colramos-amd
143 rader
5.7 KiB
Python
143 rader
5.7 KiB
Python
##############################################################################bl
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# MIT License
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#
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# Copyright (c) 2021 - 2024 Advanced Micro Devices, Inc. All Rights Reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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##############################################################################el
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import os
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import glob
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import re
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import subprocess
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import pandas as pd
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from utils.utils import console_error, console_debug, console_log
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cache = dict()
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# Note: shortener is now dependent on a rocprof install with llvm
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def kernel_name_shortener(workload_dir, level):
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def shorten_file(df, level):
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global cache
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column_name = ""
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if "Kernel_Name" in df:
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column_name = "Kernel_Name"
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if "Name" in df:
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column_name = "Name"
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if column_name == "Kernel_Name" or column_name == "Name":
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# loop through all indices
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for index in df.index:
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original_name = df.loc[index, column_name]
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if original_name in cache:
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continue
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cmd = [cpp_filt, original_name]
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proc = subprocess.Popen(
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cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE
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)
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demangled_name, e = proc.communicate()
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demangled_name = str(demangled_name, "UTF-8").strip()
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# cache miss, add the shortened name to the dictionary
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new_name = ""
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matches = ""
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names_and_args = re.compile(
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r"(?P<name>[( )A-Za-z0-9_]+)([ ,*<>()]+)(::)?"
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)
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# works for name Kokkos::namespace::init_lock_array_kernel_threadid(int) [clone .kd]
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if names_and_args.search(demangled_name):
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matches = names_and_args.findall(demangled_name)
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else:
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# Works for first case '__amd_rocclr_fillBuffer.kd'
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cache[original_name] = new_name
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if new_name == None or new_name == "":
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cache[original_name] = demangled_name
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continue
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current_level = 0
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for name in matches:
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##can cause errors if a function name or argument is equal to 'clone'
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if name[0] == "clone":
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continue
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if len(name) == 3:
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if name[2] == "::":
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continue
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if current_level < level:
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new_name += name[0]
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# closing '>' is to be taken account by the while loop
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if name[1].count(">") == 0:
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if current_level < level:
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if not (
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current_level == level - 1 and name[1].count("<") > 0
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):
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new_name += name[1]
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current_level += name[1].count("<")
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curr_index = 0
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# cases include '>' '> >, ' have to go in depth here to not lose account of commas and current level
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while name[1].count(">") > 0 and curr_index < len(name[1]):
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if current_level < level:
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new_name += name[1][curr_index:]
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current_level -= name[1][curr_index:].count(">")
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curr_index = len(name[1])
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elif name[1][curr_index] == (">"):
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current_level -= 1
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curr_index += 1
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cache[original_name] = new_name
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if new_name == None or new_name == "":
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cache[original_name] = demangled_name
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df[column_name] = df[column_name].map(cache)
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return df
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# Only shorten if valid shortening level
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if level < 5:
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cpp_filt = os.path.join("/usr", "bin", "c++filt")
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if not os.path.isfile(cpp_filt):
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console_error(
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"Could not resolve c++filt in expected directory: %s" % cpp_filt
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)
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for fpath in glob.glob(workload_dir + "/[SQpmc]*.csv"):
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try:
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orig_df = pd.read_csv(
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fpath,
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on_bad_lines="skip",
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engine="python",
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)
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modified_df = shorten_file(orig_df, level)
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modified_df.to_csv(fpath, index=False)
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except pd.errors.EmptyDataError:
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console_debug(
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"profiling", "Skipping shortening on empty csv: %s" % str(fpath)
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)
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console_log("profiling", "Kernel_Name shortening complete.")
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