TheRock/rocm-libraries/projects/hipblaslt/tensilelite/Tensile/GenerateSummations.py

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import os

import pandas as pd
import numpy as np
import yaml
import subprocess
import glob

from shutil import copyfile
from copy import deepcopy

from . import LibraryIO

from . import ClientWriter
from .TensileCreateLibrary import tensileLibraryFile
from Tensile.Common import ensurePath, printExit
from Tensile.Common.Architectures import isaToGfx, gfxToSwCodename, detectGlobalCurrentISA
from Tensile.Common.GlobalParameters import assignGlobalParameters
from .SolutionStructs import ProblemSizes
from .Toolchain.Validators import ToolchainDefaults, validateToolchain


def createLibraryForBenchmark(logicPath, libraryPath, currentPath):
    """
    takes the path of existing logic files as input and adds the summation
    model for each of the solutions. This is used in the Tile Aware Metirc
    Selection.
    """

    pythonExePath = os.path.join(os.path.dirname(os.path.realpath(__file__)), "bin", "TensileCreateLibrary")
    args = [pythonExePath, \
        "--new-client-only", "--no-short-file-names", \
        "--architecture=all", "--code-object-version=default", "--library-format=yaml", \
        logicPath, libraryPath, "HIP"]

    try:
        subprocess.run(args, check=True, cwd=currentPath)
    except (subprocess.CalledProcessError, OSError) as e:
        printExit("ClientWriter Benchmark Process exited with error: {}".format(e))

def GenerateSummations(userArgs):

    inputLogicPath = userArgs[0]
    outputPath = userArgs[1]
    isaInfoMap = assignGlobalParameters({})
    cxxCompiler, cCompiler, enumerator = validateToolchain(ToolchainDefaults.CXX_COMPILER,
                                                           ToolchainDefaults.C_COMPILER,
                                                           ToolchainDefaults.DEVICE_ENUMERATOR)

    currentISA = detectGlobalCurrentISA(0, enumerator)
    gfxName = isaToGfx(currentISA)
    commonName = gfxToSwCodename(gfxName)

    globPath = os.path.join(inputLogicPath, "{}*".format(commonName))
    logicFileNames = glob.glob(globPath)

    for logicFileName in logicFileNames:

        logicFileBaseName = os.path.basename(logicFileName)
        logicFileStem, ext = os.path.splitext(logicFileBaseName)
        if (ext != ".yaml"):
            continue

        currentPath = ensurePath(os.path.join(outputPath, logicFileStem))

        libPath = ensurePath(os.path.join(currentPath, "lib"))
        finalPath = ensurePath(os.path.join(currentPath, "final"))
        localLogicPath = ensurePath(os.path.join(currentPath, "logic"))
        localLogicFilePath = os.path.join(localLogicPath, logicFileBaseName)

        # Here we read in two version of the logic the first one fills the solutions with
        # defaults and modifies some of the parameters. The final logic file should be the
        # same as the initial logic with the summation model added. To preseve the original
        # logic we also read in the raw unaltered version of the logic and stage the content
        # to write the final logic.
        logic    = LibraryIO.parseLibraryLogicFile(logicFileName, cxxCompiler, isaInfoMap)
        rawLogic = LibraryIO.rawLibraryLogic(logicFileName)

        # If we cannot read the logic file then skip it
        if rawLogic == None or logic == None:
            printExit("Error reading the file: %s. skipping." % logicFileName)

        (versionStringR, scheduleNameR, architectureNameR, deviceNamesR, problemTypeStateR,\
            solutionStatesR, indexOrderR, exactLogicR, rangeLogicR, otherFieldsR) =\
            rawLogic

        copyfile(logicFileName, localLogicFilePath)
        createLibraryForBenchmark(localLogicPath, libPath, currentPath)

        exactList = []

        solutionSummationSizes = [32,64,96,128,256,512,1024,2048,4096,8192,16384]

        for K in solutionSummationSizes:
            e = {"Exact" : [8192, 4096, 1, K]}
            exactList.append(e)

        libraryPath = libPath
        clientBuildDir = os.path.join(outputPath, "client")
        problemTypeObj = logic.problemType.state

        problemSizes = ProblemSizes(problemTypeObj, exactList)
        dataPath = ensurePath(os.path.join(outputPath, logicFileStem, "data"))
        configFilePath = ensurePath(os.path.join(outputPath, logicFileStem, "configs"))
        dataFilePath = os.path.join(dataPath, "benchmark.csv")
        configFile = os.path.join(configFilePath, "ClientParameters.ini")
        scriptPath = ensurePath(os.path.join(outputPath, logicFileStem, "script"))

        ClientWriter.CreateBenchmarkClientParametersForSizes(libraryPath, problemSizes, dataFilePath, configFile, 0, gfxName, problemTypeDict=problemTypeObj)
        ClientWriter.runNewClient(scriptPath, configFile, clientBuildDir, cxxCompiler, cCompiler)

        libraryFilePath = tensileLibraryFile(libPath, gfxName, "yaml")

        stream = open(libraryFilePath, "r")
        tensileLibrary = yaml.load(stream, yaml.SafeLoader)
        stream.close()

        libSolutions = tensileLibrary["solutions"]

        libSolutionNames = []
        for s in libSolutions:
            kernelName=s["name"]
            libSolutionNames.append(kernelName)

        working_data=pd.read_csv(dataFilePath).rename(str.strip,axis='columns')

        index_keys = working_data.SizeL.unique()
        solutionsDF = working_data.filter(like='Cij')

        perf_max = solutionsDF.max().max().item()

        solutionIndex = 0
        for s_stateR, kernelName in zip(solutionStatesR, libSolutionNames):
            solutionIndex += 1
            perf_raw = working_data[kernelName]
            perf = (1000*index_keys) / perf_raw
            model = np.polyfit(x=index_keys, y=perf, deg=1)
            slope = model[0].item()
            intercept = model[1].item()
            linearModel = {"slope": slope, "intercept": intercept, "max": perf_max}
            s_stateR["LinearModel"] = deepcopy(linearModel)

        rawLogicData = []
        rawLogicData.append(deepcopy(versionStringR))
        rawLogicData.append(deepcopy(scheduleNameR))
        rawLogicData.append(deepcopy(architectureNameR))
        rawLogicData.append(deepcopy(deviceNamesR))
        rawLogicData.append(deepcopy(problemTypeStateR))
        rawLogicData.append(deepcopy(solutionStatesR))
        rawLogicData.append(deepcopy(indexOrderR))
        rawLogicData.append(deepcopy(exactLogicR))
        rawLogicData.append(deepcopy(rangeLogicR))
        for idx in range(0, len(otherFieldsR)):
            rawLogicData.append(deepcopy(otherFieldsR[idx]))

        localFinalLogic = os.path.join(finalPath, logicFileBaseName)

        yamlWriter = LibraryIO.YAMLWriter()
        yamlWriter.write(localFinalLogic, rawLogicData)

        outputFinal = ensurePath(os.path.join(outputPath, "final"))
        finalLogic = os.path.join(outputFinal, logicFileBaseName)

        copyfile(localFinalLogic, finalLogic)