mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-03-28 21:48:12 +08:00
add sizing table
Signed-off-by: Tsai, Louie <louie.tsai@intel.com>
This commit is contained in:
Tsai, Louie
parent
db9aaa61ac
commit
f825a14d56
@ -27,6 +27,10 @@ DEFAULT_INFO_COLS = [
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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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@ -54,7 +58,6 @@ def compare_data_columns(
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raw_data_cols: List[str] = []
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compare_frames = []
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# 1) choose a canonical key list from info_cols that exists in ALL files
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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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@ -65,24 +68,20 @@ def compare_data_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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# soft fallback: use any info_cols present in the first file
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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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# 2) build a single "meta" block (keys as columns) once, aligned by the key index
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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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# Keep rows that actually have the compared metric (same as original behavior)
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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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# Stabilize numeric key columns (harmless if missing)
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for c in (
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"Input Len",
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"Output Len",
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@ -94,32 +93,26 @@ def compare_data_columns(
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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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# Ensure all key columns exist
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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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# Set index = key_cols and aggregate duplicates → unique MultiIndex
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df_idx = df.set_index(key_cols, drop=False)
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# meta (key columns), unique per key
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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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# metric series for this file, aggregated to one row per key
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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 # column label like original
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s.name = file_label
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# add meta once (from first file) so keys are the leftmost columns
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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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# debug: aligned test-name column per file
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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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@ -131,21 +124,19 @@ def compare_data_columns(
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raw_data_cols.append(file_label)
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compare_frames.append(s)
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# ratio columns: fileN / file1 (throughput) or file1 / fileN (latency)
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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 # for latency: larger means better
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ratio = base / current
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else:
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ratio = current / base # for throughput: larger means better
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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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# Ensure key/info columns appear first (in your info_cols order)
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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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@ -160,16 +151,9 @@ def compare_data_columns(
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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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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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@ -178,14 +162,12 @@ def split_json_by_tp_pp(
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df = pd.DataFrame(data)
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# Keep only "serving" tests
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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[df[name_col].astype(str).str.contains(r"serving", case=False, na=False)].copy()
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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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@ -194,7 +176,6 @@ def split_json_by_tp_pp(
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}
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df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True)
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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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@ -228,7 +209,6 @@ def _find_concurrency_col(df: pd.DataFrame) -> str:
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]:
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if c in df.columns:
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return c
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# Fallback: guess an integer-like column (harmless if unused)
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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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@ -236,7 +216,6 @@ def _find_concurrency_col(df: pd.DataFrame) -> str:
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def _highlight_threshold(df: pd.DataFrame, threshold: float) -> "pd.io.formats.style.Styler":
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"""Highlight numeric per-configuration columns with value <= threshold."""
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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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@ -257,19 +236,16 @@ def _highlight_threshold(df: pd.DataFrame, threshold: float) -> "pd.io.formats.s
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def highlight_ratio_columns(styler: "pd.io.formats.style.Styler"):
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"""Highlight entire columns whose header contains 'Ratio'."""
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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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# Highlight entire column (cells)
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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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# Highlight column headers
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styler = styler.set_table_styles(
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[
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{
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@ -284,6 +260,152 @@ def highlight_ratio_columns(styler: "pd.io.formats.style.Styler"):
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return styler
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def _apply_two_decimals(styler: "pd.io.formats.style.Styler") -> "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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# 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 = [c for c in ["Model", "Dataset Name", "Input Len", "Output Len"] if c in df.columns]
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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(df: pd.DataFrame, conc_col: str, cfg_col: str, threshold: float):
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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 df is None or conc_col not in df.columns or cfg_col not in df.columns or pd.isna(conc_value):
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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 = _config_value_columns(ttft_group_df, conc_col) if ttft_group_df is not None else []
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tpot_cols = _config_value_columns(tpot_group_df, conc_col) if tpot_group_df is not None else []
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tput_cols = _config_value_columns(tput_group_df, conc_col) if tput_group_df is not None else []
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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 = _max_concurrency_ok(ttft_group_df, conc_col, cfg, args.ttft_max_ms) if ttft_group_df is not None else pd.NA
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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
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both = pd.NA if (pd.isna(ttft_max) or pd.isna(tpot_max)) else min(ttft_max, tpot_max)
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tput_at_both = _value_at_concurrency(tput_group_df, conc_col, cfg, both) if tput_group_df is not None else pd.NA
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ttft_at_both = _value_at_concurrency(ttft_group_df, conc_col, cfg, both) if ttft_group_df is not None else pd.NA
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tpot_at_both = _value_at_concurrency(tpot_group_df, conc_col, cfg, both) if tpot_group_df is not None else pd.NA
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rows.append(
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{
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"Configuration": cfg,
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f"Max {conc_col} (TTFT ≤ {args.ttft_max_ms:g} ms)": ttft_max,
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f"Max {conc_col} (TPOT ≤ {args.tpot_max_ms:g} ms)": tpot_max,
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f"Max {conc_col} (Both)": both,
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"Output Tput @ Both (tok/s)": tput_at_both,
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"TTFT @ Both (ms)": ttft_at_both,
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"TPOT @ Both (ms)": tpot_at_both,
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}
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)
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summary_df = pd.DataFrame(rows)
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# --- Coerce numeric columns so Styler doesn't miss them due to object dtype ---
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for c in summary_df.columns:
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if c == "Configuration":
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continue
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summary_df[c] = pd.to_numeric(summary_df[c], errors="coerce")
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both_col = f"Max {conc_col} (Both)"
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# --- Strict 2-decimal formatting for ALL non-Configuration columns ---
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formatters = {}
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for c in summary_df.columns:
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if c == "Configuration":
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continue
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# default argument binds per-column formatter correctly
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formatters[c] = (lambda v: "—" if pd.isna(v) else f"{float(v):.2f}")
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styler = summary_df.style.format(formatters)
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def _green(v):
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return "background-color:#e6ffe6;font-weight:bold;" if pd.notna(v) else ""
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if both_col in summary_df.columns:
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styler = styler.map(_green, subset=[both_col])
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title = (
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f'<div style="font-size: 1.15em; font-weight: 700; margin: 12px 0 6px 0;">'
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f'Valid Max Concurrency Summary'
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f"</div>\n"
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)
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return title + styler.to_html(table_attributes='border="1" class="dataframe"')
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# -----------------------------
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# Plot helper
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# -----------------------------
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@ -295,7 +417,6 @@ def _add_limit_line(fig, y_value: float, label: str):
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annotation_text=f"{label}: {y_value} ms",
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annotation_position="top left",
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)
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# If plotly is available, add a legend entry
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if plotly_found:
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import plotly.graph_objects as go
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@ -361,7 +482,6 @@ def choose_metrics(latency: str) -> MetricPlan:
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drop_column=drop_column,
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)
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# default: p99
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return MetricPlan(
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data_cols=["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"],
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drop_column=drop_column,
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@ -387,7 +507,6 @@ def get_y_axis_col(info_cols: List[str], xaxis: str) -> str:
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def get_group_cols(output_df: pd.DataFrame, info_cols: List[str]) -> List[str]:
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# Your current grouping rule: first 4 info columns
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filtered_info_cols = info_cols[:4]
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group_cols = [c for c in filtered_info_cols if c in output_df.columns]
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if not group_cols:
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@ -399,7 +518,6 @@ def get_group_cols(output_df: pd.DataFrame, info_cols: List[str]) -> List[str]:
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def normalize_group_key(name):
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"""Pandas group key can be scalar (1 col) or tuple (N cols). Normalize to tuple."""
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return name if isinstance(name, tuple) else (name,)
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@ -437,16 +555,13 @@ def render_metric_table_html(
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else:
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styler = display_group.style
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styler = styler.format(
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{c: "{:.2f}" for c in display_group.select_dtypes("number").columns},
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na_rep="—",
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)
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styler = _apply_two_decimals(styler)
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styler = highlight_ratio_columns(styler)
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return title + styler.to_html(table_attributes='border="1" class="dataframe"')
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def write_plot(
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def maybe_write_plot(
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main_fh,
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sub_fh,
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group_df: pd.DataFrame,
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@ -476,6 +591,10 @@ def write_plot(
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markers=True,
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)
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# Ensure plot hover + y tick labels are also 2 decimals.
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fig.update_traces(hovertemplate="%{y:.2f}<extra></extra>")
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fig.update_yaxes(tickformat=".2f")
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metric_name = metric_label.lower()
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if "ttft" in metric_name:
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_add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
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@ -488,7 +607,6 @@ def write_plot(
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def build_group_keys(df: pd.DataFrame, group_cols: List[str], sort_cols: List[str] | None = None):
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"""Return a stable list of group keys from df."""
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if sort_cols:
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df = df.sort_values(by=sort_cols)
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gb = df.groupby(group_cols, dropna=False)
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@ -496,16 +614,11 @@ def build_group_keys(df: pd.DataFrame, group_cols: List[str], sort_cols: List[st
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def write_report_group_first(files: List[str], info_cols: List[str], plan: MetricPlan, args):
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"""
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Group-first layout:
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For each group, emit tok/s then TTFT then TPOT (or Median variants) together.
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"""
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name_column = "Test name"
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y_axis_col = get_y_axis_col(info_cols, args.xaxis)
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print("comparing : " + ", ".join(files))
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# Precompute per-metric dataframes once
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metric_cache: Dict[str, Tuple[pd.DataFrame, List[str]]] = {}
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group_cols_canonical: List[str] | None = None
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@ -519,7 +632,6 @@ def write_report_group_first(files: List[str], info_cols: List[str], plan: Metri
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||||
debug=args.debug,
|
||||
)
|
||||
|
||||
# plot expects y-axis column at the front
|
||||
raw_data_cols = list(raw_data_cols)
|
||||
raw_data_cols.insert(0, y_axis_col)
|
||||
|
||||
@ -527,7 +639,6 @@ def write_report_group_first(files: List[str], info_cols: List[str], plan: Metri
|
||||
if group_cols_canonical is None:
|
||||
group_cols_canonical = group_cols
|
||||
else:
|
||||
# keep intersection (stable order)
|
||||
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)
|
||||
@ -535,12 +646,10 @@ def write_report_group_first(files: List[str], info_cols: List[str], plan: Metri
|
||||
if not group_cols_canonical:
|
||||
raise ValueError("No canonical group columns found across metrics.")
|
||||
|
||||
# Canonical group keys from first metric (typically tok/s)
|
||||
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])
|
||||
|
||||
# Pre-build groupby objects per metric
|
||||
metric_groupbys = {
|
||||
metric_label: df.groupby(group_cols_canonical, dropna=False)
|
||||
for metric_label, (df, _) in metric_cache.items()
|
||||
@ -552,7 +661,6 @@ def write_report_group_first(files: List[str], info_cols: List[str], plan: Metri
|
||||
suffix = build_group_suffix(group_cols_canonical, gkey_tuple)
|
||||
sub_path = group_filename(gkey_tuple)
|
||||
|
||||
# Optional group header (separates each group visually)
|
||||
group_header = (
|
||||
f'<div style="font-size: 1.4em; font-weight: 700; margin: 18px 0 10px 0;">'
|
||||
f'{_html.escape(suffix)}'
|
||||
@ -563,6 +671,11 @@ def write_report_group_first(files: List[str], info_cols: List[str], plan: Metri
|
||||
with open(sub_path, "w") as sub_fh:
|
||||
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]
|
||||
@ -579,15 +692,24 @@ def write_report_group_first(files: List[str], info_cols: List[str], plan: Metri
|
||||
sub_fh.write(missing)
|
||||
continue
|
||||
|
||||
# Display-only: drop group columns
|
||||
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)
|
||||
|
||||
write_plot(
|
||||
maybe_write_plot(
|
||||
main_fh,
|
||||
sub_fh,
|
||||
group_df=group_df,
|
||||
@ -597,16 +719,23 @@ def write_report_group_first(files: List[str], info_cols: List[str], plan: Metri
|
||||
args=args,
|
||||
)
|
||||
|
||||
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)
|
||||
|
||||
|
||||
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)
|
||||
|
||||
# Group-first report layout
|
||||
write_report_group_first(files, info_cols, plan, args)
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user