mirror of
https://github.com/CNugteren/CLBlast.git
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209 lines
7.4 KiB
Python
209 lines
7.4 KiB
Python
#!/usr/bin/env python
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# ==================================================================================================
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# This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
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# project loosely follows the Google C++ styleguide and uses a max-width of 100 characters per line.
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#
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# Author(s):
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# Cedric Nugteren <www.cedricnugteren.nl>
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#
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# ==================================================================================================
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# System modules
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import sys
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import os.path
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import glob
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import re
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import json
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# Additional modules
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import pandas as pd
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# Constants
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ATTRIBUTES = ["device", "type", "vendor", "precision", "kernel_family", "arg_m", "arg_n", "arg_k"]
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# Pandas options
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pd.set_option('display.width', 1000)
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# ==================================================================================================
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# Database operations
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# ==================================================================================================
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# Loads the database from disk
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def LoadDatabase(filename):
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return pd.read_pickle(filename)
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# Saves the database to disk
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def SaveDatabase(df, filename):
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df.to_pickle(filename)
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# Loads JSON data from file
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def ImportDataFromFile(filename):
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with open(filename) as f:
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data = json.load(f)
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json_data = pd.DataFrame(data)
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df = pd.io.json.json_normalize(json_data["results"])
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for attribute in ATTRIBUTES:
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if attribute == "kernel_family":
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df[attribute] = re.sub(r'_\d+', '', data[attribute])
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elif attribute in data:
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df[attribute] = data[attribute]
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else:
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df[attribute] = 0
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return df
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# Returns the row-wise concatenation of two dataframes
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def ConcatenateData(df1, df2):
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return pd.concat([df1, df2])
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# Removes duplicates from a dataframe
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def RemoveDuplicates(df):
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return df.drop_duplicates()
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# Bests
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def GetBestResults(df):
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dfbest = pd.DataFrame()
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grouped = df.groupby(ATTRIBUTES+["kernel"])
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for name, dfgroup in grouped:
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bestcase = dfgroup.loc[[dfgroup["time"].idxmin()]]
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dfbest = ConcatenateData(dfbest, bestcase)
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return dfbest
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# ==================================================================================================
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# C++ header generation
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# ==================================================================================================
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# The C++ header
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def GetHeader(family):
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return("""
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// =================================================================================================
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// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
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// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
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// width of 100 characters per line.
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//
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// Author(s):
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// Database generator <database.py>
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//
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// This file populates the database with best-found tuning parameters for the '%s' kernels.
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//
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// =================================================================================================
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namespace clblast {
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// ================================================================================================="""
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% family.title())
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# The C++ footer
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def GetFooter():
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return("\n} // namespace clblast\n")
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# The start of a new C++ precision entry
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def GetPrecision(family, precision):
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precisionstring = "Single"
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if precision == "64":
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precisionstring = "Double"
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elif precision == "3232":
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precisionstring = "ComplexSingle"
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elif precision == "6464":
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precisionstring = "ComplexDouble"
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return("\n\nconst Database::DatabaseEntry Database::%s%s = {\n \"%s\", Precision::k%s, {\n"
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% (family.title(), precisionstring, family.title(), precisionstring))
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# The C++ device type and vendor
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def GetDeviceVendor(vendor, devtype):
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return(" { // %s %ss\n kDeviceType%s, kDeviceVendor%s, {\n"
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% (vendor, devtype, devtype, vendor))
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# Prints the data to a C++ database
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def PrintData(df):
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# Iterates over the kernel families: creates a new file per family
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for family, dffamily in df.groupby(["kernel_family"]):
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dffamily = dffamily.dropna(axis=1, how='all')
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f = open(family+'.h', 'w+')
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f.write(GetHeader(family))
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# Loops over the different entries for this family and prints their headers
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for precision, dfprecision in dffamily.groupby(["precision"]):
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f.write(GetPrecision(family, precision))
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for vendor, dfvendor in dfprecision.groupby(["vendor"]):
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for devtype, dfdevtype in dfvendor.groupby(["type"]):
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f.write(GetDeviceVendor(vendor, devtype))
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for device, dfdevice in dfdevtype.groupby(["device"]):
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devicename = "\"%s\"," % device
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f.write(" { %-20s { " % devicename)
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# Collects the paramaters for this case and prints them
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parameters = []
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for kernel, dfkernel in dfdevice.groupby(["kernel"]):
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dfkernel = dfkernel.dropna(axis=1)
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col_names = [col for col in list(dfkernel) if col.startswith('parameters.') and col != "parameters.PRECISION"]
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parameters += ["{\"%s\",%d}" % (p.replace("parameters.",""), dfkernel[p].iloc[0]) for p in col_names]
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f.write(", ".join(parameters))
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f.write(" } },\n")
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# Prints the footers
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f.write(" }\n },\n")
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f.write(" }\n};\n\n// =================================================================================================")
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f.write(GetFooter())
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# ==================================================================================================
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# Command-line arguments parsing and verification
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# ==================================================================================================
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# Checks for the number of command-line arguments
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if len(sys.argv) != 3:
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print "[ERROR] Usage: database.py <folder_with_json_files> <root_of_clblast>"
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sys.exit()
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# Parses the command-line arguments
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path_json = sys.argv[1]
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path_clblast = sys.argv[2]
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file_db = path_clblast+"/src/database.db"
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glob_json = path_json+"/*.json"
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# Checks whether the command-line arguments are valid; exists otherwise
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clblast_h = path_clblast+"/include/clblast.h" # Not used but just for validation
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if not os.path.isfile(clblast_h):
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print "[ERROR] The path '"+path_clblast+"' does not point to the root of the CLBlast library"
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sys.exit()
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if len(glob.glob(glob_json)) < 1:
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print "[ERROR] The path '"+path_json+"' does not contain any JSON files"
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sys.exit()
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# ==================================================================================================
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# The main body of the script
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# ==================================================================================================
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# Loads the database if it exists. If not, a new database is initialized
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db_exists = os.path.isfile(file_db)
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database = LoadDatabase(file_db) if db_exists else pd.DataFrame()
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# Loops over all JSON files in the supplied folder
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for file_json in glob.glob(glob_json):
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# Loads the newly imported data
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print "## Processing '"+file_json+"'",
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imported_data = ImportDataFromFile(file_json)
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# Adds the new data to the database
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old_size = len(database.index)
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database = ConcatenateData(database, imported_data)
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database = RemoveDuplicates(database)
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new_size = len(database.index)
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print "with "+str(new_size-old_size)+" new items"
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# Stores the new database back to disk
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SaveDatabase(database, file_db)
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# Retrieves the best performing results
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bests = GetBestResults(database)
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# TODO: Determines the defaults for other vendors and per vendor
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#defaults = CalculateDefaults(bests)
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#bests = ConcatenateData(bests, defaults)
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# Outputs the data as a C++ database
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PrintData(bests)
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# ==================================================================================================
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