_algo.max(axis=0)) # max. performance found through autotuning + max_perf[mnk] = maxperf + + return max_perf + + +# =============================================================================== +def get_baseline_performances_per_mnk(data, algorithm, gpu, autotuning): + """ + Construct dictionary: + keys: (m, n, k)-tuple, + values: baseline performance for this given (m, n, k) and the given algorithm + """ + + # Get list of different (m, n, k)s occurring in this instance + data["mnk"] = list(zip(data["m"], data["n"], data["k"])) + mnks = np.unique(data["mnk"]) + + # Get baseline performance per (m, n, k) + baseline_perf = dict() + + for mnk in mnks: + m, n, k = mnk + + baseline_pars = kernel_algorithm[algorithm].baseline(m, n, k, gpu, autotuning) + + if np.isnan(baseline_pars["tile_m"]): + idx_baseline = data[(data.m == baseline_pars["m"]) + & (data.n == baseline_pars["n"]) + & (data.k == baseline_pars["k"]) + & (data.threads == baseline_pars["threads"]) + & (data.grouping == baseline_pars["grouping"]) + & (data.minblocks == baseline_pars["minblocks"])].index.tolist() + elif np.isnan(baseline_pars["w"]): + idx_baseline = data[(data.m == baseline_pars["m"]) + & (data.n == baseline_pars["n"]) + & (data.k == baseline_pars["k"]) + & (data.threads == baseline_pars["threads"]) + & (data.grouping == baseline_pars["grouping"]) + & (data.minblocks == baseline_pars["minblocks"]) + & (data.tile_m == baseline_pars["tile_m"]) + & (data.tile_n == baseline_pars["tile_n"])].index.tolist() + else: + idx_baseline = data[(data.m == baseline_pars["m"]) + & (data.n == baseline_pars["n"]) + & (data.k == baseline_pars["k"]) + & (data.threads == baseline_pars["threads"]) + & (data.grouping == baseline_pars["grouping"]) + & (data.minblocks == baseline_pars["minblocks"]) + & (data.tile_m == baseline_pars["tile_m"]) + & (data.tile_n == baseline_pars["tile_n"]) + & (data.w == baseline_pars["w"]) + & (data.v == baseline_pars["v"])].index.tolist() + + if len(idx_baseline) < 1: + idx_baseline = data[(data.m == baseline_pars["m"]) + & (data.n == baseline_pars["n"]) + & (data.k == baseline_pars["k"]) + & (data.threads == baseline_pars["threads"])].index.tolist() + assert len(idx_baseline) > 0 + + idx_baseline = idx_baseline[0] + baseline_perf[mnk] = data["perf (Gflop/s)"][idx_baseline] + + return baseline_perf + + +# =============================================================================== +class PredictiveParameters: + """ + Class handling predictive features for the predictive modelling of libcusmm's performance + """ + + def __init__(self, params_df, gpu, autotuning, max_performances, partial_initialization=False): + """ + params_df: pandas Dataframe where each row corresponds to a kernel parameter set + """ + assert "m" in params_df.columns.values + assert "n" in params_df.columns.values + assert "k" in params_df.columns.values + self.gpu = gpu # GPU card properties + self.autotuning = autotuning # autotuning properties + self.max_performances = max_performances # dictionary of max. performances + # keys: (m, n, k)-tuple, values: maximum performance + # found over all algorithms for this given (m, n, k) + + if not partial_initialization: + assert ("threads" in params_df.columns.values), "Missing column: threads. Available columns:\n" + str( + params_df.columns.values) + assert "grouping" in params_df.columns.values, "Missing column: grouping. Available columns:\n" + str( + params_df.columns.values) + assert "minblocks" in params_df.columns.values, "Missing column: minblocks. Available columns:\n" + str( + params_df.columns.values) + algos = np.unique(params_df["algorithm"].values) + assert len(algos) == 1 + algo = algos[0] + if algo in ["small", "medium", "largeDB1", "largeDB2"]: + assert "tile_m" in params_df.columns.values, "Missing column: tile_m. Available columns:\n" + str( + params_df.columns.values) + assert "tile_n" in params_df.columns.values, "Missing column: tile_n. Available columns:\n" + str( + params_df.columns.values) + if algo in ["largeDB1", "largeDB2"]: + assert "w" in params_df.columns.values, "Missing column: w. Available columns:\n" + str( + params_df.columns.values) + assert "v" in params_df.columns.values, "Missing column: v. Available columns:\n" + str( + params_df.columns.values) + + self.params = params_df + + def get(self, feature_name): + """Generic function to compute any feature given by name""" + + if feature_name not in self.params.columns.values: + if feature_name != "perf_scaled": # not vectorizable + vget = getattr(self, "get_" + feature_name) + else: + vget = np.vectorize(getattr(self, "get_" + feature_name)) + feature_val = vget() + else: + feature_val = self.params[feature_name].values + return feature_val + + def get_features(self, feature_names): + """ + Compute a list of features given by name and return them as a pandas Dataframe. + :param feature_names: list of names of features to compute + """ + for feat in feature_names: + self.params.loc[:, feat] = self.get(feat) + return self.params[feature_names] + + # =============================================================================== + # Performances + def get_perf_scaled(self): + """ + Scale raw performances in [Gflop/s] between 0 and 1, where + 0 = 0 Gflop/s + 1 = performance equal to autotuned maximum FOR ALL ALGORITHMS + :return: numpy array of scaled performances + """ + + def scale_perf(perf, mnk): + """For a given mnk and a given performance on this mnk, return the scaled performance""" + return perf / self.max_performances[mnk] + + vec_scale_perf = np.vectorize(scale_perf) + ret = vec_scale_perf(self.get("perf (Gflop/s)"), self.get("mnk")) + return ret + + # =============================================================================== + # Matrix sizes + def get_size_a(self): + """Size of matrix A (first operand of A * B = C)""" + return self.get("m") * self.get("k") + + def get_size_b(self): + """Size of matrix B (second operand of A * B = C)""" + return self.get("k") * self.get("n") + + def get_size_c(self): + """Size of matrix B (result of of A * B = C)""" + return self.get("m") * self.get("n") + + def get_mnk(self): + """Return (m, n, k) as a tuple""" + return self.get("m"), self.get("n"), self.get("k") + + def get_mxnxk(self): + """Return the product m*n*k""" + return self.get("m") * self.get("n") * self.get("k") + + # =============================================================================== + # Launch parameters + def get_need_sync(self): + """(mn > warp_size || mk > warp_size || kn > warp_size || threads > warp_size)""" + return (np.where(self.get("size_c") > self.gpu["Threads_/_Warp"], True, False) + | np.where(self.get("size_a") > self.gpu["Threads_/_Warp"], True, False) + | np.where(self.get("size_b") > self.gpu["Threads_/_Warp"], True, False) + | np.where(self.get("threads") > self.gpu["Threads_/_Warp"], True, False)) + + def get_nblks(self): + """Number of thread blocks needed to multiply all matrices on the stack""" + return np.ceil(self.autotuning["stack_size"] / self.get("grouping")) + + def get_warps_per_blk(self): + """Number of warps per block""" + return np.ceil(self.get("threads") / self.gpu["Threads_/_Warp"]) + + def get_nwarps(self): + """Total number of warps needed to multiply all matrices on the stack""" + return self.get("warps_per_blk") * self.get("nblks") + + def get_sm_desired(self): + """ + Number of multiprocessors desired to multiply all matrices on the stack. + For more details, see https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#launch-bounds + """ + return np.ceil(self.get("nblks") / self.get("minblocks")) + + def get_nthreads(self): + """Total number of threads needed to multiply all matrices on the stack""" + return self.get("threads") * self.get("nblks") + + # =============================================================================== + # Resource usage (common) + def get_ru_param_stack_unroll_factor(self): + """Number of executions of the body of the loop that loads data from the parameter stack""" + return np.ceil(self.get("grouping") / self.get("threads")) + + def get_n_iter(self): + """Number of benchmark repetitions in autotuning procedure""" + return np.maximum(3, 12500 * (1 // (self.get("m") * self.get("n") * self.get("k")))) + + def get_Gflops(self): + """Number of floating point operations in [Gflops] carried out during autotuning""" + return (self.get("n_iter") * self.autotuning["stack_size"] * self.get("m") * self.get("n") * self.get("k") * 2 * + 10**(-9)) + + # =============================================================================== + # Resource occupancy estimations + def get_nblocks_per_sm_lim_blks_warps(self): + """Resource occupations in terms of warps and blocks (Follows CUDA calculator sheet)""" + return np.minimum( + self.gpu["Thread_Blocks_/_Multiprocessor"], + np.floor(self.gpu["Warps_/_Multiprocessor"] / self.get("warps_per_blk")), + ) + + # =============================================================================== + # Resource usage (tiny, small, medium) + def get_ru_tinysmallmed_unroll_factor_a(self): + """loop unroll factor of the loop on m*k""" + return np.ceil(self.get("size_a") / self.get("threads")) + + def get_ru_tinysmallmed_unroll_factor_b(self): + """loop unroll factor of the loop on k*m""" + return np.ceil(self.get("size_b") / self.get("threads")) + + def get_ru_tinysmallmed_unroll_factor_a_total(self): + """loop unroll factor multiplied by number of times the loop is run""" + return self.get("ru_tinysmallmed_unroll_factor_a") * self.get("grouping") + + def get_ru_tinysmallmed_unroll_factor_b_total(self): + """loop unroll factor multiplied by number of times the loop is run""" + return self.get("ru_tinysmallmed_unroll_factor_b") * self.get("grouping") + + def get_ru_tinysmallmed_unroll_factor_c_total(self): + """loop unroll factor of the loop writing back to C multiplied by number of times the loop is run""" + return self.get("k") * self.get("grouping") + + # =============================================================================== + # Resource usage (tiny) + def get_ru_tiny_max_parallel_work(self): + """Total number of iterations in each loop""" + return np.maximum.reduce([self.get("grouping"), self.get("size_a"), self.get("size_b"), self.get("size_c")]) + + def get_ru_tiny_min_threads(self): + """Minimum number of threads required to run the kernel and produce correct results""" + return self.get("size_c") + + def get_ru_tiny_buf_size(self): + """Buffer size""" + return self.get("k") * (self.get("m") + self.get("n")) + + def get_ru_tiny_smem_per_block(self): + """"Shared memory usage per block (estimate)""" + return (self.get("ru_tiny_buf_size") * self.autotuning["sizeof_double"]) + ( + self.autotuning["npars"] * self.get("grouping") * self.autotuning["sizeof_int"]) + + def get_ru_tiny_nblks_per_sm(self): + """ + Occupancy estimation: assumption (verified on a sample of mnks): nblks is always limited by number of threads + for algorithm tiny + """ + return self.get("nblocks_per_sm_lim_blks_warps") + + def get_ru_tiny_nwarps_per_sm(self): + """Number of wars per multiprocessor""" + return self.get("ru_tiny_nblks_per_sm") * self.get("warps_per_blk") + + def get_ru_tiny_nsm(self): + """Number of multiprocessors""" + return np.ceil(self.get("nblks") / self.get("ru_tiny_nblks_per_sm")) + + def get_ru_tiny_ngpu(self): + """Number of GPUs""" + return np.ceil(self.get("ru_tiny_nsm") / self.gpu["Multiprocessors"]) + + def get_ru_tiny_occupancy(self): + return self.get("ru_tiny_nwarps_per_sm") / self.gpu["Warps_/_Multiprocessor"] + + # =============================================================================== + # Resource usage (small, medium, large) + def get_ru_smallmedlarge_cmax(self): + return np.ceil(self.get("n") / self.get("tile_n")) + + def get_ru_smallmedlarge_rmax(self): + return np.ceil(self.get("m") / self.get("tile_m")) + + def get_ru_smallmedlarge_T(self): + return self.get("tile_m") * self.get("tile_n") + + def get_ru_smallmedlarge_min_threads(self): + return self.get("ru_smallmedlarge_cmax") * self.get("ru_smallmedlarge_rmax") + + # =============================================================================== + # Resource usage estimation and loop counts (small, medium) + def get_ru_smallmed_tm_max(self): + return self.get("m") + + def get_ru_smallmed_tn_max(self): + return self.get("n") + + def get_ru_smallmed_unroll_factor_c(self): + """loop unroll factor of the loop on m*n""" + return np.ceil(self.get("size_c") / self.get("threads")) + + def get_ru_smallmed_loop_matmul(self): + """Actual multiplication loop""" + return self.get("k") * self.get("tile_m") * self.get("tile_n") + + def get_ru_smallmed_max_parallel_work(self): + """Maximum parallel work""" + return np.maximum.reduce([ + self.get("grouping"), + self.get("size_a"), + self.get("size_b"), + self.get("size_c"), + self.get("ru_smallmedlarge_min_threads"), + ]) + + def get_ru_smallmed_buf_size(self): + """Buffer size""" + intermediate1 = self.get("size_a") + self.get("k") * self.get("tile_n") * self.get("ru_smallmedlarge_cmax") + intermediate2 = self.get("tile_m") * self.get("ru_smallmedlarge_rmax") * self.get("k") + 1 + return np.maximum.reduce([self.get("size_c"), intermediate1, intermediate2]) + + def get_ru_smallmed_smem_per_block(self): + """Shared memory usage per block""" + return (self.get("ru_smallmed_buf_size") * self.autotuning["sizeof_double"]) + ( + self.autotuning["npars"] * self.get("grouping") * self.autotuning["sizeof_int"]) + + def get_ru_smallmed_regs_per_thread(self): + """Register usage per thread (estimated)""" + return (self.get("tile_m") * self.get("tile_n") + + (self.get("m") * self.get("k") + self.get("threads") - 1) // self.get("threads") + + (self.get("k") * self.get("n") + self.get("threads") - 1) // self.get("threads")) + + # =============================================================================== + # Resource usage (medium) + # Loop bounds + def get_load_unroll_factor_1(self): + return self.get("size_a") // self.get("threads") + 1 + + def get_load_unroll_factor_2(self): + return self.get("size_b") // self.get("threads") + 1 + + def get_n_mkloads(self): + return self.get("size_a") // (self.get("load_unroll_factor_1") * self.get("threads")) + + def get_n_knloads(self): + return self.get("size_b") // (self.get("load_unroll_factor_2") * self.get("threads")) + + # =============================================================================== + # Resource usage (large) + def get_ru_large_Pa(self): + """Input slab size""" + return self.get("m") * self.get("w") + + def get_ru_large_Pb(self): + """Input slab size""" + return self.get("w") * self.get("n") + + def get_ru_large_Pc(self): + """Output slab size""" + return self.get("m") * self.get("v") + + def get_ru_large_unroll_factor_a(self): + return np.ceil(self.get("ru_large_Pa") / self.get("threads")) + + def get_ru_large_unroll_factor_b(self): + return np.ceil(self.get("ru_large_Pb") / self.get("threads")) + + def get_ru_large_unroll_factor_c(self): + return np.ceil(self.get("ru_large_Pc") / self.get("threads")) + + def get_ru_large_loop_matmul(self): + return self.get("w") * self.get("tile_m") * self.get("tile_n") + + def get_ru_large_max_concurrent_work(self): + """Maximum concurrent work""" + return np.maximum.reduce([ + self.get("grouping"), + self.get("ru_large_Pa"), + self.get("ru_large_Pb"), + self.get("ru_large_Pc"), + self.get("ru_smallmedlarge_T"), + ]) + + def get_ru_large_regs_per_thread(self): + """Register usage per thread (estimated)""" + return (self.get("tile_m") * self.get("tile_n") + + (self.get("w") * self.get("m") + self.get("threads") - 1) // self.get("threads") + + (self.get("w") * self.get("n") + self.get("threads") - 1) // self.get("threads")) + + def get_ru_large_n_DB_iter(self): + """Number of double-buffering iterations""" + return self.get("k") // (2 * self.get("w")) + + def get_ru_large_buf_size(self): + """Buffer size""" + intermediate1 = (self.get("w") - 1) * self.get("m") + self.get("ru_smallmedlarge_rmax") * self.get("tile_m") + intermediate2 = (self.get("m") * self.get("w") + (self.get("w") - 1) * self.get("n") + + self.get("ru_smallmedlarge_cmax") * self.get("tile_n")) + return np.maximum.reduce([self.get("ru_large_Pc"), intermediate1, intermediate2]) + + def get_ru_large_smem_per_block(self): + """Shared memory usage per block""" + return (self.get("ru_large_buf_size") * self.autotuning["sizeof_double"] + + self.autotuning["npars"] * self.get("grouping") * self.autotuning["sizeof_int"]) diff --git a/src/acc/libsmm_acc/libcusmm/kernels/gpu_properties.json b/src/acc/libsmm_acc/libcusmm/kernels/gpu_properties.json index e839b29fd84..a61546f1a40 100644 --- a/src/acc/libsmm_acc/libcusmm/kernels/gpu_properties.json +++ b/src/acc/libsmm_acc/libcusmm/kernels/gpu_properties.json @@ -1,32 +1,71 @@ { "info": { - "header": "Hardware-dependent constraints (GPU P100, compute capability 6.0)", - "source": "CUDA occupancy calculator", - "source_url": "http://developer.download.nvidia.com/compute/cuda/CUDA_Occupancy_calculator.xls" - }, + "header": "Hardware-dependent constraints (GPU P100, compute capability 6.0)", + "source": "CUDA occupancy calculator", + "source_url": [ + "http://developer.download.nvidia.com/compute/cuda/CUDA_Occupancy_calculator.xls", + "https://devblogs.nvidia.com/inside-pascal/" + ] + }, "sm_35": { - "warp_size": 32, - "maxTHREADSperBLOCK": 1024, - "maxTHREADSperSM": 2048, - "maxBLOCKSperSM": 16, - "SMEMperBLOCK": 49152, - "SMEMperSM": 49152 + "Compute_Capability": 3.5, + "SM_Version": "sm_35", + "Threads_/_Warp" : 32, + "Warps_/_Multiprocessor": 64, + "Threads_/_Multiprocessor": 2048, + "Thread_Blocks_/_Multiprocessor": 16, + "Shared_Memory_/_Multiprocessor_(bytes)": 49152, + "Max_Shared_Memory_/_Block_(bytes)": 49152, + "Register_File_Size_/_Multiprocessor_(32-bit_registers)": 65536, + "Max_Registers_/_Block": 65536, + "Register_Allocation_Unit_Size": 256, + "Register_Allocation_Granularity": "warp", + "Max_Registers_/_Thread": 255, + "Shared_Memory_Allocation_Unit_Size": 256, + "Warp_Allocation_Granularity": 4, + "Max_Thread_Block_Size": 1024, + "Shared_Memory_Size_Configurations_(bytes)": 49152, + "Warp_register_allocation_granularities": 256 }, "sm_37": { - "warp_size": 32, - "maxTHREADSperBLOCK": 1024, - "maxTHREADSperSM": 2048, - "maxBLOCKSperSM": 16, - "SMEMperBLOCK": 49152, - "SMEMperSM": 114688 + "Compute_Capability": 3.7, + "SM_Version": "sm_37", + "Threads_/_Warp" : 32, + "Warps_/_Multiprocessor": 64, + "Threads_/_Multiprocessor": 2048, + "Thread_Blocks_/_Multiprocessor": 16, + "Shared_Memory_/_Multiprocessor_(bytes)": 114688, + "Max_Shared_Memory_/_Block_(bytes)": 49152, + "Register_File_Size_/_Multiprocessor_(32-bit_registers)": 131072, + "Max_Registers_/_Block": 65536, + "Register_Allocation_Unit_Size": 256, + "Register_Allocation_Granularity": "warp", + "Max_Registers_/_Thread": 255, + "Shared_Memory_Allocation_Unit_Size": 256, + "Warp_Allocation_Granularity": 4, + "Max_Thread_Block_Size": 1024, + "Shared_Memory_Size_Configurations_(bytes)": 114688, + "Warp_register_allocation_granularities": 256 }, "sm_60": { - "warp_size": 32, - "maxTHREADSperBLOCK": 1024, - "maxTHREADSperSM": 2048, - "maxBLOCKSperSM": 32, - "SMEMperBLOCK": 49152, - "SMEMperSM": 65536 + "Compute_Capability": 6.0, + "SM_Version": "sm_60", + "Threads_/_Warp" : 32, + "Warps_/_Multiprocessor": 64, + "Threads_/_Multiprocessor": 2048, + "Thread_Blocks_/_Multiprocessor": 32, + "Shared_Memory_/_Multiprocessor_(bytes)": 65536, + "Max_Shared_Memory_/_Block_(bytes)": 49152, + "Register_File_Size_/_Multiprocessor_(32-bit_registers)": 65536, + "Max_Registers_/_Block": 65536, + "Register_Allocation_Unit_Size": 256, + "Register_Allocation_Granularity": "warp", + "Max_Registers_/_Thread": 255, + "Shared_Memory_Allocation_Unit_Size": 256, + "Warp_Allocation_Granularity": 4, + "Max_Thread_Block_Size": 1024, + "Shared_Memory_Size_Configurations_(bytes)": 65536, + "Warp_register_allocation_granularities": 256 } } diff --git a/src/acc/libsmm_acc/libcusmm/libcusmm.cpp b/src/acc/libsmm_acc/libcusmm/libcusmm.cpp index 36671bc1160..ba6ae8b7540 100644 --- a/src/acc/libsmm_acc/libcusmm/libcusmm.cpp +++ b/src/acc/libsmm_acc/libcusmm/libcusmm.cpp @@ -31,7 +31,7 @@ //=========================================================================== inline int launch_kernel_from_handle(CUfunction const& kern_func, int nblks, int threads, CUstream stream, void** args){ - CUDA_SAFE_CALL( + CU_SAFE_CALL( "cuLaunchKernel", cuLaunchKernel(kern_func, // CUfunction nblks, 1, 1, // grid dimension x, y, z @@ -59,19 +59,14 @@ inline void validate_kernel(CUfunction& kern_func, CUstream stream, int threads, double sumCPU = checkSum(h->mat_c, h->n_c, m, n); // Run the matrix-matrix multiplication kernel on the GPU - cudaMemcpy(h->d_mat_a, h->mat_a, h->n_a * m * k * sizeof(double), cudaMemcpyHostToDevice); - cudaMemcpy(h->d_mat_b, h->mat_b, h->n_b * k * n * sizeof(double), cudaMemcpyHostToDevice); - cudaMemcpy(h->d_stack, h->stack, h->n_stack * 3 * sizeof(int), cudaMemcpyHostToDevice); - cudaMemset(h->d_mat_c, 0, h->n_c * m * n * sizeof(double)); + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(h->d_mat_a, h->mat_a, h->n_a * m * k * sizeof(double), cudaMemcpyHostToDevice)); + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(h->d_mat_b, h->mat_b, h->n_b * k * n * sizeof(double), cudaMemcpyHostToDevice)); + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(h->d_stack, h->stack, h->n_stack * 3 * sizeof(int), cudaMemcpyHostToDevice)); + CUDA_SAFE_CALL("cudaMemset", cudaMemset(h->d_mat_c, 0, h->n_c * m * n * sizeof(double))); void *args[] = { &h->d_stack, &h->n_stack, &h->d_mat_a, &h->d_mat_b, &h->d_mat_c }; int res = launch_kernel_from_handle(kern_func, ((h->n_stack + grouping - 1) / grouping), threads, stream, args); - cudaMemcpy(h->mat_c, h->d_mat_c, h->n_c * m * n * sizeof(double), cudaMemcpyDeviceToHost); - cudaError_t cudaError = cudaGetLastError(); - if (cudaError != cudaSuccess){ - printf("validate_kernel: cuda_error: %s\n", cudaGetErrorString(cudaError)); - exit(1); - } + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(h->mat_c, h->d_mat_c, h->n_c * m * n * sizeof(double), cudaMemcpyDeviceToHost)); // Validate the kernel based on results double sumGPU = checkSum(h->mat_c, h->n_c, m, n); @@ -156,65 +151,113 @@ inline void jit_kernel(CUfunction& kern_func, libcusmm_algo algo, int tile_m, in // Get pointer to kernel from PTX CUmodule module; - CUDA_SAFE_CALL("cuModuleLoadDataEx", cuModuleLoadDataEx(&module, ptx, 0, 0, 0)); - delete[] ptx; - CUDA_SAFE_CALL("cuModuleGetFunction", cuModuleGetFunction(&kern_func, module, lowered_kernel_name)); + CU_SAFE_CALL("cuModuleLoadDataEx", cuModuleLoadDataEx(&module, ptx, 0, 0, 0)); + delete[] ptx; + CU_SAFE_CALL("cuModuleGetFunction", cuModuleGetFunction(&kern_func, module, lowered_kernel_name)); // Set shared memory configuration - CUDA_SAFE_CALL("cuFuncSetSharedMemConfig", cuFuncSetSharedMemConfig(kern_func, CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE)); + CU_SAFE_CALL("cuFuncSetSharedMemConfig", cuFuncSetSharedMemConfig(kern_func, CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE)); // Destroy program NVRTC_SAFE_CALL("nvrtcDestroyProgram", nvrtcDestroyProgram(&kernel_program)); } +void add_kernel_handle_to_jitted_kernels(CUfunction kern_func, CUstream stream, Triplet h_mnk, int& threads, int& grouping, bool& cpu_fallback){ + + // Check whether autotuned parameters are given for this kernel, and if so, retrieve them + if (ht.find(h_mnk) != ht.end()){ + + // Retrieve launching parameters + const KernelParameters params = ht.at(h_mnk); + libcusmm_algo algo = libcusmm_algo(params[0]); // enum {largeDB1, largeDB2, medium, small, tiny} + int tile_m = params[1]; + int tile_n = params[2]; + int w = params[3]; + int v = params[4]; + threads = params[5]; + grouping = params[6]; + int minblocks = params[7]; + + // JIT and validate the kernel + jit_kernel(kern_func, algo, tile_m, tile_n, w, v, threads, grouping, minblocks, h_mnk[0], h_mnk[1], h_mnk[2]); + validate_kernel(kern_func, stream, threads, grouping, h_mnk[0], h_mnk[1], h_mnk[2]); + + // Store the handle to the JIT-ed kernel + kernel_handles.emplace(h_mnk, kernel_launcher(kern_func, threads, grouping)); + + } else { // there exist no autotuned parameters for this (m, n, k)-triplet, fall back to CPU + + cpu_fallback = true; + + } + +} + + //=========================================================================== int libcusmm_process_d(int *param_stack, int stack_size, CUstream stream, int m, int n, int k, double *a_data, double *b_data, double *c_data){ CUfunction kern_func; - int threads, grouping; - - // Look up the kernel in the table of already JITed kernels + int threads, grouping; Triplet h_mnk = { m, n, k }; - auto kernel_it = kernel_handles.find(h_mnk); - if (kernel_it != kernel_handles.end()){ // the kernel has already been JITed - - // Retrieve kernel launching parameters - kern_func = kernel_it->second.kernel_function; - threads = kernel_it->second.threads; - grouping = kernel_it->second.grouping; - - } else { // the kernel has not been JIT-ed yet - // Check whether autotuned parameters are given for this kernel, and if so, retrieve them - auto params_it = ht.find(h_mnk); - if (params_it != ht.end()){ + // Look up the kernel in the table of already JITed kernels + if (kernel_handles.find(h_mnk) == kernel_handles.end()){ // the kernel has not been JIT-ed yet + + static bool cpu_fallback = false; + + // Add lock to table of locks and initialize it + // (Some serialization here) +#if defined _OPENMP + #pragma omp critical + { + if(kernel_locks.find(h_mnk) == kernel_locks.end()){ + omp_lock_t lock_kernel_mnk; + kernel_locks.emplace(h_mnk, lock_kernel_mnk); + omp_init_lock(&kernel_locks.at(h_mnk)); + } + } - // Retrieve launching parameters - const KernelParameters params = ht.at(h_mnk); - libcusmm_algo algo = libcusmm_algo(params[0]); // enum {largeDB1, largeDB2, medium, small, tiny} - int tile_m = params[1]; - int tile_n = params[2]; - int w = params[3]; - int v = params[4]; - threads = params[5]; - grouping = params[6]; - int minblocks = params[7]; + // Set lock if it exists + if(kernel_locks.find(h_mnk) != kernel_locks.end()){ + omp_set_lock(&kernel_locks.at(h_mnk)); + } - // JIT and validate the kernel - jit_kernel(kern_func, algo, tile_m, tile_n, w, v, threads, grouping, minblocks, m, n, k); - validate_kernel(kern_func, stream, threads, grouping, m, n, k); + // JIT the kernel using a single thread + if(kernel_handles.find(h_mnk) == kernel_handles.end()){ +#endif + add_kernel_handle_to_jitted_kernels(kern_func, stream, h_mnk, threads, grouping, cpu_fallback); +#if defined _OPENMP + } - // Store the handle to the JIT-ed kernel - kernel_handles.emplace(h_mnk, kernel_launcher(kern_func, threads, grouping)); + // Unset lock and destroy + if(kernel_locks.find(h_mnk) != kernel_locks.end()){ + omp_unset_lock(&kernel_locks.at(h_mnk)); + } - } else { // there exist no autotuned parameters for this (m, n, k)-triplet + #pragma omp critical + { + if(kernel_locks.find(h_mnk) == kernel_locks.end()){ + omp_destroy_lock(&kernel_locks.at(h_mnk)); + auto it = kernel_locks.find(h_mnk); + kernel_locks.erase(it); + } + } +#endif + if(cpu_fallback) return -2; // fall back to CPU - } + } // now the kernel has been jitted - } + // Look up the kernel in the table of already JITed kernels + auto kernel_it = kernel_handles.find(h_mnk); + + // Retrieve kernel launching parameters + kern_func = kernel_it->second.kernel_function; + threads = kernel_it->second.threads; + grouping = kernel_it->second.grouping; // Construct argument pointer list and launch kernel void *args[] = { ¶m_stack, &stack_size, &a_data, &b_data, &c_data }; @@ -267,12 +310,12 @@ void jit_transpose_handle(CUfunction& kern_func, int m, int n){ // Get pointer to kernel from PTX CUmodule module; - CUDA_SAFE_CALL("cuModuleLoadDataEx", cuModuleLoadDataEx(&module, ptx, 0, 0, 0)); + CU_SAFE_CALL("cuModuleLoadDataEx", cuModuleLoadDataEx(&module, ptx, 0, 0, 0)); delete[] ptx; - CUDA_SAFE_CALL("cuModuleGetFunction", cuModuleGetFunction(&kern_func, module, lowered_kernel_name)); + CU_SAFE_CALL("cuModuleGetFunction", cuModuleGetFunction(&kern_func, module, lowered_kernel_name)); // Set shared memory configuration - CUDA_SAFE_CALL("cuFuncSetSharedMemConfig", cuFuncSetSharedMemConfig(kern_func, CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE)); + CU_SAFE_CALL("cuFuncSetSharedMemConfig", cuFuncSetSharedMemConfig(kern_func, CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE)); // Destroy program NVRTC_SAFE_CALL("nvrtcDestroyProgram", nvrtcDestroyProgram(&kernel_program)); @@ -287,25 +330,24 @@ int libcusmm_transpose_d(int *trs_stack, int offset, int nblks, // Look up the kernel in the table of already JITed kernels Triplet h_mnk = { m, n, 0 }; - auto kernel_it = transpose_handles.find(h_mnk); + auto kernel_it = transpose_handles.find(h_mnk); if(kernel_it != transpose_handles.end()){ // the kernel has already been JITed kern_func = kernel_it->second; // retrieve handle - } else { // the kernel has not been JIT-ed yet + } else { // the kernel has not been JIT-ed yet // JIT and store a kernel for this transposition jit_transpose_handle(kern_func, m, n); transpose_handles.emplace(h_mnk, kern_func); } - + // Construct argument pointer list and lauch function int* trs_stack_ = trs_stack + offset; void *args[] = { &trs_stack_, &buffer}; - int res = launch_kernel_from_handle(kern_func, nblks, 128, stream, args); - return(cudaGetLastError()); + return launch_kernel_from_handle(kern_func, nblks, 128, stream, args); } diff --git a/src/acc/libsmm_acc/libcusmm/libcusmm.h b/src/acc/libsmm_acc/libcusmm/libcusmm.h index ac7794bba5b..d0a63abfbb2 100644 --- a/src/acc/libsmm_acc/libcusmm/libcusmm.h +++ b/src/acc/libsmm_acc/libcusmm/libcusmm.h @@ -19,26 +19,21 @@ #include #include +#if defined _OPENMP +#include +#endif + +// Macros for CUDA error handling +// Wrap calls to CUDA NVRTC API #define NVRTC_SAFE_CALL(name, x) \ do { \ nvrtcResult result = x; \ if (result != NVRTC_SUCCESS) { \ - printf("\nerror: %s failed with error %s\n", \ + printf("\nNVRTC ERROR: %s failed with error %s\n", \ name, nvrtcGetErrorString(result)); \ exit(1); \ } \ } while(0) -#define CUDA_SAFE_CALL(name, x) \ - do { \ - CUresult result = x; \ - if (result != CUDA_SUCCESS) { \ - const char *msg; \ - cuGetErrorName(result, &msg); \ - printf("\nerror: %s failed with error %s\n", \ - name, msg); \ - exit(1); \ - } \ - } while(0) enum libcusmm_algo { largeDB1 = 1, @@ -56,6 +51,9 @@ struct kernel_launcher { }; static std::unordered_map kernel_handles; +#if defined _OPENMP +static std::unordered_map kernel_locks; +#endif int libcusmm_process_d(int *param_stack, int stack_size, CUstream stream, int m, int n, int k, diff --git a/src/acc/libsmm_acc/libcusmm/libcusmm_benchmark.cu b/src/acc/libsmm_acc/libcusmm/libcusmm_benchmark.cu index c6c6ebb5de1..7f66b51266d 100644 --- a/src/acc/libsmm_acc/libcusmm/libcusmm_benchmark.cu +++ b/src/acc/libsmm_acc/libcusmm/libcusmm_benchmark.cu @@ -57,35 +57,30 @@ void libcusmm_benchmark_init(libcusmm_benchmark_t** handle, benchmark_mode mode, h->stack_trs_a = (int*) malloc(h->n_stack_trs_a * sizeof(int)); h->stack_trs_b = (int*) malloc(h->n_stack_trs_b * sizeof(int)); - cudaMalloc(&h->d_mat_a, h->n_a * max_m * max_k * sizeof(double)); - cudaMalloc(&h->d_mat_b, h->n_b * max_k * max_n * sizeof(double)); - cudaMalloc(&h->d_mat_c, h->n_c * max_m * max_n * sizeof(double)); - cudaMalloc(&h->d_stack, h->n_stack * 3 * sizeof(int)); - cudaMalloc(&h->d_stack_trs_a, h->n_stack_trs_a * sizeof(int)); - cudaMalloc(&h->d_stack_trs_b, h->n_stack_trs_b * sizeof(int)); - - cuEventCreate(&h->t_start, CU_EVENT_DEFAULT); - cuEventCreate(&h->t_stop, CU_EVENT_DEFAULT); - - cudaError_t cudaError = cudaGetLastError(); - if (cudaError != cudaSuccess){ - printf("libcusmm_benchmark_init: %s\n", cudaGetErrorString(cudaError)); - exit(1); - } + CUDA_SAFE_CALL("cudaMalloc", cudaMalloc(&h->d_mat_a, h->n_a * max_m * max_k * sizeof(double))); + CUDA_SAFE_CALL("cudaMalloc", cudaMalloc(&h->d_mat_b, h->n_b * max_k * max_n * sizeof(double))); + CUDA_SAFE_CALL("cudaMalloc", cudaMalloc(&h->d_mat_c, h->n_c * max_m * max_n * sizeof(double))); + CUDA_SAFE_CALL("cudaMalloc", cudaMalloc(&h->d_stack, h->n_stack * 3 * sizeof(int))); + CUDA_SAFE_CALL("cudaMalloc", cudaMalloc(&h->d_stack_trs_a, h->n_stack_trs_a * sizeof(int))); + CUDA_SAFE_CALL("cudaMalloc", cudaMalloc(&h->d_stack_trs_b, h->n_stack_trs_b * sizeof(int))); + + CU_SAFE_CALL("cuEventCreate", cuEventCreate(&h->t_start, CU_EVENT_DEFAULT)); + CU_SAFE_CALL("cuEventCreate", cuEventCreate(&h->t_stop, CU_EVENT_DEFAULT)); + } //=========================================================================== // Free memory and cuda events void libcusmm_benchmark_finalize(libcusmm_benchmark_t* handle){ - cudaEventDestroy(handle->t_stop); - cudaEventDestroy(handle->t_start); - cudaFree(handle->d_stack_trs_b); - cudaFree(handle->d_stack_trs_a); - cudaFree(handle->d_stack); - cudaFree(handle->d_mat_c); - cudaFree(handle->d_mat_b); - cudaFree(handle->d_mat_a); + CU_SAFE_CALL("cuEventDestroy", cuEventDestroy(handle->t_start)); + CU_SAFE_CALL("cuEventDestroy", cuEventDestroy(handle->t_stop)); + CUDA_SAFE_CALL("cudaFree", cudaFree(handle->d_stack_trs_b)); + CUDA_SAFE_CALL("cudaFree", cudaFree(handle->d_stack_trs_a)); + CUDA_SAFE_CALL("cudaFree", cudaFree(handle->d_stack)); + CUDA_SAFE_CALL("cudaFree", cudaFree(handle->d_mat_c)); + CUDA_SAFE_CALL("cudaFree", cudaFree(handle->d_mat_b)); + CUDA_SAFE_CALL("cudaFree", cudaFree(handle->d_mat_a)); free(handle->stack_trs_b); free(handle->stack_trs_a); free(handle->stack); @@ -95,11 +90,6 @@ void libcusmm_benchmark_finalize(libcusmm_benchmark_t* handle){ free(handle->mat_trs_a); free(handle->mat_a); free(handle); - cudaError_t cudaError = cudaGetLastError(); - if (cudaError != cudaSuccess){ - printf("libcusmm_benchmark_finalize: %s\n", cudaGetErrorString(cudaError)); - exit(1); - } } @@ -286,8 +276,8 @@ int libcusmm_benchmark(libcusmm_benchmark_t* h, break; } - CUstream stream; - cuStreamCreate(&stream, CU_STREAM_DEFAULT); + CUstream stream; + CU_SAFE_CALL("cuStreamCreate", cuStreamCreate(&stream, CU_STREAM_DEFAULT)); int error_counter = 0; int best_kernel = -1; @@ -313,9 +303,9 @@ int libcusmm_benchmark(libcusmm_benchmark_t* h, sumCPU = checkSum(h->mat_c, h->n_c, mat_m, mat_n); - cudaMemcpy(h->d_mat_a, h->mat_a, h->n_a * mat_m * mat_k * sizeof(double), cudaMemcpyHostToDevice); - cudaMemcpy(h->d_mat_b, h->mat_b, h->n_b * mat_k * mat_n * sizeof(double), cudaMemcpyHostToDevice); - cudaMemcpy(h->d_stack, h->stack, h->n_stack * 3 * sizeof(int), cudaMemcpyHostToDevice); + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(h->d_mat_a, h->mat_a, h->n_a * mat_m * mat_k * sizeof(double), cudaMemcpyHostToDevice)); + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(h->d_mat_b, h->mat_b, h->n_b * mat_k * mat_n * sizeof(double), cudaMemcpyHostToDevice)); + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(h->d_stack, h->stack, h->n_stack * 3 * sizeof(int), cudaMemcpyHostToDevice)); // d_mat_c gets zeroed after warmup run for(int ikern=0; ikern < nkernels; ikern++){ @@ -323,31 +313,24 @@ int libcusmm_benchmark(libcusmm_benchmark_t* h, // Warmup run (more often if n_iter is small) for(int i=0; id_stack, h->n_stack, stream, mat_m, mat_n, mat_k, h->d_mat_a, h->d_mat_b, h->d_mat_c); - cudaMemset(h->d_mat_c, 0, h->n_c * mat_m * mat_n * sizeof(double)); + CUDA_SAFE_CALL("cudaMemset", cudaMemset(h->d_mat_c, 0, h->n_c * mat_m * mat_n * sizeof(double))); - cuEventRecord(h->t_start, stream); + CU_SAFE_CALL("cuEventRecord", cuEventRecord(h->t_start, stream)); for(int i=0; id_stack, h->n_stack, stream, mat_m, mat_n, mat_k, h->d_mat_a, h->d_mat_b, h->d_mat_c); - cuEventRecord(h->t_stop, stream); - cuEventSynchronize(h->t_stop); - cuEventElapsedTime(&t_duration, h->t_start, h->t_stop); + CU_SAFE_CALL("cuEventRecord", cuEventRecord(h->t_stop, stream)); + CU_SAFE_CALL("cuEventSynchronize", cuEventSynchronize(h->t_stop)); + CU_SAFE_CALL("cuEventElapsedTime", cuEventElapsedTime(&t_duration, h->t_start, h->t_stop)); - cudaMemcpy(h->mat_c, h->d_mat_c, h->n_c * mat_m * mat_n * sizeof(double), cudaMemcpyDeviceToHost); + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(h->mat_c, h->d_mat_c, h->n_c * mat_m * mat_n * sizeof(double), cudaMemcpyDeviceToHost)); clean_string(kernel_descr[ikern], descr); if(h->mode == tune) sprintf(msg_prefix, "params %d / %d\n",ikern+1, nkernels); - cudaError = cudaGetLastError(); - if (cudaError != cudaSuccess){ - printf("%sERROR %s cuda_error: %s\n", msg_prefix, descr, cudaGetErrorString(cudaError)); - error_counter++; - continue; - } - sumGPU = checkSum(h->mat_c, h->n_c, mat_m, mat_n); if(sumGPU != sumCPU){ printf("%sERROR %s checksum_diff: %g\n",msg_prefix, descr, sumGPU-sumCPU); @@ -362,7 +345,7 @@ int libcusmm_benchmark(libcusmm_benchmark_t* h, best_gflops = gflops; best_kernel = ikern; } - }else{ + } else { printf("%sOK %s\n", msg_prefix, descr); } } @@ -375,7 +358,6 @@ int libcusmm_benchmark(libcusmm_benchmark_t* h, printf("WINNER: None\n"); } printf("Number of errors: %d\n", error_counter); - cudaDeviceReset(); } return(error_counter); @@ -395,7 +377,7 @@ int libcusmm_benchmark_transpose_(int n_stack, int* stack, int* d_stack, } CUstream stream; - cuStreamCreate(&stream, CU_STREAM_DEFAULT); + CU_SAFE_CALL("cuStreamCreate", cuStreamCreate(&stream, CU_STREAM_DEFAULT)); int offset = 0; int n_warm = 0; @@ -404,7 +386,6 @@ int libcusmm_benchmark_transpose_(int n_stack, int* stack, int* d_stack, double sumCPU, sumGPU; float t_duration; char descr[1000], msg_prefix[100]=""; - cudaError_t cudaError; // Matrix and stack initialization matInit(mat, n, mat_m, mat_n, 42); @@ -416,38 +397,26 @@ int libcusmm_benchmark_transpose_(int n_stack, int* stack, int* d_stack, sumCPU = checkSumTransp(mat_trs, n, n_stack, mat_m, mat_n); // Compute on GPU - cudaMemcpy(d_mat, mat, n * mat_m * mat_n * sizeof(double), cudaMemcpyHostToDevice); - cudaMemcpy(d_stack, stack, n_stack * sizeof(int), cudaMemcpyHostToDevice); - cudaError = cudaGetLastError(); - if (cudaError != cudaSuccess){ - printf("%sERROR %s cuda_error: %s\n", msg_prefix, descr, cudaGetErrorString(cudaError)); - error_counter++; - } + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(d_mat, mat, n * mat_m * mat_n * sizeof(double), cudaMemcpyHostToDevice)); + CUDA_SAFE_CALL("cudaMemcpy", cudaMemcpy(d_stack, stack, n_stack * sizeof(int), cudaMemcpyHostToDevice)); // Warmup run for(int i=0; imode == tune){ - printf("Tune mode not supported for benchmarking of transpose"); - exit(1); + printf("Tune mode not supported for benchmarking of transpose"); + exit(1); } int errors = 0; diff --git a/src/acc/libsmm_acc/libcusmm/libcusmm_benchmark.h b/src/acc/libsmm_acc/libcusmm/libcusmm_benchmark.h index deef105db87..d38915220e5 100644 --- a/src/acc/libsmm_acc/libcusmm/libcusmm_benchmark.h +++ b/src/acc/libsmm_acc/libcusmm/libcusmm_benchmark.h @@ -14,6 +14,29 @@ #define MAX_BLOCK_DIM 80 +// Macros for CUDA error handling +// Wrap calls to CUDA APIs (CUDA driver API and CUDA runtime API) +#define CU_SAFE_CALL(name, x) \ + do { \ + CUresult result = x; \ + if (result != CUDA_SUCCESS) { \ + const char *msg; \ + cuGetErrorName(result, &msg); \ + printf("\nCUDA DRIVER API ERROR: %s failed with error %s\n",\ + name, msg); \ + exit(1); \ + } \ + } while(0) +#define CUDA_SAFE_CALL(name, x) \ + do { \ + cudaError_t result = x; \ + if (result != cudaSuccess) { \ + printf("\nCUDA RUNTIME API error: %s failed with error %s\n",\ + name, cudaGetErrorName(result)); \ + exit(1); \ + } \ + } while(0) + typedef int (*KernelLauncher)(int *param_stack, int stack_size, CUstream stream, int m_max, int n_max, int k_max, double *a_data, double *b_data, double *c_data); diff --git a/src/acc/libsmm_acc/libcusmm/merge.py b/src/acc/libsmm_acc/libcusmm/merge.py deleted file mode 100755 index e8b18b51d12..00000000000 --- a/src/acc/libsmm_acc/libcusmm/merge.py +++ /dev/null @@ -1,60 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -#################################################################################################### -# Copyright (C) by the DBCSR developers group - All rights reserved # -# This file is part of the DBCSR library. # -# # -# For information on the license, see the LICENSE file. # -# For further information please visit https://dbcsr.cp2k.org # -# SPDX-License-Identifier: GPL-2.0+ # -#################################################################################################### - -import sys -import json -from optparse import OptionParser -from kernels.cusmm_dnt_helper import params_dict_to_kernel - - -def main(): - usage = "Write a new kernel parameter file as an unique merge of an old parameter file and a new one called " + \ - "parameters.json as created by collect.py. If a kernel (m, n, k) is listed in both the old parameter" + \ - "file and the new parameter file, retain its parameters as defined in the new parameter file." - parser = OptionParser(usage) - parser.add_option("-p", "--params", metavar="filename.json", - default="parameters_P100.json", - help="Default: %default") - - (options, args) = parser.parse_args() - assert len(args) == 0 - param_fn = options.params - - # Read new kernel parameters - with open("parameters.json") as f: - new_kernels = [params_dict_to_kernel(**params) for params in json.load(f)] - - # Read old kernel parameters - with open(param_fn) as f: - old_kernels = [params_dict_to_kernel(**params) for params in json.load(f)] - - # Merge two parameter lists - kernels_dict = dict(zip([(k.m, k.n, k.k) for k in old_kernels], old_kernels)) - new_kernels_dict = dict(zip([(k.m, k.n, k.k) for k in new_kernels], new_kernels)) - kernels_dict.update(new_kernels_dict) - - # Write kernel parameters to new file - new_file = "parameters.new.json" - with open(new_file, 'w') as f: - s = json.dumps([kernels_dict[kernel].as_dict for kernel in sorted(kernels_dict.keys())]) - s = s.replace('}, ', '},\n') - s = s.replace('[', '[\n') - s = s.replace(']', '\n]') - f.write(s) - - print("Wrote", new_file) - - -#=============================================================================== -if len(sys.argv)==2 and sys.argv[-1]=="--selftest": - pass #TODO implement selftest -else: - main() diff --git a/src/acc/libsmm_acc/libcusmm/notebooks/README.md b/src/acc/libsmm_acc/libcusmm/notebooks/README.md new file mode 100644 index 00000000000..1c2e4f48243 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/notebooks/README.md @@ -0,0 +1,12 @@ +# libcusmm/notebooks + +Notebooks for exploring data generated from autotuning and prediction. + +**Requirements** +Python version required: python 3.6 + +Install all python packages required (if you do not want this project's requirements to interfere with your other Python projects, consider doing so in a [virtual environment](https://docs.python.org/3/tutorial/venv.html)), using + +```%bash +pip install -r requirements.txt +``` diff --git a/src/acc/libsmm_acc/libcusmm/notebooks/inspect_autotuned_parameters.ipynb b/src/acc/libsmm_acc/libcusmm/notebooks/inspect_autotuned_parameters.ipynb new file mode 100644 index 00000000000..eeecb385b1e --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/notebooks/inspect_autotuned_parameters.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# `libcusmm`: explore the space of autotuned parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook can be used to explore the space of autotuned parameters, stored in files named `parameters_GPU.json`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Library imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import json, os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Choose a GPU" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "GPU = 'P100' # Options: K20X, K40, K80, P100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "params = '../parameters_' + GPU + '.json' \n", + "assert os.path.exists(params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Read autotuned parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open(params) as f:\n", + " all_parameters = pd.DataFrame([params for params in json.load(f)])\n", + "autotuned_parameters = all_parameters[all_parameters['source'] == 'autotuned']\n", + "print(\"Reading autotuned data from\", params)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "ordered_columns = ['m', 'n', 'k', 'perf', 'algorithm', 'threads', 'grouping', 'minblocks', 'tile_m', 'tile_n', 'v', 'w']\n", + "autotuned_parameters = autotuned_parameters[ordered_columns]\n", + "print('Autotuned parameters:')\n", + "display(autotuned_parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Description" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Numer of columns:', len(autotuned_parameters.columns), '\\nNumber of rows:', len(autotuned_parameters.index.values))\n", + "print('\\nColumn names:')\n", + "for c in autotuned_parameters.columns.values: \n", + " print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autotuned_parameters.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import pandas_profiling \n", + "pandas_profiling.ProfileReport(autotuned_parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot performances" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline \n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autotuned_parameters['mnk'] = autotuned_parameters['m'] * autotuned_parameters['n'] * autotuned_parameters['k']\n", + "plt.semilogx(autotuned_parameters['mnk'], autotuned_parameters['perf'], '.', markersize=3)\n", + "plt.xlabel('Training (m, n, k) triplets (in order of increasing m*n*k)')\n", + "plt.ylabel('Performance [Gflops]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ignore the 'threads' parameter since it has to be adapted to the size of matrix C\n", + "parameter_set = ['algorithm', 'grouping', 'minblocks', 'tile_m', 'tile_n', 'v', 'w']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Most frequent parameter sets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_par_set(algorithm, grouping, minblocks, tile_m, tile_n, v, w):\n", + " par_set= algorithm + '_' + str(int(grouping)) + '_' + str(int(minblocks)) \n", + " if not np.isnan(tile_m):\n", + " par_set += '_' + str(int(tile_m)) + '_' + str(int(tile_n))\n", + " if not np.isnan(v):\n", + " par_set += '_' + str(int(v)) + '_' + str(int(w))\n", + " return par_set\n", + " \n", + "vget = np.vectorize(get_par_set)\n", + "autotuned_parameters['param_set'] = vget(*[a for a in autotuned_parameters[parameter_set].values.transpose()])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "param_set_freq = autotuned_parameters['param_set'].value_counts(dropna=True)\n", + "autotuned_parameters['param_set_freq'] = autotuned_parameters['param_set'].apply(lambda item: param_set_freq[item])\n", + "autotuned_parameters.sort_values(by='param_set_freq', ascending=False, inplace=True)\n", + "autotuned_parameters.iloc[:50,:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Most frequent parameters (independently of each other)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "most_frequent_values = dict()\n", + "for c in autotuned_parameters.columns.values: \n", + " plt.figure\n", + " plt.hist(autotuned_parameters[c].dropna(), bins=50)\n", + " plt.title(c)\n", + " plt.show()\n", + " if c in parameter_set: \n", + " col = autotuned_parameters[c].dropna().values\n", + " values, counts = np.unique(col, return_counts=True)\n", + " ind_most_freq = np.argmax(counts)\n", + " most_freq_val = values[ind_most_freq]\n", + " most_frequent_values[c] = most_freq_val" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/src/acc/libsmm_acc/libcusmm/notebooks/inspect_training_data.ipynb b/src/acc/libsmm_acc/libcusmm/notebooks/inspect_training_data.ipynb new file mode 100644 index 00000000000..00741762b38 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/notebooks/inspect_training_data.ipynb @@ -0,0 +1,618 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# `libcusmm` explore training data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook allows you to explore the training data collected from autotuning before proceeding to training." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import re, sys, os, json\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Path to autotuning data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Provide the path to the autotuning data:\n", + "- You can use the bash cell below to navigate your filetree. \n", + "- Then, copy what you've replaced `AUTOTUNING_DATA_PATH` with in the Python variable `autotuning_data_path` below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "ls -ad AUTOTUNING_DATA_PATH/tune_dataset/tune_*x*x*/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "ls -ad ~/scratch/tune_dataset/tune_*x*x*/ | wc -l " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autotuning_data_path = '/users/alicej/scratch/tune_dataset' # may not recognize '~', provide absolute complete paths\n", + "assert os.path.exists(autotuning_data_path)\n", + "assert len(os.listdir(autotuning_data_path)) > 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set options\n", + "\n", + "Set the following options appropriately:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "to_read = 100 # How many / which data folders to read. Options: \n", + " # - 'all': reads from all available data folders. \n", + " # Beware, this might result in memory errors if large amounts of data are made available\n", + " # - a number: reads this number of data folders (e.g. 100)\n", + " # - a regex: reads the data folders with matching regex (e.g. tune_4x*x*)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kernel_folder_pattern = re.compile('tune_(\\d+)x(\\d+)x(\\d+)$')\n", + "\n", + "# Get the list of folders to read\n", + "if to_read == 'all':\n", + " folders_to_read = [os.path.join(autotuning_data_path, f) for f in os.listdir(autotuning_data_path) \n", + " if kernel_folder_pattern.match(f) is not None]\n", + "elif isinstance(to_read, int):\n", + " folders_to_read = [os.path.join(autotuning_data_path, f) for f in os.listdir(autotuning_data_path) \n", + " if kernel_folder_pattern.match(f) is not None]\n", + " folders_to_read = folders_to_read[:to_read]\n", + "elif isinstance(to_read, str):\n", + " to_read = re.compile(to_read)\n", + " folders_to_read = [os.path.join(autotuning_data_path, f) for f in os.listdir(autotuning_data_path) \n", + " if to_read.match(f) is not None] \n", + "else: \n", + " assert False, \"Cannot recognize option: \" + to_read\n", + "\n", + "num_folders_to_read = len(folders_to_read)\n", + "assert num_folders_to_read > 0\n", + "print(\"Data folders to be read from (total: {:,})\\n\".format(num_folders_to_read))\n", + "[print(f) for f in folders_to_read]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "algorithm = 'all' # algorithms to explore. Options: all, tiny, small, medium\n", + " #-------------------------------------------------------------------\n", + "algo_to_read = [algorithm] if algorithm != 'all' else ['tiny', 'small', 'medium', 'largeDB1', 'largeDB2']\n", + "print(\"Algorithm(s) to explore:\")\n", + "[print(a) for a in algo_to_read]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "thresh = 300000 # do not perform very long operations on row counts above this threshold\n", + " #-------------------------------------------------------------------" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read training data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataframe_list = list()\n", + "kernel_folder_pattern = re.compile('tune_(\\d+x\\d+x\\d+)$')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for i, kernel_folder in enumerate(folders_to_read):\n", + " print('\\nfrom {}, read ({}/{:,})'.format(kernel_folder, i+1, num_folders_to_read))\n", + " \n", + " for name_algo in algo_to_read: \n", + "\n", + " mnk_string = kernel_folder_pattern.search(kernel_folder).groups()[0]\n", + " raw_file_base = 'raw_training_data_' + mnk_string + '_' + name_algo + '.csv'\n", + " raw_file = os.path.join(kernel_folder, raw_file_base)\n", + " derived_file_base = 'training_data_' + mnk_string + '_' + name_algo + '.csv'\n", + " derived_file = os.path.join(kernel_folder, derived_file_base) \n", + "\n", + " if os.path.exists(raw_file) and os.path.exists(derived_file):\n", + "\n", + " # Read raw parameters file\n", + " raw = pd.read_csv(raw_file, index_col=0)\n", + " raw['algorithm'] = np.array([name_algo]*len(raw.index.values))\n", + " print('\\t{:50} number of lines: {:>8,}'.format(raw_file_base, len(raw.index.values)))\n", + "\n", + " # Read derived parameters file\n", + " derived = pd.read_csv(derived_file, index_col=0)\n", + " print('\\t{:50} number of lines: {:>8,}'.format(derived_file_base, len(raw.index.values)))\n", + " dataframe_list.append(pd.concat([raw, derived], axis=1))\n", + "\n", + " else: \n", + " \n", + " if not os.path.exists(raw_file):\n", + " print('\\t...{:50} no file'.format(raw_file_base))\n", + " if not os.path.exists(derived_file):\n", + " print('\\t...{:50} no file'.format(derived_file_base))\n", + " \n", + "print('Read all csv files, merging dataframes')\n", + "data = pd.concat(dataframe_list, ignore_index=True)\n", + "data.dropna(axis=1, how='all', inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data head" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "page_width = 5 # columns per output line\n", + "for i in range(0, len(data.columns.values), page_width):\n", + " display(data.iloc[:,i:i+page_width].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data description" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "print('Data size :', sys.getsizeof(data)/10**6, 'MB')\n", + "print('Number of columns:', len(data.columns.values))\n", + "print('Number of rows : {:,}'.format(len(data.index.values)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for i in range(0, len(data.columns.values), page_width):\n", + " display(data.iloc[:,i:i+page_width].describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print('Number of columns:', len(data.columns), '\\nNumber of rows:', len(data.index.values), '\\n')\n", + "for col in data.columns: \n", + " print('{:<40} {}'.format(col, data[col].dtype))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Feature categories\n", + "mnk = ['m', 'n', 'k']\n", + "kernel_pars = ['algorithm', 'threads_per_blk', 'grouping', 'minblocks',\n", + " 'tile_m', 'tile_n', 'w', 'v', 'nbytes_smem', 'nbytes_cmem', 'regs_per_thread']\n", + "kernel_pars = list(set(kernel_pars) & set(data.columns.values))\n", + "perf = ['perf (Gflop/s)', 'perf_squared', 'perf_scaled', 'perf_scaled_by_algo']\n", + "common = ['Gflops', 'mxnxk', 'size_a', 'size_b', 'size_c', 'nblks', \n", + " 'warps_per_blk', 'nwarps', 'sm_desired', 'nthreads', 'ru_param_stack_unroll_factor']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "other_columns = list(set(data.columns.values) - set(kernel_pars + mnk + perf + common))\n", + "irow = 0#random.sample(range(len(data.index.values)), 1)[0]\n", + "row = data.iloc[irow,:]\n", + "print(row[mnk], '\\n')\n", + "print(row[kernel_pars], '\\n')\n", + "print(row[perf], '\\n')\n", + "print(row[common], '\\n')\n", + "print(row[other_columns], '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Features\n", + "\n", + "Features in the left-most column correspond to \"raw\" parameters\n", + "* **green** kernel parameters \n", + "* **grey** CUDA card properties (taken from CUDA documentation) \n", + "* **pink** autotuning parameters (taken from DBCSR codebase) \n", + "\n", + "Other features correspond to derived parameters, computed from the \"raw\" parameters\n", + "* **yellow** matrix sizes\n", + "* **light grey** launch parameters\n", + "* **blue** and **purple** estimations of resource usages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![parameters dependency graph](libcusmm_parameters_and_memory.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "data_profiling = data\n", + "if len(data.index.values) > thresh: # if it is a very large dataframe, perform op on subsampled rows \n", + " sampled_rows = random.sample(data_profiling.index.values.tolist(), thresh)\n", + " data_profiling = data.iloc[sampled_rows,:]\n", + "\n", + "import pandas_profiling \n", + "pandas_profiling.ProfileReport(data_profiling)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data visualization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.semilogx(data['mxnxk'], data['perf (Gflop/s)'], '.', markersize=1)\n", + "plt.xlabel('Training (m, n, k) triplets (in order of increasing m*n*k)')\n", + "plt.ylabel('Performance [Gflops]')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.semilogx(data['mxnxk'], data['perf_squared'], '.', markersize=1)\n", + "plt.xlabel('Training (m, n, k) triplets (in order of increasing m*n*k)')\n", + "plt.ylabel('Performance squared [Gflops]^2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data visualization (scaled performance)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(data['mxnxk'], data['perf_scaled'], '.', markersize=1)\n", + "plt.xlabel('Training (m, n, k) triplets (in order of increasing m*n*k)')\n", + "plt.ylabel('Performance scaled (overall)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "algos = np.unique(data['algorithm'])\n", + "for algo in algos:\n", + " algo_data = data[data['algorithm'] == algo]\n", + " plt.figure()\n", + " plt.semilogx(algo_data['mxnxk'], algo_data['perf_scaled_by_algo'], '.', markersize=1)\n", + " plt.xlabel('Training (m, n, k) triplets (in order of increasing m*n*k)')\n", + " plt.ylabel('Performance scaled (by algorithm)')\n", + " plt.title(algo)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performance profile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose (m, n, k) triplet\n", + "m_plot, n_plot, k_plot = (4, 4, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_mnk = data[data['m'] == m_plot][ \n", + " data['n'] == n_plot][ \n", + " data['k'] == k_plot]\n", + "data_mnk.sort_values(by='perf (Gflop/s)', ascending=True, inplace=True)\n", + "plt.plot(data_mnk['perf (Gflop/s)'].values)\n", + "plt.xlabel('parameter set')\n", + "plt.ylabel('perf (Gflop/s)')\n", + "plt.title('Performance profile for kernel ' + str(m_plot) + 'x'+ str(n_plot) + 'x'+ str(k_plot))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Histograms with Bokeh\n", + "from bokeh.plotting import figure \n", + "from bokeh.models import ColumnDataSource, HoverTool\n", + "from bokeh.io import output_notebook, show\n", + "output_notebook()\n", + "\n", + "# Create histogram\n", + "num_bins = 100 \n", + "hist, edges = np.histogram(data_mnk['perf (Gflop/s)'], bins=num_bins)\n", + "df_hist = pd.DataFrame({'hist': hist, 'left': edges[:-1], 'right': edges[1:]})\n", + "source = ColumnDataSource(df_hist)\n", + "\n", + "# Create tool \n", + "hover = HoverTool(tooltips=[('# occurences', '@hist'), ('low', '@left'), ('high', '@right')])\n", + "\n", + "# Create the figure\n", + "p = figure(plot_width=800, plot_height=800, title=\"Performance histogram\",\n", + " toolbar_location=None, tools=\"\")\n", + "p.xgrid.grid_line_color = None\n", + "p.xaxis.axis_label = \"Performance (GFlop/s)\"\n", + "p.xaxis.major_label_orientation = 1.2\n", + "p.yaxis.axis_label = \"# occurrences\"\n", + "p.quad(source=source, bottom=0, top='hist', left='left', right='right', fill_color='blue')\n", + "p.add_tools(hover)\n", + "show(p)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Histograms with Bokeh\n", + "from bokeh.plotting import figure \n", + "from bokeh.models import ColumnDataSource, HoverTool\n", + "from bokeh.io import output_notebook, show\n", + "output_notebook()\n", + "\n", + "# Create histogram\n", + "num_bins = 100 \n", + "hist, edges = np.histogram(data_mnk['perf_scaled'], bins=num_bins)\n", + "df_hist = pd.DataFrame({'hist': hist, 'left': edges[:-1], 'right': edges[1:]})\n", + "source = ColumnDataSource(df_hist)\n", + "\n", + "# Create tool \n", + "hover = HoverTool(tooltips=[('# occurences', '@hist'), ('low', '@left'), ('high', '@right')])\n", + "\n", + "# Create the figure\n", + "p = figure(plot_width=800, plot_height=800, title=\"Performance histogram\",\n", + " toolbar_location=None, tools=\"\")\n", + "p.xgrid.grid_line_color = None\n", + "p.xaxis.axis_label = \"Performance scaled\"\n", + "p.xaxis.major_label_orientation = 1.2\n", + "p.yaxis.axis_label = \"# occurrences\"\n", + "p.quad(source=source, bottom=0, top='hist', left='left', right='right', fill_color='blue')\n", + "p.add_tools(hover)\n", + "show(p)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Top slices of perf. distribution\n", + "pars_autotuning_top = {\n", + " 5: list(), \n", + " 2: list(), \n", + " 1: list(), \n", + " 0.5: list()\n", + "}\n", + "max_perf = float(data_mnk['perf (Gflop/s)'].max())\n", + "max_perf_idx = data_mnk['perf (Gflop/s)'].idxmax()\n", + "max_perf_row = data_mnk.loc[max_perf_idx]\n", + "max_perf_cond = max_perf_row[mnk + kernel_pars + ['perf (Gflop/s)']]\n", + "\n", + "print('Maximally performing parameter set:')\n", + "display(max_perf_cond)\n", + "for perc in pars_autotuning_top.keys():\n", + " lim = max_perf - max_perf*perc/100\n", + " blob = data_mnk.loc[data_mnk['perf (Gflop/s)'] >= lim]\n", + " print('\\ntop', perc, '%')\n", + " display(blob[kernel_pars + ['perf (Gflop/s)']].describe())\n", + " pars_autotuning_top[perc].append(blob)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pair plot " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_pairplot = data\n", + "if len(data.index.values) > thresh: # if it is a very large dataframe, perform op on subsampled rows \n", + " sampled_rows = random.sample(data_pairplot.index.values.tolist(), thresh)\n", + " data_pairplot = data.iloc[sampled_rows,:]\n", + "\n", + "sns.pairplot(data_pairplot[mnk + kernel_pars + perf].dropna())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/src/acc/libsmm_acc/libcusmm/notebooks/libcusmm_parameters_and_memory.png b/src/acc/libsmm_acc/libcusmm/notebooks/libcusmm_parameters_and_memory.png new file mode 120000 index 00000000000..9d26ae9ff4e --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/notebooks/libcusmm_parameters_and_memory.png @@ -0,0 +1 @@ +../../../../../docs/images/libcusmm_predictive_modeling_features.png \ No newline at end of file diff --git a/src/acc/libsmm_acc/libcusmm/notebooks/requirements.txt b/src/acc/libsmm_acc/libcusmm/notebooks/requirements.txt new file mode 100644 index 00000000000..b4d8d789134 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/notebooks/requirements.txt @@ -0,0 +1,19 @@ +bokeh==1.0.4 +cycler==0.10.0 +Jinja2==2.10 +kiwisolver==1.0.1 +MarkupSafe==1.1.0 +matplotlib==3.0.2 +numpy==1.16.0 +packaging==18.0 +pandas==0.23.4 +pandas-profiling==1.4.1 +Pillow==5.4.1 +pyparsing==2.3.1 +python-dateutil==2.7.5 +pytz==2018.9 +PyYAML==4.2b1 +scipy==1.2.0 +seaborn==0.9.0 +six==1.12.0 +tornado==5.1.1 diff --git a/src/acc/libsmm_acc/libcusmm/parameters_K20X.json b/src/acc/libsmm_acc/libcusmm/parameters_K20X.json index 96666b0582e..50cea719a0a 100644 --- a/src/acc/libsmm_acc/libcusmm/parameters_K20X.json +++ b/src/acc/libsmm_acc/libcusmm/parameters_K20X.json @@ -1,3145 +1,3146 @@ [ -{"m": 4, "n": 4, "k": 4, 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a/src/acc/libsmm_acc/libcusmm/parameters_K40.json +++ b/src/acc/libsmm_acc/libcusmm/parameters_K40.json @@ -1,21 +1,21 @@ [ -{"m": 5, "n": 5, "k": 5, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "tiny", "perf": 30.4899}, -{"m": 6, "n": 6, "k": 6, "threads": 128, "grouping": 16, "minblocks": 1, "algorithm": "tiny", "perf": 44.1711}, -{"m": 7, "n": 7, "k": 7, "threads": 96, "grouping": 16, "minblocks": 1, "algorithm": "tiny", "perf": 64.2483}, -{"m": 8, "n": 8, "k": 8, "tile_m": 1, "tile_n": 1, "threads": 64, "grouping": 16, "minblocks": 4, "algorithm": "small", "perf": 95.1028}, -{"m": 9, "n": 9, "k": 9, "tile_m": 1, "tile_n": 1, "threads": 96, "grouping": 16, "minblocks": 4, "algorithm": "small", "perf": 111.384}, -{"m": 10, "n": 10, "k": 10, "tile_m": 1, "tile_n": 1, "threads": 128, "grouping": 16, "minblocks": 4, "algorithm": "small", "perf": 132.089}, -{"m": 11, "n": 11, "k": 11, "tile_m": 1, "tile_n": 1, "threads": 128, "grouping": 16, "minblocks": 4, "algorithm": 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"largeDB2", "source": "predicted"}, +{"m": 45, "n": 45, "k": 40, "tile_m": 5, "tile_n": 7, "w": 8, "v": 6, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "source": "predicted"}, +{"m": 45, "n": 45, "k": 41, "tile_m": 5, "tile_n": 7, "w": 8, "v": 6, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "source": "predicted"}, +{"m": 45, "n": 45, "k": 42, "tile_m": 5, "tile_n": 7, "w": 8, "v": 6, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "source": "predicted"}, +{"m": 45, "n": 45, "k": 43, "tile_m": 5, "tile_n": 7, "w": 8, "v": 6, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "source": "predicted"}, +{"m": 45, "n": 45, "k": 44, "tile_m": 5, "tile_n": 7, "w": 8, "v": 6, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "source": "predicted"}, +{"m": 45, "n": 45, "k": 45, "tile_m": 6, "tile_n": 6, "w": 12, "v": 18, "threads": 64, "grouping": 16, "minblocks": 2, "algorithm": "largeDB2", "perf": 2690.9, "source": "autotuned"}, +{"m": 55, "n": 16, "k": 16, "tile_m": 1, "tile_n": 4, "threads": 256, "grouping": 4, "minblocks": 5, "algorithm": "medium", "perf": 1448.91, "source": "autotuned"}, +{"m": 55, "n": 16, "k": 29, "tile_m": 2, "tile_n": 8, "w": 8, "v": 8, "threads": 64, "grouping": 16, "minblocks": 8, "algorithm": "largeDB2", "perf": 1544.22, "source": "autotuned"}, +{"m": 55, "n": 16, "k": 55, "tile_m": 2, "tile_n": 8, "w": 8, "v": 12, "threads": 64, "grouping": 16, "minblocks": 4, "algorithm": "largeDB2", "perf": 1652.51, "source": "autotuned"}, +{"m": 55, "n": 29, "k": 16, "tile_m": 7, "tile_n": 4, "w": 8, "v": 22, "threads": 64, "grouping": 16, "minblocks": 2, "algorithm": "largeDB2", "perf": 1916.32, "source": "autotuned"}, +{"m": 55, "n": 29, "k": 29, "tile_m": 3, "tile_n": 6, "w": 10, "v": 18, "threads": 96, "grouping": 16, "minblocks": 4, "algorithm": "largeDB2", "perf": 2161.11, "source": "autotuned"}, +{"m": 55, "n": 29, "k": 32, "tile_m": 7, "tile_n": 4, "w": 8, "v": 22, "threads": 64, "grouping": 16, "minblocks": 2, "algorithm": "largeDB2", "perf": 2241.72, "source": "autotuned"}, +{"m": 55, "n": 29, "k": 55, "tile_m": 3, "tile_n": 6, "w": 14, "v": 16, "threads": 96, "grouping": 16, "minblocks": 2, "algorithm": "largeDB2", "perf": 2370.43, "source": "autotuned"}, +{"m": 55, "n": 32, "k": 29, "tile_m": 7, "tile_n": 4, "w": 14, "v": 20, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "perf": 2315.88, "source": "autotuned"}, +{"m": 55, "n": 32, "k": 32, "tile_m": 7, "tile_n": 4, "w": 8, "v": 24, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "perf": 2400.75, "source": "autotuned"}, +{"m": 55, "n": 32, "k": 55, "tile_m": 7, "tile_n": 4, "w": 6, "v": 12, "threads": 64, "grouping": 16, "minblocks": 4, "algorithm": "largeDB2", "perf": 2531.29, "source": "autotuned"}, +{"m": 55, "n": 55, "k": 16, "tile_m": 7, "tile_n": 4, "w": 8, "v": 32, "threads": 128, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "perf": 2371.04, "source": "autotuned"}, +{"m": 55, "n": 55, "k": 29, "tile_m": 7, "tile_n": 7, "w": 10, "v": 28, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "perf": 2634.65, "source": "autotuned"}, +{"m": 55, "n": 55, "k": 32, "tile_m": 7, "tile_n": 7, "w": 10, "v": 28, "threads": 64, "grouping": 16, "minblocks": 2, "algorithm": "largeDB2", "perf": 2696.5, "source": "autotuned"}, +{"m": 55, "n": 55, "k": 55, "tile_m": 7, "tile_n": 7, "w": 10, "v": 24, "threads": 64, "grouping": 16, "minblocks": 1, "algorithm": "largeDB2", "perf": 2934.2, "source": "autotuned"}, +{"m": 64, "n": 64, "k": 64, "tile_m": 9, "tile_n": 4, "w": 22, "v": 32, "threads": 128, "grouping": 16, "minblocks": 2, "algorithm": "largeDB2", "perf": 3072.53, "source": "autotuned"}, +{"m": 78, "n": 78, "k": 78, "tile_m": 10, "tile_n": 5, "w": 8, "v": 30, "threads": 128, "grouping": 16, "minblocks": 1, "algorithm": "largeDB1", "perf": 3319.77, "source": "autotuned"} +] diff --git a/src/acc/libsmm_acc/libcusmm/predict.md b/src/acc/libsmm_acc/libcusmm/predict.md new file mode 100644 index 00000000000..f6a3445933c --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/predict.md @@ -0,0 +1,148 @@ +# Training Procedure for Predictive Modelling of Optimal Parameters in `libcusmm` + +The performance of the matrix-matrix multiplication kernels is highly dependant on the choice of algorithm and parameters, this is why [*autotuning*](https://www.cp2k.org/howto:libcusmm) is used to find optimal kernel parameters. + +However, the autotuning procedure is expensive, and the space of (m,n,k)-triplets to explore is large. This predictive modeling procedure is set up to predict optimal parameters for (m,n,k)-triplets that have not been autotuned from the data gathered from autotuning other (m,n,k)-triplets. + +--- + +### Requirements + +Python version required: `python 3.6` + +Install all python packages required (if you do not want this project's requirements to interfere with your other Python projects, consider doing so in a [virtual environment](https://docs.python.org/3/tutorial/venv.html)), using + +```%bash +pip install -r requirements.txt +``` + + + +--- + +### Predictive parameters + +The input features for the predictive models can be 'raw' parameters (left-most-column in the figure below), or hand-engineered features 'derived' from the raw features (matrix sizes, launch parameters and resource usage estimations). + +![libcusmm_predictive_modeling_features](../../../../docs/images/libcusmm_predictive_modeling_features.png) + + + +--- + +### Predictive modeling procedure + +#### 1. Get the data + +Get the data to be used for training, either by downloading data from the [dedicated repository](https://github.com/cp2k/dbcsr-data), or by autotuning new kernels yourself and combining them with pre-existing data. + + + +##### 1.a Download pre-collected data from dedicated repository + +- Download data from the dedicated repository: + + ```%bash + wget https://github.com/cp2k/dbcsr-data/blob/master/GPU/raw_training_data_ALGORITHM.csv + ``` + +- Compute derived parameters from raw parameters and create a record of baseline and maximum performances: run [`predict_derivepars.py`](predict_derivepars.py) , providing the CUDA architecture number and the location of the downloaded data: + + ```%bash + ./predict_derivepars.py # –arch 60 --folder /scratch/autotuning_dataset, e.g. + ``` + + + +##### 1.b (optional) Aquire data from autotuning + +- We would appreciate if you would upload the data resulting from your autotuning procedure to the [dedicated repository](https://github.com/cp2k/dbcsr-data). For this, please take note, at this stage, of the [information required to upload your data](https://github.com/cp2k/dbcsr-data/blob/master/git-commit.template). + +- If you're autotuning data for a new GPU, make sure that the GPU's compute architecture properties are given in the file [`kernels/gpu_properties.json`](kernels/gpu_properties.json). If not, please add them. + +- Follow the [instructions for autotuning](https://www.cp2k.org/howto:libcusmm). + +- If all went well, you now have directories named `tune_mxnxk` containing log files in which parameter sets and their corresponding measured performances are recorded. + +- Collect the information in all the `tune_mxnxk` directories into CSV files: run [`predict_collect.py`](predict_collect.py), providing the CUDA architecture number and the location of the autotuning data: + + ```%bash + ./predict_collect.py # –arch 60 --folder /scratch/autotuning_dataset, e.g. + ``` + +- Follow the instructions given at the end to merge the CSV files. + +##### At the end, you should end up with the following files: + +- `raw_training_data_ALGORITHM.csv` (containing all *raw* parameters for training a model for algorithm ALGORITHM) +- `training_data_ALGORITHM.csv` (containing all *derived* parameters for training a model for algorithm ALGORITHM) + + + +#### 2. (optional) Explore the data + +Explore the data interactively using the [provided jupyter notebook](notebooks/inspect_training_data.ipynb). + + + +#### 3. Train + +For each algorithm, build a predictive model using decision trees and feature selection based on the features' permutation importance. + + +```%bash +./predict_train.py # –algo medium --folder /scratch/autotuning_dataset, e.g. +``` + +Repeat this step for all algorithms. +This may take several hours. For example, training algorithm 'medium' for the P100 took 11 hours on a single Greina (CSCS) node. +Moreover, depending on the size of the training data, large amounts of memory may be needed. For example, training algorithm 'medium' for the P100 was run on a 192 GB node. + + + +#### 4. Generate optimal parameters + +Given predictive models (in the form of serialized [scikit-learn](https://scikit-learn.org/) model objects) for all unseen (m,n,k)s, generate or update a file of optimal parameters + +```%bash +./predict_genpars.py -c 5000 \ # chunk size + --largeDB2 /scratch/largeDB2/feature_tree_refit.p \ # path to models + --largeDB1 /scratch/largeDB1/feature_tree_refit.p \ + --medium /scratch/medium/feature_tree_refit.p \ + --small /scratch/small/feature_tree_refit.p \ + --tiny /scratch/tiny/feature_tree_refit.p +``` + +This may take several hours. For example, generating parameters for the P100 took 8 hours on a single Piz Daint (CSCS) node. + + + +#### 5. Evaluate the predicted parameters + +```%bash +./predict_evaluate.py -f libcusmm_predicted.out -n libcusmm_baseline.out +``` + + + +#### 6. Contribute your new parameters and data + +##### Contribute training data + +See [instructions](https://github.com/cp2k/dbcsr-data#contributing) in our [dedicated repository](https://github.com/cp2k/dbcsr-data) + +##### Contribute predicted parameters + +Submit a pull request updating the `parameters_GPU.json` file in question. + + + +--- + +### Contributing to the training procedure + +##### Adding a new predictive feature + +- Choose the new feature's name +- Add the feature as a method of `class PredictiveParameters`, named `get_NAME` +- Add the derived feature to the data structure `derived_parameters` in [`kernels/cusmm_predict.py`](kernels/cusmm_predict.py) diff --git a/src/acc/libsmm_acc/libcusmm/predict_collect.py b/src/acc/libsmm_acc/libcusmm/predict_collect.py new file mode 100755 index 00000000000..04aa4aafbd7 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/predict_collect.py @@ -0,0 +1,317 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import os +import re +import json +import argparse +import pandas as pd +from kernels.cusmm_predict import ( + get_max_performances_per_mnk, + get_baseline_performances_per_mnk, + to_string, + PredictiveParameters, + derived_parameters, + kernel_algorithm, +) + + +# =============================================================================== +def main(tunedir, arch): + """ + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md + + Once autotuning of new kernels has been run, + - collect the parameter information and performance from log files, + - dump them to CSV files for data analysis and training of a predictive model + - write the max_performances and baseline_performances to JSON files + """ + # =============================================================================== + # Read GPU properties and autotuning properties + with open("kernels/gpu_properties.json") as f: + gpu_properties = json.load(f)["sm_" + str(arch)] + with open("kernels/autotuning_properties.json") as f: + autotuning_properties = json.load(f) + + # =============================================================================== + # Find all the 'tune_MxNxK' folders + kernel_folder_pattern = re.compile(r"tune_(\d+)x(\d+)x(\d+)$") + kernel_folders = [ + os.path.join(tunedir, ak) for ak in os.listdir(tunedir) if kernel_folder_pattern.match(ak) is not None + ] + n_kernels = len(kernel_folders) + print("Found {:,} kernel folders".format(n_kernels)) + max_performances_per_mnk = dict() + baseline_performances_per_algo_per_mnk = { + "tiny": dict(), + "small": dict(), + "medium": dict(), + "largeDB1": dict(), + "largeDB2": dict(), + } + + # =============================================================================== + # Collect information and write to csv + collect_training_data( + kernel_folders, + kernel_folder_pattern, + gpu_properties, + autotuning_properties, + max_performances_per_mnk, + baseline_performances_per_algo_per_mnk, + ) + + # =============================================================================== + # Print max performance dictionaries + max_performances_per_mnk_file = os.path.join(tunedir, "max_performances.json") + if os.path.exists(max_performances_per_mnk_file): + # If this file already exists, read its contents and merge them with the content to write + print("Found {}, reading and merging into new data...".format(max_performances_per_mnk_file)) + with open(max_performances_per_mnk_file, "r") as f: + max_performances_per_mnk_to_merge = json.load(f) + max_performances_per_mnk.update(max_performances_per_mnk_to_merge) + with open(max_performances_per_mnk_file, "w") as f: + json.dump(max_performances_per_mnk, f) + print("\nWrote maximum performances to:\n", max_performances_per_mnk_file) + + # Print baseline performance dictionaries + baseline_performances_per_algo_per_mnk_file = os.path.join(tunedir, "baseline_performances_by_algo.json") + if os.path.exists(baseline_performances_per_algo_per_mnk_file): + # If this file already exists, read its contents and merge them with the content to write + print("Found {}, reading and merging into new data...".format(baseline_performances_per_algo_per_mnk_file)) + with open(baseline_performances_per_algo_per_mnk_file, "r") as f: + baseline_performances_per_algo_per_mnk_to_merge = json.load(f) + baseline_performances_per_algo_per_mnk.update(baseline_performances_per_algo_per_mnk_to_merge) + with open(baseline_performances_per_algo_per_mnk_file, "w") as f: + json.dump(baseline_performances_per_algo_per_mnk, f) + print("\nWrote baseline performances to:\n", baseline_performances_per_algo_per_mnk_file) + + # =============================================================================== + # Print commands to merge CSVs into one big CSV for training data + print("Merge all individual CSV files into one by running the following commands:\n") + print_merging_commands(kernel_folders, kernel_folder_pattern, tunedir) + + +# =============================================================================== +# Helper variables and functions (formatting & writing) +autotuning_line = re.compile(r"OK Kernel_dnt_(\w+) m (\d+)\s+n (\d+)\s+k (\d+)\s+" + + r"(?:tile_m (\d+)\s+tile_n (\d+)\s+(?:w (\d+)\s+v (\d+)\s+)?)?" + + r"threads (\d+)\s+grouping (\d+)\s+minblocks (\d+)\s+GFlop/s (\d+(?:\.\d+)?)") + + +def read_log_file(log_folder, m, n, k): + """ + Given a folder of kernel autotuning, read and parse the autotuning information in the log file + and return it in the form of a pandas Dataframe. + :param log_folder: folder of kernel autotuning + :return: pandas Dataframe containing autotuning information + """ + # Find log files in the log folder + log_files = [f for f in os.listdir(log_folder) if f[-4:] == ".log"] + assert len(log_files) > 0 + log_files = sorted(log_files) + + # Parse the log files and collect data + data = list() + for log_file in log_files: + + print("Processing log file", log_file) + with open(os.path.join(log_folder, log_file), "r") as f: + log_file_content = f.read().splitlines() + + for l in log_file_content: + + if "OK" in l: # this line contains autotuning data + + # Parse the line + match = autotuning_line.match(l) + assert match is not None, "Found null match: " + l + + # Get algorithm, parameters, and performance + data.append({ + "m": m, + "n": n, + "k": k, + "algorithm": match.group(1), + "threads": int(match.group(9)), + "grouping": int(match.group(10)), + "minblocks": int(match.group(11)), + "tile_m": int(match.group(5)) if match.group(5) is not None else None, + "tile_n": int(match.group(6)) if match.group(6) is not None else None, + "w": int(match.group(7)) if match.group(7) is not None else None, + "v": int(match.group(8)) if match.group(8) is not None else None, + "perf (Gflop/s)": float(match.group(12)), + }) + + print("Autotuning lines found: ", len(data)) + + # Merge dictionaries into a pandas dataframe + dataframe = pd.DataFrame(data) + + return dataframe + + +def collect_training_data( + kernel_folders, + kernel_folder_pattern, + gpu_properties, + autotuning_properties, + max_performances_per_mnk, + baseline_performances_per_algo_per_mnk, +): + """ + Collect training data from log files resulting of autotuning + """ + + n_kernels = len(kernel_folders) + + # For each folder: + for i, kernel_folder in enumerate(kernel_folders): + + print("\nProcess folder {} ({}/{:,})".format(kernel_folder, i + 1, n_kernels)) + + # Find (m, n, k) + match = kernel_folder_pattern.search(kernel_folder).groups() + m = int(match[0]) + n = int(match[1]) + k = int(match[2]) + + # =============================================================================== + # Collect info from log files + data = read_log_file(kernel_folder, m, n, k) + + # Collect max performances per (m, n, k) + max_performances = get_max_performances_per_mnk(data) + max_performances_per_mnk.update(dict(zip(to_string(*max_performances.keys()), max_performances.values()))) + + # =============================================================================== + # Write parameters to CSV + for name_algo, kernel_algo in kernel_algorithm.items(): + + # if applicable to this mnk + if name_algo in data["algorithm"].values: + + # Get the data corresponding to this algorithm + data_algo = data[data["algorithm"] == name_algo] + + # Collect baseline performances per algo, per (m, n, k) + baseline_performances_algo = get_baseline_performances_per_mnk(data_algo, name_algo, gpu_properties, + autotuning_properties) + baseline_performances_per_algo_per_mnk[name_algo].update( + dict(zip(to_string(*baseline_performances_algo.keys()), baseline_performances_algo.values()))) + + # Does collected csv file exist already? + raw_parameters_file_name = os.path.join( + kernel_folder, "raw_training_data_" + to_string(m, n, k) + "_" + name_algo + ".csv") + derived_parameters_file_name = os.path.join( + kernel_folder, "training_data_" + to_string(m, n, k) + "_" + name_algo + ".csv") + + if os.path.exists(raw_parameters_file_name): + print("\tFound csv file:", raw_parameters_file_name, ", skipping ...") + + else: + + # Write raw parameters + pars_to_get = kernel_algo.launch_parameters + ["perf (Gflop/s)"] + data_algo[pars_to_get].to_csv(raw_parameters_file_name, index=False) + print("\tWrote", raw_parameters_file_name) + + if os.path.exists(derived_parameters_file_name): + print("\tFound csv file:", derived_parameters_file_name, ", skipping ...") + + else: + # Compute derived parameters + parameter_sets = PredictiveParameters(data_algo, gpu_properties, autotuning_properties, + max_performances) + pars_to_get = derived_parameters["common"] + derived_parameters[name_algo] + new_df = parameter_sets.get_features(pars_to_get) + data_algo.merge(new_df) + + # Write derived parameters + data_algo[pars_to_get].to_csv(derived_parameters_file_name, index=False) + print("\tWrote", derived_parameters_file_name) + + +# =============================================================================== +def print_merging_commands(kernel_folders, kernel_folder_pattern, tunedir): + """ + Print commands to execute in order to merge CSV files + """ + for algorithm in kernel_algorithm.keys(): + for data_type in ("raw_", ""): + + data_type_name = "raw" if data_type == "raw_" else "for predictive modelling" + print("\n$ # Merge instructions for algorithm", algorithm, "(", data_type_name, ")") + training_data_file = "{data_type}training_data_{algorithm}.csv".format( + data_type=data_type, algorithm=algorithm) + + if os.path.exists(training_data_file): + print("$ # Found {}, append new training data to this file:".format(training_data_file)) + + else: + + # Find an (m, n, k) for this algorithm to get its header line + for i, kernel_folder in enumerate(kernel_folders): + + # Find (m, n, k) + match = kernel_folder_pattern.search(kernel_folder).groups() + m = int(match[0]) + n = int(match[1]) + k = int(match[2]) + + file_name = os.path.join( + kernel_folder, + "{data_type}training_data_{mnk}_{algorithm}.csv".format( + data_type=data_type, mnk=to_string(m, n, k), algorithm=algorithm), + ) + if os.path.exists(file_name): + print("$ head -1 {base_file} > {training_data_file}".format( + base_file=file_name, training_data_file=training_data_file)) + break + else: + print("None: did not find any existing files for algorithm", algorithm, "and data", data_type_name) + continue + + print("$ tail -n +2 -q {tunedir}tune_*/{data_type}training_data_*_{algorithm}.csv >> {training_data_file}". + format( + tunedir=tunedir, data_type=data_type, algorithm=algorithm, training_data_file=training_data_file)) + + +# =============================================================================== +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description=""" + Collect matrix-matrix multiplication parameters and performance from the log files resulting from autotuning. + + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + "-f", + "--folder", + metavar="FOLDER", + type=str, + default=".", + help="Folder in which the folders tune_*x*x*x/ are to be found", + ) + parser.add_argument( + "-a", + "--arch", + metavar="ARCHITECTURE_NUMBER", + type=int, + default=60, + help="CUDA architecture number. Options: 35, 37, 60", + ) + + args = parser.parse_args() + main(args.folder, args.arch) diff --git a/src/acc/libsmm_acc/libcusmm/predict_derivepars.py b/src/acc/libsmm_acc/libcusmm/predict_derivepars.py new file mode 100755 index 00000000000..4bd6b4599f2 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/predict_derivepars.py @@ -0,0 +1,156 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import os +import json +import pandas as pd +import argparse +from kernels.cusmm_predict import ( + get_max_performances_per_mnk, + get_baseline_performances_per_mnk, + to_string, + PredictiveParameters, + derived_parameters, + kernel_algorithm, +) + + +# =============================================================================== +def main(tunedir, arch): + """ + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md. + + After downloading raw data from the dedicated repository, use this script to + - Compute derived training data and write it to a CSV file + - Record maximum and baseline performances of (m,n,k)-triplets in JSON files + """ + # =============================================================================== + # Read GPU properties and autotuning properties + with open("kernels/gpu_properties.json") as f: + gpu_properties = json.load(f)["sm_" + str(arch)] + with open("kernels/autotuning_properties.json") as f: + autotuning_properties = json.load(f) + + # =============================================================================== + # Loop over algorithms + max_performances_per_mnk = dict() + baseline_performances_per_algo_per_mnk = { + "tiny": dict(), + "small": dict(), + "medium": dict(), + "largeDB1": dict(), + "largeDB2": dict(), + } + for name_algo, kernel_algo in kernel_algorithm.items(): + + raw_training_data_filename = os.path.join(tunedir, "raw_training_data_{}.csv".format(name_algo)) + print("\nReading from {}".format(raw_training_data_filename)) + + # Read CSV and loop over chunks + chunk_size = 10000 # Number of rows of CSV file to process at a time + chunk_count = 0 + + for data_chunk in pd.read_csv(raw_training_data_filename, chunksize=chunk_size): + + # Print progress + chunk_count += 1 + print("Read chunk {:5>}".format(chunk_count)) + + # Get max_performance_per_mnk + max_performances = get_max_performances_per_mnk(data_chunk) + max_performances_per_mnk.update(dict(zip(to_string(*max_performances.keys()), max_performances.values()))) + + # Get baseline_per_mnk + baseline_performances_algo = get_baseline_performances_per_mnk(data_chunk, name_algo, gpu_properties, + autotuning_properties) + baseline_performances_per_algo_per_mnk[name_algo].update( + dict(zip(to_string(*baseline_performances_algo.keys()), baseline_performances_algo.values()))) + + # Compute derived parameters + data_chunk["algorithm"] = [name_algo] * len(data_chunk.index) # add 'algorithm' column manually + parameter_sets = PredictiveParameters(data_chunk, gpu_properties, autotuning_properties, max_performances) + pars_to_get = derived_parameters["common"] + derived_parameters[name_algo] + new_data = parameter_sets.get_features(pars_to_get) + + # Write derived parameters + derived_training_data_filename = os.path.join(tunedir, "training_data_{}_{}.csv".format( + name_algo, chunk_count - 1)) + new_data[pars_to_get].to_csv(derived_training_data_filename, index=False) + print("\tWrote", derived_training_data_filename) + + # =============================================================================== + print("\nRead all raw and computed all derived data") + + # Print header lines & merge instructions + print("\n$ # Merge instructions:") + print("$ cd {}".format(tunedir)) + for name_algo, kernel_algo in kernel_algorithm.items(): + + # Print header line + derived_training_data_filename_base = "training_data_{}_{}.csv" + derived_training_data_filename_chunk = derived_training_data_filename_base.format(name_algo, 0) + with open(derived_training_data_filename_chunk, "r") as f: + header_line = f.readline() + derived_training_data_filename = "training_data_{}.csv".format(name_algo) + with open(derived_training_data_filename, "w") as f: + f.write(header_line) + print("$ # Wrote header line to {}".format(derived_training_data_filename)) + + # Print merge instructions + print("$ # Wrote header line to {}".format(derived_training_data_filename)) + print("$ # Append training data chunks to {} by running:".format(derived_training_data_filename)) + derived_training_data_filename_wildcard = derived_training_data_filename_base.format(name_algo, "*") + print("$ tail -n +2 -q {to_merge} >> {training_data_file}".format( + to_merge=derived_training_data_filename_wildcard, training_data_file=derived_training_data_filename)) + + # Print max performances + max_performances_per_mnk_file = os.path.join(tunedir, "max_performances.json") + with open(max_performances_per_mnk_file, "w") as f: + json.dump(max_performances_per_mnk, f) + print("\nWrote maximum performances to:\n", max_performances_per_mnk_file) + + # Print baseline + baseline_performances_per_algo_per_mnk_file = os.path.join(tunedir, "baseline_performances_by_algo.json") + with open(baseline_performances_per_algo_per_mnk_file, "w") as f: + json.dump(baseline_performances_per_algo_per_mnk, f) + print("\nWrote baseline performances to:\n", baseline_performances_per_algo_per_mnk_file) + + +# =============================================================================== +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description=""" + Compute derived training data from raw parameters. + + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + "-f", + "--folder", + metavar="FOLDER", + type=str, + default=".", + help="Folder in which the folders tune_*x*x*x/ are to be found", + ) + parser.add_argument( + "-a", + "--arch", + metavar="ARCHITECTURE_NUMBER", + type=int, + default="60", + help="CUDA architecture number. Options: 35, 37, 60", + ) + + args = parser.parse_args() + main(args.folder, args.arch) diff --git a/src/acc/libsmm_acc/libcusmm/predict_evaluate.py b/src/acc/libsmm_acc/libcusmm/predict_evaluate.py new file mode 100755 index 00000000000..28b151e5565 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/predict_evaluate.py @@ -0,0 +1,145 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import re +import numpy as np +import argparse +from predict_helpers import ( + performance_gain, + relative_performance_gain, + plot_absolute_performance_gain, + plot_relative_performance_gain, + plot_performance_gains, +) + + +# =============================================================================== +def main(file, file_baseline): + """ + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md + + Given a file containing the results of the LIBCUSMM performance test, perform evaluation of the predictive model. + """ + # =============================================================================== + # Read optimal-parameter-prediction result file + with open(file) as f: + result_file = f.read().splitlines() + results_predictive_model = read_result_file(result_file) + + # Read baseline result file + with open(file_baseline) as f: + result_file = f.read().splitlines() + results_baseline = read_result_file(result_file) + + # =============================================================================== + # Performance comparison quantities + improved_over_baseline = dict( + zip( + sorted(results_predictive_model.keys()), + [ + results_predictive_model[(m, n, k)] > results_baseline[(m, n, k)] + for m, n, k in sorted(results_predictive_model.keys()) + ], + )) + perf_gain_over_baseline = performance_gain(results_baseline, results_predictive_model) + rel_perf_gain_over_baseline = relative_performance_gain(results_baseline, results_predictive_model) + + # =============================================================================== + # Print results + header = "m, n, k: baseline perf. [Gflops], predictive model perf. [Gflops], performance gain [? ]" + print(header) + line = ("{m:>2}, {n:>2}, {k:>2}: {baseline_perf:>7.2f}, {predictive_model_perf:>7.2f}, " + + "{performance_gain:>7.2f}, {better}") + for m, n, k in sorted(results_predictive_model.keys()): + print( + line.format( + m=m, + n=n, + k=k, + baseline_perf=results_baseline[(m, n, k)], + predictive_model_perf=results_predictive_model[(m, n, k)], + performance_gain=perf_gain_over_baseline[(m, n, k)], + better=improved_over_baseline[(m, n, k)], + )) + + print( + "\nKernel performances improved by predictive model:", + list(improved_over_baseline.values()).count(True), + "/", + len(results_predictive_model.keys()), + ) + perf_gain_improved = [pg for pg in perf_gain_over_baseline.values() if pg > 0] + print("Mean performance gain amongst improved kernels: {:.2f} Gflops".format(np.mean(perf_gain_improved))) + + print( + "\nKernel performances reduced by predictive model:", + list(improved_over_baseline.values()).count(False), + "/", + len(results_predictive_model.keys()), + ) + perf_gain_deteriorated = [pg for pg in perf_gain_over_baseline.values() if pg < 0] + print("Mean performance loss amongst deteriorated kernels: {:.2f} Gflops".format(np.mean(perf_gain_deteriorated))) + + print("\nMean performance gain overall: {:.2f} Gflops".format(np.mean(list(perf_gain_over_baseline.values())))) + + # =============================================================================== + # Plot results (testing set: predictive modelling VS naïve) + plot_absolute_performance_gain(perf_gain_over_baseline, "non-autotuned", "baseline", "predictive model") + plot_relative_performance_gain(rel_perf_gain_over_baseline, "non-autotuned", "baseline", "predictive model") + plot_performance_gains(results_predictive_model, results_baseline, "non-autotuned", "baseline", "predictive model") + + +# =============================================================================== +def read_result_file(file): + results = dict() + result_line = re.compile(r"OK (\d+) x (\d+) x (\d+) GFlop/s (\d+(?:\.\d+)?)") + for line in file: + match = result_line.match(line) + if match is not None: + m = int(match.group(1)) + n = int(match.group(2)) + k = int(match.group(3)) + perf = float(match.group(4)) + results[(m, n, k)] = perf + + return results + + +# =============================================================================== +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description=""" + Given a file containing the results of the LIBCUSMM performance test, perform evaluation of the predictive + model. + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + "-f", + "--file", + metavar="filename.out", + type=str, + default="", + help="Result file to evaluate. Output of tests/libcusmm_timer_multiply.cu", + ) + parser.add_argument( + "-n", + "--file_baseline", + metavar="filename.out", + type=str, + default="", + help="Baseline performance file to compare against.", + ) + + args = parser.parse_args() + main(args.file, args.file_baseline) diff --git a/src/acc/libsmm_acc/libcusmm/predict_genpars.py b/src/acc/libsmm_acc/libcusmm/predict_genpars.py new file mode 100755 index 00000000000..5b84a3b1493 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/predict_genpars.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import gc +import sys +import json +import numpy as np +import pandas as pd +from itertools import product +import argparse +from joblib import Parallel, delayed +from predict_helpers import safe_pickle_load +from kernels.cusmm_predict import arch_number, kernel_algorithm, params_dict_to_kernel, PredictiveParameters + + +# =============================================================================== +def main(params, njobs, baseline, paths_to_models, chunk_size): + """ + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md + + Update parameter file with new optimal parameter predictions given newly trained decision trees + """ + # =============================================================================== + # Load GPU and autotuning properties + assert params in arch_number.keys(), "Cannot find compute version for file " + str(params) + arch = arch_number[params] + with open("kernels/gpu_properties.json") as f: + gpu_properties = json.load(f)["sm_" + str(arch)] + with open("kernels/autotuning_properties.json") as f: + autotuning_properties = json.load(f) + + # Load autotuned kernel parameters + with open(params) as f: + all_kernels = [params_dict_to_kernel(**params) for params in json.load(f)] + print("Libcusmm: Found %d existing parameter sets." % len(all_kernels)) + autotuned_mnks = [(k.m, k.n, k.k) for k in all_kernels if k.autotuned] + autotuned_kernels_ = [k for k in all_kernels if k.autotuned] + autotuned_kernels = dict(zip(autotuned_mnks, autotuned_kernels_)) + + # =============================================================================== + # Evaluation + mnks = combinations(list(range(4, 46))) + mnks_to_predict = list() + kernels_to_print = dict() + + for m, n, k in mnks: + if (m, n, k) in autotuned_kernels.keys(): + kernels_to_print[(m, n, k)] = autotuned_kernels[(m, n, k)] + else: + mnks_to_predict.append((m, n, k)) + + if baseline: + kernels = get_baseline_kernels(mnks_to_predict, gpu_properties, autotuning_properties) + else: + kernels = get_optimal_kernels(mnks_to_predict, njobs, chunk_size, paths_to_models, gpu_properties, + autotuning_properties, 1) + + kernels_to_print.update(kernels) + + # =============================================================================== + # Write to file + with open(params, "w") as f: + s = json.dumps( + [kernels_to_print[kernel].as_dict_for_parameters_json for kernel in sorted(kernels_to_print.keys())]) + s = s.replace("}, ", "},\n") + s = s.replace("[", "[\n") + s = s.replace("]", "\n]") + f.write(s) + print("Wrote new predicted parameters to file", params) + + +# =============================================================================== +# Helpers +def combinations(sizes): + return list(product(sizes, sizes, sizes)) + + +def find_optimal_kernel(mnk, algo, tree, tree_features, gpu_properties, autotuning_properties): + """ + Find the optimal kernel parameter set for a given (m, n, k) and a given algorithm + :return: optimal_kernels: dictionary, keys: (m, n, k), values: Kernel object describing best parameters + """ + + # Get parameter space for this (m, n, k) and this algorithm + m, n, k = mnk + parameter_space_ = kernel_algorithm[algo].promising_parameters(m, n, k, gpu_properties, autotuning_properties) + parameter_space = pd.DataFrame(parameter_space_) + del parameter_space_ + parameter_space["algorithm"] = [algo] * len(parameter_space.index) # Add "algorithm" column + if len(parameter_space.index) == 0: + optimal_kernels = dict() + + else: + + # Get predictor features from raw parameters + parameter_sets = PredictiveParameters(parameter_space, gpu_properties, autotuning_properties, None) + predictors = np.array(parameter_sets.get_features(tree_features)) + + # Predict performances + performances_scaled = tree.predict(predictors) + del predictors + parameter_performances = parameter_sets.params + del parameter_sets + parameter_performances["perf"] = performances_scaled + del performances_scaled + + # Pick optimal kernel + optimal_kernel = max(parameter_performances.to_dict("records"), key=lambda x: x["perf"]) + del parameter_performances + optimal_kernels = dict() + optimal_kernels[(m, n, k)] = params_dict_to_kernel(**optimal_kernel, source="predicted") + + return optimal_kernels + + +def get_optimal_kernels(mnks_to_predict, njobs, chunk_size, paths_to_models, gpu_properties, autotuning_properties, + top_k): + # optimal_kernels_list is a list of dictionaries + # - keys: (m, n, k), + # - values: Kernel object describing best parameters + # - number of elements in each dictionary = top_k + # each element of the list corresponds to the search of optimal kernels for a given mnk and a given algorithm + + print("Getting optimal kernels") + + # =============================================================================== + # Load predictive trees and feature list + tree = dict() + kernel_to_investigate = dict() + for algo in kernel_algorithm.keys(): + path_to_model = paths_to_models[algo] + if path_to_model is not None: + print("Algorithm: {:<8}, loading model from: {}".format(algo, path_to_model)) + tree[algo] = dict() + tree[algo]["file"] = path_to_model + features, tree[algo]["tree"] = safe_pickle_load(tree[algo]["file"]) + tree[algo]["features"] = features.tolist() + kernel_to_investigate[algo] = kernel_algorithm[algo] + else: + print("Algorithm: {:<8}, no model found.".format(algo)) + + if len(kernel_to_investigate) == 0: + print("No model found. Specify path to predictive models using ") + sys.exit(1) + + # =============================================================================== + optimal_kernels_list = list() + mnk_by_algo = list(product(mnks_to_predict, kernel_to_investigate.keys())) + num_mnks_by_algo = len(mnk_by_algo) + if njobs == 1: + + # Ignore joblib and run serially: + for mnk, algo in mnk_by_algo: + gc.collect() + print("Find optimal kernels for mnk=", mnk, ", algo=", algo) + optimal_kernels_list.append( + find_optimal_kernel(mnk, algo, tree[algo]["tree"], tree[algo]["features"], gpu_properties, + autotuning_properties)) + else: + + # Chunk up tasks + for i in range(0, num_mnks_by_algo + 1, chunk_size): + start_chunk = i + end_chunk = int(min(start_chunk + chunk_size, num_mnks_by_algo + 1)) + print("Completed {:,} tasks out of {:,}".format(i, num_mnks_by_algo)) + + # Run prediction tasks in parallel with joblib + optimal_kernels_list_ = Parallel( + n_jobs=njobs, verbose=2)(delayed(find_optimal_kernel, check_pickle=True)( + mnk, algo, tree[algo]["tree"], tree[algo]["features"], gpu_properties, autotuning_properties) + for mnk, algo in mnk_by_algo[start_chunk:end_chunk]) + + optimal_kernels_list += optimal_kernels_list_ + + print("Finished gathering candidates for optimal parameter space") + + # Group optimal kernel candidates by (m,n,k) in a dictionary + optimal_kernels_mnk_algo = dict() + for optimal_kernel_mnk in optimal_kernels_list: + for mnk, kernels_mnk in optimal_kernel_mnk.items(): + m, n, k = mnk + if (m, n, k) in optimal_kernels_mnk_algo.keys(): + optimal_kernels_mnk_algo[(m, n, k)].append(kernels_mnk) + else: + optimal_kernels_mnk_algo[(m, n, k)] = [kernels_mnk] + + # Find optimal kernel per mnk among the different algorithm possibilities + optimal_kernels = dict() + for mnk, candidate_kernels in optimal_kernels_mnk_algo.items(): + m, n, k = mnk + optimal_kernel_mnk = sorted(candidate_kernels, key=lambda x: x.perf, reverse=True)[:top_k] + optimal_kernels[(m, n, k)] = optimal_kernel_mnk[0] + + return optimal_kernels + + +def get_baseline_kernels(mnks_to_predict, gpu_propertes, autotuning_properties): + + print("Getting baseline kernels") + baseline_algorithm = "medium" + baseline_kernels = list() + for m, n, k in mnks_to_predict: + baseline_kernels[(m, n, k)] = kernel_algorithm[baseline_algorithm].baseline(m, n, k, gpu_propertes, + autotuning_properties) + + return baseline_kernels + + +# =============================================================================== +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description=""" + Update parameter file with new optimal parameter predictions given newly trained decision trees. + + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + + parser.add_argument( + "-p", + "--params", + metavar="parameters_GPU.json", + default="parameters_P100.json", + help="Parameter file to read and update with predictions", + ) + parser.add_argument("-j", "--njobs", type=int, default=-1, help="Number of joblib jobs") + parser.add_argument( + "--baseline", + default=False, + help="Generate a parameter file corresponding to the baseline of a predictive model", + ) + parser.add_argument( + "--tiny", default=None, help="Path to model trained for algorithm 'tiny'. If not given, ignore this algorithm.") + parser.add_argument( + "--small", + default=None, + help="Path to model trained for algorithm 'small'. If not given, ignore this algorithm.", + ) + parser.add_argument( + "--medium", + default=None, + help="Path to model trained for algorithm 'medium'. If not given, ignore this algorithm.", + ) + parser.add_argument( + "--largeDB1", + default=None, + help="Path to model trained for algorithm 'largeDB1'. If not given, ignore this algorithm.", + ) + parser.add_argument( + "--largeDB2", + default=None, + help="Path to model trained for algorithm 'largeDB2'. If not given, ignore this algorithm.", + ) + parser.add_argument( + "-c", + "--chunk_size", + type=int, + default=20000, + help="Chunk size for dispatching joblib jobs. If memory errors are experienced, reduce this number", + ) + + args = parser.parse_args() + paths_to_models = dict() + for algo in kernel_algorithm.keys(): + paths_to_models[algo] = args.__dict__[algo] + main(args.params, args.njobs, args.baseline, paths_to_models, args.chunk_size) diff --git a/src/acc/libsmm_acc/libcusmm/predict_helpers.py b/src/acc/libsmm_acc/libcusmm/predict_helpers.py new file mode 100644 index 00000000000..9d7b913a0c2 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/predict_helpers.py @@ -0,0 +1,231 @@ +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import os +import pickle +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from kernels.cusmm_predict import to_string + + +# =============================================================================== +# I/O helpers +def safe_pickle(data, file): + """ + Pickle big files safely by processing them in chunks. + This wrapper is a workaround for a bug on OSX (https://bugs.python.org/issue24658) + + :param data: data to be pickled + :param file: file to pickle it into + """ + max_bytes = 2**31 - 1 # Maximum number of bytes to write in one chunk + pickle_out = pickle.dumps(data) + n_bytes = len(pickle_out) + with open(file, "wb") as f: + count = 0 + for i in range(0, n_bytes, max_bytes): + f.write(pickle_out[i:min(n_bytes, i + max_bytes)]) + count += 1 + + +def safe_pickle_load(file_path): + """ + Load big pickled files safely by processing them in chunks + This wrapper is a workaround a bug on OSX (https://bugs.python.org/issue24658) + + :param data: data to be loaded through pickle + :param file: file to read from + """ + max_bytes = 2**31 - 1 # Maximum number of bytes to read in one chunk + bytes_in = bytearray(0) + input_size = os.path.getsize(file_path) + with open(file_path, "rb") as f: + for _ in range(0, input_size, max_bytes): + bytes_in += f.read(max_bytes) + return pickle.loads(bytes_in) + + +# =============================================================================== +# Model evaluation helpers +def performance_gain(baseline, current): + """ + Compute the absolute perfomance gain, in Gflop/s between a baseline and a 'current' + :param baseline, current: dictionary, keys: (m, n, k), values: performance in Gflop/s + :return: dictionary, keys: (m, n, k), values: performance difference in Gflop/s + """ + return dict( + zip(sorted(current.keys()), [current[(m, n, k)] - baseline[(m, n, k)] for m, n, k in sorted(current.keys())])) + + +def plot_training_data(Y, X_mnk, algo, folder=""): + import re + import matplotlib.pyplot as plt + + print("Plotting training data...") + + mnks_strings = X_mnk["mnk"].values + mnks = list() + mnk_str = re.compile(r"(\d+)x(\d+)x(\d+)") + for mnk_s in mnks_strings: + match = mnk_str.match(mnk_s) + mnks.append((int(match.group(1)), int(match.group(2)), int(match.group(3)))) + + perf_scaled = zip(mnks, Y["perf_scaled"]) + mnk_products_perf_sorted = [(mnk[0] * mnk[1] * mnk[2], p) + for mnk, p in sorted(perf_scaled, key=lambda x: x[0][0] * x[0][1] * x[0][2])] + tmp = list(zip(*mnk_products_perf_sorted)) + mnk_products_sorted = tmp[0] + perf_scaled_sorted = tmp[1] + + # Plot + plt.plot(mnk_products_sorted, 100 * np.array(perf_scaled_sorted), ".", markersize=1) + plt.xlabel("Training (m, n, k) triplets (in order of increasing m*n*k)") + plt.ylabel("Scaled performance [%]") + plt.title("Scaled performance on training data (" + algo + ")") + if folder != "": + file_name = os.path.join(folder, "y_scaled.svg") + plt.savefig(file_name) + print(file_name) + else: + plt.show() + + +def relative_performance_gain(baseline, current): + """ + Compute the relative perfomance gain (no units), between a baseline and a 'current' + :param baseline, current: dictionary, keys: (m, n, k), values: performance in Gflop/s + :return: dictionary, keys: (m, n, k), values: relative performance difference (no units) + """ + return dict( + zip( + sorted(current.keys()), + [(current[(m, n, k)] - baseline[(m, n, k)]) / baseline[(m, n, k)] for m, n, k in sorted(current.keys())], + )) + + +def plot_absolute_performance_gain(perf_gain, mnk_names, baseline_name, current_name, pp=None): + mnk_products = [m * n * k for m, n, k in sorted(perf_gain.keys(), key=lambda x: x[0] * x[1] * x[2])] + + plt.figure() + plt.plot(mnk_products, list(perf_gain.values()), ".", markersize=3) + plt.plot([mnk_products[0], mnk_products[-1]], [0, 0], "-r") + plt.xlabel(mnk_names + " (m, n, k) triplets (in order of increasing m*n*k)") + plt.ylabel("Performance Gain [Gflops]") + plt.title("Performance gain of " + current_name + " VS " + baseline_name + " parameter set") + if pp is not None: + pp.savefig() + else: + plt.show() + + +def plot_relative_performance_gain(rel_perf_gain, mnk_names, baseline_name, current_name, pp=None): + mnk_products = [m * n * k for m, n, k in sorted(rel_perf_gain.keys(), key=lambda x: x[0] * x[1] * x[2])] + + plt.figure() + plt.plot(mnk_products, 100 * np.array(list(rel_perf_gain.values())), ".", markersize=3) + plt.plot([mnk_products[0], mnk_products[-1]], [0, 0], "-r") + plt.xlabel(mnk_names + " (m, n, k) triplets (in order of increasing m*n*k)") + plt.ylabel("Performance Gain [%]") + plt.title("Relative performance gain of " + current_name + " VS " + baseline_name + " parameter set") + if pp is not None: + pp.savefig() + else: + plt.show() + + +def plot_performance_gains(perf_gain1, perf_gain2, mnk_names, perf_gain1_name, perf_gain2_name, pp=None): + mnks = [(m, n, k) for m, n, k in sorted(perf_gain2.keys(), key=lambda x: x[0] * x[1] * x[2])] + mnk_products = [m * n * k for m, n, k in sorted(perf_gain2.keys(), key=lambda x: x[0] * x[1] * x[2])] + res1 = [perf_gain1[mnk] for mnk in mnks] + res2 = [perf_gain2[mnk] for mnk in mnks] + + marker_size = 3 + plt.figure() + plt.plot(mnk_products, res1, ".", markersize=marker_size) + plt.plot(mnk_products, res2, ".", color="#d62728", markersize=marker_size) + plt.xlabel(mnk_names + " (m, n, k) triplets (in order of increasing m*n*k)") + plt.ylabel("Performance [Gflops]") + plt.xscale("log") + plt.legend([perf_gain1_name, perf_gain2_name]) + plt.title("Performance of " + perf_gain1_name + " and " + perf_gain2_name + " parameter set") + if pp is not None: + pp.savefig() + else: + plt.show() + + +def plot_scaled_performance_gains(perf_gain1, perf_gain2, mnk_names, perf_gain1_name, perf_gain2_name, pp=None): + mnks = [(m, n, k) for m, n, k in sorted(perf_gain2.keys(), key=lambda x: x[0] * x[1] * x[2])] + mnk_products = [m * n * k for m, n, k in sorted(perf_gain2.keys(), key=lambda x: x[0] * x[1] * x[2])] + res1 = np.array([perf_gain1[mnk] for mnk in mnks]) + res2 = np.array([perf_gain2[mnk] for mnk in mnks]) + + marker_size = 3 + plt.figure() + plt.plot(mnk_products, 100 * res1, ".", markersize=marker_size) + plt.plot(mnk_products, 100 * res2, ".", color="#d62728", markersize=marker_size) + plt.xlabel(mnk_names + " (m, n, k) triplets (in order of increasing m*n*k)") + plt.ylabel("Scaled performance [%]") + plt.xscale("log") + plt.legend([perf_gain1_name, perf_gain2_name]) + plt.title("Performance of " + perf_gain1_name + " and " + perf_gain2_name + " parameter set") + if pp is not None: + pp.savefig() + else: + plt.show() + + +def plot_choice_goodness(m, n, k, baseline_performances, max_performances, y_true, y_pred, train, pp, scaled=True): + + # Sort in ascending performances + data_mnk = pd.DataFrame() + if scaled: + data_mnk["perf_true"] = (100 * y_true.flatten()).tolist() + data_mnk["perf_pred"] = (100 * y_pred).tolist() + else: + data_mnk["perf_true"] = y_true.flatten().tolist() + data_mnk["perf_pred"] = y_pred.tolist() + data_mnk.sort_values(by="perf_true", inplace=True) + + # Plot + plt.figure() + marker_size = 1 + par_set_ids = range(len(data_mnk.index.values)) + plt.plot(par_set_ids, data_mnk["perf_true"], "b.", markersize=marker_size, label="measured performances") + plt.xlabel("Parameter set id") + plt.ylabel("Performance scaled [%]") + type = "train" if train else "test" + plt.title("Performance profile of parameter sets for " + str((m, n, k)) + "-triplet (" + type + ")") + + # Annotate + x = [0, len(y_true)] + y = np.array([1, 1]) + perf_num = "{:2.2f}" + + # autotuning + perf_autotuned_algo = data_mnk["perf_true"].max() + plt.plot(x, perf_autotuned_algo * y, "k-", label="max (for this algo): " + perf_num.format(perf_autotuned_algo)) + + # chosen + idx_perf_chosen = data_mnk["perf_pred"].idxmax() + perf_chosen = data_mnk["perf_true"][idx_perf_chosen] + plt.plot(x, perf_chosen * y, "r-", label="chosen: " + perf_num.format(perf_chosen)) + + # baseline + if scaled: + # baseline = per algo, scale it to 0-1 + perf_baseline = 100 * baseline_performances[to_string(m, n, k)] / max_performances["{}x{}x{}".format(m, n, k)] + else: + perf_baseline = baseline_performances[to_string(m, n, k)] + plt.plot(x, perf_baseline * y, "g-", label="baseline: " + perf_num.format(perf_baseline)) + + plt.legend(loc="lower right") + pp.savefig() diff --git a/src/acc/libsmm_acc/libcusmm/predict_train.py b/src/acc/libsmm_acc/libcusmm/predict_train.py new file mode 100755 index 00000000000..cc39109234b --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/predict_train.py @@ -0,0 +1,1172 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import os +import sys +import datetime +import json +import random +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import argparse +from predict_helpers import ( + safe_pickle, + safe_pickle_load, + plot_training_data, + plot_choice_goodness, + plot_performance_gains, + plot_scaled_performance_gains, + plot_absolute_performance_gain, + plot_relative_performance_gain, + performance_gain, +) +from kernels.cusmm_predict import to_tuple, to_string + + +# =============================================================================== +def main(datadir, algo, plot_all, model_args, nrows, prefitted_model): + """ + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md + + """ + # =============================================================================== + # Create folder to store results of this training and start a log + folder, log_file, log = get_log_folder(datadir, algo) + + # =============================================================================== + # Override algorithm option if working on a pre-fitted model, and log program options + log += print_and_log("----------------------------------------------------------------------------") + algo, model_args, nrows, log = dump_or_load_options(algo, model_args, prefitted_model, nrows, folder, log) + + # =============================================================================== + # Get maximum and baseline performances + max_performances, max_performances_algo, max_performances_ref, baseline_performances_algo = \ + get_reference_performances( + datadir, algo + ) + + # =============================================================================== + # Read data + log += print_and_log("----------------------------------------------------------------------------") + X, X_mnk, Y, log, data_nrows = read_data(algo, datadir, nrows, plot_all, folder, log) + + # =============================================================================== + # Get or train model + log += print_and_log("----------------------------------------------------------------------------") + if len(prefitted_model) == 0: # train a model + + log += print_and_log("\nPreparing to fit model...") + X_train, Y_train, X_mnk_train, X_test, Y_test, X_mnk_test, model, log = train_model( + X, X_mnk, Y, algo, model_args, folder, log) + + else: # load pre-trained model + + log += print_and_log("\nReading pre-fitted model from " + prefitted_model) + X_train, Y_train, X_mnk_train, X_test, Y_test, X_mnk_test, model, log = fetch_pre_trained_model( + X, X_mnk, Y, prefitted_model, log) + + # =============================================================================== + # Evaluate model + log = evaluate_model( + model, + X_train, + X_mnk_train, + Y_train, + X_test, + X_mnk_test, + Y_test, + max_performances_ref, + max_performances_algo, + baseline_performances_algo, + data_nrows, + plot_all, + log, + folder, + ) + + # =============================================================================== + # Refit to the entire dataset + log += print_and_log("----------------------------------------------------------------------------") + log += print_and_log("\nRefit to the entire dataset:") + X = pd.concat([X_train, X_test], ignore_index=True) + X_mnk = pd.concat([X_mnk_train, X_mnk_test], ignore_index=True) + Y = pd.concat([Y_train, Y_test], ignore_index=True) + model.fit(X, Y) + results_file = os.path.join(folder, "feature_tree_refit.p") + safe_pickle([X.columns.values, model], results_file) + + # =============================================================================== + # Evaluate refit-model + log = evaluate_model( + model, + X, + X_mnk, + Y, + None, + None, + None, + max_performances_ref, + max_performances_algo, + baseline_performances_algo, + data_nrows, + plot_all, + log, + folder, + ) + + # =============================================================================== + # Print log + log += print_and_log("----------------------------------------------------------------------------") + with open(log_file, "w") as f: + f.write(log) + + +# =============================================================================== +# Model hyperparameters +optimized_hyperparameters = { # chosen by common sense, then trial and error + "tiny": {"max_depth": 16, "min_samples_leaf": 2, "min_samples_split": 15, "n_features_to_drop": 2}, + "small": {"max_depth": 16, "min_samples_leaf": 2, "min_samples_split": 15, "n_features_to_drop": 16}, + "medium": {"max_depth": 18, "min_samples_leaf": 2, "min_samples_split": 13, "n_features_to_drop": 12}, + "largeDB1": {"max_depth": 18, "min_samples_leaf": 2, "min_samples_split": 15, "n_features_to_drop": 12}, + "largeDB2": {"max_depth": 18, "min_samples_leaf": 2, "min_samples_split": 15, "n_features_to_drop": 12}, +} + + +# =============================================================================== +# Printing and dumping helpers +def get_log_folder(algo, prefitted_model_folder): + """Create a unique log folder for this run in which logs, plots etc. will be stored """ + if len(prefitted_model_folder) == 0: + + # Create a new folder for this model + file_signature = datetime.datetime.now().strftime("%Y-%m-%d--%H-%M") + folder = os.path.join("model_selection", os.path.join(algo, file_signature)) + log_file = os.path.join(folder, "log.txt") + if not os.path.exists(folder): + os.makedirs(folder) + + else: + + # If loading a pre-fitted model, use this pre-fitted model's folder as a log folder, but create a new log file + folder = prefitted_model_folder + log_file_signature = datetime.datetime.now().strftime("%Y-%m-%d--%H-%M") + log_file = os.path.join(folder, "log_" + log_file_signature + ".txt") + + # Log folder and file + log = "" + log += print_and_log("\nLogging to:") + log += print_and_log("\t" + folder) + log += print_and_log("\t" + log_file) + + return folder, log_file, log + + +def dump_or_load_options(algo, model_args, prefitted_model, nrows, folder, log): + + options_file_name = os.path.join(folder, "options.json") + pgm_options = {"folder": folder, "algo": algo, "nrows": nrows} + pgm_options.update(model_args) + + if len(prefitted_model) == 0: + # if we're training a model, dump options to folder so they can be reloaded in another run + print("Dump options to", options_file_name) + with open(options_file_name, "w") as f: + json.dump(pgm_options, f) + + else: + # if we're using a pre-fitted model, load options from that model + print("Read options from", options_file_name) + with open(options_file_name, "r") as f: + pgm_options = json.load(f) + + algo = pgm_options["algo"] + model_args_list = [ + "model", + "splits", + "ntrees", + "njobs", + ] + model_args = dict() + for m in model_args_list: + model_args[m] = pgm_options[m] + nrows = pgm_options["nrows"] + + # Log options + log += print_and_log("Predict-train running with options:") + for opt, opt_val in pgm_options.items(): + log += print_and_log("{:<15}: {}".format(opt, opt_val)) + + return algo, model_args, nrows, log + + +def print_and_log(msg): + if not isinstance(msg, str): + msg = str(msg) + log = "\n" + msg + print(msg) + return log + + +# =============================================================================== +# Custom loss functions and scorers +def perf_loss(y_true, y_pred, top_k, X_mnk, scaled=True): + """ + Compute the relative performance losses per mnk if one were to measure the top-k best predicted sets of parameters + and pick the best out of this top-k + + :param y_true: ground truth performances (performance scaled between 0 and 1) + :param y_pred: estimated performances (performance scaled between 0 and 1) + :param top_k: number of top performances to measure + :param X_mnk: corresponding mnks + :return: perf_losses: array of relative performance losses (in %), one array element per mnk + """ + assert len(y_true.index) == y_pred.flatten().size + assert len(y_true.index) == len(X_mnk.index) + + perf_losses = list() + mnks = np.unique(X_mnk["mnk"].values) + for mnk in mnks: + + # Get performances per mnk + idx_mnk = np.where(X_mnk == mnk)[0].tolist() + assert len(idx_mnk) > 0, "idx_mnk is empty" + y_true_mnk = y_true.iloc[idx_mnk] + y_pred_mnk = y_pred[idx_mnk] + + # Get top-k best predicted performances + if top_k != 1: + top_k_idx = np.argpartition(-y_pred_mnk, top_k)[:top_k] + else: + top_k_idx = np.argmax(y_pred_mnk) + y_correspmax = y_true_mnk.iloc[top_k_idx] + + # Chosen max perf. among predicted max performances + maxperf_chosen = np.amax(y_correspmax) + + # True Max. performances + if not scaled: + maxperf = float(y_true_mnk.max(axis=0)) + assert maxperf >= 0, "Found non-positive value for maxperf: " + str(maxperf) + perf_loss = (maxperf - maxperf_chosen) / maxperf + else: + perf_loss = 1.0 - maxperf_chosen + + # Relative performance loss incurred by using model-predicted parameters instead of autotuned ones [%] + perf_losses.append(100 * perf_loss) + + return perf_losses + + +def worse_rel_perf_loss_of_k(y_true, y_pred, top_k, X_mnk, scaled=True): + y = np.array(perf_loss(y_true, y_pred, top_k, X_mnk, scaled)) + return float(y.max(axis=0)) + + +def mean_rel_perf_loss_of_k(y_true, y_pred, top_k, X_mnk, scaled=True): + y = np.array(perf_loss(y_true, y_pred, top_k, X_mnk, scaled)) + return float(y.mean(axis=0)) + + +def worse_case_scorer(estimator, X, y, top_k): + """ + :param estimator: the model that should be evaluated + :param X: validation data + :param y: ground truth target for X + :return: score: a floating point number that quantifies the estimator prediction quality on X, with reference to y + """ + mnk = pd.DataFrame() + mnk["mnk"] = X["mnk"].copy() + y_pred = estimator.predict(X.drop(["mnk"], axis=1)) + score = worse_rel_perf_loss_of_k(y, y_pred, top_k, mnk) + return -score # by scikit-learn convention, higher numbers are better, so the value should be negated + + +def worse_case_scorer_top1(estimator, X, y): + return worse_case_scorer(estimator, X, y, 1) + + +def mean_scorer(estimator, X, y, top_k): + """ + :param estimator: the model that should be evaluated + :param X: validation data + :param y: ground truth target for X + :return: score: a floating point number that quantifies the estimator prediction quality on X, with reference to y + """ + mnk = pd.DataFrame() + mnk["mnk"] = X["mnk"].copy() + y_pred = estimator.predict(X.drop(["mnk"], axis=1)) + score = mean_rel_perf_loss_of_k(y, y_pred, top_k, mnk) + return -score # by scikit-learn convention, higher numbers are better, so the value should be negated + + +def mean_scorer_top1(estimator, X, y): + return mean_scorer(estimator, X, y, 1) + + +# =============================================================================== +# Read and prepare data +def get_reference_performances(folder, algo): + import json + + maxperf_file = os.path.join(folder, "max_performances.json") + with open(maxperf_file) as f: + max_performances = json.load(f) + + maxperf_file = os.path.join(folder, "max_performances_by_algo.json") + with open(maxperf_file) as f: + max_performances_algo = json.load(f)[algo] + + max_performances_ref = max_performances + + baseline_file = os.path.join(folder, "baseline_performances_by_algo.json") + with open(baseline_file) as f: + baseline_performances_algo = json.load(f)[algo] + + return (max_performances, max_performances_algo, max_performances_ref, baseline_performances_algo) + + +def read_data(algo, read_from, nrows, plot_all, folder, log): + + # =============================================================================== + # Read raw and derived data from CSV + raw_data_file = os.path.join(read_from, "raw_training_data_" + algo + ".csv") + log += print_and_log("\nRead raw data from " + raw_data_file) + raw_data = pd.read_csv(raw_data_file, index_col=False, nrows=nrows) + log += print_and_log("raw data : {:>8,} x {:>8,} ({:>2.3} MB)".format(raw_data.shape[0], raw_data.shape[1], + sys.getsizeof(raw_data) / 10**6)) + + derived_data_file = os.path.join(read_from, "training_data_" + algo + ".csv") + log += print_and_log("\nRead training data from " + derived_data_file) + derived_data = pd.read_csv(derived_data_file, index_col=False, nrows=nrows) + log += print_and_log("derived data : {:>8,} x {:>8,} ({:>2.3} MB)".format(derived_data.shape[0], + derived_data.shape[1], + sys.getsizeof(derived_data) / 10**6)) + + # =============================================================================== + # Get 'X' + X = pd.concat( + [ + raw_data.drop(["perf (Gflop/s)"], axis=1), + derived_data.drop(["perf_scaled"], axis=1), + ], + axis=1, + ) + # If there are unique-valued columns, drop these since they do not contribute to any prediction + for col in X.columns.values.tolist(): + if len(X[col].unique().tolist()) == 1: + X = X.drop(col, axis=1) + + log += print_and_log("X : {:>8,} x {:>8,} ({:>2.2} MB)".format(X.shape[0], X.shape[1], sys.getsizeof(X) / 10**6)) + n_features = len(list(X.columns)) + predictor_names = X.columns.values + log += print_and_log("\nPredictor variables: (" + str(n_features) + ")") + for i, p in enumerate(predictor_names): + log += print_and_log("\t{:2}) {}".format(i + 1, p)) + + # =============================================================================== + # Get 'Y' + log += print_and_log("\nExtract Y") + Y = pd.DataFrame() + Y["perf_scaled"] = derived_data["perf_scaled"] + Y.dropna(axis=0, inplace=True) + log += print_and_log("Y : {:>8,} ({:>2.2} MB)".format(Y.size, sys.getsizeof(Y) / 10**6)) + + # =============================================================================== + # Get 'X_mnk' + log += print_and_log("\nWrite X_mnk") + X_mnk = pd.DataFrame() + X_mnk["mnk"] = X["m"].astype(str) + "x" + X["n"].astype(str) + "x" + X["k"].astype(str) + log += print_and_log("X_mnk : {:>8,} ({:>2.2} MB)".format(X_mnk.size, sys.getsizeof(X_mnk) / 10**6)) + + if plot_all: + plot_training_data(Y, X_mnk, algo, folder) + + nrows_data = len(X.index) + return X, X_mnk, Y, log, nrows_data + + +# =============================================================================== +# Predictive modelling +def get_DecisionTree_model(algo, n_features): + from itertools import chain + + # Fixed parameters + model_name = "Decision_Tree" + splitting_criterion = "mse" + splitter = "best" + max_features = None + max_leaf_nodes = None + + # Hyper-parameters to optimize + if algo == "medium": + max_depth = [10, 13, 16, 18, 21, 24] + min_samples_split = [2, 8, 12, 18] + min_samples_leaf = [2, 8, 12, 18] + else: + if algo == "tiny": + step = 3 + max_depth = chain( + range(4, int(np.ceil(0.75 * n_features)), step), + range(int(np.ceil(0.75 * n_features)), int(np.ceil(n_features)), step), + ) + min_samples_split = range(2, 21, step) + min_samples_leaf = range(2, 21, step) + else: + max_depth = chain(range(4, 13, 2), range(15, 19, 3)) + min_samples_split = [2, 5, 13, 18] + min_samples_leaf = [2, 5, 13, 18] + param_grid = { + model_name + "__estimator__" + "max_depth": list(max_depth), + model_name + "__estimator__" + "min_samples_split": list(min_samples_split), + model_name + "__estimator__" + "min_samples_leaf": list(min_samples_leaf), + } + + # Tree model + from sklearn.tree import DecisionTreeRegressor + + model = DecisionTreeRegressor( + criterion=splitting_criterion, + splitter=splitter, + min_samples_split=optimized_hyperparameters[algo]["min_samples_split"], + min_samples_leaf=optimized_hyperparameters[algo]["min_samples_leaf"], + max_depth=optimized_hyperparameters[algo]["max_depth"], + max_features=max_features, + max_leaf_nodes=max_leaf_nodes, + ) + + from eli5.sklearn import PermutationImportance + + model_perm = PermutationImportance(model, cv=None) + + return model_perm, model_name, param_grid + + +def get_RandomForest_model(algo, njobs, ntrees): + from itertools import chain + from sklearn.ensemble import RandomForestRegressor + + # Fixed parameters + model_name = "Random_Forest" + bootstrap = True + splitting_criterion = "mse" + max_features = "sqrt" + + # Parameters to optimize + step_big = 50 + step_small = 5 + n_estimators = chain(range(1, 10, step_small), range(50, 200, step_big)) + param_grid = {model_name + "__" + "n_estimators": list(n_estimators)} + + # Random Forest model + model = RandomForestRegressor( + criterion=splitting_criterion, + n_estimators=ntrees, + min_samples_split=optimized_hyperparameters[algo]["min_samples_split"], + min_samples_leaf=optimized_hyperparameters[algo]["min_samples_leaf"], + max_depth=optimized_hyperparameters[algo]["max_depth"], + bootstrap=bootstrap, + max_features=max_features, + n_jobs=njobs, + ) + + return model, model_name, param_grid + + +def get_train_test_partition(to_partition, test, train=None): + """ + Perform train/test partition + :param to_partition: sequence of objects to partition + :param test: ndarray, test-indices + :param train (optional): ndarray + :return: + """ + if train is None: # Retrieve training indices + all_indices = set(range(len(to_partition[0].index))) + train = list(all_indices - set(test)) + + partitioned = list() + for df in to_partition: + df_train = df.iloc[train, :] # train: use for hyper-parameter optimization (via CV) and training + partitioned.append(df_train) + df_test = df.iloc[test, :] # test : use for evaluation of 'selected/final' model + partitioned.append(df_test) + + return partitioned + + +def train_model(X, X_mnk, Y, algo, model_options, folder, log): + + # =============================================================================== + # Get options + results_file = os.path.join(folder, "feature_tree.p") + + # =============================================================================== + # Testing splitter (train/test-split) + from sklearn.model_selection import GroupShuffleSplit + + cv = GroupShuffleSplit(n_splits=2, test_size=0.2) + train_test_splits = cv.split(X, Y, groups=X_mnk["mnk"]) + train, test = next(train_test_splits) + X_train, X_test, Y_train, Y_test, X_mnk_train, X_mnk_test = get_train_test_partition([X, Y, X_mnk], test, train) + plot_train_test_partition(test, train, X_mnk, folder) + log += print_and_log("\nComplete train/test split, total size=" + str(X.shape) + ", test size=" + + str(X_test.shape) + ", train_size=" + str(X_train.shape)) + del X, X_mnk, Y # free memory + predictor_names = X_train.columns.values + n_predictors = len(predictor_names) + + # =============================================================================== + # Predictive model + model_to_train = model_options["model"] + if model_to_train == "DT": + model, model_name, param_grid = get_DecisionTree_model(algo, len(X_train.columns.values)) + elif model_to_train == "RF": + model, model_name, param_grid = get_RandomForest_model(algo, model_options["njobs"], model_options["ntrees"]) + else: + assert False, "Cannot recognize model: " + model_to_train + ". Options: DT, RF" + log += print_and_log("\nStart tune/train for model " + model_name + " with parameters:") + log += print_and_log(model) + + # =============================================================================== + # Cross-validation splitter (train/validation-split) + test_size = 0.3 + cv = GroupShuffleSplit(n_splits=model_options["splits"], test_size=test_size) + + # =============================================================================== + # Feature selection: SelectFromModel + from sklearn.feature_selection import SelectFromModel + + feature_importance_threshold = -np.inf + max_features = n_predictors - optimized_hyperparameters[algo]["n_features_to_drop"] + model.cv = cv.split(X_train.values, Y_train.values, groups=X_mnk_train.values) + model.fit(X_train.values, Y_train.values) + model_fs = SelectFromModel(model, threshold=feature_importance_threshold, max_features=max_features, prefit=True) + print(model_fs) + model.cv = None + + # =============================================================================== + # Info on feature selection + all_feature_names = X_train.columns.values.tolist() + feature_support = model_fs.get_support() + features_importances = model.feature_importances_ + feature_name_importance = zip(all_feature_names, features_importances, feature_support) + feature_name_importance = sorted(feature_name_importance, key=lambda x: x[1], reverse=True) + + log += print_and_log("\n----------------------------------------------------------------------------") + n_selected_features = np.sum(feature_support) + log += print_and_log("Optimal number of features : {}".format(n_selected_features)) + + # Selected features + log += print_and_log("\nFeatures:") + selected_features = list() + selected_feature_importances = list() + for i, (feat_name, feat_imp, feat_in) in enumerate(feature_name_importance): + in_or_out = "accepted" if feat_in else " x rejected" + log += print_and_log("{:>2}) {:<40}, imp: {:>1.3f} {}".format(i + 1, feat_name, feat_imp, in_or_out)) + if feat_in: + selected_features.append(feat_name) + selected_feature_importances.append(feat_imp) + + # Drop non-selected features + features_to_drop = [f for f in predictor_names if f not in selected_features] + X_train = X_train.drop(features_to_drop, axis=1) + X_test = X_test.drop(features_to_drop, axis=1) + + # =============================================================================== + # Fit + log += print_and_log("----------------------------------------------------------------------------") + log += print_and_log("\nStart fitting model with predictors:\n") + for i, p in enumerate(X_train.columns.values): + log += print_and_log("\t{:>2}) {}".format(i + 1, p)) + + model.fit(X_train, Y_train) + + safe_pickle([X_train.columns.values, model, test], results_file) + log += print_and_log("\nCompleted fit, wrote results to " + results_file) + log += print_and_log("----------------------------------------------------------------------------") + return_model = model + + # Return + if "mnk" in X_train.columns.values: + X_train.drop("mnk", axis=1, inplace=True) + if "mnk" in X_test.columns.values: + X_train.drop("mnk", axis=1, inplace=True) + + return X_train, Y_train, X_mnk_train, X_test, Y_test, X_mnk_test, return_model, log + + +def fetch_pre_trained_model(X, X_mnk, Y, model_path, log): + + # Load pre-trained model, selected features and indices of test-set + features, model, test_indices = safe_pickle_load(os.path.join(model_path, "feature_tree.p")) + if "mnk" in features: + features.remove("mnk") + + log += print_and_log("\nPerform train/test split") + X_train, X_test, Y_train, Y_test, X_mnk_train, X_mnk_test = get_train_test_partition([X, Y, X_mnk], test_indices) + log += print_and_log("\nComplete train/test split, total size=" + str(X.shape) + ", test size=" + + str(X_test.shape) + ", train_size=" + str(X_train.shape)) + assert X_test.shape[0] < X_train.shape[0] + + log += print_and_log("\nDrop non-selected features") + predictor_names = X_train.columns.values.tolist() + features_to_drop = [f for f in predictor_names if f not in features] + X_train.drop(features_to_drop, axis=1, inplace=True) + X_test.drop(features_to_drop, axis=1, inplace=True) + + return X_train, Y_train, X_mnk_train, X_test, Y_test, X_mnk_test, model, log + + +# =============================================================================== +# Describe and evaluate model +def describe_hpo(gs, X, Y, log, plot_all): + predictor_names = X.columns.values.tolist() + log += print_and_log("\n----------------------------------------------------------------------------") + log += print_and_log("Available predictor variables:") + for p in predictor_names: + log += print_and_log("\t{}".format(p)) + + log += print_and_log("\nBest parameters set found on development set:") + for bestpar_name, bestpar_value in gs.best_params_.items(): + log += print_and_log("\t{}: {}".format(bestpar_name, bestpar_value)) + + log += print_and_log("\nBest estimator:") + best_estimator = gs.best_estimator_._final_estimator + log += print_and_log(best_estimator) + log += print_and_log("----------------------------------------------------------------------------") + + # Export tree SVG + if plot_all: + from dtreeviz.trees import dtreeviz + + log += print_and_log("\nExport tree to SVG:") + viz = dtreeviz(best_estimator, X.values, Y.values.ravel(), target_name="perf", feature_names=predictor_names) + viz.save("trytree.svg") + viz.view() + + return log + + +def describe_model(model, X, Y, log, plot_all): + predictor_names = X.columns.values.tolist() + log += print_and_log("Model:") + log += print_and_log(model) + + log += print_and_log("Predictor variables:") + for p in predictor_names: + log += print_and_log("\t{}".format(p)) + + # Export tree SVG + if plot_all: + from dtreeviz.trees import dtreeviz + + log += print_and_log("\nExport tree to SVG:") + viz = dtreeviz(model, X.values, Y.values.ravel(), target_name="perf", feature_names=predictor_names) + viz.save("trytree.svg") + viz.view() + + return log + + +def print_error(y_true, y_pred, X_mnk, log, scaled=True): + result_line = ( + "Relative performance loss compared to autotuned max:\n" + "top-{}: worse: {:>6.3f} [%], mean: {:>6.3f} [%]") + for top_k in [1]: + log += print_and_log( + result_line.format( + top_k, + worse_rel_perf_loss_of_k(y_true, y_pred, top_k, X_mnk, scaled), + mean_rel_perf_loss_of_k(y_true, y_pred, top_k, X_mnk, scaled), + )) + return log + + +def scale_back(y_scaled, x_mnk, max_performances): + corresponding_maxperf = np.array([max_performances[mnk] for mnk in x_mnk["mnk"].values.tolist()]) + return y_scaled * corresponding_maxperf + + +def plot_train_test_partition(test_idx, train_idx, X_mnk, folder): + + import matplotlib.pyplot as plt + + mnks_string_train = X_mnk["mnk"].iloc[train_idx].unique() + mnks_train = to_tuple(*mnks_string_train) + mnks_string_test = X_mnk["mnk"].iloc[test_idx].unique() + mnks_test = to_tuple(*mnks_string_test) + + y_train_product = dict() # keys: m*n*k, values: how many times this mnk-product appears in training-mnks + for m, n, k in mnks_train: + mxnxk = m * n * k + if mxnxk in y_train_product.keys(): + y_train_product[mxnxk] += 1 + else: + y_train_product[mxnxk] = 1 + + train_mnks = list() + train_counts = list() + for mnk, count in y_train_product.items(): + for c in range(count): + train_mnks.append(mnk) + train_counts.append(c + 1) + + y_test_product = dict() + for m, n, k in mnks_test: + mxnxk = m * n * k + if mxnxk in y_test_product.keys(): + y_test_product[mxnxk] += 1 + else: + y_test_product[mxnxk] = 1 + + test_mnks = list() + test_counts = list() + for mnk, count in y_test_product.items(): + for c in range(count): + test_mnks.append(mnk) + if mnk in y_train_product.keys(): + test_counts.append(y_train_product[mnk] + c + 1) + else: + test_counts.append(c + 1) + + plt.figure(figsize=(30, 5)) + markersize = 12 + plt.plot( + train_mnks, + train_counts, + "o", + markersize=markersize, + color="blue", + label="training mnks (" + str(len(train_mnks)) + ")", + ) + plt.plot( + test_mnks, + test_counts, + "o", + markersize=markersize, + color="red", + label="testing mnks (" + str(len(test_mnks)) + ")", + ) + plot_file_path = os.path.join(folder, "train-test_split.svg") + plt.xlabel("m * n * k triplets") + plt.ylabel("number of occurences in data set") + plt.title("Train/test split") + maxcount = max(max(test_counts), max(train_counts)) + 1 + plt.ylim([0, maxcount]) + plt.legend() + plt.savefig(plot_file_path) + + +def plot_rfecv(rfecv, folder): + # Plot number of features VS. cross-validation scores + plt.figure() + plt.xlabel("Number of features selected") + plt.ylabel("Cross validation score (nb of correct classifications)") + plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_) + plot_file_path = os.path.join(folder, "rfecv_scores.svg") + plt.savefig(plot_file_path) + print(plot_file_path) + + +def plot_feature_importance(importances, names, folder): + + plt.rcdefaults() + fig, ax = plt.subplots() + + ax.set_title("Feature importances") + ax.barh(range(len(names)), importances, color="g", align="center") + ax.set_yticks(np.arange(len(importances))) + ax.set_yticklabels(names) + ax.invert_yaxis() + plot_file_path = os.path.join(folder, "feature_importance.svg") + plt.savefig(plot_file_path) + print(plot_file_path) + + +def plot_loss_histogram(y_true, y_pred, X_mnk, folder): + import matplotlib.pyplot as plt + + # Get losses + top_k = 1 + y = np.array(perf_loss(y_true, y_pred, top_k, X_mnk, False)) + + # Losses-histogram + num_bins = 100 + plt.figure() + plt.hist(y, num_bins, facecolor="green", alpha=0.75) + plt.xlabel("relative performance loss [%]") + plt.ylabel("# occurrences") + plt.title("Performance losses for top-k=" + str(top_k) + " (" + str(len(y)) + " test mnks)") + plot_file_path = os.path.join(folder, "result_losses.svg") + plt.savefig(plot_file_path) + print(plot_file_path) + + +def plot_prediction_accuracy(m, n, k, y_true, y_pred, train, pp): + + plt.figure() + if train: + plt.plot(100 * y_true, 100 * y_pred, "b.", label="truth") + else: + plt.plot(100 * y_true, 100 * y_pred, "r.", label="truth") + plt.xlabel("true scaled performance [%]") + plt.ylabel("predicted scaled performance [%]") + type = "train" if train else "test" + plt.title("Prediction accuracy for kernel " + str((m, n, k)) + " (" + type + ")") + pp.savefig() + + +def get_predive_model_performances(y_true, y_pred, x_mnk, max_performances_ref, max_performances_algo): + + predictive_model_perf_scaled = dict() + + for mnk_string in x_mnk["mnk"].unique(): + + idx_mnk = np.where(x_mnk == mnk_string)[0].tolist() + assert len(idx_mnk) > 0, "idx_mnk is empty" + m, n, k = to_tuple(mnk_string) + + perf_chosen_idx = np.argmax(y_pred[idx_mnk]) + perf_effective = y_true.iloc[idx_mnk].iloc[perf_chosen_idx].values.item() + predictive_model_perf_scaled[(m, n, k)] = perf_effective # 'scaled' between 0 and 1 + + predictive_model_perf = dict( + zip( + predictive_model_perf_scaled.keys(), + [ + perf_scaled * max_performances_ref[to_string(mnk)] + for mnk, perf_scaled in predictive_model_perf_scaled.items() + ], + )) + + # Re-scale performances by algorithm for a fair comparison + predictive_model_perf_scaled = dict( + zip( + predictive_model_perf.keys(), + [perf / max_performances_algo[mnk] for mnk, perf in predictive_model_perf.items()], + )) + + return predictive_model_perf, predictive_model_perf_scaled + + +# =============================================================================== +def evaluate_model( + model, + X_train, + X_mnk_train, + Y_train, + X_test, + X_mnk_test, + Y_test, + max_performances_ref, + max_performances_algo, + baseline_performances_algo, + data_nrows, + plot_all, + log, + folder, +): + """Main evaluation function""" + + log += print_and_log("----------------------------------------------------------------------------") + log += print_and_log("Start model evaluation") + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + log = describe_model(model, X_test, Y_test, log, plot_all) + + # Training error + y_train_pred = model.predict(X_train) + log += print_and_log("\nTraining error: (train&val)") + log = print_error(Y_train, y_train_pred, X_mnk_train, log, True) + + # Test error + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + y_test_pred = model.predict(X_test) + log += print_and_log("\nTesting error:") + log = print_error(Y_test, y_test_pred, X_mnk_test, log, True) + + # Training error (scaled-back) + log += print_and_log("\nTraining error (scaled back): (train&val)") + y_train_pred_scaled_back = scale_back(y_train_pred, X_mnk_train, max_performances_ref) + y_train_scaled_back = pd.DataFrame(scale_back(Y_train.values.flatten(), X_mnk_train, max_performances_ref)) + log = print_error(y_train_scaled_back, y_train_pred_scaled_back, X_mnk_train, log, False) + + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + # Test error (scaled-back) + log += print_and_log("\nTesting error (scaled back): (test&val)") + y_test_pred_scaled_back = scale_back(y_test_pred, X_mnk_test, max_performances_ref) + y_test_scaled_back = pd.DataFrame(scale_back(Y_test.values.flatten(), X_mnk_test, max_performances_ref)) + log = print_error(y_test_scaled_back, y_test_pred_scaled_back, X_mnk_test, log, False) + + # =============================================================================== + # Print histogram for "best" estimator + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + log += print_and_log("\nPlot result histogram:") + plot_loss_histogram(Y_test, y_test_pred, X_mnk_test, folder) + + # =============================================================================== + # Plot prediction accuracy and goodness of choice for a few mnks (training-set) + n_samples = 10 if data_nrows < 100000000 else 2 + mnks_to_plot = random.sample(X_mnk_train["mnk"].values.tolist(), n_samples) + + from matplotlib.backends.backend_pdf import PdfPages + + plot_file_path = os.path.join(folder, "evaluation_by_mnk_refit.pdf") + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + plot_file_path = os.path.join(folder, "evaluation_by_mnk.pdf") + pp = PdfPages(plot_file_path) + + for mnk_string in mnks_to_plot: + + # Get performances per mnk + idx_mnk = np.where(X_mnk_train == mnk_string)[0].tolist() + assert len(idx_mnk) > 0, "idx_mnk is empty" + m_, n_, k_ = to_tuple(mnk_string) + + log += print_and_log("Prediction accuracy plot: " + str(mnk_string)) + plot_prediction_accuracy(m_, n_, k_, Y_train.iloc[idx_mnk], y_train_pred[idx_mnk], True, pp) + + log += print_and_log("Goodness plot: " + str(mnk_string)) + plot_choice_goodness( + m_, + n_, + k_, + baseline_performances_algo, + max_performances_ref, + Y_train.iloc[idx_mnk].values, + y_train_pred[idx_mnk], + True, + pp, + True, + ) + + # =============================================================================== + # Plot prediction accuracy for a few mnks (testing-set) + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + mnks_to_plot = random.sample(X_mnk_test["mnk"].values.tolist(), n_samples) + for mnk_string in mnks_to_plot: + + # Get performances per mnk + idx_mnk = np.where(X_mnk_test == mnk_string)[0].tolist() + assert len(idx_mnk) > 0, "idx_mnk is empty" + m_, n_, k_ = to_tuple(mnk_string) + + log += print_and_log("Prediction accuracy plot: " + str(mnk_string)) + plot_prediction_accuracy(m_, n_, k_, Y_test.iloc[idx_mnk], y_test_pred[idx_mnk], False, pp) + + log += print_and_log("Goodness plot: " + str(mnk_string)) + plot_choice_goodness( + m_, + n_, + k_, + baseline_performances_algo, + max_performances_ref, + Y_test.iloc[idx_mnk].values, + y_test_pred[idx_mnk], + False, + pp, + True, + ) + + pp.close() + + # =============================================================================== + # Scale baseline and max performances + max_performances_algo = dict( + zip([to_tuple(mnk_string) for mnk_string in max_performances_algo.keys()], max_performances_algo.values())) + max_performances_algo_scaled = dict(zip(max_performances_algo.keys(), [1.0] * len(max_performances_algo))) + baseline_performances_algo = dict( + zip( + [to_tuple(mnk_string) for mnk_string in baseline_performances_algo.keys()], + baseline_performances_algo.values(), + )) + baseline_performances_algo_scaled = dict( + zip( + [(m, n, k) for m, n, k in baseline_performances_algo.keys()], + [perf / max_performances_algo[(m, n, k)] for (m, n, k), perf in baseline_performances_algo.items()], + )) + + # =============================================================================== + # Compare max performances and baseline + from matplotlib.backends.backend_pdf import PdfPages + + plot_file_path = os.path.join(folder, "evaluation_by_overall_refit.pdf") + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + plot_file_path = os.path.join(folder, "evaluation_overall.pdf") + pp = PdfPages(plot_file_path) + + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + plot_performance_gains( + max_performances_algo, + baseline_performances_algo, + "trained", + "max. performance per algorithm", + "baseline per algorithm", + pp, + ) + plot_scaled_performance_gains( + max_performances_algo_scaled, + baseline_performances_algo_scaled, + "trained", + "max. performance per algorithm", + "baseline per algorithm", + pp, + ) + + # =============================================================================== + # 'Results' = y_true ( y_chosen ) + predictive_model_perf_train, predictive_model_perf_train_scaled = get_predive_model_performances( + Y_train, y_train_pred, X_mnk_train, max_performances_ref, max_performances_algo) + + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + predictive_model_perf_test, predictive_model_perf_test_scaled = get_predive_model_performances( + Y_test, y_test_pred, X_mnk_test, max_performances_ref, max_performances_algo) + + # =============================================================================== + # Plot results (training set: predictive modelling VS naïve) + log += print_and_log("\nPredictive model VS baseline: ") + + perf_gain_pred_train_over_baseline = performance_gain(baseline_performances_algo, predictive_model_perf_train) + plot_absolute_performance_gain(perf_gain_pred_train_over_baseline, "trained", "baseline per algorithm", + "predictive model", pp) + + scaled_perf_gain_pred_train_over_baseline = performance_gain(baseline_performances_algo_scaled, + predictive_model_perf_train_scaled) + plot_relative_performance_gain(scaled_perf_gain_pred_train_over_baseline, "trained", "baseline per algorithm", + "predictive model", pp) + + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + perf_gain_pred_test_over_baseline = performance_gain(baseline_performances_algo, predictive_model_perf_test) + plot_absolute_performance_gain(perf_gain_pred_test_over_baseline, "tested", "baseline per algorithm", + "predictive model", pp) + + scaled_perf_gain_pred_test_over_baseline = performance_gain(baseline_performances_algo_scaled, + predictive_model_perf_test_scaled) + plot_relative_performance_gain(scaled_perf_gain_pred_test_over_baseline, "tested", "baseline per algorithm", + "predictive model", pp) + + log += print_and_log("\nPredictive model VS autotuned: ") + perf_gain_pred_train_over_max = performance_gain(max_performances_algo, predictive_model_perf_train) + plot_absolute_performance_gain(perf_gain_pred_train_over_max, "trained", "max. performance per algorithm", + "predictive model", pp) + scaled_perf_gain_pred_train_over_max = performance_gain(max_performances_algo_scaled, + predictive_model_perf_train_scaled) + plot_relative_performance_gain(scaled_perf_gain_pred_train_over_max, "trained", "max. performance per algorithm", + "predictive model", pp) + + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + perf_gain_pred_test_over_max = performance_gain(max_performances_algo, predictive_model_perf_test) + plot_absolute_performance_gain(perf_gain_pred_test_over_max, "tested", "max. performance per algorithm", + "predictive model", pp) + scaled_perf_gain_pred_test_over_max = performance_gain(max_performances_algo_scaled, + predictive_model_perf_test_scaled) + plot_relative_performance_gain(scaled_perf_gain_pred_test_over_max, "tested", "max. performance per algorithm", + "predictive model", pp) + + log += print_and_log("\nCompare performances: ") + plot_performance_gains( + baseline_performances_algo, + predictive_model_perf_train, + "trained", + "baseline per algorithm", + "predictive model", + pp, + ) + plot_performance_gains( + max_performances_algo, + predictive_model_perf_train, + "trained", + "max. performance per algorithm", + "predictive model", + pp, + ) + + if all([x is not None for x in [X_test, X_mnk_test, Y_test]]): + plot_performance_gains( + baseline_performances_algo, + predictive_model_perf_test, + "tested", + "baseline per algorithm", + "predictive model", + pp, + ) + plot_performance_gains( + max_performances_algo, + predictive_model_perf_test, + "tested", + "max. performance per algorithm", + "predictive model", + pp, + ) + + pp.close() + + return log + + +# =============================================================================== +if __name__ == '__main__': + + parser = argparse.ArgumentParser( + description=""" + Train predictive model on autotuning data + + This script is part of the workflow for predictive modelling of optimal libcusmm parameters. + For more details, see predict.md. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + "-f", "--folder", metavar="FOLDER", type=str, default=".", help="Folder from which to read data") + parser.add_argument("-a", "--algo", metavar="algoname", default="", help="Algorithm to train on") + parser.add_argument( + "-c", + "--plot_all", + default=False, + help="Plot more stuff (Warning: can be very slow for large trees and create very large files)", + ) + parser.add_argument( + "-m", "--model", default="DT", help="Model to train. Options: DT (Decision Trees), RF (Random Forests)") + parser.add_argument( + "-s", + "--splits", + default=3, + metavar="NUMBER", + type=int, + help="Number of cross-validation splits used in RFECV and GridSearchCV", + ) + parser.add_argument("-e", "--ntrees", default=3, metavar="NUMBER", type=int, help="Number of estimators in RF") + parser.add_argument( + "-j", + "--njobs", + default=-1, + metavar="NUMBER", + type=int, + help="Number of cross-validation splits used in RFECV and GridSearchCV", + ) + parser.add_argument( + "-r", + "--nrows", + default=None, + metavar="NUMBER", + type=int, + help="Number of rows of data to load. Default: None (load all)", + ) + parser.add_argument( + "-g", + "--prefitted_model", + metavar="filename", + default="", + help="Path to pickled GridSearchCV object to load instead of recomputing", + ) + + args = parser.parse_args() + model_args = {'model': args.model, 'splits': args.splits, 'ntrees': args.ntrees, 'njobs': args.njobs} + main(args.folder, args.algo, args.plot_all, model_args, args.nrows, args.prefitted_model) diff --git a/src/acc/libsmm_acc/libcusmm/requirements.txt b/src/acc/libsmm_acc/libcusmm/requirements.txt new file mode 100644 index 00000000000..778108ad0b1 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/requirements.txt @@ -0,0 +1,19 @@ +attrs==18.2.0 +cycler==0.10.0 +eli5==0.8.1 +graphviz==0.10.1 +Jinja2==2.10 +joblib==0.13.1 +kiwisolver==1.0.1 +MarkupSafe==1.1.0 +matplotlib==3.0.2 +numpy==1.16.0 +pandas==0.23.4 +pyparsing==2.3.1 +python-dateutil==2.7.5 +pytz==2018.9 +scikit-learn==0.20.2 +scipy==1.2.0 +six==1.12.0 +tabulate==0.8.2 +typing==3.6.6 diff --git a/src/acc/libsmm_acc/libcusmm/submit.py b/src/acc/libsmm_acc/libcusmm/submit.py deleted file mode 100755 index e1230a30832..00000000000 --- a/src/acc/libsmm_acc/libcusmm/submit.py +++ /dev/null @@ -1,53 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -#################################################################################################### -# Copyright (C) by the DBCSR developers group - All rights reserved # -# This file is part of the DBCSR library. # -# # -# For information on the license, see the LICENSE file. # -# For further information please visit https://dbcsr.cp2k.org # -# SPDX-License-Identifier: GPL-2.0+ # -#################################################################################################### - -import sys, os -from os import path -from glob import glob - -from subprocess import Popen, PIPE - - -#=============================================================================== -def main(): - do_it = sys.argv[-1] == "doit!" - - cmd = ["squeue", "--user", os.environ['USER'], "--format=%j", "--nohead"] - p = Popen(cmd, stdout=PIPE) - submitted = p.stdout.read() - - n_submits = 0 - for d in glob("tune_*"): - if not path.isdir(d): - continue - - if len(glob(d+"/slurm-*.out"))>0: - print("%20s: Found slurm file(s)"%d) - continue - - if d in submitted: - print("%20s: Found submitted job"%d) - continue - - n_submits += 1 - if do_it: - print("%20s: Submitting"%d) - assert os.system("cd %s; sbatch *.job"%d)==0 - else: - print('%20s: Would submit, run with "doit!"'%d) - - print("Number of jobs submitted: %d"%n_submits) - -#=============================================================================== -if len(sys.argv)==2 and sys.argv[-1]=="--selftest": - pass #TODO implement selftest -else: - main() diff --git a/src/acc/libsmm_acc/libcusmm/tune.py b/src/acc/libsmm_acc/libcusmm/tune.py deleted file mode 100755 index c9559240ba3..00000000000 --- a/src/acc/libsmm_acc/libcusmm/tune.py +++ /dev/null @@ -1,280 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -#################################################################################################### -# Copyright (C) by the DBCSR developers group - All rights reserved # -# This file is part of the DBCSR library. # -# # -# For information on the license, see the LICENSE file. # -# For further information please visit https://dbcsr.cp2k.org # -# SPDX-License-Identifier: GPL-2.0+ # -#################################################################################################### - -import sys -import os -import json -from glob import glob -from itertools import product -from optparse import OptionParser -from kernels.cusmm_dnt_helper import kernel_algorithm, params_dict_to_kernel -from kernels.cusmm_dnt_tiny import Kernel_dnt_tiny -from kernels.cusmm_dnt_small import Kernel_dnt_small -from kernels.cusmm_dnt_largeDB1 import Kernel_dnt_largeDB1 -from kernels.cusmm_dnt_largeDB2 import Kernel_dnt_largeDB2 - -ALL_KERNELS = tuple(kernel_algorithm.values()) - -#=============================================================================== -# Correspondance between CUDA compute versions and parameter_file -arch_number = { - "parameters_K20X.json": 35, - "parameters_K40.json": 35, - "parameters_K80.json": 37, - "parameters_P100.json": 60 -} - -#=============================================================================== -def main(): - usage = "Usage: tune.py ... " - parser = OptionParser(usage) - parser.add_option("-p", "--params", metavar="filename.json", - default="parameters_P100.json", - help="Default: %default") - - (options, args) = parser.parse_args(sys.argv) - if len(sys.argv) < 2: - print(usage) - sys.exit(1) - - # read existing parameters - param_fn = options.params - assert param_fn in arch_number.keys(), "Cannot find compute version for file " + param_fn - arch = arch_number[param_fn] - with open('kernels/gpu_properties.json') as f: - gpu_properties = json.load(f)["sm_" + str(arch)] - with open(param_fn) as f: - all_kernels = [params_dict_to_kernel(**params) for params in json.load(f)] - print("Libcusmm: Found %d existing parameter sets."%len(all_kernels)) - - blocksizes = [int(i) for i in args[1:]] - assert len(set(blocksizes)) == len(blocksizes) - blocksizes.sort() - - triples = combinations(*blocksizes) - - for (m, n, k) in triples: - existing = [kern for kern in all_kernels if kern.can_handle(m,n,k)] - if existing: - print("Found existing parameter set for %dx%dx%d, skipping."%(m,n,k)) - continue - - outdir = "tune_%dx%dx%d/"%(m,n,k) - if os.path.exists(outdir): - print("Directory %s exists already, skipping."%outdir) - continue - os.mkdir(outdir) - gen_benchmark(outdir, gpu_properties, m, n, k) - gen_jobfile(outdir, m, n, k) - gen_makefile(outdir, arch) - - #gen_collect(outdir, triples) - - -#=============================================================================== -def format_params(params): - output = [] - order = ['m', 'n', 'k', 'tile_m', 'tile_n', 'w', 'v', 'split_thread', 'threads', 'blockdim', 'grouping'] - for k in order: - if k in params: - output.append("%s=%d"%(k, params[k])) - - for k in params.keys(): - if k not in order: - output.append("%s=%d"%(k, params[k])) - - return "(" + ", ".join(output) +")" - - - -#=============================================================================== -def gen_benchmark(outdir, gpu_properties, m, n, k): - includes = [] - launcher_codes = [] - launchers = [] - kernel_descr = [] - - # Get the kernels compatible with the given size: - compatible_kernels = list(ALL_KERNELS) - max_sizes = max(m*k, n*k, m*n) - if max_sizes > 64: - compatible_kernels.remove(Kernel_dnt_tiny) - if max_sizes > 128: - compatible_kernels.remove(Kernel_dnt_small) - if max_sizes < 250: - compatible_kernels.remove(Kernel_dnt_largeDB1) - compatible_kernels.remove(Kernel_dnt_largeDB2) - - - for kernclass in compatible_kernels: - params = kernclass.promising_parameters(m, n, k, gpu_properties) - if(params == 0): - continue - - for p in params: - kern = kernclass(**p) - includes.append("../kernels/"+kern.include) - launcher_codes.append(kern.launcher_code) - launchers.append("launch_"+kern.name) - kernel_descr.append(kernclass.__name__ + format_params(p)) - - print("Found %d parameter sets for %dx%dx%d"%(len(launchers), m, n, k)) - if len(launchers)==0: return - #assert(len(launchers)> 0) - - incl_output = '#include "../kernels/cusmm_common.h"\n' - for i in set(includes): - incl_output += '#include "%s"\n'%i - incl_output += "\n\n" - - MAX_LAUNCHERS_PER_EXE = 10000 - LAUNCHERS_PER_OBJ = 100 - - n_exe_files = len(launcher_codes)//MAX_LAUNCHERS_PER_EXE + 1 - launchers_per_exe = len(launcher_codes) // n_exe_files + 1 - - for i in range(n_exe_files): - A = i * launchers_per_exe - B = min((i+1)*launchers_per_exe, len(launcher_codes)) - for j in range((B-A)//LAUNCHERS_PER_OBJ + 1): - output = incl_output - a = A + j*LAUNCHERS_PER_OBJ - b = min(A + (j+1)*LAUNCHERS_PER_OBJ, B) - output += "\n\n".join(launcher_codes[a:b]) - fn = outdir+"/tune_%dx%dx%d_exe%d_part%d.cu"%(m, n, k, i, j) - writefile(fn, output) - - output = '#include "../libcusmm_benchmark.h"\n\n' - for l in launchers: - output += "int "+ l + "(int *param_stack, int stack_size, cudaStream_t stream, int m_max, int n_max, int k_max, double *a_data, double *b_data, double *c_data);\n" - - output += "\n" - output += "int main(int argc, char** argv){\n" - output += "libcusmm_benchmark_t* handle;\n" - output += "KernelLauncher launchers[%d];\n"%(B-A) - output += "char *kernel_descr[%d];\n"%(B-A) - - for j in range(B-A): - output += "launchers[%d] = %s;\n"%(j, launchers[A+j]) - output += 'kernel_descr[%d] = (char *) "%s";\n'%(j, kernel_descr[A+j]) - output += "libcusmm_benchmark_init(&handle, tune, %d, %d, %d);\n"%(m, n, k) - output += "return libcusmm_benchmark(handle, %d, %d, %d, %d, launchers, kernel_descr);\n"%(m, n, k, B-A) - output += "libcusmm_benchmark_finalize(handle);\n" - output += "}\n" - - fn = outdir+"/tune_%dx%dx%d_exe%d_main.cu"%(m, n, k, i) - writefile(fn, output) - -#=============================================================================== -def gen_jobfile(outdir, m, n, k): - t = "/tune_%dx%dx%d"%(m,n,k) - all_exe_src = [os.path.basename(fn) for fn in glob(outdir+t+"_*_main.cu")] - all_exe = sorted([fn.replace("_main.cu", "") for fn in all_exe_src]) - - output = "#!/bin/bash -l\n" - output += "#SBATCH --nodes=%d\n"%len(all_exe) - output += "#SBATCH --time=0:30:00\n" - output += "#SBATCH --account=s238\n" - output += "#SBATCH --partition=normal\n" - output += "#SBATCH --constraint=gpu\n" - output += "\n" - output += "source ${MODULESHOME}/init/sh;\n" - output += "module load daint-gpu\n" - output += "module unload PrgEnv-cray\n" - output += "module load PrgEnv-gnu/6.0.3\n" - output += "module load cudatoolkit/8.0.54_2.2.8_ga620558-2.1\n" - output += "module list\n" - output += "export CRAY_CUDA_MPS=1\n" - output += "cd $SLURM_SUBMIT_DIR \n" - output += "\n" - output += "date\n" - for exe in all_exe: - output += "srun --nodes=1 --bcast=/tmp/${USER} --ntasks=1 --ntasks-per-node=1 --cpus-per-task=12 make -j 24 %s &\n"%exe - output += "wait\n" - output += "date\n" - output += "\n" - for exe in all_exe: - output += "srun --nodes=1 --bcast=/tmp/${USER} --ntasks=1 --ntasks-per-node=1 --cpus-per-task=1 ./"+exe+" >"+exe+".log 2>&1 & \n" - output += "wait\n" - output += "date\n" - output += "\n" - output += "echo Over all winner:\n" - output += 'grep WINNER .'+t+'_exe*.log | sort -n --field-separator="#" -k 2 | tail -n 1\n' - output += "\n" - output += "#EOF\n" - - fn = outdir+t+".job" - writefile(fn, output) - - - -#=============================================================================== -def gen_makefile(outdir, arch): - output = ".SECONDARY:\n" - output += "vpath %.cu ../\n\n" - all_exe_src = sorted([os.path.basename(fn) for fn in glob(outdir+"/tune_*_main.cu")]) - build_targets = [fn.replace("_main.cu", "") for fn in all_exe_src] - - output += ".PHONY: do_nothing build_all \n\n" - output += "do_nothing:\n\n" - output += "build_all: " + " ".join(build_targets) + "\n\n" - - output += "libcusmm_benchmark.o : libcusmm_benchmark.cu\n" - output += "\tnvcc -O3 -arch=sm_" + str(arch) + " -w -c -std=c++11 $<\n\n" - - headers = " ".join( ["."+fn for fn in glob("./kernels/*.h")] ) - output += "%.o : %.cu "+headers+"\n" - output += "\tnvcc -O3 -arch=sm_" + str(arch) + " -w -c $<\n\n" - - for exe_src in all_exe_src: - absparts = sorted(glob(outdir+"/"+exe_src.replace("_main.cu", "_part*"))) - parts = [os.path.basename(fn) for fn in absparts] - deps = [exe_src, "libcusmm_benchmark.cu"] + parts - deps_obj = " ".join([fn.replace(".cu", ".o") for fn in deps]) - exe = exe_src.replace("_main.cu", "") - output += exe + " : " + deps_obj +"\n" - output += "\tnvcc -O3 -arch=sm_" + str(arch) + " -w -o $@ $^ -L $(CUDA_PATH) -lcuda\n\n" - - writefile(outdir+"/Makefile", output) - - -#=============================================================================== -def gen_collect(outdir, triples): - output = "#!/bin/bash\n" - for (m, n, k) in triples: - t = "/tune_%dx%dx%d"%(m,n,k) - output += 'grep WINNER .'+t+'_exe*.log | sort -n --field-separator="#" -k 2 | tail -n 1\n' - output += "#EOF\n" - fn = outdir+"/collect_winners.sh" - writefile(fn, output) - os.system("chmod +x "+fn) - - -#=============================================================================== -def writefile(fn, content): - if os.path.exists(fn): - with open(fn, "r") as f: - old_content = f.read() - if old_content == content: - return - - with open(fn, "w") as f: - f.write(content) - -#=============================================================================== -def combinations(*sizes): - return list(product(sizes, sizes, sizes)) - -#=============================================================================== -if(len(sys.argv)==2 and sys.argv[-1]=="--selftest"): - pass #TODO implement selftest -else: - main() diff --git a/src/acc/libsmm_acc/libcusmm/tune_collect.py b/src/acc/libsmm_acc/libcusmm/tune_collect.py new file mode 100755 index 00000000000..9c34a4fb091 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/tune_collect.py @@ -0,0 +1,110 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import os +from glob import glob +import re +import json +import argparse +from kernels.cusmm_predict import descr_to_kernel + +re_mnk = re.compile(r"tune_(\d+)x(\d+)x(\d+)_") +re_winner = re.compile(r"\nWINNER: \d+ (.+)\n") +re_gflops = re.compile(r"# ([0-9.]+) GFlop/s") +re_errors = re.compile(r"Number of errors: (\d+)\n") + + +# =============================================================================== +def main(): + winners = dict() + + for d in glob("tune_*"): + if not os.path.isdir(d): + continue + + for exe_fn in glob(d + "/tune_*main.cu"): + mnk = tuple([int(i) for i in re_mnk.search(exe_fn).groups()]) + log_fn = exe_fn.replace("_main.cu", ".log") + error_code = 0 + if not os.path.exists(log_fn): + winners[mnk] = "log missing: " + log_fn + print("Missing log:", log_fn, ", please re-run (cd tune_mxnxk; sbatch tune_mxnxk.job)") + error_code = 1 + else: + error_code += process_log(log_fn, mnk, winners) + + if error_code > 0: + print("Missing or incomplete logs") + return + + # Get kernel objects from list of strings + kernels = [descr_to_kernel(kernel_descr) for kernel_descr in winners.values()] + kernels_dict = dict(zip([(k.m, k.n, k.k) for k in kernels], kernels)) + new_file = "parameters.json" + with open(new_file, "w") as f: + s = json.dumps([kernels_dict[kernel].as_dict_for_parameters_json for kernel in sorted(kernels_dict.keys())]) + s = s.replace("}, ", "},\n") + s = s.replace("[", "[\n") + s = s.replace("]", "\n]") + f.write(s) + + print("\n") + print("Wrote", new_file) + + +# =============================================================================== +def process_log(log_fn, mnk, winners): + print("Reading: " + log_fn) + + with open(log_fn) as f: + content = f.read() + + m = re_errors.search(content) + if not m: + winners[mnk] = "log incomplete: " + log_fn + print("Found incomplete log:", log_fn, ", please re-run (cd tune_mxnxk; sbatch tune_mxnxk.job)") + return 1 + + n_errors = int(m.group(1)) + if n_errors != 0: + winners[mnk] = "errors: " + log_fn + return 0 + + old_gflops = 0.0 + if mnk in winners.keys(): + m = re_gflops.search(winners[mnk]) + if not m: + return 0 + old_gflops = float(m.group(1)) + + new_winner = re_winner.search(content).group(1).strip().replace("GFlops", "GFlop/s") + new_gflops = float(re_gflops.search(new_winner).group(1)) + + if new_gflops > old_gflops: + winners[mnk] = new_winner + return 0 + + +# =============================================================================== +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description=""" + Collect autotuning results: parse the log files contained in folders tune_*x*x* + to determine the best kernel for each block size, and store the results in a + file "parameters.json". + + This script is part of the workflow for autotuning optimal libcusmm parameters. + For more details, see README.md#autotuning-procedure. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + args = parser.parse_args() + main() diff --git a/src/acc/libsmm_acc/libcusmm/tune_merge.py b/src/acc/libsmm_acc/libcusmm/tune_merge.py new file mode 100755 index 00000000000..bacbaa3022a --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/tune_merge.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import json +import argparse +from kernels.cusmm_predict import params_dict_to_kernel + + +def main(param_fn): + + # Read new kernel parameters + param_new = "parameters.json" + with open(param_new) as f: + new_kernels = [params_dict_to_kernel(**params) for params in json.load(f)] + + # Read old kernel parameters + with open(param_fn) as f: + old_kernels = [params_dict_to_kernel(**params) for params in json.load(f)] + + # Merge two parameter lists + print("Merging", param_new, "with", param_fn) + kernels_dict = dict(zip([(k.m, k.n, k.k) for k in old_kernels], old_kernels)) + new_kernels_dict = dict(zip([(k.m, k.n, k.k) for k in new_kernels], new_kernels)) + kernels_dict.update(new_kernels_dict) + + # Write kernel parameters to new file + new_file = "parameters.new.json" + with open(new_file, "w") as f: + s = json.dumps([kernels_dict[kernel].as_dict_for_parameters_json for kernel in sorted(kernels_dict.keys())]) + s = s.replace("}, ", "},\n") + s = s.replace("[", "[\n") + s = s.replace("]", "\n]") + f.write(s) + + print("Wrote", new_file) + + +# =============================================================================== +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description=""" + Write a new kernel parameter file (parameters.new.json) as a unique merge of an + already-existing parameter file (specified by `-p parameters_GPU.json`) and a new + one (parameters.json) created by tune_collect.py. If a kernel (m, n, k) is listed + in both the original parameter file and the new parameter file, retain its parameters + as defined in the new parameter file. + + This script is part of the workflow for autotuning optimal libcusmm parameters. + For more details, see README.md#autotuning-procedure. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + "-p", + "--params", + metavar="parameters_GPU.json", + type=str, + default="parameters_P100.json", + help="parameter file in which to merge the newly obtained autotuned parameters") + + args = parser.parse_args() + main(args.params) diff --git a/src/acc/libsmm_acc/libcusmm/tune_setup.py b/src/acc/libsmm_acc/libcusmm/tune_setup.py new file mode 100755 index 00000000000..fc839f999ea --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/tune_setup.py @@ -0,0 +1,323 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import os +import json +from glob import glob +from itertools import product +import argparse +from kernels.cusmm_predict import arch_number, kernel_algorithm, params_dict_to_kernel, compatible_mnk + + +# =============================================================================== +def main(param_fn, cpus_per_node, max_num_nodes, blocksizes): + + # Read existing parameters + assert param_fn in arch_number.keys(), "Cannot find compute version for file " + param_fn + arch = arch_number[param_fn] + with open("kernels/gpu_properties.json") as f: + gpu_properties = json.load(f)["sm_" + str(arch)] + with open("kernels/autotuning_properties.json") as f: + autotuning_properties = json.load(f) + with open(param_fn) as f: + all_kernels = [params_dict_to_kernel(**params) for params in json.load(f)] + print("Reading parameters from %s" % param_fn) + autotuned_kernels = [k for k in all_kernels if k.autotuned] + predicted_kernels = [k for k in all_kernels if not k.autotuned] + print("Libcusmm: Found %d existing parameter sets, of which %d are autotuned and %d are predicted." % + (len(all_kernels), len(autotuned_kernels), len(predicted_kernels))) + + # Get blocksizes to be autotuned + assert len(set(blocksizes)) == len(blocksizes) + blocksizes.sort() + + # Get (m, n, k) triplets to be autotuned + triples = combinations(*blocksizes) + + for (m, n, k) in triples: + existing = [kern for kern in autotuned_kernels if kern.can_handle(m, n, k)] + if existing: + print("Found existing autotuned parameter set for %dx%dx%d, skipping." % (m, n, k)) + continue + + outdir = "tune_%dx%dx%d/" % (m, n, k) + if os.path.exists(outdir): + print("Directory %s exists already, skipping." % outdir) + continue + os.mkdir(outdir) + gen_benchmark(outdir, gpu_properties, autotuning_properties, m, n, k) + gen_jobfile(outdir, m, n, k, cpus_per_node, max_num_nodes) + gen_makefile(outdir, arch) + + +# =============================================================================== +def format_params(params): + output = [] + order = ["m", "n", "k", "tile_m", "tile_n", "w", "v", "split_thread", "threads", "blockdim", "grouping"] + for k in order: + if k in params.keys(): + output.append("%s=%d" % (k, params[k])) + + for k in params.keys(): + if k not in order: + output.append("%s=%d" % (k, params[k])) + + return "(" + ", ".join(output) + ")" + + +# =============================================================================== +def gen_benchmark(outdir, gpu_properties, autotuning_properties, m, n, k): + includes = [] + launcher_codes = [] + launchers = [] + kernel_descr = [] + + # Get the kernel algorithms compatible with the given size: + compatible_kernels = [ + kernel_algorithm[kernclass] for kernclass in kernel_algorithm.keys() if compatible_mnk(kernclass, m, n, k) + ] + + # Get the parameter sets to measure for this (m,n,k) + for kernclass in compatible_kernels: + params = kernclass.promising_parameters(m, n, k, gpu_properties, autotuning_properties) + if params == 0: + continue + + for p in params: + kern = kernclass(**p, source="autotuning_candidate", perf=0) + includes.append("../kernels/" + kern.include) + launcher_codes.append(kern.launcher_code) + launchers.append("launch_" + kern.name) + kernel_descr.append(kernclass.__name__ + format_params(p)) + + print("Found %d parameter sets for %dx%dx%d" % (len(launchers), m, n, k)) + if len(launchers) == 0: + return + + # Compose the "include" line of the benchmark code + incl_output = '#include "../kernels/cusmm_common.h"\n' + for i in set(includes): + incl_output += '#include "%s"\n' % i + incl_output += "\n\n" + max_launchers_per_exe = 10000 + # Compose the benchmark code + launchers_per_obj = 100 + n_exe_files = int(len(launcher_codes) / max_launchers_per_exe) + 1 + launchers_per_exe = int(len(launcher_codes) / n_exe_files) + 1 + + # Compose source code for each executable file + for i in range(n_exe_files): + chunk_a = i * launchers_per_exe + chunk_b = min((i + 1) * launchers_per_exe, len(launcher_codes)) + n_obj_files = int((chunk_b - chunk_a) / launchers_per_obj) + 1 + + # Compose source code for each object file + for j in range(n_obj_files): + a = chunk_a + j * launchers_per_obj + b = min(chunk_a + (j + 1) * launchers_per_obj, chunk_b) + output = incl_output + output += "\n\n".join(launcher_codes[a:b]) + fn = outdir + "/tune_%dx%dx%d_exe%d_part%d.cu" % (m, n, k, i, j) + writefile(fn, output) + +# Compose source code for "main" of executable file + output = '#include "../libcusmm_benchmark.h"\n\n' + for l in launchers: + output += ( + "int " + l + "(int *param_stack, int stack_size, cudaStream_t stream, int m_max, int n_max, int k_max," + + " double *a_data, double *b_data, double *c_data);\n") + + output += "\n" + output += "int main(int argc, char** argv){\n" + output += "libcusmm_benchmark_t* handle;\n" + output += "KernelLauncher launchers[%d];\n" % (chunk_b - chunk_a) + output += "char *kernel_descr[%d];\n" % (chunk_b - chunk_a) + + for j in range(chunk_b - chunk_a): + output += "launchers[%d] = %s;\n" % (j, launchers[chunk_a + j]) + output += 'kernel_descr[%d] = (char *) "%s";\n' % (j, kernel_descr[chunk_a + j]) + output += "libcusmm_benchmark_init(&handle, tune, %d, %d, %d);\n" % (m, n, k) + output += "int result = libcusmm_benchmark(handle, %d, %d, %d, %d, launchers, kernel_descr);\n" % ( + m, + n, + k, + chunk_b - chunk_a, + ) + output += "libcusmm_benchmark_finalize(handle);\n" + output += "return result;" + output += "}\n" + + fn = outdir + "/tune_%dx%dx%d_exe%d_main.cu" % (m, n, k, i) + writefile(fn, output) + + +# =============================================================================== +def gen_jobfile(outdir, m, n, k, cpus_per_node=12, max_num_nodes=0): + t = "/tune_%dx%dx%d" % (m, n, k) + all_exe_src = [os.path.basename(fn) for fn in glob(outdir + t + "_*_main.cu")] + all_exe = sorted([fn.replace("_main.cu", "") for fn in all_exe_src]) + if max_num_nodes > 0: + num_nodes = min(len(all_exe), max_num_nodes) + else: + num_nodes = len(all_exe) + + output = "#!/bin/bash -l\n" + output += "#SBATCH --nodes=%d\n" % num_nodes + output += "#SBATCH --ntasks-per-core=1\n" + output += "#SBATCH --ntasks-per-node=1\n" + output += "#SBATCH --cpus-per-task=" + "%d\n" % cpus_per_node + if num_nodes < 3: + output += "#SBATCH --time=1:30:00\n" + else: + output += "#SBATCH --time=0:30:00\n" + output += "#SBATCH --account=s238\n" + output += "#SBATCH --partition=normal\n" + output += "#SBATCH --constraint=gpu\n" + output += "\n" + output += "source ${MODULESHOME}/init/sh;\n" + output += "module load daint-gpu\n" + output += "module unload PrgEnv-cray\n" + output += "module load PrgEnv-gnu/6.0.3\n" + output += "module load cudatoolkit/8.0.54_2.2.8_ga620558-2.1\n" + output += "module list\n" + output += "export CRAY_CUDA_MPS=1\n" + output += "cd $SLURM_SUBMIT_DIR \n" + output += "\n" + output += "date\n" + + # Compilation + num_nodes_busy = 0 + for exe in all_exe: + output += ( + "srun --nodes=1 --bcast=/tmp/${USER} --ntasks=1 --ntasks-per-node=1 --cpus-per-task=%d make -j %d %s &\n" % + (cpus_per_node, 2 * cpus_per_node, exe)) + num_nodes_busy += 1 + if num_nodes_busy == num_nodes: + output += "wait\n" + num_nodes_busy = 0 + + output += "wait\n" + output += "date\n" + output += "\n" + + # Execution + for exe in all_exe: + output += ("srun --nodes=1 --bcast=/tmp/${USER} --ntasks=1 --ntasks-per-node=1 --cpus-per-task=1 ./" + exe + + " >" + exe + ".log 2>&1 & \n") + num_nodes_busy += 1 + if num_nodes_busy == num_nodes: + output += "wait\n" + num_nodes_busy = 0 + + output += "wait\n" + output += "date\n" + output += "\n" + + # Winner + output += "echo Over all winner:\n" + output += "grep WINNER ." + t + '_exe*.log | sort -n --field-separator="#" -k 2 | tail -n 1\n' + output += "\n" + output += "#EOF\n" + + fn = outdir + t + ".job" + writefile(fn, output) + + +# =============================================================================== +def gen_makefile(outdir, arch): + + output = ".SECONDARY:\n" + output += "vpath %.cu ../\n\n" + all_exe_src = sorted([os.path.basename(fn) for fn in glob(outdir + "/tune_*_main.cu")]) + build_targets = [fn.replace("_main.cu", "") for fn in all_exe_src] + + output += ".PHONY: do_nothing build_all \n\n" + output += "do_nothing:\n\n" + output += "build_all: " + " ".join(build_targets) + "\n\n" + + output += "libcusmm_benchmark.o : libcusmm_benchmark.cu\n" + output += "\tnvcc -O3 -arch=sm_" + str(arch) + " -w -c -std=c++11 $<\n\n" + + headers = " ".join(["." + fn for fn in glob("./kernels/*.h")]) + output += "%.o : %.cu " + headers + "\n" + output += "\tnvcc -O3 -arch=sm_" + str(arch) + " -w -c $<\n\n" + + for exe_src in all_exe_src: + absparts = sorted(glob(outdir + "/" + exe_src.replace("_main.cu", "_part*"))) + parts = [os.path.basename(fn) for fn in absparts] + deps = [exe_src, "libcusmm_benchmark.cu"] + parts + deps_obj = " ".join([fn.replace(".cu", ".o") for fn in deps]) + exe = exe_src.replace("_main.cu", "") + output += exe + " : " + deps_obj + "\n" + output += "\tnvcc -O3 -arch=sm_" + str(arch) + " -w -o $@ $^ -L $(CUDA_PATH) -lcuda\n\n" + + writefile(outdir + "/Makefile", output) + + +# =============================================================================== +def gen_collect(outdir, triples): + output = "#!/bin/bash\n" + for (m, n, k) in triples: + t = "/tune_%dx%dx%d" % (m, n, k) + output += "grep WINNER ." + t + '_exe*.log | sort -n --field-separator="#" -k 2 | tail -n 1\n' + output += "#EOF\n" + fn = outdir + "/collect_winners.sh" + writefile(fn, output) + os.system("chmod +x " + fn) + + +# =============================================================================== +def writefile(fn, content): + if os.path.exists(fn): + with open(fn, "r") as f: + old_content = f.read() + if old_content == content: + return + + with open(fn, "w") as f: + f.write(content) + + +# =============================================================================== +def combinations(*sizes): + return list(product(sizes, sizes, sizes)) + + +# =============================================================================== +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description=""" + Set up the autotuning of specified blocksizes. This script produces folders (tune_*x*x*) + containing the code, Makefile and jobfiles for the autotuning of a given (m, n, k)-triplet. + + This script is part of the workflow for autotuning optimal libcusmm parameters. + For more details, see README.md#autotuning-procedure. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + "-p", + "--params", + metavar="parameters_GPU.json", + default="parameters_P100.json", + help="Parameter file to extend by this autotuning (pick the right GPU)") + parser.add_argument("-c", "--cpus_per_node", metavar="INT", default=12, type=int, help="Number of CPUs per node") + parser.add_argument( + "-n", + "--nodes", + metavar="INT", + default=0, + type=int, + help="Maximum number of nodes an slurm allocation can get. 0: not a limiting factor" + + "(choose this option if you can allocate jobs of 20-30 nodes without a problem.") + parser.add_argument('blocksizes', metavar="BLOCKSIZE", nargs='+', type=int, help='Blocksize(s) to autotune') + + args = parser.parse_args() + main(args.params, args.cpus_per_node, args.nodes, args.blocksizes) diff --git a/src/acc/libsmm_acc/libcusmm/tune_submit.py b/src/acc/libsmm_acc/libcusmm/tune_submit.py new file mode 100755 index 00000000000..16877dee3c1 --- /dev/null +++ b/src/acc/libsmm_acc/libcusmm/tune_submit.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import os +import argparse +from os import path +from glob import glob + +from subprocess import Popen, PIPE + + +# =============================================================================== +def main(submit_jobs): + + cmd = ["squeue", "--user", os.environ["USER"], "--format=%j", "--nohead"] + p = Popen(cmd, stdout=PIPE) + submitted = p.stdout.read() + submitted = submitted.decode("utf-8") + + n_submits = 0 + for d in glob("tune_*"): + if not path.isdir(d): + continue + + if len(glob(d + "/slurm-*.out")) > 0: + print("%20s: Found slurm file(s)" % d) + continue + + if d in submitted: + print("%20s: Found submitted job" % d) + continue + + n_submits += 1 + if submit_jobs: + print("%20s: Submitting" % d) + assert os.system("cd %s; sbatch *.job" % d) == 0 + else: + if len(glob(d + "/*.job")) == 1: + print('%20s: Would submit, run with "doit!"' % d) + elif len(glob(d + "/*.job")) == 0: + print('%20s: Cannot find jobfile, delete this folder and re-create with tune_setup.py"' % d) + else: + print('%20s: Found multiple jobfiles, delete this folder and re-create with tune_setup.py"' % d) + + print("Number of jobs submitted: %d" % n_submits) + + +# =============================================================================== +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description=""" + Submit autotuning jobs: Each tune-directory contains a job file. Since there might be many tune-directories, the + convenience script submit.py can be used. It will go through all the tune_*-directories and check if it has + already been submitted or run. For this the script calls squeue in the background and it searches for + slurm-*.out files. + + This script is part of the workflow for autotuning optimal libcusmm parameters. + For more details, see README.md#autotuning-procedure. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('doit', metavar="doit!", nargs='?', type=str) + + args = parser.parse_args() + submit_jobs = True if args.doit == "doit!" else False + main(submit_jobs) diff --git a/src/utils/dbcsr_toollib.F b/src/utils/dbcsr_toollib.F index aa6b60bea4b..9019f21d893 100644 --- a/src/utils/dbcsr_toollib.F +++ b/src/utils/dbcsr_toollib.F @@ -205,7 +205,7 @@ END FUNCTION lcm FUNCTION joaat_hash(key) RESULT(hash_index) ! LIBXSMM: at least v1.9.0-6 is required #if defined(__LIBXSMM) && TO_VERSION(1, 10, 0) <= TO_VERSION(LIBXSMM_CONFIG_VERSION_MAJOR, LIBXSMM_CONFIG_VERSION_MINOR, LIBXSMM_CONFIG_VERSION_UPDATE) - USE libxsmm + USE libxsmm, ONLY: libxsmm_hash INTEGER, PARAMETER :: seed = 0 INTEGER, DIMENSION(:), INTENT(IN) :: key INTEGER :: hash_index diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 649ce384af0..399cc2d2cc1 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -34,6 +34,7 @@ endforeach () set(DBCSR_TESTS dbcsr_unittest1 dbcsr_unittest2 + dbcsr_unittest3 dbcsr_tensor_unittest dbcsr_test_csr_conversions ) @@ -47,6 +48,7 @@ set(dbcsr_unittest_common_SRCS # For each test, set a variable testname_SRCS defining the sources of that test set(dbcsr_unittest1_SRCS dbcsr_unittest1.F) set(dbcsr_unittest2_SRCS dbcsr_unittest2.F) +set(dbcsr_unittest3_SRCS dbcsr_unittest3.F) set(dbcsr_tensor_unittest_SRCS dbcsr_tensor_unittest.F) set(dbcsr_test_csr_conversions_SRCS dbcsr_test_csr_conversions.F) @@ -78,9 +80,24 @@ if (USE_CUDA) libcusmm_timer_multiply ) + add_custom_command( + OUTPUT ${CMAKE_CURRENT_SOURCE_DIR}/libcusmm_unittest_multiply.cu + COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/generate_libcusmm_unittest_multiply.py --base_folder ${CMAKE_CURRENT_SOURCE_DIR}/.. --gpu_version=${WITH_GPU} + DEPENDS libcusmm_unittest_multiply.template generate_libcusmm_unittest_multiply.py + COMMENT "Generate tests/libcusmm_unittest_multiply.cu" + ) + + add_custom_command( + OUTPUT ${CMAKE_CURRENT_SOURCE_DIR}/libcusmm_timer_multiply.cu + COMMAND ${CMAKE_CURRENT_SOURCE_DIR}/generate_libcusmm_timer_multiply.py --base_folder ${CMAKE_CURRENT_SOURCE_DIR}/.. --gpu_version=${WITH_GPU} + DEPENDS libcusmm_timer_multiply.template generate_libcusmm_timer_multiply.py + COMMENT "Generate tests/libcusmm_timer_multiply.cu" + ) + foreach (libcusmm_test ${LIBCUSMM_TESTS}) - add_executable(${libcusmm_test} ${libcusmm_test}.cu) + add_executable(${libcusmm_test} ${CMAKE_CURRENT_SOURCE_DIR}/${libcusmm_test}.cu) target_link_libraries(${libcusmm_test} dbcsr) add_test(NAME ${libcusmm_test} COMMAND ./${libcusmm_test}) endforeach () + endif () diff --git a/tests/Makefile.inc b/tests/Makefile.inc index da3141c351a..dff55130d19 100644 --- a/tests/Makefile.inc +++ b/tests/Makefile.inc @@ -1,6 +1,6 @@ - # Add unit tests to be executed by test target in Makefile -UNITTESTS = dbcsr_unittest1 dbcsr_unittest2 dbcsr_tensor_unittest dbcsr_test_csr_conversions +UNITTESTS = dbcsr_unittest1 dbcsr_unittest2 dbcsr_unittest3 dbcsr_tensor_unittest dbcsr_test_csr_conversions + # Input tests for the dbcsr_performance_driver PERFTESTS = $(wildcard $(TESTSDIR)/inputs/*.perf) @@ -10,10 +10,11 @@ dbcsr_performance_driver: BIN_DEPS = dbcsr_performance_multiply.o SRC_TESTS += dbcsr_test_csr_conversions.F dbcsr_test_csr_conversions: BIN_DEPS = -SRC_TESTS += dbcsr_unittest1.F dbcsr_unittest2.F +SRC_TESTS += dbcsr_unittest1.F dbcsr_unittest2.F dbcsr_unittest3 dbcsr_unittest1: BIN_DEPS = dbcsr_test_add.o dbcsr_test_multiply.o dbcsr_unittest2: BIN_DEPS = dbcsr_test_multiply.o -.NOTPARALLEL: dbcsr_unittest1 dbcsr_unittest2 # Dependency because both tests share an object file +dbcsr_unittest3: BIN_DEPS = dbcsr_test_multiply.o +.NOTPARALLEL: dbcsr_unittest1 dbcsr_unittest2 dbcsr_unittest3 # Dependency because both tests share an object file SRC_TESTS += dbcsr_tensor_unittest.F dbcsr_tensor_unittest: BIN_DEPS = @@ -21,12 +22,22 @@ dbcsr_tensor_unittest: BIN_DEPS = ifneq ($(NVCC),) UNITTESTS += libcusmm_unittest_multiply libcusmm_unittest_transpose libcusmm_timer_multiply +libcusmm_unittest_multiply.cu: $(DBCSRHOME)/tests/generate_libcusmm_unittest_multiply.py $(DBCSRHOME)/tests/libcusmm_unittest_multiply.template + $(DBCSRHOME)/tests/generate_libcusmm_unittest_multiply.py --base_folder $(DBCSRHOME) --gpu_version=$(GPUVER) + +libcusmm_unittest_multiply: libcusmm_unittest_multiply.cu + SRC_TESTS += libcusmm_unittest_multiply.cu libcusmm_unittest_multiply: BIN_DEPS = SRC_TESTS += libcusmm_unittest_transpose.cu libcusmm_unittest_transpose: BIN_DEPS = +libcusmm_timer_multiply.cu: $(DBCSRHOME)/tests/generate_libcusmm_timer_multiply.py $(DBCSRHOME)/tests/libcusmm_timer_multiply.template + $(DBCSRHOME)/tests/generate_libcusmm_timer_multiply.py --base_folder $(DBCSRHOME) --gpu_version=$(GPUVER) + +libcusmm_timer_multiply: libcusmm_timer_multiply.cu + SRC_TESTS += libcusmm_timer_multiply.cu libcusmm_timer_multiply: BIN_DEPS = endif diff --git a/tests/dbcsr_unittest3.F b/tests/dbcsr_unittest3.F new file mode 100644 index 00000000000..a8309184a0b --- /dev/null +++ b/tests/dbcsr_unittest3.F @@ -0,0 +1,144 @@ +!--------------------------------------------------------------------------------------------------! +! Copyright (C) by the DBCSR developers group - All rights reserved ! +! This file is part of the DBCSR library. ! +! ! +! For information on the license, see the LICENSE file. ! +! For further information please visit https://dbcsr.cp2k.org ! +! SPDX-License-Identifier: GPL-2.0+ ! +!--------------------------------------------------------------------------------------------------! + +! ************************************************************************************************** +!> \brief Tests for DBCSR's libcusmm backend +!> \author VW +!> \date 2010 +!> \version 1.0 +!> +!> Modification history: +!> - Created 2010 +!> - 2015 Rewrote into standalone unittest (Ole Schuett) +!> - 2019 Adapted to test the libcusmm backend (Shoshana Jakobovits) +! ************************************************************************************************** +PROGRAM dbcsr_unittest + + USE dbcsr_acc_device, ONLY: acc_get_ndevices, & + acc_set_active_device + USE dbcsr_kinds, ONLY: dp + USE dbcsr_lib, ONLY: dbcsr_finalize_lib, & + dbcsr_init_lib + USE dbcsr_machine, ONLY: default_output_unit + USE dbcsr_mp_methods, ONLY: dbcsr_mp_new, & + dbcsr_mp_release + USE dbcsr_mpiwrap, ONLY: mp_cart_create, & + mp_cart_rank, & + mp_comm_free, & + mp_environ, & + mp_world_finalize, & + mp_world_init + USE dbcsr_test_methods, ONLY: dbcsr_reset_randmat_seed + USE dbcsr_test_multiply, ONLY: dbcsr_test_multiplies + USE dbcsr_types, ONLY: dbcsr_mp_obj +#include "base/dbcsr_base_uses.f90" + +!$ USE OMP_LIB, ONLY: omp_get_max_threads, omp_get_thread_num, omp_get_num_threads + + IMPLICIT NONE + + INTEGER :: mp_comm, group, numnodes, mynode, & + prow, pcol, io_unit, handle + INTEGER, DIMENSION(2) :: npdims, myploc + INTEGER, DIMENSION(:, :), POINTER :: pgrid + TYPE(dbcsr_mp_obj) :: mp_env + + CHARACTER(len=*), PARAMETER :: routineN = 'dbcsr_check_multiply' + + !*************************************************************************************** + + ! + ! initialize libdbcsr errors + CALL timeset(routineN, handle) + + ! + ! initialize mpi + CALL mp_world_init(mp_comm) + + ! setup the mp environment + npdims(:) = 0 + CALL mp_cart_create(mp_comm, 2, npdims, myploc, group) + CALL mp_environ(numnodes, mynode, group) + ALLOCATE (pgrid(0:npdims(1) - 1, 0:npdims(2) - 1)) + DO prow = 0, npdims(1) - 1 + DO pcol = 0, npdims(2) - 1 + CALL mp_cart_rank(group, (/prow, pcol/), pgrid(prow, pcol)) + ENDDO + ENDDO + CALL dbcsr_mp_new(mp_env, pgrid, group, mynode, numnodes, & + myprow=myploc(1), mypcol=myploc(2)) + DEALLOCATE (pgrid) + ! + ! set standard output parameters + io_unit = 0 + IF (mynode .EQ. 0) io_unit = default_output_unit + + ! + ! initialize libdbcsr + IF (acc_get_ndevices() > 0) & + CALL acc_set_active_device(MOD(mynode, acc_get_ndevices())) + CALL dbcsr_init_lib() + + CALL dbcsr_reset_randmat_seed() + + ! run tests + + ! multiply ------------------------------------------------------------------ + + ! ... + CALL dbcsr_test_multiplies("blocks_4_5_7", & + group, mp_env, npdims, io_unit, matrix_sizes=(/496, 48, 48/), & + sparsities=(/0.5_dp, 0.5_dp, 0.5_dp/), retain_sparsity=.FALSE., & + alpha=CMPLX(1.0_dp, 0.0_dp, dp), beta=CMPLX(0.0_dp, 0.0_dp, dp), & + bs_m=(/1, 4, 1, 5, 1, 7/), bs_n=(/1, 4, 1, 5, 1, 7/), bs_k=(/1, 4, 1, 5, 1, 7/), & + limits=(/1, 496, 1, 48, 1, 48/)) + CALL dbcsr_test_multiplies("blocks_5_8_9", & + group, mp_env, npdims, io_unit, matrix_sizes=(/506, 44, 44/), & + sparsities=(/0.5_dp, 0.5_dp, 0.5_dp/), retain_sparsity=.FALSE., & + alpha=CMPLX(1.0_dp, 0.0_dp, dp), beta=CMPLX(0.0_dp, 0.0_dp, dp), & + bs_m=(/1, 5, 1, 8, 1, 9/), bs_n=(/1, 5, 1, 8, 1, 9/), bs_k=(/1, 5, 1, 8, 1, 9/), & + limits=(/1, 506, 1, 44, 1, 44/)) + CALL dbcsr_test_multiplies("blocks_4_13_25", & + group, mp_env, npdims, io_unit, matrix_sizes=(/504, 42, 42/), & + sparsities=(/0.5_dp, 0.5_dp, 0.5_dp/), retain_sparsity=.FALSE., & + alpha=CMPLX(1.0_dp, 0.0_dp, dp), beta=CMPLX(0.0_dp, 0.0_dp, dp), & + bs_m=(/1, 4, 1, 13, 1, 25/), bs_n=(/1, 4, 1, 13, 1, 25/), bs_k=(/1, 4, 1, 13, 1, 25/), & + limits=(/1, 504, 1, 42, 1, 42/)) + CALL dbcsr_test_multiplies("blocks_14_29_32", & + group, mp_env, npdims, io_unit, matrix_sizes=(/525, 75, 75/), & + sparsities=(/0.5_dp, 0.5_dp, 0.5_dp/), retain_sparsity=.FALSE., & + alpha=CMPLX(1.0_dp, 0.0_dp, dp), beta=CMPLX(0.0_dp, 0.0_dp, dp), & + bs_m=(/1, 14, 1, 29, 1, 32/), bs_n=(/1, 14, 1, 29, 1, 32/), bs_k=(/1, 14, 1, 29, 1, 32/), & + limits=(/1, 525, 1, 75, 1, 75/)) + CALL dbcsr_test_multiplies("blocks_H2O", & + group, mp_env, npdims, io_unit, matrix_sizes=(/552, 46, 46/), & + sparsities=(/0.5_dp, 0.5_dp, 0.5_dp/), retain_sparsity=.FALSE., & + alpha=CMPLX(1.0_dp, 0.0_dp, dp), beta=CMPLX(0.0_dp, 0.0_dp, dp), & + bs_m=(/1, 23/), bs_n=(/1, 23/), bs_k=(/1, 23/), & + limits=(/1, 552, 1, 46, 1, 46/)) + + ! end of test cases --------------------------------------------------------- + + ! finalize libdbcsr + CALL dbcsr_finalize_lib(mp_comm, io_unit) + + ! + ! clean mp environment + CALL dbcsr_mp_release(mp_env) + + ! + ! finalize mpi + CALL mp_comm_free(group) + CALL mp_world_finalize() + + ! + ! finalize libdbcsr errors + CALL timestop(handle) + +END PROGRAM dbcsr_unittest diff --git a/tests/generate_libcusmm_timer_multiply.py b/tests/generate_libcusmm_timer_multiply.py new file mode 100755 index 00000000000..f2f5c33bfaa --- /dev/null +++ b/tests/generate_libcusmm_timer_multiply.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import os +import random +import json +import argparse + + +def format_to_cpp(kernels): + """Given a list of Kernels represented as dictionaries, return a string representing them as C++ vector of vectors + using initializer lists""" + kernels = sorted(kernels, key=lambda k: (k["m"], k["n"], k["k"])) + out = "" + init_list_line = " {{{m:>2}, {n:>2}, {k:>2}}},\n" + for k in kernels: + out += init_list_line.format(m=k["m"], n=k["n"], k=k["k"]) + return out + + +# =============================================================================== +def main(basedir, gpu_version, nsamples): + """ + Generate a performance test of libcusmm in the form of a CUDA file, using libcusmm_timer_multiply.template + as a template + """ + + # Read parameter file + print("GPU version: {}".format(gpu_version)) + base_dir = os.path.join(basedir, "src/acc/libsmm_acc/libcusmm/") + param_fn = os.path.join(base_dir, "parameters_{}.json".format(gpu_version)) + with open(param_fn, "r") as f: + all_kernels = json.load(f) + + # Get the autotuned kernels to test + autotuned_kernels = [k for k in all_kernels if k["source"] == "autotuned"] + print("Found {:,} autotuned kernels".format(len(autotuned_kernels))) + kernels_to_print_autotuned = format_to_cpp(autotuned_kernels) + + # Get the non-autotuned kernels to test + predicted_kernels = [k for k in all_kernels if k["source"] != "autotuned"] + print("Found {:,} predicted kernels".format(len(predicted_kernels))) + num_predicted_kernels = len(predicted_kernels) + if num_predicted_kernels > 0: + if nsamples >= num_predicted_kernels: + nsamples = num_predicted_kernels + kernels_to_test_predicted = random.sample(predicted_kernels, nsamples) + kernels_to_print_predicted = format_to_cpp(kernels_to_test_predicted) + else: + kernels_to_test_predicted = list() + kernels_to_print_predicted = '' + + # Print to test file + test_directory = os.path.join(basedir, "tests") + file_template = os.path.join(test_directory, "libcusmm_timer_multiply.template") + file_generate = os.path.join(test_directory, "libcusmm_timer_multiply.cu") + with open(file_template, "r") as f: + test = f.read() + test = test.replace("[[AUTOTUNED_KERNELS_HERE]]", kernels_to_print_autotuned.lstrip()) + test = test.replace("[[PREDICTED_KERNELS_HERE]]", kernels_to_print_predicted.lstrip()) + with open(file_generate, "w") as f: + f.write(test) + print("Wrote {:,} test kernels to {}".format(len(autotuned_kernels + kernels_to_test_predicted), file_generate)) + + +# =============================================================================== +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description=""" + Generate a performance test of libcusmm in the form of a CUDA file, using libcusmm_timer_multiply.template + as a template + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("-f", "--base_folder", metavar="DBCSRHOME", default="") + parser.add_argument( + "-g", + "--gpu_version", + metavar="GPU_VERSION", + default="P100", + help="GPU card version, used to select the appropriate libcusmm parameters file", + ) + parser.add_argument( + "-n", + "--nsamples", + default=1000, + help="Number of samples from the matrix sizes space 4 <= m,n,k <= 45 (except autotuned kernels)" + + " to sample for performance testing", + ) + + args = parser.parse_args() + main(args.base_folder, args.gpu_version, args.nsamples) diff --git a/tests/generate_libcusmm_unittest_multiply.py b/tests/generate_libcusmm_unittest_multiply.py new file mode 100755 index 00000000000..04789a5bfe5 --- /dev/null +++ b/tests/generate_libcusmm_unittest_multiply.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +#################################################################################################### +# Copyright (C) by the DBCSR developers group - All rights reserved # +# This file is part of the DBCSR library. # +# # +# For information on the license, see the LICENSE file. # +# For further information please visit https://dbcsr.cp2k.org # +# SPDX-License-Identifier: GPL-2.0+ # +#################################################################################################### + +import os +import random +import json +import argparse + + +def format_to_cpp(kernels): + """Given a list of Kernels represented as dictionaries, return a string representing them as C++ vector of vectors + using initializer lists""" + kernels = sorted(kernels, key=lambda k: (k["m"], k["n"], k["k"])) + out = "" + init_list_line = " {{{m:>2}, {n:>2}, {k:>2}}},\n" + for k in kernels: + out += init_list_line.format(m=k["m"], n=k["n"], k=k["k"]) + return out + + +# =============================================================================== +def main(basedir, gpu_version, nsamples): + """ + Generate a performance test of libcusmm in the form of a CUDA file, using libcusmm_timer_multiply.template + as a template + """ + + # Read parameter file + print("GPU version: {}".format(gpu_version)) + base_dir = os.path.join(basedir, "src/acc/libsmm_acc/libcusmm/") + param_fn = os.path.join(base_dir, "parameters_{}.json".format(gpu_version)) + with open(param_fn, "r") as f: + all_kernels = json.load(f) + + # Get the autotuned kernels to test + autotuned_kernels = [k for k in all_kernels if k["source"] == "autotuned"] + print("Found {:,} autotuned kernels".format(len(autotuned_kernels))) + + # Get the non-autotuned kernels to test + predicted_kernels = [k for k in all_kernels if k["source"] != "autotuned"] + print("Found {:,} predicted kernels".format(len(predicted_kernels))) + num_predicted_kernels = len(predicted_kernels) + if num_predicted_kernels > 0: + if nsamples >= num_predicted_kernels: + nsamples = num_predicted_kernels + kernels_to_test_predicted = random.sample(predicted_kernels, nsamples) + else: + kernels_to_test_predicted = list() + kernels_to_print = format_to_cpp(autotuned_kernels + kernels_to_test_predicted) + + # Print to test file + test_directory = os.path.join(basedir, "tests") + file_template = os.path.join(test_directory, "libcusmm_unittest_multiply.template") + file_generate = os.path.join(test_directory, "libcusmm_unittest_multiply.cu") + with open(file_template, "r") as f: + test = f.read() + test = test.replace("[[UNITTEST_KERNELS_HERE]]", kernels_to_print.lstrip()) + with open(file_generate, "w") as f: + f.write(test) + print("Wrote {:,} test kernels to {}".format(len(kernels_to_print), file_generate)) + + +# =============================================================================== +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description=""" + Generate a performance test of libcusmm in the form of a CUDA file, using libcusmm_timer_multiply.template + as a template + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("-f", "--base_folder", metavar="DBCSRHOME", default="") + parser.add_argument( + "-g", + "--gpu_version", + metavar="GPU_VERSION", + default="P100", + help="GPU card version, used to select the appropriate libcusmm parameters file", + ) + parser.add_argument( + "-n", + "--nsamples", + default=1000, + help="Number of samples from the matrix sizes space 4 <= m,n,k <= 45 (except autotuned kernels)" + + " to sample for performance testing", + ) + + args = parser.parse_args() + main(args.base_folder, args.gpu_version, args.nsamples) diff --git a/tests/inputs/test_H2O.perf b/tests/inputs/test_H2O.perf new file mode 100644 index 00000000000..ac2a7e2366e --- /dev/null +++ b/tests/inputs/test_H2O.perf @@ -0,0 +1,60 @@ +# npcols MPI grid, 0 leaves MPI to find the best grid. +# Note that the total number of processors must be divisible per npcols +0 +# operation +dbcsr_multiply +# matrix sizes (M, N, K) +2208 +2208 +2208 +# sparsity (A, B, C) +0.2d0 +0.2d0 +0.2d0 +# transposes +N +N +# symmetries +N +N +N +# data type +3 +# alpha (real, imag) +1.0d0 +0.0d0 +# beta (real, imag) +1.0d0 +0.0d0 +# limits (0 means full size) +# row +0 +0 +# col +0 +0 +# k +0 +0 +# retain sparsity (T/F) +F +# number of repetitions +3 +# number of different blocks to read (m, n, k) +1 +1 +1 +# the m blocks (multiplicity, block size, ...) +1 +23 +# the n blocks (multiplicity, block size, ...) +1 +23 +# the k blocks (multiplicity, block size, ...) +1 +23 +# checksum (check, threshold, references) +F +0. +0. +0. diff --git a/tests/inputs/test_rect1_dense.perf b/tests/inputs/test_rect1_dense.perf index d5a77f171ef..179c33a8a8d 100644 --- a/tests/inputs/test_rect1_dense.perf +++ b/tests/inputs/test_rect1_dense.perf @@ -39,7 +39,7 @@ N # retain sparsity (T/F) F # number of repetitions -1 +150 # number of different blocks to read (m, n, k) 1 1 diff --git a/tests/inputs/test_rect1_sparse.perf b/tests/inputs/test_rect1_sparse.perf index 2d460bc293c..ecbab12e175 100644 --- a/tests/inputs/test_rect1_sparse.perf +++ b/tests/inputs/test_rect1_sparse.perf @@ -39,7 +39,7 @@ N # retain sparsity (T/F) F # number of repetitions -1 +150 # number of different blocks to read (m, n, k) 1 1 diff --git a/tests/inputs/test_rect2_dense.perf b/tests/inputs/test_rect2_dense.perf index 89852f7212f..80f0da677e0 100644 --- a/tests/inputs/test_rect2_dense.perf +++ b/tests/inputs/test_rect2_dense.perf @@ -39,7 +39,7 @@ N # retain sparsity (T/F) F # number of repetitions -1 +150 # number of different blocks to read (m, n, k) 1 1 diff --git a/tests/inputs/test_rect2_sparse.perf b/tests/inputs/test_rect2_sparse.perf index be13c330eae..0a0d69d4bf3 100644 --- a/tests/inputs/test_rect2_sparse.perf +++ b/tests/inputs/test_rect2_sparse.perf @@ -39,7 +39,7 @@ N # retain sparsity (T/F) F # number of repetitions -1 +150 # number of different blocks to read (m, n, k) 1 1 diff --git a/tests/inputs/test_square_dense.perf b/tests/inputs/test_square_dense.perf index 7d1dfb2289f..b87d9ffc65d 100644 --- a/tests/inputs/test_square_dense.perf +++ b/tests/inputs/test_square_dense.perf @@ -39,7 +39,7 @@ N # retain sparsity (T/F) F # number of repetitions -1 +150 # number of different blocks to read (m, n, k) 1 1 diff --git a/tests/inputs/test_square_sparse.perf b/tests/inputs/test_square_sparse.perf index dbfdf74e84c..c6d875cb221 100644 --- a/tests/inputs/test_square_sparse.perf +++ b/tests/inputs/test_square_sparse.perf @@ -39,7 +39,7 @@ N # retain sparsity (T/F) F # number of repetitions -1 +150 # number of different blocks to read (m, n, k) 1 1 diff --git a/tests/inputs/test_square_sparse_bigblocks.perf b/tests/inputs/test_square_sparse_bigblocks.perf index 933679e7d89..e90fa85a328 100644 --- a/tests/inputs/test_square_sparse_bigblocks.perf +++ b/tests/inputs/test_square_sparse_bigblocks.perf @@ -39,7 +39,7 @@ N # retain sparsity (T/F) F # number of repetitions -1 +150 # number of different blocks to read (m, n, k) 1 1 diff --git a/tests/libcusmm_timer_multiply.cu b/tests/libcusmm_timer_multiply.template similarity index 57% rename from tests/libcusmm_timer_multiply.cu rename to tests/libcusmm_timer_multiply.template index b814298b6b8..df0498d683b 100644 --- a/tests/libcusmm_timer_multiply.cu +++ b/tests/libcusmm_timer_multiply.template @@ -18,13 +18,13 @@ #include "acc/libsmm_acc/libcusmm/parameters.h" std::vector combinations(std::vector to_combine){ - - std::vector v; + + std::vector v; int len = to_combine.size(); for(size_t i=0; i combinations(std::vector to_combine){ } /****************************************************************************\ - \brief Checks correctness of and measures performance of common libcusmm multiplication kernels + \brief Checks correctness of and measures performance of randomly selected libcusmm multiplication kernels \****************************************************************************/ int main(int argc, char** argv){ + if(argc < 2){ + printf("Usage: %s autotuned/predicted\n", argv[0]); + return 1; + } + + printf("Time kernels: %s\n", argv[1]); + + std::vector libcusmm_triplets; + if(argv[1] == std::string("autotuned")){ + + libcusmm_triplets = { + [[AUTOTUNED_KERNELS_HERE]] + }; + + } else if(argv[1] == std::string("predicted")){ + + libcusmm_triplets = { + [[PREDICTED_KERNELS_HERE]] + }; + + } else { + + printf("Unrecognized option: %s, exiting ...\n", argv[1]); + return 1; + + } + + // Build benchmark KernelLauncher launcher = libcusmm_process_d; char buffer[1000]; char * kernel_descr[1] = {buffer}; - // Choose which triplets to include in timer - std::vector triplets = { - {23, 23, 23}, - {6, 6, 6}, - {64, 64, 64}, - {78, 78, 78}, - {12, 12, 12} - }; - std::vector to_add = combinations(std::vector({14, 16, 29, 32})); - triplets.insert(triplets.end(), to_add.begin(), to_add.end()); - to_add = combinations(std::vector({5, 32, 13, 24, 26})); - triplets.insert(triplets.end(), to_add.begin(), to_add.end()); - to_add = combinations(std::vector({9, 32, 22})); - triplets.insert(triplets.end(), to_add.begin(), to_add.end()); - to_add = combinations(std::vector({13, 14, 25, 26, 32})); - triplets.insert(triplets.end(), to_add.begin(), to_add.end()); - - // Remove triplets for which no autotuned parameters exist - std::vector blocksizes; - get_libcusmm_triplets(blocksizes, ht); - for(int i=0; i libcusmm_triplets; - get_libcusmm_triplets(libcusmm_triplets, ht); + std::vector libcusmm_triplets = { + [[UNITTEST_KERNELS_HERE]] + }; int n_triplets = libcusmm_triplets.size(); printf("# Libcusmm has %d blocksizes for multiplication\n", n_triplets); diff --git a/tests/libcusmm_unittest_transpose.cu b/tests/libcusmm_unittest_transpose.cu index c172ff0c29d..3fd421c9336 100644 --- a/tests/libcusmm_unittest_transpose.cu +++ b/tests/libcusmm_unittest_transpose.cu @@ -20,7 +20,7 @@ /****************************************************************************\ - \brief Checks correctness of every libcusmm transpose kernel. + \brief Checks correctness of all libcusmm transpose kernels \****************************************************************************/ int main(int argc, char** argv){ @@ -46,7 +46,7 @@ int main(int argc, char** argv){ libcusmm_benchmark_init(&handle, test, max_m, max_n, max_k); // Get (m,n) pairs to test transposition - std::vector > libcusmm_transpose_pairs; + std::vector > libcusmm_transpose_pairs; for(int i=0; i a, std::pair b) { - return (a.first > b.first) || (a.first == b.first && a.second > b.second) ; + return (a.first > b.first) || (a.first == b.first && a.second > b.second) ; }); auto last = std::unique(libcusmm_transpose_pairs.begin(), libcusmm_transpose_pairs.end()); - libcusmm_transpose_pairs.erase(last, libcusmm_transpose_pairs.end()); + libcusmm_transpose_pairs.erase(last, libcusmm_transpose_pairs.end()); int n_pairs = libcusmm_transpose_pairs.size(); printf("# Libcusmm has %d blocksizes for transposition\n", n_pairs); + // Sort (m,n) pairs in growing order + std::sort(libcusmm_transpose_pairs.begin(), libcusmm_transpose_pairs.end(), + [](std::pair mn1, std::pair mn2){ + if(mn1.first != mn2.first){ + return mn1.first < mn2.first; + } else { + return mn1.second < mn2.second; + } + }); + int errors = 0; for(int i=0; i ") - sys.exit(1) - main(sys.argv[1], sys.argv[2], sys.argv[3]) +# =============================================================================== +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description=""" + Parse files and package manifests in the source tree to create rules for objects and executables + + This script is part of the build utility scripts for DBCSR. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("ar_executable", metavar="ar_executable", type=str) + parser.add_argument("src_dir", metavar="src_dir", type=str) + parser.add_argument("lib_dir", metavar="lib_dir", type=str) + + args = parser.parse_args() + main(ar_exe=args.ar_executable, src_dir=args.src_dir, lib_dir=args.lib_dir) diff --git a/tools/build_utils/makedep.py b/tools/build_utils/makedep.py index a5e20af38bc..0a90971ed40 100755 --- a/tools/build_utils/makedep.py +++ b/tools/build_utils/makedep.py @@ -7,6 +7,7 @@ from os import path from os.path import dirname, basename, normpath import glob +import argparse # pre-compiled regular expressions re_module = re.compile(r"(?:^|\n)\s*module\s+(\w+)\s.*\n\s*end\s*module", re.DOTALL) @@ -21,43 +22,43 @@ # ============================================================================ def main(out_fn, project_name, mod_format, mode, archive_ext, src_dir, src_files): messages = [] - # process command line arguments + # process arguments src_files = [path.join(src_dir, f) for f in src_files] - if mod_format not in ('lower', 'upper', 'no'): + if mod_format not in ("lower", "upper", "no"): error('Module filename format must be eighter of "lower", "upper", or "no".') - if mode not in ('normal', 'hackdep', 'mod_compiler'): + if mode not in ("normal", "hackdep", "mod_compiler"): error('Mode must be eighter of "normal", "hackdep", or "mod_compiler".') for fn in src_files: if not fn.startswith("/"): - error("Path of source-file not absolut: "+fn) + error("Path of source-file not absolut: " + fn) src_basenames = [basename(fn).rsplit(".", 1)[0] for fn in src_files] for bfn in src_basenames: if src_basenames.count(bfn) > 1: - error("Multiple source files with the same basename: "+bfn) + error("Multiple source files with the same basename: " + bfn) # parse files parsed_files = dict() for fn in src_files: - parse_file(parsed_files, fn, src_dir) #parses also included files + parse_file(parsed_files, fn, src_dir) # parses also included files messages.append("Parsed %d files" % len(parsed_files)) # create table mapping fortan module-names to file-name mod2fn = dict() for fn in src_files: - for m in parsed_files[fn]['module']: + for m in parsed_files[fn]["module"]: if m in mod2fn.keys(): - error('Multiple declarations of module "%s"'%m) + error('Multiple declarations of module "%s"' % m) mod2fn[m] = fn messages.append("Created mod2fn table, found %d modules." % len(mod2fn)) # check "one module per file"-convention for m, fn in mod2fn.items(): - if basename(fn) != m+".F": - error("Names of module and file do not match: "+fn) + if basename(fn) != m + ".F": + error("Names of module and file do not match: " + fn) # read package manifests packages = dict() @@ -70,34 +71,34 @@ def main(out_fn, project_name, mod_format, mode, archive_ext, src_dir, src_files n_deps = 0 for fn in src_files: p = normpath(dirname(fn)) - if not parsed_files[fn]['program']: - packages[p]['objects'].append(src2obj(basename(fn))) + if not parsed_files[fn]["program"]: + packages[p]["objects"].append(src2obj(basename(fn))) deps = collect_include_deps(parsed_files, fn, src_dir) deps += [mod2fn[m] for m in collect_use_deps(parsed_files, fn, src_dir) if m in mod2fn.keys()] n_deps += len(deps) for d in deps: dp = normpath(dirname(d)) - if dp not in packages[p]['allowed_deps']: - error("Dependency forbidden according to package manifest: %s -> %s"%(fn, d)) + if dp not in packages[p]["allowed_deps"]: + error("Dependency forbidden according to package manifest: %s -> %s" % (fn, d)) if dp != p and "public_files" in packages[dp].keys(): if basename(d) not in packages[dp]["public_files"]: - error("File not public according to package manifest: %s -> %s"%(fn, d)) - messages.append("Checked %d dependencies"%n_deps) + error("File not public according to package manifest: %s -> %s" % (fn, d)) + messages.append("Checked %d dependencies" % n_deps) # check for circular dependencies for fn in parsed_files.keys(): find_cycles(parsed_files, mod2fn, fn, src_dir) # write messages as comments - makefile = "".join(["#makedep: %s\n"%m for m in messages]) + makefile = "".join(["#makedep: %s\n" % m for m in messages]) makefile += "\n" # write rules for archives for p in packages.keys(): - if packages[p]['objects']: - makefile += "# Package %s\n"%p - makefile += "$(LIBDIR)/%s : "%(packages[p]['archive']+archive_ext) - makefile += " ".join(packages[p]['objects']) + "\n\n" + if packages[p]["objects"]: + makefile += "# Package %s\n" % p + makefile += "$(LIBDIR)/%s : " % (packages[p]["archive"] + archive_ext) + makefile += " ".join(packages[p]["objects"]) + "\n\n" # write rules for public files for p in packages.keys(): @@ -111,29 +112,29 @@ def main(out_fn, project_name, mod_format, mode, archive_ext, src_dir, src_files # write rules for executables archive_postfix = archive_ext.rsplit(".", 1)[0] for fn in src_files: - if not parsed_files[fn]['program']: + if not parsed_files[fn]["program"]: continue bfn = basename(fn).rsplit(".", 1)[0] - makefile += "# Program %s\n"%fn - makefile += "$(EXEDIR)/%s.$(ONEVERSION) : %s.o "%(bfn, bfn) + makefile += "# Program %s\n" % fn + makefile += "$(EXEDIR)/%s.$(ONEVERSION) : %s.o " % (bfn, bfn) p = normpath(dirname(fn)) deps = collect_pkg_deps(packages, p) - makefile += " ".join(["$(LIBDIR)/"+a+archive_ext for a in deps]) + "\n" + makefile += " ".join(["$(LIBDIR)/" + a + archive_ext for a in deps]) + "\n" makefile += "\t" + "$(LD) $(LDFLAGS)" if fn.endswith(".c") or fn.endswith(".cu"): makefile += " $(LDFLAGS_C)" - makefile += " -L$(LIBDIR) -o $@ %s.o "%bfn + makefile += " -L$(LIBDIR) -o $@ %s.o " % bfn makefile += "$(EXTERNAL_OBJECTS) " assert all([a.startswith("lib") for a in deps]) - makefile += " ".join(["-l"+a[3:]+archive_postfix for a in deps]) + makefile += " ".join(["-l" + a[3:] + archive_postfix for a in deps]) makefile += " $(LIBS)\n\n" # write rules for objects for fn in src_files: deps = " ".join(collect_include_deps(parsed_files, fn, src_dir)) mods = collect_use_deps(parsed_files, fn, src_dir) - mods.sort(key=cmp_mods) # sort mods to speedup compilation + mods.sort(key=cmp_mods) # sort mods to speedup compilation for m in mods: if m in mod2fn.keys(): deps += " " + mod2modfile(m, mod_format) @@ -141,11 +142,11 @@ def main(out_fn, project_name, mod_format, mode, archive_ext, src_dir, src_files deps = "" bfn = basename(fn) - makefile += "# Object %s\n"%bfn - provides = [mod2modfile(m, mod_format) for m in parsed_files[fn]['module']] + makefile += "# Object %s\n" % bfn + provides = [mod2modfile(m, mod_format) for m in parsed_files[fn]["module"]] for mfn in provides: - makefile += "%s : %s "%(mfn, bfn) + deps + "\n" - makefile += "%s : %s "%(src2obj(bfn), bfn) + deps + makefile += "%s : %s " % (mfn, bfn) + deps + "\n" + makefile += "%s : %s " % (src2obj(bfn), bfn) + deps if mode == "mod_compiler": makefile += " " + " ".join(provides) makefile += "\n\n" @@ -175,7 +176,7 @@ def parse_file(parsed_files, fn, src_dir): content_lower = content.lower() # all files are parsed for cpp includes - incls = re_incl_cpp.findall(content) #CPP includes (case-sensitiv) + incls = re_incl_cpp.findall(content) # CPP includes (case-sensitiv) mods = [] uses = [] @@ -183,14 +184,14 @@ def parse_file(parsed_files, fn, src_dir): if fn[-2:] == ".F" or fn[-4:] == ".f90" or fn[-5:] == ".fypp": mods += re_module.findall(content_lower) - prog = re_program.search(content_lower) != None + prog = True if re_program.search(content_lower) is not None else False uses += re_use.findall(content_lower) - incls += re_incl_fypp.findall(content) # Fypp includes (case-sensitiv) - incl_fort_iter = re_incl_fort.finditer(content_lower) # fortran includes + incls += re_incl_fypp.findall(content) # Fypp includes (case-sensitiv) + incl_fort_iter = re_incl_fort.finditer(content_lower) # fortran includes incls += [content[m.start(1):m.end(1)] for m in incl_fort_iter] if fn[-2:] == ".c" or fn[-3:] == ".cu": - prog = re_main.search(content) != None # C is case-sensitiv + prog = True if re_main.search(content) is not None else False # C is case-sensitiv # exclude included files from outside the source tree def incl_fn(i): @@ -203,9 +204,13 @@ def incl_fn_src(i): existing_incl_src = [i for i in incls if path.exists(incl_fn_src(i))] # store everything in parsed_files cache - parsed_files[fn] = {'module':mods, 'program': prog, 'use':uses, - 'include':existing_incl, - 'include_src':existing_incl_src} + parsed_files[fn] = { + "module": mods, + "program": prog, + "use": uses, + "include": existing_incl, + "include_src": existing_incl_src, + } # parse included files for i in existing_incl: @@ -228,13 +233,13 @@ def read_pkg_manifest(project_name, packages, p): content = fhandle.read() packages[p] = ast.literal_eval(content) - packages[p]['objects'] = [] + packages[p]["objects"] = [] if "archive" not in packages[p].keys(): - packages[p]['archive'] = "lib{}{}".format(project_name, basename(p)) - packages[p]['allowed_deps'] = [normpath(p)] - packages[p]['allowed_deps'] += [normpath(path.join(p, r)) for r in packages[p]['requires']] + packages[p]["archive"] = "lib{}{}".format(project_name, basename(p)) + packages[p]["allowed_deps"] = [normpath(p)] + packages[p]["allowed_deps"] += [normpath(path.join(p, r)) for r in packages[p]["requires"]] - for r in packages[p]['requires']: + for r in packages[p]["requires"]: read_pkg_manifest(project_name, packages, normpath(path.join(p, r))) if "public" in packages[p].keys(): @@ -243,15 +248,16 @@ def read_pkg_manifest(project_name, packages, p): public_files += glob.glob(path.join(p, fn)) packages[p]["public_files"] = [basename(fn) for fn in public_files] + # ============================================================================ def mod2modfile(m, mod_format): - if mod_format == 'no': + if mod_format == "no": return "" - if mod_format == 'lower': + if mod_format == "lower": return m.lower() + ".mod" - if mod_format == 'upper': + if mod_format == "upper": return m.upper() + ".mod" assert False # modeps unknown @@ -267,13 +273,13 @@ def collect_include_deps(parsed_files, fn, src_dir): pf = parsed_files[fn] incs = [] - for i in pf['include']: + for i in pf["include"]: fn_inc = normpath(path.join(dirname(fn), i)) if fn_inc in parsed_files.keys(): incs.append(fn_inc) incs += collect_include_deps(parsed_files, fn_inc, src_dir) - for i in pf['include_src']: + for i in pf["include_src"]: fn_inc = normpath(path.join(src_dir, i)) if fn_inc in parsed_files.keys(): incs.append(fn_inc) @@ -285,14 +291,14 @@ def collect_include_deps(parsed_files, fn, src_dir): # ============================================================================ def collect_use_deps(parsed_files, fn, src_dir): pf = parsed_files[fn] - uses = pf['use'] + uses = pf["use"] - for i in pf['include']: + for i in pf["include"]: fn_inc = normpath(path.join(dirname(fn), i)) if fn_inc in parsed_files.keys(): uses += collect_use_deps(parsed_files, fn_inc, src_dir) - for i in pf['include_src']: + for i in pf["include_src"]: fn_inc = normpath(path.join(src_dir, i)) if fn_inc in parsed_files.keys(): uses += collect_use_deps(parsed_files, fn_inc, src_dir) @@ -303,26 +309,26 @@ def collect_use_deps(parsed_files, fn, src_dir): # ============================================================================ def find_cycles(parsed_files, mod2fn, fn, src_dir, S=None): pf = parsed_files[fn] - if 'visited' in pf.keys(): + if "visited" in pf.keys(): return if not S: S = [] - for m in pf['module']: + for m in pf["module"]: if m in S: i = S.index(m) - error("Circular dependency: "+ " -> ".join(S[i:] + [m])) + error("Circular dependency: " + " -> ".join(S[i:] + [m])) S.append(m) for m in collect_use_deps(parsed_files, fn, src_dir): if m in mod2fn.keys(): find_cycles(parsed_files, mod2fn, mod2fn[m], src_dir, S) - for m in pf['module']: + for m in pf["module"]: S.pop() - pf['visited'] = True + pf["visited"] = True # ============================================================================ @@ -333,40 +339,56 @@ def collect_pkg_deps(packages, p, archives=None, S=None): if not S: S = [] - a = packages[p]['archive'] + a = packages[p]["archive"] if a in archives: return archives if a in S: i = S.index(a) - error("Circular package dependency: "+ " -> ".join(S[i:] + [a])) + error("Circular package dependency: " + " -> ".join(S[i:] + [a])) S.append(a) - for r in packages[p]['requires']: + for r in packages[p]["requires"]: d = normpath(path.join(p, r)) collect_pkg_deps(packages, d, archives, S) S.pop() - if packages[p]['objects']: - archives.insert(0, packages[p]['archive']) + if packages[p]["objects"]: + archives.insert(0, packages[p]["archive"]) return archives # ============================================================================ def error(msg): - sys.stderr.write("makedep error: %s\n"%msg) + sys.stderr.write("makedep error: %s\n" % msg) sys.exit(1) -# ============================================================================ - -if __name__ == '__main__': - if len(sys.argv) < 8: - print("Usage: {} [ ...]" - .format(sys.argv[0])) - sys.exit(1) - main(out_fn=sys.argv[1], project_name=sys.argv[2], - mod_format=sys.argv[3], mode=sys.argv[4], archive_ext=sys.argv[5], - src_dir=sys.argv[6], src_files=sys.argv[7:]) +# ============================================================================ +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description=""" + Parse files and package manifests in the source tree to create rules for objects and executables + + This script is part of the build utility scripts for DBCSR. + """, + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("outfile", metavar="outfile", type=str) + parser.add_argument("project_name", metavar="project_name", type=str) + parser.add_argument("format", metavar="format", type=str) + parser.add_argument("mode", metavar="mode", type=str) + parser.add_argument("archive_ext", metavar="archive_ext", type=str) + parser.add_argument("src_dir", metavar="src_dir", type=str) + parser.add_argument("src_file", metavar="src_file", nargs="+", type=str) + + args = parser.parse_args() + main( + out_fn=args.outfile, + project_name=args.project_name, + mod_format=args.format, + mode=args.mode, + archive_ext=args.archive_ext, + src_dir=args.src_dir, + src_files=args.src_file) diff --git a/tools/build_utils/test_makedep.py b/tools/build_utils/test_makedep.py index e1a22e10554..979d4afaa46 100755 --- a/tools/build_utils/test_makedep.py +++ b/tools/build_utils/test_makedep.py @@ -1,6 +1,5 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- - """ Unittests for the makedep.py script """ @@ -49,13 +48,10 @@ def test_empty_dir(self): os.mkdir(my_dir) - makedep.main( - out_fn, "empty_project", - "lower", "normal", ".a", - my_dir, []) + makedep.main(out_fn, "empty_project", "lower", "normal", ".a", my_dir, []) - with open(out_fn, 'r') as fhandle: - no_comment_lines = [l.strip() for l in fhandle if not l.startswith('#') and l.strip()] + with open(out_fn, "r") as fhandle: + no_comment_lines = [l.strip() for l in fhandle if not l.startswith("#") and l.strip()] self.assertEqual(len(no_comment_lines), 0) @@ -69,34 +65,22 @@ def test_single_empty(self): single_fn = path.join(my_dir, "single.F") os.mkdir(my_dir) - with open(single_fn, 'w') as fhandle: + with open(single_fn, "w") as fhandle: fhandle.write("\n") with self.assertRaises(SystemExit): # should throw an exception due to missing PACKAGES - makedep.main( - out_fn, "faulty_project", - "lower", "normal", ".a", - my_dir, ["./single.F"]) - - _mkpkg({ - "description": "Nothing", - "archive": "test", - "public": ["*.F"], - "requires": [] - }, my_dir) - - makedep.main( - out_fn, "single_empty", - "lower", "normal", ".a", - my_dir, ["./single.F"]) - - with open(out_fn, 'r') as fhandle: - no_comment_lines = [l.strip() for l in fhandle if not l.startswith('#') and l.strip()] + makedep.main(out_fn, "faulty_project", "lower", "normal", ".a", my_dir, ["./single.F"]) - self.assertEqual( - no_comment_lines, - ['$(LIBDIR)/test.a : single.o', 'install: PUBLICFILES += *.F', 'single.o : single.F']) + _mkpkg({"description": "Nothing", "archive": "test", "public": ["*.F"], "requires": []}, my_dir) + + makedep.main(out_fn, "single_empty", "lower", "normal", ".a", my_dir, ["./single.F"]) + + with open(out_fn, "r") as fhandle: + no_comment_lines = [l.strip() for l in fhandle if not l.startswith("#") and l.strip()] + + self.assertEqual(no_comment_lines, + ["$(LIBDIR)/test.a : single.o", "install: PUBLICFILES += *.F", "single.o : single.F"]) def test_unicode(self): """ @@ -108,28 +92,26 @@ def test_unicode(self): single_fn = path.join(my_dir, "single.F") os.mkdir(my_dir) - with io.open(single_fn, 'w', encoding='utf8') as fhandle: + with io.open(single_fn, "w", encoding="utf8") as fhandle: fhandle.write(u"! Ångström\n") - _mkpkg("""{ + _mkpkg( + """{ "description": "unicode test with just an Ångström 😉", "archive": "test", "public": ["*.F"], "requires": [] -}""", my_dir) +}""", + my_dir, + ) - makedep.main( - out_fn, "unicode_project", - "lower", "normal", ".a", - my_dir, ["./single.F"]) + makedep.main(out_fn, "unicode_project", "lower", "normal", ".a", my_dir, ["./single.F"]) - with open(out_fn, 'r') as fhandle: - no_comment_lines = [l.strip() for l in fhandle if not l.startswith('#') and l.strip()] - - self.assertEqual( - no_comment_lines, - ['$(LIBDIR)/test.a : single.o', 'install: PUBLICFILES += *.F', 'single.o : single.F']) + with open(out_fn, "r") as fhandle: + no_comment_lines = [l.strip() for l in fhandle if not l.startswith("#") and l.strip()] + self.assertEqual(no_comment_lines, + ["$(LIBDIR)/test.a : single.o", "install: PUBLICFILES += *.F", "single.o : single.F"]) def test_subpackage(self): """ @@ -146,62 +128,62 @@ def test_subpackage(self): os.mkdir(sub_dir) for fname in [single_fn, sub_fn]: - with open(fname, 'w') as fhandle: + with open(fname, "w") as fhandle: fhandle.write("") - _mkpkg("""{ + _mkpkg( + """{ "description": "test with a simple subpackage", "archive": "test", "public": ["*.F"], "requires": ["./sub"] -}""", my_dir) +}""", + my_dir, + ) - _mkpkg("""{ + _mkpkg( + """{ "description": "the sub package", "requires": [""] -}""", sub_dir) +}""", + sub_dir, + ) - makedep.main( - out_fn, "sub_pkg_without_dep", - "lower", "normal", ".a", - my_dir, ["./single.F"]) + makedep.main(out_fn, "sub_pkg_without_dep", "lower", "normal", ".a", my_dir, ["./single.F"]) - with open(out_fn, 'r') as fhandle: - no_comment_lines = [l.strip() for l in fhandle if not l.startswith('#') and l.strip()] + with open(out_fn, "r") as fhandle: + no_comment_lines = [l.strip() for l in fhandle if not l.startswith("#") and l.strip()] - self.assertEqual( - no_comment_lines, - ['$(LIBDIR)/test.a : single.o', 'install: PUBLICFILES += *.F', 'single.o : single.F']) + self.assertEqual(no_comment_lines, + ["$(LIBDIR)/test.a : single.o", "install: PUBLICFILES += *.F", "single.o : single.F"]) - with open(single_fn, 'w') as fhandle: + with open(single_fn, "w") as fhandle: fhandle.write("""module single use :: sub end module""") - with open(sub_fn, 'w') as fhandle: + with open(sub_fn, "w") as fhandle: fhandle.write("""module sub end module""") - makedep.main( - out_fn, "sub_pkg_with_dep", - "lower", "normal", ".a", - my_dir, ["./single.F", "./sub/sub.F"]) + makedep.main(out_fn, "sub_pkg_with_dep", "lower", "normal", ".a", my_dir, ["./single.F", "./sub/sub.F"]) - with open(out_fn, 'r') as fhandle: - no_comment_lines = [l.strip() for l in fhandle if not l.startswith('#') and l.strip()] + with open(out_fn, "r") as fhandle: + no_comment_lines = [l.strip() for l in fhandle if not l.startswith("#") and l.strip()] self.assertEqual( sorted(no_comment_lines), [ - '$(LIBDIR)/libsub_pkg_with_depsub.a : sub.o', - '$(LIBDIR)/test.a : single.o', - 'install: PUBLICFILES += *.F', - 'single.mod : single.F', - 'single.o : single.F', - 'sub.o : sub.F', - ] - ) - - -if __name__ == '__main__': + "$(LIBDIR)/libsub_pkg_with_depsub.a : sub.o", + "$(LIBDIR)/test.a : single.o", + "install: PUBLICFILES += *.F", + "single.mod : single.F", + "single.o : single.F", + "sub.o : sub.F", + ], + ) + + +# ============================================================================ +if __name__ == "__main__": unittest.main()