248 líneas
12 KiB
Python
248 líneas
12 KiB
Python
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##############################################################################bl
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# MIT License
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#
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# Copyright (c) 2021 - 2023 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 pandas as pd
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from pathlib import Path
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from tabulate import tabulate
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import sys
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import copy
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from utils import parser
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hidden_columns = ["Tips", "coll_level"]
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hidden_sections = [1900, 2000]
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def string_multiple_lines(source, width, max_rows):
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"""
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Adjust string with multiple lines by inserting '\n'
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"""
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idx = 0
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lines = []
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while idx < len(source) and len(lines) < max_rows:
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lines.append(source[idx : idx + width])
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idx += width
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if idx < len(source):
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last = lines[-1]
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lines[-1] = last[0:-3] + "..."
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return "\n".join(lines)
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def show_all(args, runs, archConfigs, output):
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"""
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Show all panels with their data in plain text mode.
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"""
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comparable_columns = parser.build_comparable_columns(args.time_unit)
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for panel_id, panel in archConfigs.panel_configs.items():
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# Skip panels that don't support baseline comparison
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if panel_id in hidden_sections:
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continue
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ss = "" # store content of all data_source from one pannel
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for data_source in panel["data source"]:
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for type, table_config in data_source.items():
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# take the 1st run as baseline
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base_run, base_data = next(iter(runs.items()))
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base_df = base_data.dfs[table_config["id"]]
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df = pd.DataFrame(index=base_df.index)
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for header in list(base_df.keys()):
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if (
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(not args.cols)
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or (args.cols and base_df.columns.get_loc(header) in args.cols)
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or (type == "raw_csv_table")
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):
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if header in hidden_columns:
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pass
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elif header not in comparable_columns:
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if (
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type == "raw_csv_table"
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and table_config["source"] == "pmc_kernel_top.csv"
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and header == "KernelName"
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):
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# NB: the width of kernel name might depend on the header of the table.
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adjusted_name = base_df["KernelName"].apply(
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lambda x: string_multiple_lines(x, 40, 3)
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)
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df = pd.concat([df, adjusted_name], axis=1)
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elif type == "raw_csv_table" and header == "Info":
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for run, data in runs.items():
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cur_df = data.dfs[table_config["id"]]
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df = pd.concat([df, cur_df[header]], axis=1)
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else:
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df = pd.concat([df, base_df[header]], axis=1)
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else:
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for run, data in runs.items():
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cur_df = data.dfs[table_config["id"]]
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if (type == "raw_csv_table") or (
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type == "metric_table"
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and (not header in hidden_columns)
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):
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if run != base_run:
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# calc percentage over the baseline
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base_df[header] = [
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float(x) if x != "" else float(0)
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for x in base_df[header]
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]
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cur_df[header] = [
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float(x) if x != "" else float(0)
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for x in cur_df[header]
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]
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t_df = pd.concat(
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[
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base_df[header],
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cur_df[header],
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],
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axis=1,
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)
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diff = t_df.iloc[:, 1] - t_df.iloc[:, 0]
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t_df = diff / t_df.iloc[:, 0].replace(0, 1)
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if args.verbose >= 2:
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print("---------", header, t_df)
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t_df_pretty = (
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t_df.astype(float)
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.mul(100)
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.round(args.decimal)
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)
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# show value + percentage
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# TODO: better alignment
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t_df = (
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cur_df[header]
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.astype(float)
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.round(args.decimal)
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.map(str)
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+ " ("
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+ t_df_pretty.map(str)
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+ "%)"
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)
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df = pd.concat([df, t_df], axis=1)
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# DEBUG: When in a CI setting and flag is set,
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# then verify metrics meet threshold requirement
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if args.report_diff:
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if (
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t_df_pretty.abs()
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.gt(args.report_diff)
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.any()
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):
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violation_idx = t_df_pretty.index[t_df_pretty.abs() > args.report_diff]
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print(
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"DEBUG ERROR: Dataframe diff exceeds {} threshold requirement\nSee metric {}".format(
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str(args.report_diff) + "%",
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violation_idx.to_numpy()
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)
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)
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print(df)
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sys.exit(1)
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else:
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cur_df_copy = copy.deepcopy(cur_df)
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cur_df_copy[header] = [
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round(float(x), args.decimal)
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if x != ""
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else x
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for x in base_df[header]
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]
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df = pd.concat([df, cur_df_copy[header]], axis=1)
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if not df.empty:
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# subtitle for each table in a panel if existing
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table_id_str = (
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str(table_config["id"] // 100)
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+ "."
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+ str(table_config["id"] % 100)
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)
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if "title" in table_config and table_config["title"]:
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ss += table_id_str + " " + table_config["title"] + "\n"
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if args.df_file_dir:
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p = Path(args.df_file_dir)
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if not p.exists():
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p.mkdir()
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if p.is_dir():
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if "title" in table_config and table_config["title"]:
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table_id_str += "_" + table_config["title"]
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df.to_csv(
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p.joinpath(table_id_str.replace(" ", "_") + ".csv"),
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index=False,
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)
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# NB:
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# "columnwise: True" is a special attr of a table/df
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# For raw_csv_table, such as system_info, we transpose the
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# df when load it, because we need those items in column.
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# For metric_table, we only need to show the data in column
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# fash for now.
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ss += (
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tabulate(
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df.transpose()
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if type != "raw_csv_table"
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and "columnwise" in table_config
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and table_config["columnwise"] == True
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else df,
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headers="keys",
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tablefmt="fancy_grid",
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floatfmt="." + str(args.decimal) + "f",
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)
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+ "\n"
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)
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if ss:
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print("\n" + "-" * 80, file=output)
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print(str(panel_id // 100) + ". " + panel["title"], file=output)
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print(ss, file=output)
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def show_kernels(args, runs, archConfigs, output):
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"""
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Show the kernels from top stats.
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"""
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print("\n" + "-" * 80, file=output)
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print("Detected Kernels", file=output)
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df = pd.DataFrame()
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for panel_id, panel in archConfigs.panel_configs.items():
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for data_source in panel["data source"]:
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for type, table_config in data_source.items():
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for run, data in runs.items():
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single_df = data.dfs[table_config["id"]]
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# NB:
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# For pmc_kernel_top.csv, have to sort here if not
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# sorted when load_table_data.
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df = pd.concat([df, single_df["KernelName"]], axis=1)
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print(
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tabulate(
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df,
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headers="keys",
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tablefmt="fancy_grid",
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floatfmt="." + str(args.decimal) + "f",
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),
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file=output,
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)
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