biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Benchmark Suite] improve cpu Benchmark Suite tests and comparison report for 0.12.0 (#30994)

Browse Source 
Signed-off-by: Tsai, Louie <louie.tsai@intel.com>
This commit is contained in:
Louie Tsai
2025-12-25 00:51:45 -08:00
committed by GitHub
parent ba25a65992
commit f15185fbdb
4 changed files with 666 additions and 222 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
.buildkite/performance-benchmarks/README.md
View File

@@ -176,19 +176,6 @@ If you do not see the table, please wait till the benchmark finish running.
The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.
If only one benchmark_results.json is passed, `compare-json-results.py` compares different TP and PP configurations in the benchmark_results.json instead.
#### Performance Results Comparison
Here is an example using the script to compare result_a and result_b with Model, Dataset name, input/output length, max concurrency and qps.
`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
| | Model | Dataset Name | Input Len | Output Len | # of max concurrency | qps | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio |
|----|---------------------------------------|--------|-----|-----|------|-----|-----------|----------|----------|
| 0 | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | 1 | 142.633982 | 156.526018 | 1.097396 |
| 1 | meta-llama/Meta-Llama-3.1-8B-Instruct | random | 128 | 128 | 1000 | inf| 241.620334 | 294.018783 | 1.216863 |
A comparison diagram will be generated below the table.
Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
<img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />
Follow the instructions in [performance results comparison](https://docs.vllm.ai/en/latest/benchmarking/dashboard/#performance-results-comparison) to analyze performance results and the sizing guide.

775
.buildkite/performance-benchmarks/scripts/compare-json-results.py
View File

@@ -1,8 +1,13 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from __future__ import annotations
import argparse
import html as _html
import json
import os
from dataclasses import dataclass
from importlib import util
import pandas as pd
@@ -10,27 +15,49 @@ import pandas as pd
pd.options.display.float_format = "{:.2f}".format
plotly_found = util.find_spec("plotly.express") is not None
DEFAULT_INFO_COLS = [
"Model",
"Dataset Name",
"Input Len",
"Output Len",
# "TP Size",
# "PP Size",
"# of max concurrency.",
"qps",
]
# Safety net: if any DataFrame leaks into to_html(), keep precision at 2.
pd.set_option("display.precision", 2)
pd.set_option("display.float_format", lambda x: f"{x:.2f}")
# -----------------------------
# Core data compare
# -----------------------------
def compare_data_columns(
files, name_column, data_column, info_cols, drop_column, debug=False
files: list[str],
name_column: str,
data_column: str,
info_cols: list[str],
drop_column: str,
debug: bool = False,
):
"""
Align concatenation by keys derived from info_cols instead of row order.
- Pick one canonical key list: subset of info_cols present in ALL files.
- For each file: set index to those keys, aggregate duplicates
- (mean for metric, first for names).
(mean for metric, first for names).
- Concat along axis=1 (indexes align), then reset_index so callers can
- group by columns.
group by columns.
- If --debug, add a <file_label>_name column per file.
"""
print("\ncompare_data_column:", data_column)
frames = []
raw_data_cols = []
raw_data_cols: list[str] = []
compare_frames = []
# 1) choose a canonical key list from info_cols that exists in ALL files
cols_per_file = []
cols_per_file: list[set] = []
for f in files:
try:
df_tmp = pd.read_json(f, orient="records")
@@ -40,24 +67,20 @@ def compare_data_columns(
key_cols = [c for c in info_cols if all(c in cset for cset in cols_per_file)]
if not key_cols:
# soft fallback: use any info_cols present in the first file
key_cols = [c for c in info_cols if c in list(cols_per_file[0])]
if not key_cols:
raise ValueError(
"No common key columns found from info_cols across the input files."
)
# 2) build a single "meta" block (keys as columns) once, aligned by the key index
meta_added = False
for file in files:
df = pd.read_json(file, orient="records")
# Keep rows that actually have the compared metric (same as original behavior)
if drop_column in df.columns:
df = df.dropna(subset=[drop_column], ignore_index=True)
# Stabilize numeric key columns (harmless if missing)
for c in (
"Input Len",
"Output Len",
@@ -69,32 +92,26 @@ def compare_data_columns(
if c in df.columns:
df[c] = pd.to_numeric(df[c], errors="coerce")
# Ensure all key columns exist
for c in key_cols:
if c not in df.columns:
df[c] = pd.NA
# Set index = key_cols and aggregate duplicates → unique MultiIndex
df_idx = df.set_index(key_cols, drop=False)
# meta (key columns), unique per key
meta = df_idx[key_cols]
if not meta.index.is_unique:
meta = meta.groupby(level=key_cols, dropna=False).first()
# metric series for this file, aggregated to one row per key
file_label = "/".join(file.split("/")[:-1]) or os.path.basename(file)
s = df_idx[data_column]
if not s.index.is_unique:
s = s.groupby(level=key_cols, dropna=False).mean()
s.name = file_label # column label like original
s.name = file_label
# add meta once (from first file) so keys are the leftmost columns
if not meta_added:
frames.append(meta)
meta_added = True
# (NEW) debug: aligned test-name column per file
if debug and name_column in df_idx.columns:
name_s = df_idx[name_column]
if not name_s.index.is_unique:
@@ -106,26 +123,19 @@ def compare_data_columns(
raw_data_cols.append(file_label)
compare_frames.append(s)
# Generalize ratio: for any file N>=2, add ratio (fileN / file1)
if len(compare_frames) >= 2:
base = compare_frames[0]
current = compare_frames[-1]
if "P99" in data_column or "Median" in data_column:
ratio = base / current # for latency
ratio = base / current
else:
ratio = current / base
ratio = ratio.mask(base == 0) # avoid inf when baseline is 0
ratio = ratio.mask(base == 0)
ratio.name = f"Ratio 1 vs {len(compare_frames)}"
frames.append(ratio)
# 4) concat on columns with aligned MultiIndex;
# then reset_index to return keys as columns
concat_df = pd.concat(frames, axis=1)
concat_df = concat_df.reset_index(drop=True).reset_index()
if "index" in concat_df.columns:
concat_df = concat_df.drop(columns=["index"])
concat_df = pd.concat(frames, axis=1).reset_index(drop=True)
# Ensure key/info columns appear first (in your info_cols order)
front = [c for c in info_cols if c in concat_df.columns]
rest = [c for c in concat_df.columns if c not in front]
concat_df = concat_df[front + rest]
@@ -134,20 +144,15 @@ def compare_data_columns(
return concat_df, raw_data_cols
# -----------------------------
# Split helper
# -----------------------------
def split_json_by_tp_pp(
input_file: str = "benchmark_results.json", output_root: str = "."
) -> list[str]:
"""
Split a benchmark JSON into separate folders by (TP Size, PP Size).
Creates: <output_root>/tp{TP}_pp{PP}/benchmark_results.json
Returns: list of file paths written.
"""
# Load JSON data into DataFrame
with open(input_file, encoding="utf-8") as f:
data = json.load(f)
# If the JSON is a dict with a list under common keys, use that list
if isinstance(data, dict):
for key in ("results", "serving_results", "benchmarks", "data"):
if isinstance(data.get(key), list):
@@ -156,7 +161,6 @@ def split_json_by_tp_pp(
df = pd.DataFrame(data)
# Keep only "serving" tests
name_col = next(
(c for c in ["Test name", "test_name", "Test Name"] if c in df.columns), None
)
@@ -165,7 +169,6 @@ def split_json_by_tp_pp(
df[name_col].astype(str).str.contains(r"serving", case=False, na=False)
].copy()
# Handle alias column names
rename_map = {
"tp_size": "TP Size",
"tensor_parallel_size": "TP Size",
@@ -176,21 +179,14 @@ def split_json_by_tp_pp(
columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True
)
# Ensure TP/PP columns exist (default to 1 if missing)
if "TP Size" not in df.columns:
df["TP Size"] = 1
if "PP Size" not in df.columns:
df["PP Size"] = 1
# make sure TP/PP are numeric ints with no NaN
df["TP Size"] = (
pd.to_numeric(df.get("TP Size", 1), errors="coerce").fillna(1).astype(int)
)
df["PP Size"] = (
pd.to_numeric(df.get("PP Size", 1), errors="coerce").fillna(1).astype(int)
)
df["TP Size"] = pd.to_numeric(df["TP Size"], errors="coerce").fillna(1).astype(int)
df["PP Size"] = pd.to_numeric(df["PP Size"], errors="coerce").fillna(1).astype(int)
# Split into separate folders
saved_paths: list[str] = []
for (tp, pp), group_df in df.groupby(["TP Size", "PP Size"], dropna=False):
folder_name = os.path.join(output_root, f"tp{int(tp)}_pp{int(pp)}")
@@ -203,32 +199,9 @@ def split_json_by_tp_pp(
return saved_paths
def _add_limit_line(fig, y_value, label):
# Visible dashed line + annotation
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",
)
# Optional: add a legend item (as a transparent helper trace)
if plot and 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=f"{label}",
)
)
# -----------------------------
# Styling helpers
# -----------------------------
def _find_concurrency_col(df: pd.DataFrame) -> str:
for c in [
"# of max concurrency.",
@@ -239,7 +212,6 @@ def _find_concurrency_col(df: pd.DataFrame) -> str:
]:
if c in df.columns:
return c
# Fallback: guess an integer-like column (harmless if unused)
for c in df.columns:
if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
return c
@@ -248,8 +220,7 @@ def _find_concurrency_col(df: pd.DataFrame) -> str:
def _highlight_threshold(
df: pd.DataFrame, threshold: float
) -> "pd.io.formats.style.Styler":
"""Highlight numeric per-configuration columns with value <= threshold."""
) -> pd.io.formats.style.Styler:
conc_col = _find_concurrency_col(df)
key_cols = [
c
@@ -260,6 +231,7 @@ def _highlight_threshold(
c for c in df.columns if c not in key_cols and not str(c).startswith("Ratio")
]
conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
return df.style.map(
lambda v: "background-color:#e6ffe6;font-weight:bold;"
if pd.notna(v) and v <= threshold
@@ -268,7 +240,264 @@ def _highlight_threshold(
)
if __name__ == "__main__":
def highlight_ratio_columns(styler: pd.io.formats.style.Styler):
ratio_cols = [c for c in styler.data.columns if "ratio" in str(c).lower()]
if not ratio_cols:
return styler
styler = styler.apply(
lambda _: ["background-color: #fff3b0"] * len(styler.data),
subset=ratio_cols,
axis=0,
)
styler = styler.set_table_styles(
[
{
"selector": f"th.col_heading.level0.col{i}",
"props": [("background-color", "#fff3b0")],
}
for i, col in enumerate(styler.data.columns)
if col in ratio_cols
],
overwrite=False,
)
return styler
def _apply_two_decimals(
styler: pd.io.formats.style.Styler,
) -> pd.io.formats.style.Styler:
df = styler.data
num_cols = df.select_dtypes("number").columns
if len(num_cols) == 0:
return styler
return styler.format({c: "{:.2f}" for c in num_cols}, na_rep="")
# -----------------------------
# Valid max concurrency summary helpers
# -----------------------------
def _config_value_columns(df: pd.DataFrame, conc_col: str) -> list[str]:
key_cols = [
c
for c in ["Model", "Dataset Name", "Input Len", "Output Len"]
if c in df.columns
]
exclude = set(key_cols + [conc_col, "qps", "QPS"])
cols: list[str] = []
for c in df.columns:
if c in exclude:
continue
lc = str(c).lower()
if lc.startswith("ratio"):
continue
if lc.endswith("_name") or lc == "test name" or lc == "test_name":
continue
if pd.api.types.is_numeric_dtype(df[c]):
cols.append(c)
return cols
def _max_concurrency_ok(
df: pd.DataFrame, conc_col: str, cfg_col: str, threshold: float
):
if df is None or conc_col not in df.columns or cfg_col not in df.columns:
return pd.NA
d = df[[conc_col, cfg_col]].copy()
d[conc_col] = pd.to_numeric(d[conc_col], errors="coerce")
d[cfg_col] = pd.to_numeric(d[cfg_col], errors="coerce")
d = d.dropna(subset=[conc_col, cfg_col])
if d.empty:
return pd.NA
ok = d[d[cfg_col] <= threshold]
if ok.empty:
return pd.NA
return ok[conc_col].max()
def _value_at_concurrency(df: pd.DataFrame, conc_col: str, cfg_col: str, conc_value):
if (
df is None
or conc_col not in df.columns
or cfg_col not in df.columns
or pd.isna(conc_value)
):
return pd.NA
d = df[[conc_col, cfg_col]].copy()
d[conc_col] = pd.to_numeric(d[conc_col], errors="coerce")
d[cfg_col] = pd.to_numeric(d[cfg_col], errors="coerce")
conc_value = pd.to_numeric(conc_value, errors="coerce")
if pd.isna(conc_value):
return pd.NA
hit = d[d[conc_col] == conc_value]
if hit.empty:
return pd.NA
return hit[cfg_col].iloc[0]
def build_valid_max_concurrency_summary_html(
tput_group_df: pd.DataFrame | None,
ttft_group_df: pd.DataFrame | None,
tpot_group_df: pd.DataFrame | None,
conc_col: str,
args,
) -> str:
if ttft_group_df is None and tpot_group_df is None:
return ""
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,
}
)
summary_df = pd.DataFrame(rows)
# --- Coerce numeric columns so Styler doesn't miss them due to object dtype ---
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)"
# --- Strict 2-decimal formatting for ALL non-Configuration columns ---
formatters = {}
for c in summary_df.columns:
if c == "Configuration":
continue
# default argument binds per-column formatter correctly
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"')
# -----------------------------
# 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"
@@ -308,149 +537,289 @@ if __name__ == "__main__":
default=100.0,
help="Reference limit for TPOT plots (ms)",
)
return parser
args = parser.parse_args()
def choose_metrics(latency: str) -> MetricPlan:
latency = (latency or "").lower()
drop_column = "P99"
name_column = "Test name"
info_cols = [
"Model",
"Dataset Name",
"Input Len",
"Output Len",
"TP Size",
"PP Size",
"# of max concurrency.",
"qps",
]
if "median" in args.latency:
data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
html_msgs_for_data_cols = [
"Compare Output Tokens /n",
"Median TTFT /n",
"Median TPOT /n",
]
drop_column = "P99"
elif "p99" in args.latency:
data_cols_to_compare = ["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"]
html_msgs_for_data_cols = [
"Compare Output Tokens /n",
"P99 TTFT /n",
"P99 TPOT /n",
]
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,
)
# Ensure plot hover + y tick labels are also 2 decimals.
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))
debug = args.debug
plot = args.plot
# For Plot feature, assign y axis from one of info_cols
y_axis_index = info_cols.index(args.xaxis) if args.xaxis in info_cols else 6
with open("perf_comparison.html", "w") as text_file:
for i in range(len(data_cols_to_compare)):
output_df, raw_data_cols = compare_data_columns(
files,
name_column,
data_cols_to_compare[i],
info_cols,
drop_column,
debug=debug,
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()
}
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"
)
# For Plot feature, insert y axis from one of info_cols
raw_data_cols.insert(0, info_cols[y_axis_index])
main_fh.write(group_header)
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
filtered_info_cols = info_cols[:-2]
existing_group_cols = [
c for c in filtered_info_cols if c in output_df.columns
]
if not existing_group_cols:
raise ValueError(
f"No valid group-by columns "
f"Expected subset: {filtered_info_cols}, "
f"but DataFrame has: {list(output_df.columns)}"
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,
)
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,
)
# output_df_sorted = output_df.sort_values(by=existing_group_cols)
output_df_sorted = output_df.sort_values(by=args.xaxis)
output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
for name, group in output_groups:
group_name = (
",".join(map(str, name)).replace(",", "_").replace("/", "-")
)
group_html_name = "perf_comparison_" + group_name + ".html"
if summary_html:
main_fh.write(summary_html)
sub_fh.write(summary_html)
metric_name = str(data_cols_to_compare[i]).lower()
if "tok/s" in metric_name:
html = group.to_html()
elif "ttft" in metric_name:
styler = _highlight_threshold(group, args.ttft_max_ms).format(
{c: "{:.2f}" for c in group.select_dtypes("number").columns},
na_rep="—",
)
html = styler.to_html(
table_attributes='border="1" class="dataframe"'
)
elif (
"tpot" in metric_name
or "median" in metric_name
or "p99" in metric_name
):
styler = _highlight_threshold(group, args.tpot_max_ms).format(
{c: "{:.2f}" for c in group.select_dtypes("number").columns},
na_rep="—",
)
html = styler.to_html(
table_attributes='border="1" class="dataframe"'
)
text_file.write(html_msgs_for_data_cols[i])
text_file.write(html)
with open(group_html_name, "a+") as sub_text_file:
sub_text_file.write(html_msgs_for_data_cols[i])
sub_text_file.write(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)
write_report_group_first(files, info_cols, plan, args)
if plot and plotly_found:
import plotly.express as px
df = group[raw_data_cols]
df_sorted = df.sort_values(by=info_cols[y_axis_index])
# Melt DataFrame for plotting
df_melted = df_sorted.melt(
id_vars=info_cols[y_axis_index],
var_name="Configuration",
value_name=data_cols_to_compare[i],
)
title = (
data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
)
# Create Plotly line chart
fig = px.line(
df_melted,
x=info_cols[y_axis_index],
y=data_cols_to_compare[i],
color="Configuration",
title=title,
markers=True,
)
# ---- Add threshold lines based on metric name ----
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")
# Export to HTML
text_file.write(
fig.to_html(full_html=True, include_plotlyjs="cdn")
)
sub_text_file.write(
fig.to_html(full_html=True, include_plotlyjs="cdn")
)
if __name__ == "__main__":
main()

43
.buildkite/performance-benchmarks/tests/serving-tests-cpu.json
View File

@@ -19,10 +19,8 @@
"block_size": 128,
"trust_remote_code": "",
"disable_log_stats": "",
"enforce_eager": "",
"max_num_batched_tokens": 2048,
"max_num_seqs": 256,
"load_format": "dummy"
"max_num_seqs": 256
},
"client_parameters": {
"model": "meta-llama/Llama-3.1-8B-Instruct",
@@ -151,6 +149,45 @@
"random-output-len": 128
}
},
{
"test_name": "serving_llama8B_int4_tp1_random_128_128",
"server_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"tensor_parallel_size": 1
},
"client_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 128
}
},
{
"test_name": "serving_llama8B_int4_tp2_random_128_128",
"server_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"tensor_parallel_size": 2
},
"client_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 128
}
},
{
"test_name": "serving_llama8B_int4_tp4_random_128_128",
"server_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"tensor_parallel_size": 4
},
"client_parameters": {
"model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
"dataset_name": "random",
"random-input-len": 128,
"random-output-len": 128
}
},
{
"test_name": "serving_llama3B_tp1_random_128_128",
"server_parameters": {