vLLM Benchmark suite improvement (#22119)
Signed-off-by: Tsai, Louie <louie.tsai@intel.com> Signed-off-by: Louie Tsai <louie.tsai@intel.com> Co-authored-by: Li, Jiang <bigpyj64@gmail.com>
This commit is contained in:
Louie Tsai
@@ -1,24 +1,38 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import argparse
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import json
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import os
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import pandas as pd
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def compare_data_columns(
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files, name_column, data_column, drop_column, ignore_test_name=False
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files, name_column, data_column, info_cols, drop_column, debug=False
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):
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print("\ncompare_data_column: " + data_column)
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frames = []
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raw_data_cols = []
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compare_frames = []
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for file in files:
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data_df = pd.read_json(file)
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serving_df = data_df.dropna(subset=[drop_column], ignore_index=True)
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if ignore_test_name is False:
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# Show all info columns in the first couple columns
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if not frames:
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for col in info_cols:
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if col not in serving_df.columns:
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print(f"Skipping missing column: {col}")
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continue
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frames.append(serving_df[col])
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# only show test name under debug mode
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if debug is True:
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serving_df = serving_df.rename(columns={name_column: file + "_name"})
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frames.append(serving_df[file + "_name"])
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file = "/".join(file.split("/")[:-1])
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serving_df = serving_df.rename(columns={data_column: file})
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frames.append(serving_df[file])
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raw_data_cols.append(file)
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compare_frames.append(serving_df[file])
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if len(compare_frames) >= 2:
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# Compare numbers among two files
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@@ -27,7 +41,68 @@ def compare_data_columns(
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compare_frames.pop(1)
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concat_df = pd.concat(frames, axis=1)
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return concat_df
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print(raw_data_cols)
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return concat_df, raw_data_cols
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def split_json_by_tp_pp(
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input_file: str = "benchmark_results.json", output_root: str = "."
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) -> list[str]:
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"""
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Split a benchmark JSON into separate folders by (TP Size, PP Size).
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Creates: <output_root>/tp{TP}_pp{PP}/benchmark_results.json
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Returns: list of file paths written.
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"""
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# Load JSON data into DataFrame
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with open(input_file, encoding="utf-8") as f:
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data = json.load(f)
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# If the JSON is a dict with a list under common keys, use that list
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if isinstance(data, dict):
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for key in ("results", "serving_results", "benchmarks", "data"):
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if isinstance(data.get(key), list):
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data = data[key]
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break
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df = pd.DataFrame(data)
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# Handle alias column names
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rename_map = {
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"tp_size": "TP Size",
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"tensor_parallel_size": "TP Size",
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"pp_size": "PP Size",
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"pipeline_parallel_size": "PP Size",
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}
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df.rename(
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columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True
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)
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# Ensure TP/PP columns exist (default to 1 if missing)
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if "TP Size" not in df.columns:
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df["TP Size"] = 1
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if "PP Size" not in df.columns:
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df["PP Size"] = 1
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# make sure TP/PP are numeric ints with no NaN
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df["TP Size"] = (
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pd.to_numeric(df.get("TP Size", 1), errors="coerce").fillna(1).astype(int)
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)
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df["PP Size"] = (
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pd.to_numeric(df.get("PP Size", 1), errors="coerce").fillna(1).astype(int)
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)
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# Split into separate folders
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saved_paths: list[str] = []
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for (tp, pp), group_df in df.groupby(["TP Size", "PP Size"], dropna=False):
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folder_name = os.path.join(output_root, f"tp{int(tp)}_pp{int(pp)}")
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os.makedirs(folder_name, exist_ok=True)
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filepath = os.path.join(folder_name, "benchmark_results.json")
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group_df.to_json(filepath, orient="records", indent=2, force_ascii=False)
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print(f"Saved: {filepath}")
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saved_paths.append(filepath)
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return saved_paths
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if __name__ == "__main__":
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@@ -36,31 +111,105 @@ if __name__ == "__main__":
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"-f", "--file", action="append", type=str, help="input file name"
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)
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parser.add_argument(
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"--ignore_test_name", action="store_true", help="ignore_test_name or not"
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"--debug", action="store_true", help="show all information for debugging"
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)
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parser.add_argument(
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"--plot",
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action=argparse.BooleanOptionalAction,
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default=True,
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help="plot perf diagrams or not --no-plot --plot",
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)
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parser.add_argument(
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"-x",
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"--xaxis",
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type=str,
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default="# of max concurrency.",
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help="column name to use as X Axis in comparision graph",
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)
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args = parser.parse_args()
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files = args.file
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print("comparing : " + ", ".join(files))
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drop_column = "P99"
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name_column = "Test name"
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info_cols = [
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"Model",
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"Dataset Name",
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"Input Len",
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"Output Len",
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"TP Size",
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"PP Size",
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"# of max concurrency.",
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"qps",
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]
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data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
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html_msgs_for_data_cols = [
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"Compare Output Tokens /n",
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"Median TTFT /n",
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"Median TPOT /n",
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]
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ignore_test_name = args.ignore_test_name
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if len(args.file) == 1:
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files = split_json_by_tp_pp(args.file[0], output_root="splits")
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info_cols = [c for c in info_cols if c not in ("TP Size", "PP Size")]
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else:
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files = args.file
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print("comparing : " + ", ".join(files))
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debug = args.debug
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plot = args.plot
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# For Plot feature, assign y axis from one of info_cols
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y_axis_index = info_cols.index(args.xaxis) if args.xaxis in info_cols else 6
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with open("perf_comparison.html", "w") as text_file:
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for i in range(len(data_cols_to_compare)):
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output_df = compare_data_columns(
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output_df, raw_data_cols = compare_data_columns(
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files,
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name_column,
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data_cols_to_compare[i],
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info_cols,
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drop_column,
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ignore_test_name=ignore_test_name,
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debug=debug,
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)
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print(output_df)
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html = output_df.to_html()
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text_file.write(html_msgs_for_data_cols[i])
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text_file.write(html)
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# For Plot feature, insert y axis from one of info_cols
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raw_data_cols.insert(0, info_cols[y_axis_index])
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filtered_info_cols = info_cols[:-2]
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existing_group_cols = [
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c for c in filtered_info_cols if c in output_df.columns
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]
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if not existing_group_cols:
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raise ValueError(
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f"No valid group-by columns "
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f"Expected subset: {filtered_info_cols}, "
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f"but DataFrame has: {list(output_df.columns)}"
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)
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output_df_sorted = output_df.sort_values(by=existing_group_cols)
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output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
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for name, group in output_groups:
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html = group.to_html()
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text_file.write(html_msgs_for_data_cols[i])
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text_file.write(html)
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if plot is True:
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import pandas as pd
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import plotly.express as px
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df = group[raw_data_cols]
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df_sorted = df.sort_values(by=info_cols[y_axis_index])
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# Melt DataFrame for plotting
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df_melted = df_sorted.melt(
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id_vars=info_cols[y_axis_index],
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var_name="Configuration",
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value_name=data_cols_to_compare[i],
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)
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title = data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
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# Create Plotly line chart
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fig = px.line(
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df_melted,
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x=info_cols[y_axis_index],
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y=data_cols_to_compare[i],
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color="Configuration",
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title=title,
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markers=True,
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)
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# Export to HTML
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text_file.write(fig.to_html(full_html=True, include_plotlyjs="cdn"))
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