1117 lines
34 KiB
Python
1117 lines
34 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from __future__ import annotations
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import argparse
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import html as _html
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import json
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import os
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from dataclasses import dataclass
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from importlib import util
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from pathlib import Path
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import pandas as pd
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import regex as re
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pd.options.display.float_format = "{:.2f}".format
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plotly_found = util.find_spec("plotly.express") is not None
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DEFAULT_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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# Safety net: if any DataFrame leaks into to_html(), keep precision at 2.
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pd.set_option("display.precision", 2)
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pd.set_option("display.float_format", lambda x: f"{x:.2f}")
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# -----------------------------
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# Core data compare
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# -----------------------------
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def compare_data_columns(
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files: list[str],
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name_column: str,
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data_column: str,
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info_cols: list[str],
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drop_column: str,
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debug: bool = False,
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):
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"""
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Align concatenation by keys derived from info_cols instead of row order.
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- Pick one canonical key list: subset of info_cols present in ALL files.
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- For each file: set index to those keys, aggregate duplicates
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(mean for metric, first for names).
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- Concat along axis=1 (indexes align), then reset_index so callers can
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group by columns.
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- If --debug, add a <file_label>_name column per file.
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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: list[str] = []
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compare_frames = []
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cols_per_file: list[set] = []
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for f in files:
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try:
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df_tmp = pd.read_json(f, orient="records")
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except Exception as err:
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raise ValueError(f"Failed to read {f}") from err
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cols_per_file.append(set(df_tmp.columns))
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key_cols = [c for c in info_cols if all(c in cset for cset in cols_per_file)]
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if not key_cols:
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key_cols = [c for c in info_cols if c in list(cols_per_file[0])]
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if not key_cols:
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raise ValueError(
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"No common key columns found from info_cols across the input files."
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)
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meta_added = False
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for file in files:
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df = pd.read_json(file, orient="records")
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if drop_column in df.columns:
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df = df.dropna(subset=[drop_column], ignore_index=True)
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for c in (
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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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if c in df.columns:
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df[c] = pd.to_numeric(df[c], errors="coerce")
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for c in key_cols:
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if c not in df.columns:
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df[c] = pd.NA
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df_idx = df.set_index(key_cols, drop=False)
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meta = df_idx[key_cols]
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if not meta.index.is_unique:
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meta = meta.groupby(level=key_cols, dropna=False).first()
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file_label = "/".join(file.split("/")[:-1]) or os.path.basename(file)
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s = df_idx[data_column]
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if not s.index.is_unique:
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s = s.groupby(level=key_cols, dropna=False).mean()
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s.name = file_label
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if not meta_added:
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frames.append(meta)
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meta_added = True
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if debug and name_column in df_idx.columns:
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name_s = df_idx[name_column]
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if not name_s.index.is_unique:
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name_s = name_s.groupby(level=key_cols, dropna=False).first()
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name_s.name = f"{file_label}_name"
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frames.append(name_s)
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frames.append(s)
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raw_data_cols.append(file_label)
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compare_frames.append(s)
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if len(compare_frames) >= 2:
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base = compare_frames[0]
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current = compare_frames[-1]
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if "P99" in data_column or "Median" in data_column:
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ratio = base / current
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else:
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ratio = current / base
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ratio = ratio.mask(base == 0)
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ratio.name = f"Ratio 1 vs {len(compare_frames)}"
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frames.append(ratio)
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concat_df = pd.concat(frames, axis=1).reset_index(drop=True)
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front = [c for c in info_cols if c in concat_df.columns]
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rest = [c for c in concat_df.columns if c not in front]
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concat_df = concat_df[front + rest]
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print(raw_data_cols)
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return concat_df, raw_data_cols
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# -----------------------------
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# Split helper
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# -----------------------------
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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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with open(input_file, encoding="utf-8") as f:
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data = json.load(f)
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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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name_col = next(
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(c for c in ["Test name", "test_name", "Test Name"] if c in df.columns), None
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)
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if name_col:
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df = df[
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df[name_col].astype(str).str.contains(r"serving", case=False, na=False)
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].copy()
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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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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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df["TP Size"] = pd.to_numeric(df["TP Size"], errors="coerce").fillna(1).astype(int)
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df["PP Size"] = pd.to_numeric(df["PP Size"], errors="coerce").fillna(1).astype(int)
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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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# -----------------------------
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# Styling helpers
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# -----------------------------
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def _find_concurrency_col(df: pd.DataFrame) -> str:
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for c in [
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"# of max concurrency.",
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"# of max concurrency",
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"Max Concurrency",
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"max_concurrency",
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"Concurrency",
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]:
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if c in df.columns:
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return c
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for c in df.columns:
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if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
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return c
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return "# of max concurrency."
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def _highlight_threshold(
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df: pd.DataFrame, threshold: float
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) -> pd.io.formats.style.Styler:
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conc_col = _find_concurrency_col(df)
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key_cols = [
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c
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for c in ["Model", "Dataset Name", "Input Len", "Output Len", conc_col]
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if c in df.columns
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]
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conf_cols = [
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c for c in df.columns if c not in key_cols and not str(c).startswith("Ratio")
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]
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conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
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return df.style.map(
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lambda v: "background-color:#e6ffe6;font-weight:bold;"
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if pd.notna(v) and v <= threshold
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else "",
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subset=conf_cols,
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)
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def highlight_ratio_columns(styler: pd.io.formats.style.Styler):
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ratio_cols = [c for c in styler.data.columns if "ratio" in str(c).lower()]
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if not ratio_cols:
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return styler
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styler = styler.apply(
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lambda _: ["background-color: #fff3b0"] * len(styler.data),
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subset=ratio_cols,
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axis=0,
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)
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styler = styler.set_table_styles(
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[
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{
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"selector": f"th.col_heading.level0.col{i}",
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"props": [("background-color", "#fff3b0")],
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}
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for i, col in enumerate(styler.data.columns)
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if col in ratio_cols
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],
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overwrite=False,
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)
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return styler
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def _apply_two_decimals(
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styler: pd.io.formats.style.Styler,
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) -> pd.io.formats.style.Styler:
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df = styler.data
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num_cols = df.select_dtypes("number").columns
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if len(num_cols) == 0:
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return styler
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return styler.format({c: "{:.2f}" for c in num_cols}, na_rep="")
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# -----------------------------
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# Export helpers (Excel + CSV)
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# -----------------------------
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def _sanitize_sheet_name(name: str) -> str:
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"""
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Excel sheet constraints:
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- max 31 chars
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- cannot contain: : \ / ? * [ ]
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- cannot be empty
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"""
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name = "sheet" if name is None else str(name)
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name = re.sub(r"[:\\/?*\[\]]", "_", name)
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name = name.strip().strip("'")
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name = re.sub(r"\s+", " ", name)
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if not name:
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name = "sheet"
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return name[:31]
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def _group_to_sheet_base(group_cols: list[str], gkey_tuple) -> str:
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d = dict(zip(group_cols, gkey_tuple))
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model = d.get("Model", "model")
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model_short = str(model).split("/")[-1]
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ilen = d.get("Input Len", "")
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olen = d.get("Output Len", "")
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lens = f"_{ilen}x{olen}" if ilen != "" and olen != "" else ""
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return _sanitize_sheet_name(f"{model_short}{lens}")
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def _write_tables_to_excel_sheet(
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writer: pd.ExcelWriter, sheet: str, blocks: list[tuple[str, pd.DataFrame]]
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):
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startrow = 0
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for title, df in blocks:
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pd.DataFrame([[title]]).to_excel(
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writer, sheet_name=sheet, index=False, header=False, startrow=startrow
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)
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startrow += 1
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df.to_excel(writer, sheet_name=sheet, index=False, startrow=startrow)
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startrow += len(df) + 3
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def _safe_filename(s: str) -> str:
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s = re.sub(r"[^\w\-.]+", "_", str(s).strip())
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return s[:180] if len(s) > 180 else s
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# -----------------------------
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# vLLM environment export helper
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# -----------------------------
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def _parse_vllm_env_txt(env_path: Path) -> pd.DataFrame:
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"""Parse vllm_env.txt into a flat table (Section, Key, Value).
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Supports:
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- section headers as standalone lines (no ':' or '=')
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- key-value lines like 'OS: Ubuntu ...'
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- env var lines like 'HF_HOME=/data/hf'
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"""
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lines = env_path.read_text(encoding="utf-8", errors="replace").splitlines()
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section = "General"
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rows: list[dict] = []
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def set_section(s: str):
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nonlocal section
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s = (s or "").strip()
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if s:
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section = s
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for raw in lines:
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stripped = raw.strip()
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if not stripped:
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continue
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# divider lines like =====
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if set(stripped) <= {"="}:
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continue
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# section header heuristic: short standalone line
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if ":" not in stripped and "=" not in stripped and len(stripped) <= 64:
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if stripped.lower().startswith("collecting environment information"):
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continue
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set_section(stripped)
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continue
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# env var style: KEY=VALUE (and not a URL with :)
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if "=" in stripped and ":" not in stripped:
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k, v = stripped.split("=", 1)
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k = k.strip()
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v = v.strip()
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if k:
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rows.append({"Section": section, "Key": k, "Value": v})
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continue
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# key: value
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if ":" in stripped:
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k, v = stripped.split(":", 1)
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k = k.strip()
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v = v.strip()
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if k:
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rows.append({"Section": section, "Key": k, "Value": v})
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continue
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return pd.DataFrame(rows, columns=["Section", "Key", "Value"])
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def _load_env_df_for_inputs(args, files: list[str]) -> pd.DataFrame | None:
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"""Load vllm_env.txt next to the *original* input JSON file.
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Note: when only one -f is provided, the script may split JSON into ./splits/...,
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but vllm_env.txt typically lives next to the original benchmark_results.json.
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"""
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base_dir: Path | None = None
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if getattr(args, "file", None):
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base_dir = Path(args.file[0]).resolve().parent
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elif files:
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base_dir = Path(files[0]).resolve().parent
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if base_dir is None:
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return None
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env_path = base_dir / "vllm_env.txt"
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if not env_path.exists():
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return None
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df = _parse_vllm_env_txt(env_path)
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return df
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# -----------------------------
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# Valid max concurrency summary helpers
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# -----------------------------
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def _config_value_columns(df: pd.DataFrame, conc_col: str) -> list[str]:
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key_cols = [
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c
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for c in ["Model", "Dataset Name", "Input Len", "Output Len"]
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if c in df.columns
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]
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exclude = set(key_cols + [conc_col, "qps", "QPS"])
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cols: list[str] = []
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for c in df.columns:
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if c in exclude:
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continue
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lc = str(c).lower()
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if lc.startswith("ratio"):
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continue
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if lc.endswith("_name") or lc == "test name" or lc == "test_name":
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continue
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if pd.api.types.is_numeric_dtype(df[c]):
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cols.append(c)
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return cols
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def _max_concurrency_ok(
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df: pd.DataFrame, conc_col: str, cfg_col: str, threshold: float
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):
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if df is None or conc_col not in df.columns or cfg_col not in df.columns:
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return pd.NA
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d = df[[conc_col, cfg_col]].copy()
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d[conc_col] = pd.to_numeric(d[conc_col], errors="coerce")
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d[cfg_col] = pd.to_numeric(d[cfg_col], errors="coerce")
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d = d.dropna(subset=[conc_col, cfg_col])
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if d.empty:
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return pd.NA
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ok = d[d[cfg_col] <= threshold]
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if ok.empty:
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return pd.NA
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return ok[conc_col].max()
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def _value_at_concurrency(df: pd.DataFrame, conc_col: str, cfg_col: str, conc_value):
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if (
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df is None
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or conc_col not in df.columns
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or cfg_col not in df.columns
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or pd.isna(conc_value)
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):
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return pd.NA
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d = df[[conc_col, cfg_col]].copy()
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d[conc_col] = pd.to_numeric(d[conc_col], errors="coerce")
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d[cfg_col] = pd.to_numeric(d[cfg_col], errors="coerce")
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conc_value = pd.to_numeric(conc_value, errors="coerce")
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if pd.isna(conc_value):
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return pd.NA
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hit = d[d[conc_col] == conc_value]
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if hit.empty:
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return pd.NA
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return hit[cfg_col].iloc[0]
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def build_valid_max_concurrency_summary_html(
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tput_group_df: pd.DataFrame | None,
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ttft_group_df: pd.DataFrame | None,
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tpot_group_df: pd.DataFrame | None,
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conc_col: str,
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args,
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) -> str:
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if ttft_group_df is None and tpot_group_df is None:
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return ""
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ttft_cols = (
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_config_value_columns(ttft_group_df, conc_col)
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if ttft_group_df is not None
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else []
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)
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tpot_cols = (
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_config_value_columns(tpot_group_df, conc_col)
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if tpot_group_df is not None
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else []
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)
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tput_cols = (
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_config_value_columns(tput_group_df, conc_col)
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if tput_group_df is not None
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else []
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)
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if ttft_group_df is not None and tpot_group_df is not None:
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cfg_cols = [c for c in ttft_cols if c in tpot_cols]
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if tput_group_df is not None:
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cfg_cols = [c for c in cfg_cols if c in tput_cols] or cfg_cols
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else:
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cfg_cols = ttft_cols or tpot_cols
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if not cfg_cols:
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cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)
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rows = []
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for cfg in cfg_cols:
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ttft_max = (
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_max_concurrency_ok(ttft_group_df, conc_col, cfg, args.ttft_max_ms)
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if ttft_group_df is not None
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|
else pd.NA
|
|
)
|
|
tpot_max = (
|
|
_max_concurrency_ok(tpot_group_df, conc_col, cfg, args.tpot_max_ms)
|
|
if tpot_group_df is not None
|
|
else pd.NA
|
|
)
|
|
both = (
|
|
pd.NA
|
|
if (pd.isna(ttft_max) or pd.isna(tpot_max))
|
|
else min(ttft_max, tpot_max)
|
|
)
|
|
|
|
tput_at_both = (
|
|
_value_at_concurrency(tput_group_df, conc_col, cfg, both)
|
|
if tput_group_df is not None
|
|
else pd.NA
|
|
)
|
|
ttft_at_both = (
|
|
_value_at_concurrency(ttft_group_df, conc_col, cfg, both)
|
|
if ttft_group_df is not None
|
|
else pd.NA
|
|
)
|
|
tpot_at_both = (
|
|
_value_at_concurrency(tpot_group_df, conc_col, cfg, both)
|
|
if tpot_group_df is not None
|
|
else pd.NA
|
|
)
|
|
|
|
rows.append(
|
|
{
|
|
"Configuration": cfg,
|
|
f"Max {conc_col} (TTFT ≤ {args.ttft_max_ms:g} ms)": ttft_max,
|
|
f"Max {conc_col} (TPOT ≤ {args.tpot_max_ms:g} ms)": tpot_max,
|
|
f"Max {conc_col} (Both)": both,
|
|
"Output Tput @ Both (tok/s)": tput_at_both,
|
|
"TTFT @ Both (ms)": ttft_at_both,
|
|
"TPOT @ Both (ms)": tpot_at_both,
|
|
}
|
|
)
|
|
|
|
summary_df = pd.DataFrame(rows)
|
|
|
|
for c in summary_df.columns:
|
|
if c == "Configuration":
|
|
continue
|
|
summary_df[c] = pd.to_numeric(summary_df[c], errors="coerce")
|
|
|
|
both_col = f"Max {conc_col} (Both)"
|
|
|
|
formatters = {}
|
|
for c in summary_df.columns:
|
|
if c == "Configuration":
|
|
continue
|
|
formatters[c] = lambda v: "" if pd.isna(v) else f"{float(v):.2f}"
|
|
|
|
styler = summary_df.style.format(formatters)
|
|
|
|
def _green(v):
|
|
return "background-color:#e6ffe6;font-weight:bold;" if pd.notna(v) else ""
|
|
|
|
if both_col in summary_df.columns:
|
|
styler = styler.map(_green, subset=[both_col])
|
|
|
|
title = (
|
|
'<div style="font-size: 1.15em; font-weight: 700; margin: 12px 0 6px 0;">'
|
|
"Valid Max Concurrency Summary"
|
|
"</div>\n"
|
|
)
|
|
return title + styler.to_html(table_attributes='border="1" class="dataframe"')
|
|
|
|
|
|
def build_valid_max_concurrency_summary_df(
|
|
tput_group_df: pd.DataFrame | None,
|
|
ttft_group_df: pd.DataFrame | None,
|
|
tpot_group_df: pd.DataFrame | None,
|
|
conc_col: str,
|
|
args,
|
|
) -> pd.DataFrame | None:
|
|
if ttft_group_df is None and tpot_group_df is None:
|
|
return None
|
|
|
|
ttft_cols = (
|
|
_config_value_columns(ttft_group_df, conc_col)
|
|
if ttft_group_df is not None
|
|
else []
|
|
)
|
|
tpot_cols = (
|
|
_config_value_columns(tpot_group_df, conc_col)
|
|
if tpot_group_df is not None
|
|
else []
|
|
)
|
|
tput_cols = (
|
|
_config_value_columns(tput_group_df, conc_col)
|
|
if tput_group_df is not None
|
|
else []
|
|
)
|
|
|
|
if ttft_group_df is not None and tpot_group_df is not None:
|
|
cfg_cols = [c for c in ttft_cols if c in tpot_cols]
|
|
if tput_group_df is not None:
|
|
cfg_cols = [c for c in cfg_cols if c in tput_cols] or cfg_cols
|
|
else:
|
|
cfg_cols = ttft_cols or tpot_cols
|
|
|
|
if not cfg_cols:
|
|
cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)
|
|
|
|
rows = []
|
|
for cfg in cfg_cols:
|
|
ttft_max = (
|
|
_max_concurrency_ok(ttft_group_df, conc_col, cfg, args.ttft_max_ms)
|
|
if ttft_group_df is not None
|
|
else pd.NA
|
|
)
|
|
tpot_max = (
|
|
_max_concurrency_ok(tpot_group_df, conc_col, cfg, args.tpot_max_ms)
|
|
if tpot_group_df is not None
|
|
else pd.NA
|
|
)
|
|
both = (
|
|
pd.NA
|
|
if (pd.isna(ttft_max) or pd.isna(tpot_max))
|
|
else min(ttft_max, tpot_max)
|
|
)
|
|
|
|
tput_at_both = (
|
|
_value_at_concurrency(tput_group_df, conc_col, cfg, both)
|
|
if tput_group_df is not None
|
|
else pd.NA
|
|
)
|
|
ttft_at_both = (
|
|
_value_at_concurrency(ttft_group_df, conc_col, cfg, both)
|
|
if ttft_group_df is not None
|
|
else pd.NA
|
|
)
|
|
tpot_at_both = (
|
|
_value_at_concurrency(tpot_group_df, conc_col, cfg, both)
|
|
if tpot_group_df is not None
|
|
else pd.NA
|
|
)
|
|
|
|
rows.append(
|
|
{
|
|
"Configuration": cfg,
|
|
f"Max {conc_col} (TTFT ≤ {args.ttft_max_ms:g} ms)": ttft_max,
|
|
f"Max {conc_col} (TPOT ≤ {args.tpot_max_ms:g} ms)": tpot_max,
|
|
f"Max {conc_col} (Both)": both,
|
|
"Output Tput @ Both (tok/s)": tput_at_both,
|
|
"TTFT @ Both (ms)": ttft_at_both,
|
|
"TPOT @ Both (ms)": tpot_at_both,
|
|
}
|
|
)
|
|
|
|
df = pd.DataFrame(rows)
|
|
for c in df.columns:
|
|
if c != "Configuration":
|
|
df[c] = pd.to_numeric(df[c], errors="coerce")
|
|
return df
|
|
|
|
|
|
# -----------------------------
|
|
# Plot helper
|
|
# -----------------------------
|
|
def _add_limit_line(fig, y_value: float, label: str):
|
|
fig.add_hline(
|
|
y=y_value,
|
|
line_dash="dash",
|
|
line_color="red" if "ttft" in label.lower() else "blue",
|
|
annotation_text=f"{label}: {y_value} ms",
|
|
annotation_position="top left",
|
|
)
|
|
if plotly_found:
|
|
import plotly.graph_objects as go
|
|
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=[None],
|
|
y=[None],
|
|
mode="lines",
|
|
line=dict(
|
|
dash="dash",
|
|
color="red" if "ttft" in label.lower() else "blue",
|
|
),
|
|
name=label,
|
|
)
|
|
)
|
|
|
|
|
|
# -----------------------------
|
|
# Refactored main + group-first report
|
|
# -----------------------------
|
|
@dataclass(frozen=True)
|
|
class MetricPlan:
|
|
data_cols: list[str]
|
|
drop_column: str
|
|
|
|
|
|
def build_parser() -> argparse.ArgumentParser:
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"-f", "--file", action="append", type=str, help="input file name"
|
|
)
|
|
parser.add_argument(
|
|
"--debug", action="store_true", help="show all information for debugging"
|
|
)
|
|
parser.add_argument(
|
|
"--plot",
|
|
action=argparse.BooleanOptionalAction,
|
|
default=True,
|
|
help="plot perf diagrams or not --no-plot --plot",
|
|
)
|
|
parser.add_argument(
|
|
"-x",
|
|
"--xaxis",
|
|
type=str,
|
|
default="# of max concurrency.",
|
|
help="column name to use as X Axis in comparison graph",
|
|
)
|
|
parser.add_argument(
|
|
"-l",
|
|
"--latency",
|
|
type=str,
|
|
default="p99",
|
|
help="take median|p99 for latency like TTFT/TPOT",
|
|
)
|
|
parser.add_argument(
|
|
"--ttft-max-ms",
|
|
type=float,
|
|
default=3000.0,
|
|
help="Reference limit for TTFT plots (ms)",
|
|
)
|
|
parser.add_argument(
|
|
"--tpot-max-ms",
|
|
type=float,
|
|
default=100.0,
|
|
help="Reference limit for TPOT plots (ms)",
|
|
)
|
|
|
|
# ---- NEW: export options ----
|
|
parser.add_argument(
|
|
"--excel-out",
|
|
type=str,
|
|
default="perf_comparison.xlsx",
|
|
help="Write one sheet per (Model, Dataset, Input Len, Output Len).",
|
|
)
|
|
parser.add_argument(
|
|
"--csv-out-dir",
|
|
type=str,
|
|
default="",
|
|
help="If set, write per-group per-metric CSVs into this directory.",
|
|
)
|
|
|
|
return parser
|
|
|
|
|
|
def choose_metrics(latency: str) -> MetricPlan:
|
|
latency = (latency or "").lower()
|
|
drop_column = "P99"
|
|
|
|
if "median" in latency:
|
|
return MetricPlan(
|
|
data_cols=["Output Tput (tok/s)", "Median TTFT (ms)", "Median"],
|
|
drop_column=drop_column,
|
|
)
|
|
|
|
return MetricPlan(
|
|
data_cols=["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"],
|
|
drop_column=drop_column,
|
|
)
|
|
|
|
|
|
def prepare_input_files(args, info_cols: list[str]) -> tuple[list[str], list[str]]:
|
|
if not args.file:
|
|
raise ValueError("No input files provided. Use -f/--file.")
|
|
|
|
if len(args.file) == 1:
|
|
files = split_json_by_tp_pp(args.file[0], output_root="splits")
|
|
info_cols = [c for c in info_cols if c not in ("TP Size", "PP Size")]
|
|
else:
|
|
files = args.file
|
|
|
|
return files, info_cols
|
|
|
|
|
|
def get_y_axis_col(info_cols: list[str], xaxis: str) -> str:
|
|
y_axis_index = info_cols.index(xaxis) if xaxis in info_cols else 6
|
|
return info_cols[y_axis_index]
|
|
|
|
|
|
def get_group_cols(output_df: pd.DataFrame, info_cols: list[str]) -> list[str]:
|
|
filtered_info_cols = info_cols[:4]
|
|
group_cols = [c for c in filtered_info_cols if c in output_df.columns]
|
|
if not group_cols:
|
|
raise ValueError(
|
|
f"No valid group-by columns. Expected subset: {filtered_info_cols}, "
|
|
f"but DataFrame has: {list(output_df.columns)}"
|
|
)
|
|
return group_cols
|
|
|
|
|
|
def normalize_group_key(name):
|
|
return name if isinstance(name, tuple) else (name,)
|
|
|
|
|
|
def group_filename(name, prefix: str = "perf_comparison_") -> str:
|
|
name_vals = normalize_group_key(name)
|
|
safe = ",".join(map(str, name_vals)).replace(",", "_").replace("/", "-")
|
|
return f"{prefix}{safe}.html"
|
|
|
|
|
|
def build_group_suffix(group_cols: list[str], name) -> str:
|
|
name_vals = normalize_group_key(name)
|
|
return " , ".join(f"{col} : [ {val} ] " for col, val in zip(group_cols, name_vals))
|
|
|
|
|
|
def render_metric_table_html(
|
|
display_group: pd.DataFrame,
|
|
metric_label: str,
|
|
group_suffix: str,
|
|
args,
|
|
) -> str:
|
|
title = (
|
|
f'<div style="font-size: 1.25em; font-weight: 600; margin: 12px 0;">'
|
|
f"{_html.escape(metric_label)}"
|
|
f" — {_html.escape(group_suffix)}"
|
|
f"</div>\n"
|
|
)
|
|
|
|
metric_name = metric_label.lower()
|
|
if "ttft" in metric_name:
|
|
styler = _highlight_threshold(display_group, args.ttft_max_ms)
|
|
elif ("tpot" in metric_name) or ("median" in metric_name) or ("p99" in metric_name):
|
|
styler = _highlight_threshold(display_group, args.tpot_max_ms)
|
|
else:
|
|
styler = display_group.style
|
|
|
|
styler = _apply_two_decimals(styler)
|
|
styler = highlight_ratio_columns(styler)
|
|
|
|
return title + styler.to_html(table_attributes='border="1" class="dataframe"')
|
|
|
|
|
|
def maybe_write_plot(
|
|
main_fh,
|
|
sub_fh,
|
|
group_df: pd.DataFrame,
|
|
raw_data_cols: list[str],
|
|
metric_label: str,
|
|
y_axis_col: str,
|
|
args,
|
|
):
|
|
if not (args.plot and plotly_found):
|
|
return
|
|
|
|
import plotly.express as px
|
|
|
|
df = group_df[raw_data_cols].sort_values(by=y_axis_col)
|
|
df_melted = df.melt(
|
|
id_vars=y_axis_col,
|
|
var_name="Configuration",
|
|
value_name=metric_label,
|
|
)
|
|
|
|
fig = px.line(
|
|
df_melted,
|
|
x=y_axis_col,
|
|
y=metric_label,
|
|
color="Configuration",
|
|
title=f"{metric_label} vs {y_axis_col}",
|
|
markers=True,
|
|
)
|
|
|
|
fig.update_traces(hovertemplate="%{y:.2f}<extra></extra>")
|
|
fig.update_yaxes(tickformat=".2f")
|
|
|
|
metric_name = metric_label.lower()
|
|
if "ttft" in metric_name:
|
|
_add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
|
|
elif ("tpot" in metric_name) or ("median" in metric_name) or ("p99" in metric_name):
|
|
_add_limit_line(fig, args.tpot_max_ms, "TPOT limit")
|
|
|
|
html = fig.to_html(full_html=True, include_plotlyjs="cdn")
|
|
main_fh.write(html)
|
|
sub_fh.write(html)
|
|
|
|
|
|
def build_group_keys(
|
|
df: pd.DataFrame, group_cols: list[str], sort_cols: list[str] | None = None
|
|
):
|
|
if sort_cols:
|
|
df = df.sort_values(by=sort_cols)
|
|
gb = df.groupby(group_cols, dropna=False)
|
|
return [k for k, _ in gb]
|
|
|
|
|
|
def write_report_group_first(
|
|
files: list[str], info_cols: list[str], plan: MetricPlan, args
|
|
):
|
|
name_column = "Test name"
|
|
y_axis_col = get_y_axis_col(info_cols, args.xaxis)
|
|
|
|
print("comparing : " + ", ".join(files))
|
|
|
|
metric_cache: dict[str, tuple[pd.DataFrame, list[str]]] = {}
|
|
group_cols_canonical: list[str] | None = None
|
|
|
|
for metric_label in plan.data_cols:
|
|
output_df, raw_data_cols = compare_data_columns(
|
|
files,
|
|
name_column,
|
|
metric_label,
|
|
info_cols,
|
|
plan.drop_column,
|
|
debug=args.debug,
|
|
)
|
|
|
|
raw_data_cols = list(raw_data_cols)
|
|
raw_data_cols.insert(0, y_axis_col)
|
|
|
|
group_cols = get_group_cols(output_df, info_cols)
|
|
if group_cols_canonical is None:
|
|
group_cols_canonical = group_cols
|
|
else:
|
|
group_cols_canonical = [c for c in group_cols_canonical if c in group_cols]
|
|
|
|
metric_cache[metric_label] = (
|
|
output_df.sort_values(by=args.xaxis),
|
|
raw_data_cols,
|
|
)
|
|
|
|
if not group_cols_canonical:
|
|
raise ValueError("No canonical group columns found across metrics.")
|
|
|
|
first_metric = plan.data_cols[0]
|
|
first_df_sorted, _ = metric_cache[first_metric]
|
|
group_keys = build_group_keys(
|
|
first_df_sorted, group_cols_canonical, sort_cols=[args.xaxis]
|
|
)
|
|
|
|
metric_groupbys = {
|
|
metric_label: df.groupby(group_cols_canonical, dropna=False)
|
|
for metric_label, (df, _) in metric_cache.items()
|
|
}
|
|
|
|
csv_dir = Path(args.csv_out_dir) if args.csv_out_dir else None
|
|
if csv_dir:
|
|
csv_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
excel_path = args.excel_out or "perf_comparison.xlsx"
|
|
with pd.ExcelWriter(excel_path, engine="openpyxl") as xw:
|
|
# ---- Environment sheet (first) ----
|
|
env_sheet = _sanitize_sheet_name("Environment")
|
|
env_df = _load_env_df_for_inputs(args, files)
|
|
if env_df is None or env_df.empty:
|
|
pd.DataFrame(
|
|
[
|
|
{
|
|
"Section": "Environment",
|
|
"Key": "vllm_env.txt",
|
|
"Value": "NOT FOUND (or empty)",
|
|
}
|
|
]
|
|
).to_excel(xw, sheet_name=env_sheet, index=False)
|
|
else:
|
|
env_df.to_excel(xw, sheet_name=env_sheet, index=False)
|
|
with open("perf_comparison.html", "w", encoding="utf-8") as main_fh:
|
|
main_fh.write('<meta charset="utf-8">\n')
|
|
for gkey in group_keys:
|
|
gkey_tuple = normalize_group_key(gkey)
|
|
suffix = build_group_suffix(group_cols_canonical, gkey_tuple)
|
|
sub_path = group_filename(gkey_tuple)
|
|
group_header = (
|
|
'<div style="font-size: 1.4em; font-weight: 700; '
|
|
'margin: 18px 0 10px 0;">'
|
|
f"{_html.escape(suffix)}"
|
|
"</div>\n"
|
|
)
|
|
|
|
main_fh.write(group_header)
|
|
|
|
sheet = _group_to_sheet_base(group_cols_canonical, gkey_tuple)
|
|
sheet_base = sheet
|
|
dedup_i = 1
|
|
while sheet in xw.sheets:
|
|
dedup_i += 1
|
|
sheet = _sanitize_sheet_name(f"{sheet_base}_{dedup_i}")
|
|
|
|
excel_blocks: list[tuple[str, pd.DataFrame]] = []
|
|
|
|
with open(sub_path, "w", encoding="utf-8") as sub_fh:
|
|
sub_fh.write('<meta charset="utf-8">\n')
|
|
sub_fh.write(group_header)
|
|
tput_group_df = None
|
|
ttft_group_df = None
|
|
tpot_group_df = None
|
|
conc_col = args.xaxis
|
|
|
|
for metric_label in plan.data_cols:
|
|
gb = metric_groupbys[metric_label]
|
|
df_sorted, raw_data_cols = metric_cache[metric_label]
|
|
|
|
try:
|
|
group_df = gb.get_group(gkey)
|
|
except KeyError:
|
|
missing = (
|
|
'<div style="font-size: 1.1em; font-weight: 600; '
|
|
'margin: 10px 0;">'
|
|
f"{_html.escape(metric_label)} — missing for this group"
|
|
"</div>\n"
|
|
)
|
|
main_fh.write(missing)
|
|
sub_fh.write(missing)
|
|
continue
|
|
|
|
if conc_col not in group_df.columns:
|
|
conc_col = _find_concurrency_col(group_df)
|
|
|
|
mn = metric_label.lower().strip()
|
|
if "tok/s" in mn:
|
|
tput_group_df = group_df
|
|
elif "ttft" in mn:
|
|
ttft_group_df = group_df
|
|
elif mn in ("p99", "median") or "tpot" in mn:
|
|
tpot_group_df = group_df
|
|
|
|
display_group = group_df.drop(
|
|
columns=group_cols_canonical, errors="ignore"
|
|
)
|
|
|
|
html = render_metric_table_html(
|
|
display_group, metric_label, suffix, args
|
|
)
|
|
main_fh.write(html)
|
|
sub_fh.write(html)
|
|
|
|
maybe_write_plot(
|
|
main_fh,
|
|
sub_fh,
|
|
group_df=group_df,
|
|
raw_data_cols=raw_data_cols,
|
|
metric_label=metric_label,
|
|
y_axis_col=y_axis_col,
|
|
args=args,
|
|
)
|
|
|
|
excel_blocks.append(
|
|
(metric_label, display_group.reset_index(drop=True))
|
|
)
|
|
if csv_dir:
|
|
fn = _safe_filename(
|
|
f"{sheet}__{metric_label}".replace(" ", "_").replace(
|
|
"/", "_"
|
|
)
|
|
)
|
|
display_group.to_csv(csv_dir / f"{fn}.csv", index=False)
|
|
|
|
summary_html = build_valid_max_concurrency_summary_html(
|
|
tput_group_df=tput_group_df,
|
|
ttft_group_df=ttft_group_df,
|
|
tpot_group_df=tpot_group_df,
|
|
conc_col=conc_col,
|
|
args=args,
|
|
)
|
|
if summary_html:
|
|
main_fh.write(summary_html)
|
|
sub_fh.write(summary_html)
|
|
|
|
summary_df = build_valid_max_concurrency_summary_df(
|
|
tput_group_df=tput_group_df,
|
|
ttft_group_df=ttft_group_df,
|
|
tpot_group_df=tpot_group_df,
|
|
conc_col=conc_col,
|
|
args=args,
|
|
)
|
|
if summary_df is not None:
|
|
excel_blocks.append(
|
|
("Valid Max Concurrency Summary", summary_df)
|
|
)
|
|
if csv_dir:
|
|
fn = _safe_filename(
|
|
f"{sheet}__Valid_Max_Concurrency_Summary"
|
|
)
|
|
summary_df.to_csv(csv_dir / f"{fn}.csv", index=False)
|
|
|
|
_write_tables_to_excel_sheet(xw, sheet, excel_blocks)
|
|
|
|
print(f"Wrote Excel: {excel_path}")
|
|
if csv_dir:
|
|
print(f"Wrote CSVs under: {csv_dir}")
|
|
|
|
|
|
def main():
|
|
args = build_parser().parse_args()
|
|
info_cols = list(DEFAULT_INFO_COLS)
|
|
plan = choose_metrics(args.latency)
|
|
files, info_cols = prepare_input_files(args, info_cols)
|
|
write_report_group_first(files, info_cols, plan, args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|